Lessons learned from time-resolved X-ray spectra of gamma ...

Doctoral Thesis in Physics

Lessons learned from time-resolved X-ray spectra of gamma-ray burstsVLASTA VALAN

Stockholm, Sweden 2022

kth royal institute of technology

Lessons learned from time-resolved X-ray spectra of gamma-ray burstsVLASTA VALAN

Doctoral Thesis in PhysicsKTH Royal Institute of TechnologyStockholm, Sweden 2022

Academic Dissertation which, with due permission of the KTH Royal Institute of Technology, is submitted for public defence for the Degree of Doctor of Philosophy on Monday 3rd of October 2022 at 1:00 PM in room FB53, AlbaNova Universitetscentrum, Roslagstullsbacken 21, Stockholm.

To Mia and Teo

Abstract

Gamma-ray bursts (GRBs) are among the brightest and most energetic phenom-ena in the Universe, observed up to great distances. The early X-ray emission ofgamma-ray bursts are usually well described by absorbed power laws. However,in some cases, additional thermal components have been identified. The origin ofthis emission is debated, with proposed explanations including supernova shockbreakout, emission from a cocoon surrounding the jet, and emission from the jetitself. A larger sample of detections is needed to constrain these different models.In this thesis, a time-resolved analysis of 199 GRBs observed by Swift XRT wasperformed to search for thermal components, which revealed 19 cases where thisthermal component is needed to describe GRB X-ray spectra fully. Due to the largespan of blackbody properties, the origin of the blackbody component is thought tobe connected with both the jet itself and the cocoon that surrounds it with nounique origin in all GRBs.

This thesis also tackles the question of where the excess X-ray absorption inGRBs originates from. There is an excess absorption above the Galactic valuein the X-ray spectra of all GRBs, and it increases with redshift. However, theexact location where it originates from is still a puzzle, with two possible, but notmutually exclusive, explanations: the host galaxy of the GRB and the inter-galacticmedium and intervening objects. I have analyzed the same sample of 199 GRBsto gain more insights into the origin of this absorption. One possible way to probewhere this excess absorption arises is to search for time variability of the excessabsorption in the X-ray spectra, which would point to the absorber being close tothe GRB. I found that this time decrease of excess absorption is a rare effect. In theGRBs that showed this time decrease, it cannot be excluded that an alternativemodel can explain the observed decrease. This implies that, in the majority ofGRBs, the absorption is happening at a larger scale in the host galaxy and/or inthe intergalactic medium.

Sammanfattning

Gammablixtar ar en av Universums mest kraftfulla explosioner. Den hoga ljusstyrkangor att blixtarna kan observeras pa mycket stora avstand. Langa gammablixtarintraffar i samband med att tunga stjarnor kollapsar, medan korta gammablixtarintraffar nar kompakta objekt slas ihop. Stralningen skickas ut i tva distinktafaser som kallas prompt stralning och efterglod. Den forsta fasen observeras framstgenom gamma- och rontgenstralning, och tros uppsta i en relativistisk jetstrale.Eftergloden uppstar senare nar jetstralen kolliderar med det omgivande mediet,vilket ger upphov till synkrotronstralning som observeras vid alla vaglander franrontgen till radio. Rontgenstralningen ar den kortaste fasen i eftergloden, mensamtidigt den fas som studerats mest, vilket beror pa att den observerats i hela 90procent av alla gammablixtar, jamfort med att ungefar halften av alla gammablix-tar observeras vid langre vaglander.

Rontgenspektra fran gammablixtar kan framgangsrikt observeras med rontgen-teleskopet XRT pa Neil Gehrels Swift rymdobservatorium. Rontgenobservationernastartar ungefar en minut efter att teleskopet triggat pa en gammablixt, vilket lateross studera bade slutet av den prompta fasen och eftergloden. Rontgenspektrumenkan oftast beskrivas val med en modell bestaende av en absorberad potenslag. Detfinns dock en liten andel gammablixtar dar denna modell inte racker till och entermisk komponent behovs for att forklara spektrumen. Ursprunget till den ovan-liga termiska komponenten ar omtvistat. En annan olost fraga ror absorptioneni spektrumen. Man vet att material utanfor Vintergatan absorberar stralningen ialla gammablixtar och att absorptionen ar starkare ju langre bort gammablixtenar. Daremot ar det oklart om merparten av absorptionen uppstar i vardgalaxernadar gammablixtarna intraffar eller i mediet mellan galaxerna.

Den har avhandlingen fokuserar pa att analysera rontgenspektra fran gammablix-tar for att adressera de tva fragorna ovan. Analysen genomfordes med hog tid-supplosning for att ta hansyn till den snabba utvecklingen av spektrumen over tid.I artiklarna I och II analyserar jag ett urval av 199 gammablixtar for att avgoraom det finns en termisk komponent i spektrumen, vilket visar sig vara fallet i 18gammablixtar. Analysen av den termiska komponentens egenskaper visar att denkan uppsta i flera olika delar av jetstralen. I artikel III analyserade jag absorptio-nen i samma urval av 199 gammablixtar for att undersoka om den avtar med tiden.En avtagande absorption kan namligen ge viktig information om var absorptionen

uppstar. Totalt 7 gammablixtar visar tecken pa en avtagande absorption, men detgar inte att utesluta att detta resultat kan forklaras med andra modeller for spek-trumen dar absorptionen istallet ar konstant. Resultaten visar tydligt att det armycket ovanligt med en avtagande absorption, vilket innebar att det mesta av detabsorberande mediet finns langt fran sjalva gammablixten i dess galax och/ellermellan galaxerna.

Acknowledgements

In the first place I would like to thank my supervisor Josefin Larsson. Thank youfor all the patience and support thought the years of my PhD. Having you as asupervisor has been inspiring.

To the people in the Particle and Astroparticle Group: thank you for all the goodtimes. To Bjorn for enduring with all my questions about anything, for listening tomy complaints about programming and great company. To Linda, Julia, Rakhee,Filip and Dennis: it was great sharing an office with you and thanks for all thelaughs!

Mom malom krugu velikih ljudi. Sasa, Naida and Igore, thank you for standingnext to me in all these years. Your encouragement and support is remarkable andcan never be taken for granted.

To my mom for all the support and endless belief in me. And finally to myhusband, Miroslav. Thank you for being my rock and safe haven. The PhD wouldhave been much more stressful without you.

Contents

Abstract v

Sammanfattning vii

Acknowledgements ix

Contents xi

Introduction xiii

List of publications xv

1 Gamma-Ray Bursts progenitors 11.1 Progenitors of short GRBs . . . . . . . . . . . . . . . . . . . . . . . 21.2 Progenitors of long GRBs . . . . . . . . . . . . . . . . . . . . . . . 2

1.2.1 Connection between long GRBs and SNe . . . . . . . . . . 31.3 General formation scenario and energetics . . . . . . . . . . . . . . 4

2 Emission phases 72.1 Prompt emission . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.2 The afterglow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

3 X-ray emission from gamma-ray bursts 113.1 X-ray light curves . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113.2 X-ray spectra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

3.2.1 Possible origins of the blackbody component . . . . . . . . 14

4 X-ray absorption 194.1 Galactic X-ray absorption . . . . . . . . . . . . . . . . . . . . . . . 204.2 Intrinsic X-ray absorption . . . . . . . . . . . . . . . . . . . . . . . 20

4.2.1 Caveats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214.3 Location of the X-ray absorption . . . . . . . . . . . . . . . . . . . 22

4.3.1 Host galaxy explanation . . . . . . . . . . . . . . . . . . . . 224.3.2 IGM explanation . . . . . . . . . . . . . . . . . . . . . . . . 23

xii Contents

5 The Neil Gehrels Swift Observatory 255.1 Swift : detectors and data reduction . . . . . . . . . . . . . . . . . . 25

5.1.1 BAT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265.1.2 XRT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

6 Data analysis and statistics 316.1 Time binning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 316.2 Spectral fitting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 326.3 Bayesian approach . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

7 Summary of publications 37

8 Conclusions and outlook 41

List of figures 43

Bibliography 45

Introduction

“Begin at the beginning,” the Kingsaid gravely, “and go on till youcome to the end: then stop.”

— Lewis Carroll, Alice inWonderland

Imagine you are in a meadow far away from any populated area. You arelooking into the night sky full of stars, planets, and the Milky Way. You reach outfor special glasses that enable you to see the sky in gamma rays. The sky lookscompletely different now. Suddenly, a bright flash of light illuminates the wholesky, blinding everything else. This flash is a gamma-ray burst (GRB), one of themost powerful explosions in the Universe.

GRBs are rare, luminous, cosmological events which are isotropically distributedover the sky. Their luminosities in the range L ∼ 1050 − 1054 erg s−1 enable us toobserve these objects at great distances and make them some of the furthest ob-jects ever observed (GRB 090429B with photospheric redshift1 z = 9.4, Cucchiaraet al. (2011) and GRB 090423 with spectroscopic redshift z = 8.2, Salvaterra et al.(2009)).

GRBs were first discovered in the late 1960s by Vela satellites as short flashes ofgamma-rays, but their existence was not published until some years later (Klebe-sadel et al., 1973). At this point, the origin of these events and even what theywere were utterly unknown. By the 1990s, there were more than 100 different the-ories explaining what these objects might be and where they may originate from.With the launch of the Compton Gamma-Ray Observatory (CGRO) in 1991 andthe instrument Burst and Transient Source Experiment (BATSE) on board thesatellite, it became clear that these objects are isotropically distributed across thesky (Briggs et al., 1996). A couple of years later, the Beppo-SAX satellite waslaunched, which discovered that GRBs have afterglows at longer wavelengths thatare long-lasting. It was from these afterglows that the redshift of the GRBs couldbe determined, which confirmed their extragalactic origin (Metzger et al., 1997).

1Redshift is a measure of distance normally used in astronomy and derived from the Dopplershift. The redshift of 1 corresponds to the distance of 10.1 billion light years.

xiv Contents

Even though significant steps were made in our understanding of GRBs, manyquestions remain unanswered. One of them is what powers the energetic jet that isthe source of the so-called prompt emission. In the current models, it is assumedthat the progenitors of long GRBs are massive, rapidly-rotating stars that undergocore collapse, but these have not been identified. Another unresolved mystery isthe existence of GRBs that have detected X-ray afterglow, but no optical and/orradio afterglow. In the X-rays the open question is the origin of the blackbodycomponent present in some X-ray spectra of GRBs. Another unanswered questionis where absorption in X-rays comes from. The last two questions related to theX-ray emission are the ones that this thesis tackles.

The thesis is structured in that it starts with a general introduction. Thenfollows Chapter 1, which is devoted to an overview of GRBs, their progenitors, andthe connection between long GRBs and supernovae (SNe). In Chapter 2, I brieflydescribe the two emission phases of GRBs: prompt emission and the afterglow.Chapter 3 deals with X-ray emission from GRBs, followed by Chapter 4, whichdescribes the X-ray absorption in GRBs and the challenges in studying it. Chapter5 describes the Neil Gehrels Swift observatory and instruments onboard it thatwere used in this work. Chapter 6 is devoted to data analysis and statistics, inChapter 7 I have summarized the papers included in the thesis and finally, Chapter8 presents the outlook. This thesis is based on my previously published Licenciatethesis (Valan, 2017). Most of the work is presented in three scientific articles at theend of the thesis. Two first articles are published in a scientific journal, and thethird is in the form of a draft submitted.

List of appended Papers

Paper IValan et al. (2017), Thermal component in theearly X-ray afterglow of GRBs: likely cocoonemission and constraints on the progenitors

Published in Monthly Notices of Royal Astronomical Society: 2017doi:10.1093/mnras/stx2920

Author contribution All data reduction and data analysis was performed byme. All figures were produced by me. I wrote the manuscript with comments fromco-authors.

Paper IIValan and Larsson. (2021), A comprehensive viewof blackbody components in the X-ray spectra ofGRBs

Published in Monthly Notices of Royal Astronomical Society: 2021https://doi.org/10.1093/mnras/staa3978

Author contribution I performed data redution, data analysis and produced ofall the figures in the paper. The manuscript was written by me, with improvementsand comments from my supervisor Josefin Larsson.

xvi Contents

Paper III (Draft)Valan et al. (2022), Investigating time variabilityof X-ray absorption in Swift GRBs

Submitted to Astrophysical Journal.

Author contribution I performed data analysis and produced of all the figuresin the paper. The code used for spectral fitting was provided by Bjorn Ahlgren.The manuscript was written by me, with improvements and comments from myco-authors.

All the work presented in this thesis was done under the guidance and supervi-sion of Josefin Larsson.

Chapter 1

Gamma-Ray Burstsprogenitors

”The ships hung in the sky in muchthe same way that bricks don’t.”

— Douglas Adams, TheHitchhiker’s Guide to the Galaxy

With the launch of BATSE, the number of detected GRBs surged (today, wedetect an average of one per day). Kouveliotou et al. (1993) identified two apparentclasses of GRBs by analyzing bursts duration: short and long. This division waseasiest seen on a histogram of burst duration on a log scale, where the two popula-tions were separated at approximately 2 s. Since then, it has become a standard tolabel all bursts with a duration of the gamma-ray emission shorter than 2 s as shortand those with longer than 2 s as long GRBs. As duration is ambiguous, T90 isused instead. It is the time during which 90 percent of the burst’s energy has beenobserved. There were some claims that there are more than two populations ofGRBs (Howell and Coward, 2013). However, the evidence for it and its statisticalsignificance was weak, and the intermediate population has been discarded by mostauthors (e.g., Bhat et al., 2016). However, there have been several observations ofGRBs with duration ≥ 10000 s, referred to as ultra-long GRBs (Levan et al., 2014).These GRBs were suggested to have different progenitors than the long GRBs, butdue to the small number of ultra-long GRBs observed, this is still inconclusive(Zhang et al., 2014). Today, GRBs are not just separated into different populationsonly by their duration but also by the prediction of their respective progenitors.

2 Chapter 1. Gamma-Ray Bursts progenitors

1.1 Progenitors of short GRBs

Short GRBs were thought to originate in mergers of two compact objects, like aneutron star–neutron star or a black hole–neutron star system (Berger et al., 2005).Due to the enormous amount of energy needed to power a GRB, single neutron starsand black holes are excluded as potential progenitors, and a black hole – black holesystem would result in a giant black hole without an accompanying electromagneticsignal. Further observations of the afterglows of these bursts placed constraints ontheir host galaxies. The association between short GRBs and elliptical galaxies wasobserved, which points to the host galaxies of short GRBs being dominated by olderstellar population (Berger, 2009; De Pasquale, 2019). However, this association isnot exclusive. Short GRBs are also found in various other types of galaxies, outsidetheir host galaxies, with a distinct separation around ∼ 5 kpc (Fong et al., 2022).Finding short GRBs outside of their host galaxies is consistent with old progenitorsbeing ”kicked” out due to the supernova (SN), after which the compact objects inthe binary merge on a long time scale (D’Avanzo, 2015).

The first hint of direct detection of this merger was a kilonova association withGRB 130603B (Tanvir et al., 2013). A kilonova (sometimes called macronova) isa day-to-week-long thermal, SN-like transient powered by the radioactive decayof neutron-rich elements. They are predicted to accompany neutron star–neutronstar or black hole–neutron star mergers (Metzger, 2017). It was not until 2017 andthe LIGO/VIRGO detection of GW170817 that was accompanied by a GRB anda kilonova, that this connection was firmly established (Abbott and al, 2017). Areview by Lazzati (2020) summarises our knowledge about short GRBs progenitorin the light of GW170817. Short GRBs are detected at lower redshifts, typicallyat z ∼ 0.6, with a handful of GRBs having redshift z ≥ 1 (Nugent et al., 2022).Unfortunately, due to their short durations, short GRBs are harder to localize, andthe sample of GRBs with determined redshift is much smaller than the long ones.

1.2 Progenitors of long GRBs

Long GRBs are thought to originate in the core collapse of massive stars. A confir-mation of this theory came in 1998 with the detection of the GRB 980425 togetherwith a supernova, SN 1998bw (Galama et al., 1998). This discovery, together withthe discovery of the link between long GRBs and star-forming regions in galaxies(Paczynski, 1998), enforced the argument that long GRBs are produced in the corecollapse of massive stars. The most popular model for explaining the production oflong GRBs is the collapsar model (Woosley, 1993; MacFadyen and Woosley, 1999).In this model, the progenitor of a GRB should be stripped of hydrogen and helium,and have a rapidly rotating stellar core, which points to progenitors being rotatingWolf–Rayet (WR) stars. However, a problem with this picture is that WR starsexhibit strong stellar winds, of order 10−5−10−4 M⊙ yr−1 (Crowther et al., 2002),which strips away the angular momentum during the star’s life. The only possibilityfor this effect not to be severe is that these WR stars are located at low metallicities

1.2. Progenitors of long GRBs 3

(Vink, 2013). On the other hand, the observations of GRB host galaxies are notlinking them exclusively to low metalicity environments (Levesque, 2012) and thereis a need for a possible explanation of how these objects are made at high metallic-ities. Another alternative is that these rapidly rotating WR stars are produced inmassive close binaries. This idea was tested by Cantiello et al. (2007) where theytested if mass accretion and quasi-chemically homogeneous evolution can producea rapidly rotating WR star. The authors also suggest that a significant fraction ofGRBs can happen in runaway stars.1 This idea was further explored by Eldridgeet al. (2011), but up to date, observational confirmations of the idea are missing.The main idea is that these stars get their high velocities when expelled from abinary system or a cluster, e.g., SN explosion (Blaauw, 1961). A rapidly-rotatinghydrogen-free massive star that undergoes pulsation pair instability until the corecollapse has been suggested as a potential progenitor of ultra-long GRBs. Theseprogenitors can have large radii ≥ 10 R⊙ (Moriya, Takashi J. et al., 2020). Areview done by Roy (2021) summarises the current knowledge and open questionsabout long GRB progenitors, and the reader is referred to it for a more in-depthdiscussion.

1.2.1 Connection between long GRBs and SNe

There have been more than a dozen connections between GRBs and SNe. Whatis striking is that all of the SNe are hydrogen and helium-poor SNe with broadspectral lines implying large ejecta velocities, i.e., Ic broad-line SN (Mazzali et al.,2008). Modjaz et al. (2016) studied the population of SNe Ic-BL with and withoutaccompanying GRB and deduced that SNe that accompany GRB have broader linesand higher velocities than those without GRBs. The majority of GRBs that havea detected SN accompanying them have equivalent isotropic γ-energies of the order1051−1052 erg cm−2 s−1. However, two exceptional GRBs were associated with SN:GRB 130427A, a very energetic GRB (Melandri et al., 2014) and GRB 111209A,which was an ultra-long GRB (Greiner et al., 2015).

As GRBs and SNe are produced in the core collapse of massive stars, we expectto observe an SN accompanying each GRB detected in the local Universe. How-ever, in 2006 two nearby GRBs were discovered: GRB 060505 at z = 0.08913 andGRB 060614 at z = 0.12514. Given their proximity, they were deeply studied inthe optical, but no sign of an SN was detected (Fynbo et al., 2006). This non-detection demonstrated that at least some GRBs are either associated with veryfaint SNe or produced differently (Della Valle et al., 2006). These two GRBs havelong durations, but spectral lags and hardness ratios that put them into the shortGRB realm (Gehrels et al., 2006). Recently, there was a detection of a long GRBaccompanied by a kilonova (Rastinejad et al., 2022). This GRB resembles the pre-vious two because it has a duration longer than 2s, while the spectral propertiesare similar to those of short GRBs. These three cases highlight the complexity of

1A runaway star is a star that is moving with extremely high velocity relative to its surroundinginterstellar medium (ISM).

4 Chapter 1. Gamma-Ray Bursts progenitors

GRB analysis. One way to detect SNe accompanying GRBs is to analyze opticalafterglows. Around 7–10 days after the trigger, the optical afterglow will changefrom a featureless spectrum to a spectrum exhibiting SN features. This is explainedby the optical afterglow of GRB fading, while the SN spectrum is slowly emerging.Also, even when no spectra are available, light curve ”bumps” on their own areinterpreted as SN signatures.

All GRBs with an accompanying SN are low-redshift GRBs, with the highestspectroscopic redshift being z = 0.677 (Schady, 2017). There is no chance to detectthe SN at redshift z > 1. The review done by Cano et al. (2017) summarizes im-portant observational aspects of the GRB-SN connection, and the reader is furtherreferred to it.

1.3 General formation scenario and energetics

One of the main characteristics of GRBs is their fast variability. This variability setsstrong constraints on the theoretical models since it narrows the size of the emittingregion (Sari and Piran, 1997). Given the small size of the emitting region and thetypically high isotropic luminosity of GRB, the radiation would be optically thickdue to pair production, and the high energy emission would be suppressed. Theonly explanation for this emission is that it is emitted in a relativistic outflow witha high Lorentz factor, typically Γ ≥ 100. This high value of Lorentz factor impliesthat the outflow is relativistic and beamed (Lithwick and Sari, 2001). Anotheradvantage of jetted emission is that it relaxes the energy requirements. The energyreleased by a GRB is enormous and challenging to explain if it is assumed to beemitted equally in all directions, i.e., isotropically. The jetted emission can reducethe total energy required by a factor of a hundred (Harrison et al., 1999).

The primary energy source for a GRB is likely accretion onto a compact object.This process is one of the most energy-efficient ones in the Universe. One of themost common theories for explaining the formation of GRBs is the collapsar scenario(Woosley, 1993). In the collapsar scenario, no matter the progenitor, we expect theformation of the black hole with an accretion disk. If we have a compact objectmerger, we expect a part of the signal to be converted to gravitational waves, whichwas observed (Abbott and al, 2017). For long GRBs, the collapsar scenario assumesthey arise when a rapidly rotating Wolf-Rayet star collapses. The first numericalsimulation of the jet launching in the collapsing Wolf-Rayet star was performed byMacFadyen and Woosley (1999). After this, there were more advanced numericalsimulations performed that included association with SNe, collimation of the jet,and the existence of the cocoon around the jet (MacFadyen et al., 2001; Zhang et al.,2003, 2004). These simulations established the collapsar model as the standardtheoretical framework of long GRBs.

Apart from creating a black hole, there is a scenario in which a fast-rotatingproto-neutron star is created. This proto-neutron star, which rotates rapidly, with arotational period of ∼ 1 ms and a strong magnetic fields of order 1015 G, is called amagnetar (Usov, 1992). The total energy released by a GRB in this model is ∼ 1052

1.3. General formation scenario and energetics 5

erg (Bucciantini, 2012), and we still lack observations that may confirm or disputethis theory. The vast majority of GRBs have total energy smaller than predictedin the magnetar model, with inferred total energies of a few 1051 erg (Shivvers andBerger, 2011). Observational support for the magnetar model is a plateau feature inthe X-ray light curves of GRBs. This feature is explained as a consequence of resid-ual magnetic energy (Stratta et al., 2018). This residual energy can inject energyinto the outflow at later times (Metzger et al., 2011). Other possible explanationsof the plateau in the X-ray light curves are discussed in Chapter 3. The magnetarmodel has also successfully explained the high luminosities of some SNe connectedto GRBs (Greiner et al., 2015; Margalit et al., 2018). While magnetars are mainlysuggested as engines of long GRBs, numerical simulations also show that they area promising short GRB engine as well (Lu et al., 2015; Mosta et al., 2020).

Chapter 2

Emission phases

”There is a theory which statesthat if ever anyone discoversexactly what the Universe is forand why it is here, it will instantlydisappear and be replaced bysomething even more bizarre andinexplicable. There is anothertheory which states that this hasalready happened.”

GRB emission is divided into two parts: prompt emission and afterglow. Promptemission is short-lasting, from milliseconds to a couple of minutes, and is mainlyobserved in the gamma-rays. On the other hand, the afterglow is longer lasting (itcan last up to years) and is observed from X-rays to the radio range.

2.1 Prompt emission

The prompt emission is observed in gamma rays. It can also be detected as X-ray emission that coincides with gamma rays. The prompt emission is thoughtto originate from the relativistic jet itself (for a detailed review of GRB promptemission, see Pe’er 2015).

The main characteristic of prompt emission is light curves. They are irregular,complex, and diverse. This makes these objects even more complex for analysisas no two GRBs are alike, and in the prompt phase, few common features couldbe associated with a particular spectral model. Since the exact physical processbehind the prompt emission was unknown, an empirical function, which was proven

8 Chapter 2. Emission phases

to fit the prompt emission spectra well, was used to fit the prompt emission spectra.This empirical model is called the ”Band model” (Band et al., 1993), defined as:

N(E) = A

100keV

exp(− E

), E < (α− β)E0[ (α− β)E0

100keV

]α−β

exp(β − α)( E

100keV

, E ≥ (α− β)E0.

This model has four free parameters: two spectral slopes, a low energy one (α)and a high energy one (β), break energy (E0), and a normalization. Note that thisis an empirical model, and conclusions about physics governing prompt emissionare hard to deduce.

As mentioned, one of the unanswered questions about GRBs is the origin ofprompt emission. In the fireball model, the standard theoretical framework for ex-plaining prompt emission, the radiation that leaves the photosphere would producea Plank function spectrum, broadened by geometric effects (Lundman et al., 2013).However, the majority of GRB spectra are broader than this model (Acuner et al.,2019). There are many ways to obtain non-thermal spectra involving different emis-sion processes and, dissipation mechanisms1. The main dissipation mechanisms are:magnetic reconnection and shocks. Magnetic reconnection is a process where themagnetic topology is rearranged, and for a detailed description of this process ap-plied to GRBs, the reader is further referred to Thompson (1994) and Giannios andSpruit (2005). Various types of shocks can dissipate energy in GRBs, with the mostcommonly used ones being internal shocks (Rees and Meszaros, 2005) and externalshocks (Rees and Meszaros, 1992).

Several authors have developed physical models to fit the GRB prompt emis-sion spectra, including models for subphotospheric dissipation with internal shocks(Ahlgren et al., 2015; Ahlgren et al., 2019), external shocks (Burgess et al., 2016),magnetic reconnection (ICMART model) (Zhang and Yan, 2011) and physical syn-chrotron model by Burgess et al. (2020). Differentiating between these models iffar from a trivial task, and one property that could help us distinguish which modelis most appropriate is polarization.

Polarization is a property of any electromagnetic wave and represents the angleof oscillation of the electric field E to some reference direction. Each of the physi-cal models suggested for explaining the prompt emission has a different predictionfor the polarization degree and the evolution of the polarization angle. Specifi-cally, photospheric models predict low polarization or even unpolarized emission.However, some photospheric models can produce low to moderate polarization, typ-ically several percent (Ito et al., 2014; Lundman et al., 2014, 2018). On the otherhand, synchrotron models can produce higher levels of polarization (Gotz et al.,2009; Yonetoku et al., 2012). However, due to the lack of adequate instrumentsfor measuring gamma-ray polarization (McConnell, 2017) and the fact that the po-larization has been measured in a small number of GRBs (Chattopadhyay et al.,

1Dissipation means that the energy is transferred to the electrons in the outflow.

2.2. The afterglow 9

2019; Burgess et al., 2019; Zhang et al., 2019; Sharma et al., 2019), there is no clearconclusion about the physical processes governing the prompt emission.

However, this thesis focuses not on the prompt emission but on the longer-lastingafterglow emission.

2.2 The afterglow

The afterglow of GRBs arises when the jet collides with the circum-stellar material(CMS) surrounding the progenitor star and sweeps it into the interstellar medium(ISM). The emission arising from this process is synchrotron emission, which isproduced when electrons are accelerated in a magnetic field. The observed after-glow at longer wavelengths is a natural consequence of the expanding shock wavesweeping up more and more material and thus losing energy. When the jet collideswith the CSM, forward and reverse shocks are formed. The forward shock will haveits peak emission in the X-rays, while the reverse shock travels slower and will peakat optical/infrared wavelengths.

The afterglows were first detected by Beppo-SAX in the X-rays, and the accuratepositions provided enabled the discovery of an optical afterglow of GRB 970227 (vanParadijs et al., 1997). The first radio afterglow was detected shortly after the firstoptical afterglow (Frail et al., 1997).

The X-ray afterglow is the first and strongest signal, but it is also the shortestand was hard to observe in the 1990s. The number of detected X-ray afterglowsincreased rapidly with the launch of the Neils Gehrels Swift satellite (Gehrels et al.,2004). Thanks to its X-ray Telescope (XRT) Swift enables rapid follow-up, preciselocalization, and provides early X-ray spectra of GRBs. The X-ray afterglow isdiscussed in more detail in Chapter 3.

Kann et al. (2010, 2011) compiled a sample of GRB optical afterglow lightcurves and concluded that the magnitude of the luminosity at day one peaks at -23R magnitude. Some early observations suggested a possible bi-modality around dayone (Liang and Zhang, 2006; Kann et al., 2006), but this disappeared as the sampleincreased (Kann et al., 2010; Zaninoni et al., 2013). Li et al. (2012) complied asynthetic optical afterglow light curve (shown in Figure 2.1) and showed that theoptical light curve has more features and more components than the X-ray syntheticlight curve (shown in Figure 3.2).

Approximately 90% of GRBs have detected X-ray afterglows, while optical andradio afterglows are available for only 30− 50% of the bursts (Piran, 2004). GRBswith no detected optical afterglow are sometimes referred to as ’dark GRBs’ in theliterature. The lack of observable optical afterglow is often attributed to significantdust extinction in the GRB host galaxies (Piran, 2004) and some studies (Perleyet al., 2009) suggest that high-z GRBs account for a small fraction of observed darkGRBs. There were several attempts to objectively define a dark GRB as a GRBwhose optical flux or upper limit is ’darker’ than the prediction from the synchrotronradiation model according to the measured X-ray flux (Jakobsson et al., 2004; Rolet al., 2005). This definition means that the GRB can be classified as ’dark’ but

10 Chapter 2. Emission phases

Figure 2.1: A syntactical optical afterglow light curve. Figure from Li et al. (2012).

still have a detected optical afterglow and vice-versa. Recent studies of the ’dark’GRBs show that they exhibit high line-of-sight dust extinction AV ≥ 2.2 − 10.6mag. This extinction is mainly due to larger scale patchy dust distribution and notassociated with GRB’s local environment (Schroeder et al., 2022).

The radio afterglow is the most long-lasting phase of the GRB emission, lastingup to years. The radio observations thus enable us to follow the complete evolutionof the fireball from its beginning to the non-relativistic phase (Frail et al., 2000;Berger et al., 2004; van der Horst, A. J. et al., 2008). A radio afterglow light curvetypically shows an early rise, peaks around 3–6 days after the trigger at frequency8.5 GHz with a median peak luminosity of 1031 erg s−1 Hz−1 in case of long GRBs.For short GRBs, the peak is about one magnitude or even more fainter (Zhang et al.,2019). Radio observations are a powerful tool in understanding GRB physics sincetheir longevity enables studies of the full broadband spectrum. Unfortunately, radioobservations suffer from a similar problem as optical, with a relatively small numberof GRBs having detected radio afterglows compared to X-ray afterglows. Reviewby Chandra and Frail (2012) summaries statistical properties of radio afterglows,and the reader is further referred to it.

Even though the afterglow is referred to as one entity, it is different in differentwavelengths in which it is observed. The focus of this thesis and the researchpresented in it is on the X-ray emission of GRBs.

Chapter 3

X-ray emission fromgamma-ray bursts

”Remember to look up at the starsand not down at your feet. Try tomake sense of what you see andwonder about what makes theuniverse exist.”

Stephen Hawkin

The exact origin of the X-ray emission from GRBs is still unknown. It is unclearwhether this emission represents the true afterglow or the late-time prompt emission(O’Brien et al., 2006). When looking into light curves of GRBs detected by XRTon board the Swift (the instrument is described in Sec. 5.1.2), it is clear that someof the lightcurves are erratic and complex, while others are smooth (see Fig. 3.1).

3.1 X-ray light curves

There are five common phases in a typical GRB X-ray light curve, although notall five are always observed (Evans et al., 2009). The emission starts with a steepdecay, followed by shallow decay (also referred to as plateau), normal decay, andpost-jet break, respectively. Flares are the fifth component superposed on the X-raylight curve (see Fig. 3.2).

The steep decay phase (I) may correspond to high-latitude emission, which ispart of the prompt emission. This theory is supported by the fact that this emissionjoins smoothly to the actual prompt emission (Barthelmy et al., 2005; O’Brien et al.,2006). Zhang et al. (2007) did a time-resolved analysis of this phase and showedstrong spectral softening with time. Such spectral softening is not expected in thesimplest high-latitude curvature effect model. However, it can be accounted for if

12 Chapter 3. X-ray emission from gamma-ray bursts

Figure 3.1: X-ray light curves of four different GRBs observed by XRT. The toptwo panels represent GRB 111123A and GRB 121211A, which have erratic lightcurves. The bottom two panels represent GRB 150727A and GRB 151027A, whichhave more smooth light curves. Adapted from Paper I.

the spectrum at the end of the prompt emission is described by a curved spectrum,such as a cutoff power law or Band model. Detailed modeling of the sample ofGRBs suggested that the mentioned model invoking a curved spectrum can explainthe steep decay phase in at least some GRBs (Zhang et al., 2009; Genet and Granot,2009; Mangano and Sbarufatti, 2011; Zhang et al., 2012).

The steep decay is followed by a shallow decay phase (II) - the plateau. Itsorigin is debated, with two main competing theories. The first theory is the late-time activity of the central engine (van Eerten, 2014), which is supported by theobservations of flares (V) over-imposed on the X-ray light curves. The flares are alsothought to represent late central engine activity (Burrows et al., 2005, 2007; Zhanget al., 2006). Margutti et al. (2010) found a luminosity-spectral lag relation for bothprompt emission and X-ray flares. This connection suggested a direct link betweenprompt emission and X-ray flares. O’Brien et al. (2006) showed by analyzing 40Swift GRBs that the flares observed in X-ray light curves are most likely due to latecentral engine activity. Many X-ray flares are simultaneously detected in gammarays by Swift BAT (for description of the instrument see Chapter 5). Thereforeit was suggested that X-ray flares should be regarded as a part of the promptemission (Peng et al., 2014). The second theory to explain the X-ray plateaus isthe magnetar origin, which gained more interest in recent years (Gibson et al., 2018;Stratta et al., 2018; Zheng et al., 2021).

The so-called normal decay phase (III) is called a ’normal’ X-ray afterglow. Inthis phase, there is no more energy injection into the flow, and, subsequently, the

3.1. X-ray light curves 13

Figure 3.2: Phases of the canonical GRB X-ray light curve. Number I representsthe steep decay; II is the plateau; III is normal decay; IV is a post-jet break, andV flares. Adapted from Zhang et al. (2006).

edge of the jet becomes visible (Evans et al., 2009). This segment often steepensto phase IV. This steepening is expected due to the jet-break effect1, which isthen called the post-jet-break phase. The origin of phase IV is again debated.The external shock models can explain its existence (e.g., Wang et al., 2015; Liet al., 2015). However, there are claims that it is produced by internal dissipationof a long-lasting central engine wind (Ghisellini et al., 2007; Kumar et al., 2008;Cannizzo and Gehrels, 2009; Lindner et al., 2010).

It was shown that for bursts detected by Swift, a correlation between promptand afterglow fluxes exists (Gehrels et al., 2008). Ghisellini et al. (2007) suggestedthat the X-ray emission was a combination of the ’late prompt’ and ’true afterglow’where the ’late prompt’ emission was due to internal shocks with lower bulk Lorentz

1In the standard fireball model it is assumed that the outflow is moving with relativistic speedin a conical jet with jet half-opening angle θj . As the burst ejecta are decelerated by the medium,the relativistic beaming angle 1/Γ increases. When 1/Γ > θj is satisfied, a steepening break inthe afterglow light curve is predicted.

factor than in the initial case. As mentioned, not all phases of the X-ray emissionare observed for all GRBs. Most often, the long-lasting afterglow, resulting fromthe forward shock is observed.

3.2 X-ray spectra

X-ray spectra of GRBs are usually very well explained by a power law with aspectral index in the range 0.5 ≤ β ≤ 1.5 (Racusin et al., 2009). The spectralindex,β, is defined as F ∝ E−β , where F is the flux and E is the energy. Thespectral index is related to the photon index (Γ) as β = Γ− 1.

However, in 2006, observations of a low-luminosity, nearby GRB, GRB 060218,showed that this power-law model is insufficient to fully explain the spectrum (Cam-pana et al., 2006). In this case, it was established that an additional blackbodycomponent is needed to explain the spectrum of GRB 060218. Since then, theblackbody component is reported to be present in dozen new cases (Starling et al.,2012; Friis and Watson, 2013; Nappo et al., 2017; Valan et al., 2018; Valan andLarsson, 2021). Even though the presence of this component in particular bursts isconfirmed, the origin of it is still extensively debated. Some theories include thatthis component originates from the jet itself, the cocoon surrounding the jet, orarises from the SN shock breakout.

3.2.1 Possible origins of the blackbody component

Shock breakout

As the massive star reaches the end of its life, it implodes and then explodes asan SN. In the first stage of the explosion, an SN shock wave is formed, and itpropagates through the star and will eventually emerge from it through the outerenvelope region in which the optical depth is low. This shock breakout happenswhen the optical depth drops below ∼ v/c where v is the shock velocity, which isexpected to happen at the star’s surface. The first light we can observe from anSN comes from the shock breaking out (Waxman and Katz, 2017). The breakoutis expected to produce a flash of X-ray/UV radiation with a blackbody spectrumon a short time scale (several seconds up to a fraction of an hour). It is followedby UV/optical emission from the expanding cooling envelope on a longer time scale(order of a day). If the SN progenitor exhibits, prior to the explosion, episodes ofenhanced mass-loss from its surface, then the shock breakout will not take place atthe surface of the star but instead on a larger radius in the CMS. In this case, thebreakout time scale may be prolonged from hours to days.

The long GRBs have been linked to SNe Ic-BL. Their presumed progenitorsare WR stars, with typical radii of 1011 cm (Sander et al., 2012). These starsare also assumed to have stronger mass loss towards the end of their lives, withobservations supporting this theory (Gal-Yam et al., 2014). There were varioustheoretical studies investigating SN shock breakout from a thick wind with expected

3.2. X-ray spectra 15

breakout radius of order ∼ 1012 cm (Balberg and Loeb, 2011), which is in agreementwith observations of SN 2008D (Modjaz et al., 2009). According to this model, theexpected energy from the shock breakout is Ebo ∼ 1044 erg, and we expect thespectrum to peak in the X-rays (Waxman and Katz, 2016).

Even though the blackbody component found in the X-ray spectra of GRBspeak in the X-rays, the problem with the shock breakout theory is that the observedexplosion radii are too big to be attributed to shock breaking out from the surfaceof the star. The assumption of the shock breakout through a thick wind needs tobe invoked. Furthermore, this theory fails to explain the observed large energiesimplied by the blackbody component detected in the X-ray spectra of GRBs. Thisis why most blackbody components detected in GRBs (except GRB 060218) areattributed to either the cocoon surrounding the jet or the jet itself.

Cocoon emission

As the jet pierces its way through the progenitor star, the majority of its energyis deposited into a cocoon surrounding it. The cocoon is the shocked jet materialaccumulating around the jet as it passes through the star. As the jet emerges fromthe star, the hot plasma composing the cocoon will swiftly escape the star andaccelerate to mildly relativistic speeds. The schematics of this process is shownin Fig. 3.3. After it emerges, the cocoon leaves a blackbody signature that isobservable in the X-rays. This signature is similar to the one produced by the SNshock breakout but much more energetic Lbo ∼ 1047− 1050 ergs−1 (De Colle et al.,2017). Another inferred observable signature of the cocoon is X-ray bumps or flaresin the early X-ray light curves of GRBs or a shallow X-ray decay phase (Pe’er et al.,2006).

Figure 3.3: Schematic illustration of different components in a long GRB withaccompanying SN. The figure is from Fig. 1 in Nakar and Piran (2017).

Recently, there were various numerical simulations of calculating light curvesand spectra expected from the cocoon. These spectra are quasi-blackbody and

peak in the soft X-rays, while the light curves exhibit a rapid rise until the peak lu-minosity of ∼ 1047erg s−1 and then slowly decay on a time-scale of 100 s (De Colleet al., 2018; De Colle et al., 2022; Suzuki and Maeda, 2022). These predicted lumi-nositites are still not high enough to explain all observed blackbody luminosities.However, these predictions are based on the post-processing of hydrodynamicalsimulations, and still, no first-principles numerical simulations are done for cocoonsfrom GRBs and a limited range of jet properties have been simulated.

Prompt emission

As was previously mentioned, the X-ray emission from GRBs could be a combina-tion of the late prompt emission and the actual afterglow emission. It is commonfor early X-ray light curves of GRBs to exhibit flares, which are proposed to bedue to late and weak central engine activity, i.e., connected to prompt emission(Chincarini et al., 2007). More recent studies of these flares showed a spectral evo-lution similar to that observed in the prompt emission (Margutti et al., 2010). Thejet origin for the blackbody component is suggested in cases where the blackbodycomponent detected is very luminous and with high temperature (Friis and Wat-son, 2013). As noted in Section 2.1, the prompt emission may arise from the jetphotosphere in some GRBs, giving rise to quasi-thermal spectra (Ryde and Pe’er,2009). From this point, it is plausible that this decay trend will continue and thatthe end of the prompt emission will appear in the soft X-rays as a blackbody com-ponent with very high luminosity and temperature (Friis and Watson, 2013). Thejet origin of the blackbody component is suggested for several bursts (Irwin andChevalier, 2016; Nappo et al., 2017).

All three scenarios give important insights into the physics behind the X-rayemission from GRBs. Therefore, it is crucial to have a clear idea of where andhow this blackbody component arises in order to be able to say something moreabout the progenitors of GRBs. This puzzle is extensively addressed in the firsttwo attached papers, where the common properties of GRBs with the detectableblackbody component are examined. In Figure 3.4, the color-coded origin of theblackbody component is presented based on the temperature and luminosity evo-lution of the blackbody.

X-ray spectra of GRBs hide more puzzles than just blackbody components. X-ray spectra at low energies are affected by absorption, and the exact location ofthis absorption is still unclear.

3.2. X-ray spectra 17

0 100 200 300 400 500 600Time since BAT trigger (s)

T BB (

Short GRBLL GRBsPrompt originUnclear originCocoon origin

0 100 200 300 400 500 600Time since BAT trigger (s)

Figure 3.4: Temperature (on the left) and luminosity (on the right) of the black-body for 19 GRBs that have reported blackbody component in their X-ray spectra.The color coding represents different possible explanations of the blackbody origin.Figure from paper II.

Chapter 4

X-ray absorption

”It seemed at first a new kind ofinvisible light. It was clearlysomething new, somethingunrecorded. There is much to do,and I am busy, very busy.”

Wilhem Rontgen

At energies below 10 keV, the most dominant absorption process is photo-electric absorption or photoionization (Wilms et al., 2000). Although commonlyparameterised by the hydrogen column density, the absorption is mainly due to lessabundant metals. The reason for this lies in the size of the cross-section. Cross sec-tion is the probability of absorption expressed as an area. The larger the area, thebigger the absorption. This, in turn, means that the heavier elements (in astronomyreferred to as metals) contribute more to absorption than hydrogen.

Optical depth τ measures how much light is blocked when it passes through acertain medium. It is defined from the following expression:

Iobs(E) = e−τIsource(E) (4.1)

where Iobs(E) is the observed intensity and Isource(E) is the intensity initially emit-ted at the source. Another way to express the optical depth is as the total absorp-tion cross section σtot divided by the total area. Let n be the number density ofabsorbers with a cross-section σ in the volume V . Let L be the distance traveledby the photons through a medium. The total absorption cross section can then beexpressed as σtot = σnV = σnAL, where A is an area surface. The optical depthcan be expressed as τ = σnL.

Due to the distribution of absorbers along the line of sight being unknown, it iscommon practice to express the absorption in terms of column density. The columndensity is the number of absorbers per unit surface area instead of per volume,

20 Chapter 4. X-ray absorption

N = nL. Then optical depth becomes: τ = σN . The effective cross-section is thesum of the different elements weighted by their cosmic abundances:

σe =1

niσi (4.2)

where σi is the cross-section for the different absorbing materials, ni/nH is thecosmic abundance of the absorber i compared to the amount of hydrogen. In thispicture Equation 4.1 becomes:

Iobs(E) = e−σe(E)NHIsource(E) (4.3)

where NH is the hydrogen column density.The effect of the absorption is that it lowers the number of photons at low

energies and this effect is the strongest at energies ≤ 2 keV. This also means thatthe low-energy photons are more easily absorbed than the high-energy ones due tothe larger cross-section. The total absorption is usually expressed as a combinationof two components: Galactic and extragalactic (intrinsic) components.

4.1 Galactic X-ray absorption

As the name suggests, the Galactic X-ray absorption is the absorption happeninginside the Milky Way. The total hydrogen column density consists of two compo-nents: atomic and molecular. The atomic absorption, accounting for about 80% ofthe absorption, is determined from the 21 cm radio emission lines1 (Kalberla et al.,2005; HI4PI Collaboration et al., 2016). From empirical studies of X-ray after-glows, Willingale et al. (2013) estimated the molecular component. In this picturethe total hydrogen column density is defined as: NH,tot = NH,I+2NH,II where NH,I

is the atomic absorption component and NH,II is the molecular component. Theirestimate was based on measurements of dust extinction and the atomic hydrogenmeasures and is set as

NH,II = NH,II,max

[1− exp

(−NH,I

)]αwhere α is a power law index and Nc, is a characteristic hydrogen column densitywhere the power law increase in NH,II flattens out to form a plateau with themaximum value corresponding to NH,II,max.

4.2 Intrinsic X-ray absorption

After analyzing X-ray spectra obtained by Beppo-SAX, it was noticed that there isstill an excess in X-ray absorption above the Galactic value (Frontera et al., 2000;

121 cm radio emission line is created by the change in the energy state of a neutral hydrogenatom due to the spin-flip transition.

4.2. Intrinsic X-ray absorption 21

Stratta et al., 2004). This ’excess absorption’ was labelled as intrinsic absorptionNH,intr. Since the launch of the Swift, it became clear that this absorption is presentin all GRBs. A well-established result is that the NH,intr inferred from opticalmeasurements is much lower than the X-ray values (see e.g. Watson et al., 2007;Fynbo et al., 2009). Another known property of this absorption is that it increaseswith redshift (see Figure 4.1). This apparent correlation between NH,intr and z wasconfirmed in many studies (see e.g., Campana et al. (2010); Starling et al. (2013);Rahin and Behar (2019); Dalton and Morris (2020) and also Paper III), but it wasalso challenged by some authors (Buchner et al., 2017). It is apparent from Figure4.1 that there is a lack of GRBs with low absorption at high redshifts. One potentialexplanation is that this is a selection effect. Campana et al. (2014) investigatedthe minimum detectable NH,intr and found that for Swift XRT, it scales as NH,intr

= NH,z=0 × (z + 1)2.34, where NH,z=0 would be the value of intrinsic absorption atz = 0. The blue line in Figure 4.1 represents this minimum detectable NH,intr forNH,z=0 = 1019 cm−2. The lack of highly absorbed GRBs at low redshifts has alsobeen attributed to selection effects. Campana et al. (2010) proposed that this lackof highly absorbed GRBs at redshift z ≤ 2 could be explained by the difficulty inobtaining spectroscopic redshifts of such highly obscured sources if the high columndensity was accompanied by high dust extinction. However, it is unlikely that theseselection effects can fully explain the observed correlation (Campana et al., 2012;Watson and Jakobsson, 2012; Starling et al., 2013).

4.2.1 Caveats

The standard procedure when fitting X-ray spectra of GRBs is to assume solarmetallicity. The reason for this was the small number of reliable metallicity mea-surements and poor constraints on metallicity evolution (Starling et al., 2013).Currently, when fitting X-ray spectra of GRBs, the solar metallicities are set tovalues from Wilms et al. (2000). As is known, GRB hosts have, on average, sub-solar metallicity, and this assumption induces systematic error, and the value ofNH,intr derived is effectively a lower limit (Starling et al., 2013; Tanga et al., 2016).

Another assumption made in the standard procedure when fitting the X-rayspectra is that all the excess absorption above the Galactic value happens at theredshift of the GRB. This assumption induces the error in the NH,intr due to theredshift difference between the GRB host and any intervening absorber. This, inturn, means that any potential error in NH,intr increases with redshift depending onthe IGM absorption, its location, and any errors in scaling law that was assumed.On top of that, the IGM hydrogen column density is uncertain since the metalfraction is poorly determined (Maiolino and Mannucci, 2019).

The third simplifying assumption is that the value of NH,intr is determinedassuming neutral absorbing gas (Behar et al., 2011; Starling et al., 2013). If theabsorber is ionized, it would have a lower cross-section in X-rays, and a largercolumn density would be needed to produce the same opacity. All these assumptionslead to the value of NH,intr being underestimated, and the values reported shouldalways be regarded as lower limits.

100 101

log(z+1)

Figure 4.1: Distribution of NH,intr for 199 GRBs observed by Swift in the time span2005–2018 with known spectroscopic redshift. The blue line represents the minimaldetectable absorption for Swift XRT based on the analysis done in Campana et al.(2014). Adapted from Paper III.

4.3 Location of the X-ray absorption

While the existence of intrinsic absorption and its relation to redshift is known, theexact location of this absorption is still a source of great debate. The two mostprominent schools of thought are that it originates in the host galaxy in the vicinityof the GRB or is dominated by absorption in the intergalactic medium (IGM). Thequestion of where the excess X-ray absorption in GRBs originates is the focus ofthe third paper attached to the thesis.

4.3.1 Host galaxy explanation

A common practice when fitting the X-ray spectra is to assume that all excess ab-sorption happens at the redshift of the GRB. This practice assumes that the excessX-ray absorption is related to dust and gas in the molecular cloud in the vicinity ofthe GRB and in its host galaxy (Waxman and Draine, 2000; Galama and Wijers,2001; Perna et al., 2003). One of the first signs that the absorption is happening inthe host galaxy was presented by Galama and Wijers (2001). By analyzing availableX-ray afterglows at the time, they found high absorption values of 1022−1023 cm−2

4.3. Location of the X-ray absorption 23

and suggested that these objects are located in dense molecular clouds. Compar-ing these values to the values obtained from optical data, they deduced that thelower value of absorption inferred from the optical spectra supports the theory thatthe hard radiation from GRBs is destroying dust and enabling optical afterglow toescape (Krongold and Prochaska, 2013).

Watson et al. (2013) analyzed the X-ray spectra of Swift GRBs and found a cor-relation between absorption and dust extinction (AV) and neutral hydrogen columndensity (NH,intr). They also found that the ratio NH,intr/AV evolves similarly withredshift as the metallicity redshift evolution in host galaxies of GRBs (althoughDalton and Morris (2020) find no metallicity evolution with redshift in more recentdata). Based on these findings, they conclude that the helium in the GRB hostgalaxy HH II regions is responsible for most of the absorption. Watson et al. (2013)also set limits on the size of this absorbing region to be ≳ a parsec but ≲ a fewtens of parsec with density 103 − 104 cm−3.

Buchner et al. (2017) proposed a model for the column density distribution ofthe hosts as an axisymmetric ellipsoid of gas with a GRB randomly placed inside.Based on the mass dependence of the absorption and the geometry, they argue thatthe galaxy-scale gas dominates the X-ray excess absorption.

There have been reports of decreasing NH,intr in several GRBs (Starling et al.,2005; Grupe et al., 2007; Campana et al., 2007, 2021). This decrease would be anatural consequence of GRB X-ray/UV radiation ionizing the surrounding medium.The afterglow at later times propagates into a more ionized medium, leading todecreased absorption with time (Perna and Loeb, 1998; Lazzati et al., 2002; Pernaet al., 2003). From the theoretical models it follows that if the GRB is very luminousand absorber compact (with radius R < 5pc, particle density nH ∼ 104 cm−3 andsize ∼ 1018 cm−3), the time scale of ionization is short (on a scale of milliseconds),and this process would be complete before the X-ray observations would start.However, it may be possible to detect a decrease in NH,intr on time scales of 50–100 s if the absorbers are dense pc-scale clouds or shells. In Paper III, we performeda time-resolved analysis of GRBs’ X-ray spectra and found that a possible decreaseof NH,intr is present in a handful of bursts. This result implies that the excessabsorption is most likely not dominated by the imminent surrounding for mostGRBs.

4.3.2 IGM explanation

Another theory is that the NH,intr is dominated by the material located in betweenthe Milky Way and the host galaxy of the GRB, i.e., the IGM and interveningobjects (Behar et al., 2011; Campana et al., 2012; Starling et al., 2013; Campanaet al., 2015; Dalton et al., 2021). Behar et al. (2011) used numerical simulationsto show that cold, neutral, metal-enriched IGM could produce excess X-ray ab-sorption at large redshift. A more realistic warm hot IGM (WHIM) was modeledby Starling et al. (2013). They showed that at redshifts greater than z ≳ 3, theIGM contribution to absorption becomes dominant if the temperature of the IGMis 105 − 106 K and metallicity Z/ZSun ∼ 0.2.

Recently, Dalton et al. (2021) developed a new model for GRB line-of-sightIGM absorption. Their analysis was based on fitting Swift X-ray spectra with aset of models for different ionization scenarios. The main ionization processes arephotoionization (PIE) and collision-ionization (CIE). In the redshift range 1.6 ≤z ≤ 6.3, they found that the CIE scenario captures most of the spectra, with similarvalues and correlation with redshift as the mean IGM density model (at redshiftz = 0 the mean density is n0 = 1.8+1.5

−1.2 × 107 cm−3). While not excluding thatthe host galaxies can contribute to the total line-of-sight absorption, Dalton et al.(2021) conclude that it is the IGM and intervening objects that constitute the bulkof the NH,intr.

These two explanations for the origin of the excess X-ray absorption are notmutually exclusive. However, both of the models rely on several simplifying as-sumptions previously mentioned.

Chapter 5

The Neil Gehrels SwiftObservatory

”A picture is worth a thousandwords. A satellite image is worth amillion dollars.”

Sarah Parcak

X-ray radiation cannot be observed on the ground because it is blocked by theEarth’s atmosphere. Our only chance of observing and detecting X-ray photons isby sending satellites into space.

5.1 Swift : detectors and data reduction

The Swift was launched in 2004 and today it is one of the main facilities for observ-ing GRBs. The satellite’s main purpose is to detect GRBs and follow them up inX-rays, UV, and optical ranges. It is placed in a low Earth orbit with a period of∼ 96 minutes. The satellite carries three instruments on board: BAT (Burst AlertTelescope) (Barthelmy et al., 2005), XRT (X-ray telescope) (Burrows et al., 2005)and UVOT (UV/Optical Telescope) (Roming et al., 2005). The satellite and thepositions of each instrument are shown in Figure 5.1. After the BAT triggers on aGRB, Swift automatically, during the next 20–70 s, slews to the position derivedfrom the trigger. Spectra and multiwavelength light curves for the afterglow dura-tion are publicly available on Leicester Swift-XRT GRB Catalogue catalog pages.1

These products are prepared for use in scientific analysis, and the user does notneed to perform their own data reduction. In the next sections, I will only describethe BAT and XRT instruments since I have not used UVOT data in the analysis.

1http://www.swift.ac.uk/xrt_live_cat/

26 Chapter 5. The Neil Gehrels Swift Observatory

Figure 5.1: The Swift with its instruments. The figure is from Figure 2 in Gehrelset al. (2004).

The XRT is described in more detail since it is the main instrument used for dataanalysis in the papers.

5.1.1 BAT

The Burst Alert Telescope (BAT) is the instrument that detects a GRB. It is ahighly sensitive, large field-of-view instrument (approximately one steradian) withthe primary purpose to monitor a large fraction of the sky for GRBs. BAT uses acoded-aperture mask which is purpose-built for Swift. It provides the burst trig-ger and the position with a precision between 1–4 arcmin (Barthelmy et al., 2005).The triggering algorithm scans the detector count rate for excess above the expectedbackground and constant sources. The algorithm continuously applies a large num-ber of criteria for detection and the table of criteria can be adjusted. The bursttrigger threshold is also adjustable, ranging from 4 − 11σ above the backgroundnoise with a typical value of 8σ.

A key feature of BAT for burst detection is its imaging capability. In order foran event to be classified as GRB, it has to correspond to a point source. After thetrigger, the onboard software checks a point source, eliminating many backgroundsources such as magnetospheric particle events and flickering in bright Galacticsources. When a burst is detected, the sky location and intensity are immediatelysent to the ground and distributed to the community for a follow-up through theGamma-Ray Burst Coordinates Network (GCN) (Barthelmy et al., 2000).

The energy range covered by BAT is 15–150 keV with an energy resolution of∼ 7 keV. The number of bursts detected by BAT is > 100 yr−1. Besides triggeringon GRBs, BAT is also accumulating an all-sky hard X-ray survey.

Reduction of BAT data

Even though the online catalog offers ready-to-use spectral files, some users mayfind it helpful to perform their own data reduction. This is, in particular, the casewhen performing time-resolved analysis.

5.1. Swift: detectors and data reduction 27

The steps for BAT data reduction are described in ”The Swift BAT SoftwareGuide” 2. The first step in the data reduction is constructing the Detector PlaneImage (DPI). After this step, a Fast Fourier Transform (FFT) is applied to decon-volve the detector plane from the coded mask pattern, resulting in an image in skycoordinates. The next step is to find and filter out the noisy detectors, which isdone using the Calibration Database. In it, the list of bad detectors is kept, andthe filtering of so-called hot pixels is applied. Finally, spectra and response filescan be made. The basic steps include extraction of the spectra from the event fileswith the specified duration of the burst that is read out of the header of the eventfile. After producing the spectra, the response matrix file needs to be built in orderto fit these spectra, which is produced using batdrmgen command. These files arethen used for spectral analysis.

5.1.2 XRT

The X-ray Telescope (XRT) is an X-ray telescope that focuses the light onto aCCD detector designed to measure GRBs’ fluxes, spectra, and light curves. Thebasic principle of operation of X-ray CCDs is as follows: the incoming X-ray isstopped by the energy converter, which produces a certain number N of visiblelight photons or electrical chargers. The CCD in XRT has a thin filter to protect itagainst optical light, as the CCD is sensitive to optical light. In addition, the CCDis protected from sunlight by a shutter which will automatically close if the CCDis pointed to a direction within 10◦ from the Sun. For a detailed description ofthe CCDs and their components, the reader is referred to Gruner et al. (2002) andtheir Figures 1 and 13. XRT covers the energy range 0.2–10 keV, and it generallystarts observing ∼ 100 s after the BAT triggered. The XRT field of view is 23.6arcmin square, much narrower than the BAT field of view (Burrows et al., 2005).The XRT effective area is shown in Figure 5.2.

The background in the instrument is a combination of the instrumental back-ground (electronic noise) and the astrophysical background. The astrophysicalbackground comprises the Cosmic X-ray background and various particles, such ascosmic rays or solar protons. The electronic noise component of the backgroundis dominant at the lower energies. However, since it is concentrated in a few badpixels, it is easily filtered out by standard software. The astrophysical background,mainly the particle-induced one, is significant at the higher energies (Moretti et al.,2007). The background spectra are extracted away from the source to account forbackground contribution. The background in XRT is generally very low comparedto the count rate of a GRB, typically 0.01% of the total count rate (see Figure 5.3).

The instrument has two main modes of operation: Photon-counting (PC) andWindowed Timing (WT) mode. WT mode has a 1.8 ms time resolution, mainlyused when the count rate is high, generally at the beginning of the XRT observa-tions. The PC mode has a worse time resolution of 2.5 s and is generally used at thelower count rate. The XRT will automatically change between different observing

2https://swift.gsfc.nasa.gov/analysis/bat_swguide_v6_3.pdf

28 Chapter 5. The Neil Gehrels Swift Observatory

Figure 5.2: Plot of the effective area of the XRT. The figure is taken from https:

//swift.gsfc.nasa.gov/about_swift/xrt_desc.html

modes as the X-ray emission fades. Should the emission become brighter later, theXRT will automatically change between modes in the reverse direction. In this the-sis, I am mainly using WT mode data, due to a better time resolution and betterstatistics in the data. These data appear as a single strip of data, oriented at thespacecraft roll angle since WT data, unlike PC data, are read out in one dimension.

Reduction of XRT WT data

Unlike BAT, where online data reduction cannot be used for time-resolved analysis,it is possible to extract time-resolved XRT spectra from Leicester Swift catalog 3.I sketch the process of manual data reduction of XRT data as some users may findit helpful to do the process themselves.

The files that can be downloaded from the Leicester Swift catalog are event filesthat contain information about each photon detected. Apart from the event files,attitude files that contain attitudes determined from the satellite star trackers areavailable. Before starting actual data reduction, it is essential to check if the dataexhibit pile-up, an effect in CCD cameras when there is a significant probabilitythat two or more photons will be registered as a single event. Pile-up occurs forthe WT mode data if the light curve has more than 100 counts s−1. The easiestway to deal with pile-up is described in Appendix A of Romano et al. (2006). Theauthors selected 5 time intervals during which the observed source count rate was< 100, 100–200, 200–300, 300-400 and > 400 counts s−1. They demonstrated byfitting these spectra with an absorbed power-law that the source is affected by

3http://www.swift.ac.uk/xrt_live_cat/

5.1. Swift: detectors and data reduction 29

Figure 5.3: Plot of the XRT background compared to the GRB spectrum for time-resolved spectra of GRB 111123A. Different colors represent different time intervals.The upper points in the plot represent the spectrum and the lower ones the back-ground.

moderate pile-up for 100–300 counts s−1 but this can be mitigated by excludingone pixel from the extraction region. The source is strongly affected by pile upfor 300–400 counts s−1 and there two pixels need to be excluded, while for > 400counts s−1 four pixels need to be excluded from the extraction region. These pixelsare excluded from the center of the Point Spread Function (PSF) since the countrate there is the highest.

From the WT image, in the first step, source and background regions are pro-duced using the xselect tool and ds9 environment. Then, these region files, togetherwith event and attitude files and possible time binning files, are fed to the xrtprod-ucts pipeline that produces spectral and background files, together with a responsefile. Swift has two types of response files: the Redistribution Matrix (RMF) andthe Ancillary Response Files (ARF). These two files contain information about theXRT effective area, which is made up of three components: the mirror effective area,the filter transmission, and the CCD quantum efficiency. The quantum efficiency isincluded in the RMFs, while the mirror effective area and the filter transmission areincluded in ARFs. Response files also contain information about channel-energyrelations. RMF and ARF files are needed to fit the XRT spectra. The final stepin the data reduction process is to flag the bad columns. These bad columns resultfrom Swift being hit by a micrometeorite in 2005, and a small number of columnsis being vetoed to prevent telemetry saturation. For the WT data, columns 0–29are marked as bad, corresponding to the energy range 0.2–0.3 keV. These files arenow ready to be used in spectral analysis.

Chapter 6

Data analysis and statistics

”However, if we’re starting withthe wrong questions, if we don’tunderstand the cause, then eventhe right answers will always steerus wrong... eventually.”

— Simon Sinek, Start With Why

6.1 Time binning

Spectral analysis of GRBs is usually made in two ways: time-averaged and time-resolved. The time-averaged analysis means that all the photons from the entirety ofthe burst are analysed together without taking into account their individual arrivaltimes. The advantage of this approach is better statistics, which leads to tighterconstraints on model parameters. The downside is that any possible temporalspectral evolution is completely ignored. Strong spectral evolution can result in adistorted time-averaged spectrum, from which erroneous conclusions may be drawn.On the other hand, the advantage of using time-resolved analysis is that one cancapture the temporal spectral evolution at the expense of lower signal-to-noise andlooser model parameter constraints.

When performing time-resolved analysis, the choice of time binning is importantas there is no guarantee that the spectrum extracted in a given time bin won’texhibit spectral evolution (see Burgess et al., 2015). One binning method that triesto address this issue is co-called Bayesian blocks (Scargle et al., 2013). Its mainpurpose is to capture local temporal variations in the count rate with Bayesianprobability theory. This in practice means that, unlike constant exposure time,the blocks may vary in duration. However, the underlying assumption I used whenapplying Bayesian blocks binning is that there is no spectral evolution when thecount rate is approximately constant. This method is non-parametric, i.e. the

32 Chapter 6. Data analysis and statistics

binning is determined by the data itself rather than some pre-setup condition (likesignal-to-noise ratio). While this method traces the evolution of flux well, somebins may end up having a poor statistics due to low count rate. Bayesian blocksbinning is the binning used in this thesis. A light curve of GRB 111225A binnedwith Bayesian blocks is shown in Fig. 6.1 as an illustration of the method. Spectraare extracted in each bin shown in the Figure 6.1.

100 150 200 250 300 350 400 450Time since BAT trigger (s)

s−1)

Figure 6.1: Light curve of GRB 111225A binned with Bayesian blocks. Adaptedfrom Paper I.

6.2 Spectral fitting

Raw spectral files are unbinned. The XRT spectral files are, by default, groupedso that one count occupies one bin. As the data are Poisson distributed, this typeof grouping allows for the use of Cash statistics (Cash, 1979). However, since thisstatistic cannot directly be interpreted as goodness-of-fit, it is common practice inthe field when using the X-ray data to group them such that each bin contains atleast ∼ 20 counts, which in turn allows usage of χ2 statistics. The need for thisparticular grouping of the data arises from the fact that χ2 statistics is strictlydefined for Gaussian-distributed data. When the number of counts per bin exceeds∼ 20, the deviation from Gaussianity become less severe (Humphrey et al., 2009).

Spectra are fitted with the standard software package XSPEC (Arnaud, 1996).The software uses source and background spectra, as well as response files. Responsefiles represent the detector response to incoming photons. An incoming photondoes not necessarily deposit all of its energy into one detector channel. Also, dueto the detector’s effective area, the distribution of energy in the detector variesprobabilistically depending on the energy of the incoming photon. This process isdescribed by the detector response matrix. Swift has two types of response files:response matrix files (RMF) and ancillary response files (ARF).

6.2. Spectral fitting 33

The detector matrix models the transition from flux f(E), measured in photon s−1

cm−2 keV−1, to detector counts C(I), measured in s−1 PHA channel−1. The sim-plest way of obtaining the photon spectrum is by inverting the RMF

f(E) = C(I)R(I, E)−1. (6.1)

However, the RMF cannot be inverted, and to fit the spectra, a procedure calledforward folding is used (Piron et al., 2001). This procedure requires a model to bepredefined. This model is then convolved with the response file, which results inthe model’s count prediction as:

C(I) =

∫ ∞

f(E)R(I, E) dE (6.2)

where C(I) is the model count rate. This prediction is then compared to thedetector counts. The results are then best-fitting parameters from the model thatgive the best agreement with the data. This leads to results being highly dependenton our assumption of the model.

The above described fitting procedure is done using Maximum Likelihood (ML).The main principle of ML is the assumption that the best-fit model parameter valuesmaximize the probability of the observed data given the model, i.e. maximize thelikelihood. The likelihood L is defined as the total probability of observing thedata given the model, L(θ) = P(d|θ), where d represents the observed data and θmodel. However, it is common practice to minimize twice the negative loglikelihood−2ln(L) due to simplicity with obtaining the same result.

By default the errors on the parameters in XSPEC are 90% confidence intervaland are calculated such that the parameter of interest is fixed and others are fitted.Then the value of the parameter of interest is changed and the fitting is doneagain. This process is then repeated until there is a desired level of significancethat corresponds to the change in statistics.

In this thesis, I explore which of the two models (simple power law or powerlaw + blackbody) better describes the data. In order to asses the significance ofthe added component (the blackbody), Monte Carlo simulations were performed.In this procedure, a large number of spectra (10 000) are simulated from the best-fitting parameters of the power-law model and then the two models are re-fittedto these fake spectra. The resulting distributions of χ2 are then compared to theones from real data and the conclusions are drawn. The biggest advantage ofthis approach is that allows for robust way of assessing significance of the addedcomponent. The main downside of this procedure is that it is computationallyexpensive. XSPEC has a built-in procedure for assessing which model fits databetter called F-test, but this procedure is not valid when assessing the significanceof the added component (Protassov et al., 2002). Another approach to performmodel comparison is to apply Bayesian statistics and set the analysis as Bayesianinference, as was done in Paper III.

6.3 Bayesian approach

The fitting procedure outlined above is referred to as the frequentist approach. TheBayesian versus frequentist discussion is still ongoing and is beyond the scope ofthis work. However, before outlining the Bayesian inference in more detail, onekey difference between the two approaches must be mentioned. In the Bayesianinterpretation, the probability of an event corresponds to our degree of belief thatthe event will happen. In the frequentist approach, the probability of an event isthe relative frequency of its outcome given that we can make infinite number oftrials. The key concept of Bayesian statistics is Bayes’ theorem, which states thatthe probability is given as:

P(θ|d) = P(θ)P(d|θ)∫P(θ)P(d|θ)dθ

∝ P(θ)P(d|θ) (6.3)

where P(θ|d) is the posterior probability of θ given data d, P(θ) is the prior andP(d|θ) is the likelihood of the observed data d, given the model θ. The denomina-tor,

∫P(θ)P(d|θ)dθ is the marginalized likelihood, also referred to as evidence Z.

The evidence is not dependent on model parameters and is left out if one is onlyestimating parameters. However, if the goal of the analysis is model comparisonand/or hypothesis testing, then the evidence plays the crucial role. When makingparameter estimation, the crucial quantity is the posterior.

As was mentioned, the probability of an event is proportional to our degree ofbelief. This information is in-coded in the prior P(θ). This can be regarded aseither updating our knowledge by considering new data, or incorporating previousknowledge when analyzing new data. Given that the choice of prior is subjectiveand that different people choose different priors for the same analysis, the usageof Bayesian inference can be controversial (Gelman, 2008). The bottom line whenchoosing a prior is to choose a probability density function for each parameter, or ajoint multivariate probability distribution, that is in line with one’s prior knowledge.

One reason why Bayesian inference was not widely used previously is the factthat it is computationally expensive due to difficulties in evaluating posterior prob-abilities. The common solution to this problem is to sample the posterior such thatthe density of samples is proportional to the posterior. One popular sampling tech-nique relies on the Monte Carlo Markov Chain (MCMC) approach, and popularalgorithms include the Metropolis-Hastings algorithm (Hastings, 1970) or a morerecent parallel-tempering technique (Altekar et al., 2004). Another approach is touse nested sampling (Skilling, 2004) and its implementation MultiNest (Feroz et al.,2009). The main purpose of nested sampling is to calculate the evidence (Z) withposterior samples as a by-product.

As was said, the posterior holds all the information in Bayesian statistics. Unlikethe frequentist approach, where one obtains point estimates and uncertainties ata certain significance level, the Bayesian approach provides additional informationin the posterior. However, extracting and visualizing this additional information isnot trivial.

6.3. Bayesian approach 35

A multidimensional posterior is usually represented by a corner plot (see Figure6.2 for an example). This corner plot shows the marginalized posterior distributionsfor all parameters and combinations of parameters. This plot has the advantage ofshowing both mean and variance of all parameters and it also allows for inspectionof any model degeneracies.

Bayesian inference is a powerful tool when performing model comparison. Inthis framework, Bayes factor is used to compare two competing models. Bayesfactor (BF) is defined as:

log(BF01) = log(p(d|M0))− log(p(d|M1)), (6.4)

for comparison between models 0 and 1. The most commonly used scale for deter-mining which model is preferred based on BF is the one defined by Jeffreys (1961).On this scale if the value of log(BF01) is < 0 then model 1 is favored, and if it is> 0 model 0 is favored. However, with values of BF close to 0, it is hard to saywhich model is preferred and in Paper III it was considered strong evidence for acertain model if BF was < −1 or > 1.

NH, intr (1022 cm 2)

2.2 2.4 2.6 2.8

Figure 6.2: Corner plot of the posterior for GRB 170607A for time-slice 55–67 s.The histograms show the marginalized posterior for each free parameter in thefitting. The contour plot shows the joint posterior for a pair of parameters. Thisparticular corner plot shows the marginalized posterior of photon index Γ andNH,intr from Paper III.

Chapter 7

Summary of publications

Paper IValan et al. (2017), Thermal components in theearly X-ray afterglow of GRBs: likely cocoonemission and constraints on the progenitors

X-ray spectra of GRBs are usually well described by an absorbed power-law model.However, previous studies showed that an additional thermal component is neededin a dozen cases to describe the spectra fully. The exact origin of the thermalcomponent is debated with three competing theories: the jet itself, a hot cocoonsurrounding it, and the shock breakout. Each of these three theories yields differentpredictions about the observable blackbody quantities.

In this paper, we have fitted time-resolved spectra of 74 GRBs observed by theSwift X-ray telescope. The sample consists of GRBs observed between 2011-Jan-01and 2015-Dec-31 with known spectroscopic redshift and above a certain flux thresh-old. For completeness, we have also analyzed GRBs that have previously reportedthermal components (an additional 9 GRBs). We compared the absorbed power-law model with an absorbed power-law plus blackbody model to systematicallysearch for a thermal component. In order to assess the significance of the thermalcomponent, we have performed Monte Carlo simulations where we simulated 10,000GRB spectra. These faked spectra were then fitted with the two models. We havecompared the distribution of ∆χ2 from fake data to the actual ∆χ2 to assess thesignificance of the thermal component. We required the thermal component to besignificant at ≥ 3σ level in at least three consecutive time bins. This conditionensured that we could follow the time evolution of the blackbody component.

We identified six new cases of thermal components and confirmed three previ-ously reported ones. From the narrow range of blackbody properties (luminosity,temperature, and radius), we speculate that the origin of the thermal component ismost probably the hot cocoon that surrounds the jet as it pierces out of the thick

38 Chapter 7. Summary of publications

wind of the progenitor star. However, two GRBs in the sample are consistent withexpectations of a thermal component arising from the jet itself.

Paper IIValan and Larsson. (2021), A comprehensive viewof blackbody components in the X-ray spectra ofGRBs

This paper is a continuation of Paper I, where we expanded the sample to analyzeGRBs observed by Swift from 2005-Apr-01 until 2018-Dec-31. The sample consistsof 116 GRBs, and together with the GRBs analyzed in Paper I, it is a combinedsample of 199 GRBs. We kept the same sample selection criteria and performedthe same time-resolved analysis as in Paper I. The only difference compared toPaper I is that we have grouped data such that one count occupies one bin. Thismeans that we have used Cash statistics and not χ2. In this paper, we have alsoinvestigated jet properties (such as Lorentz factors and photospheric radiis) to gainmore information about the systems and emission site.

We report the detection of the thermal component in 9 new GRBs. Out of these9 GRBs, one GRB is classified as short GRB. We inspect the range of blackbodyand jet properties for all 18 GRBs. We find that there is a wide spread of blackbodyand jet properties. By comparing the theoretical predictions with our results, weconclude that the observed thermal emission is not connected to the cocoon in themajority of cases. We find that approximately 1/3 of the GRBs are consistent withthe late-time prompt emission from the jet itself. In the remaining 2/3, there areGRBs consistent with cocoons, as well as GRBs that high-latitude emissions ormore energetic cocoons may explain. These findings suggest that the blackbodyemission is connected to all parts of the jet.

Paper III (Draft)Valan et al. (2022), Investigating time variabilityof X-ray absorption in Swift GRBs

X-ray spectra of GRBs are known to be absorbed in energies ≤ 2 keV. This absorp-tion is divided into Galactic absorption and absorption over this Galactic value.This excess absorption is confirmed to be increasing with redshift, but the loca-tion where it arises is still debated. One possible way to probe the location of theabsorption is to search for time variability in the excess absorption. Suppose theabsorption is happening in the vicinity of the GRB. In that case, X-ray/UV radia-tion will ionize the surrounding medium, and the afterglow will propagate later intoan already ionized medium. This will then lead to a natural decrease in absorption.

This paper analyzes the combined sample of 199 GRBs with spectroscopic red-shifts from Papers I and II. We perform a time-resolved analysis in search of adecrease in excess absorption. We find some evidence for decreasing excess ab-sorption in 7 GRBs, 2 of which have been reported previously to have a thermalcomponent in their X-ray spectra. In order to determine whether this decrease isreal, we perform fits alternative models for the underlying spectrum, but a fixedabsorption, and find that such models cannot be ruled out. Our results clearlyshow that decreasing absorption is rare and that in most GRBs, excess absorptionhappens on a larger scale in the host galaxy of the GRB and/or in the inter-galacticmedium. This also implies that the premise that the excess absorption stays con-stant in the spectral analysis is valid and safe to assume in the vast majority ofGRBs.

Chapter 8

Conclusions and outlook

”“Forty-two,” said Deep Thought,with infinite majesty and calm.”

GRBs are a fascinating phenomenon that has captured researchers’ attentionfor decades. Still, many open and unanswered questions regarding GRBs and theirphysics exist.

I have tackled two open questions about the X-ray emission from GRBs. I haveperformed a more finely time-resolved analysis in this thesis compared to mostprevious studies. I have also paid particular attention to the statistical treatmentin the analysis. The significance of the blackbody component is assessed based onMonte Carlo simulations, and Bayesian inference is used when fitting for excessabsorption in the X-ray spectra. Finally, all the work presented in this thesisrepresents systematic studies of large samples.

Firstly, while a power law describes most GRBs’ X-ray spectra, some requireadding a blackbody to fully describe their X-ray spectra. Where this blackbodycomponent originates from is debated. In Papers I and II, I have analyzed a sampleof 199 GRBs with known redshifts that were observed by Swift XRT to investigatehow many GRBs need a blackbody component and where this blackbody originates.In total, 19 GRBs needed a blackbody component to describe their X-ray spectrafully, and it is plausible that the blackbody is associated with all parts of thejet. Observed properties of blackbody components in some GRBs matched thetheoretical predictions for cocoons very well. The blackbody properties also make itpossible to determine physical properties of the systems, such as Lorentz factors andemission radii. However, improved theoretical models and a larger observationalsample are needed to fully exploit the blackbody components as probes of theprogenitors and jets.

42 Chapter 8. Conclusions and outlook

Secondly, the presence of the excess absorption above the Galactic value in GRBX-ray spectra is well known, as is its scaling with redshift. However, where thisexcess absorption, also called intrinsic absorption, originates from is still an openquestion. One way to investigate whether the absorption is happening in the hostgalaxy in the vicinity of the GRB, or on a larger scale, is by analyzing time-resolvedspectra in search of time variability in the excess absorption. In Paper III, I haveanalyzed the same sample of 199 Swift GRBs to try and shed more light on thismatter. I found that the time variability of the intrinsic absorption is a scarceeffect. Only 3% of the analyzed GRBs showed some evidence of a decrease in theintrinsic absorption, which points to the IGM and intervening objects as a dominantsource of the intrinsic absorption. In cases where a decrease of intrinsic absorptionis observed, it is likely that the pc-scale clouds in the host galaxy are a dominantsource of the absorption.

In order to learn more and make firmer conclusions about rare phenomena, weneed better data. We need X-ray spectra that start right after the GRB trigger toprobe physics more closely to the GRB. If we had such early data and observed adecrease in the intrinsic absorption, we might learn about the structure and evengeometry of the material close to the GRB. Also, early X-ray data could enableus to inspect the overlap between X-ray and γ-ray spectra. In some instances,very early X-ray spectra can be late-time prompt emission, and having more datawould help us tackle the question of the prompt emission’s origin. We also needinstruments that can offer improved sensitivity and spectral resolution.

Swift has been in orbit for 17 years, and we need a successor to it. Currently,the missions proposed with X-ray instruments on board and the ability to do spec-troscopy are Athena, SVOM, and XRISM. Athena (Advanced Telescope for HighEnergy Astrophysics) is supposed to launch in the 2030s with an observing range of0.5–12 keV (Nandra et al., 2013). However, it is not directly built for the detectionand localization of GRBs, and the observations would probe the afterglow with astart time 12 h after the trigger. XRISM (X-ray Imaging and Spectroscopy Mis-sion) is a successor to the HITOMI mission lost in 2016 (Tashiro et al., 2020). It isexpected to launch during the coming year. XRISM’s primary mission is the inves-tigation of celestial X-ray objects with imaging and high-resolution spectroscopy.The spectrograph on board XRISM will observe in the energy range of 0.3–12 keV.As GRBs are not one of the primary science goals, XRISM, similarly to Athena,might probe the afterglow several hours after the trigger. SVOM (Space VariableObjects Monitor) is planned for launch in 2023 and is primarily built for the de-tection and localization of GRBs (Wei et al., 2016). It will have an X-ray telescopeon board with an energy range of 0.2–10 keV. This energy range is similar to Swift,and hopefully, it will provide us with earlier X-ray data. Currently, this is our besthope for early X-ray data.

List of Figures

2.1 A synthetic typical optical light curve . . . . . . . . . . . . . . . . 10

3.1 X-ray light curves of four different GRBs observed by XRT. . . . . 123.2 Phases of the canonical GRB X-ray light curve. . . . . . . . . . . . 133.3 Schematic illustration of the GBR . . . . . . . . . . . . . . . . . . 153.4 Blackbody properties of GRBs with reported blackbody component 17

4.1 Distribution of NH,intr for 199 GRBs observed by Swift . . . . . . . 22

5.1 The Swift with its instruments . . . . . . . . . . . . . . . . . . . . 265.2 Plot of the effective area of the XRT . . . . . . . . . . . . . . . . . 285.3 Plot of the XRT background compared to the GRB spectrum . . . 29

6.1 Light curve of GRB 111225A binned with Bayesian blocks. Adaptedfrom Paper I. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

6.2 Example of a corner plot. . . . . . . . . . . . . . . . . . . . . . . . 36

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