Peak Luminosity Function

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Table of contents

Abstract
Résumé
I General introduction
1 Introduction
1.1 Gamma-ray bursts
1.1.1 Main observational facts
1.1.2 Theoretical framework
1.2 The progenitors of long GRBs
1.3 Use of GRBs in cosmology
1.4 Structure of the manuscript
II A population model for Long Gamma-Ray Bursts
2 Introduction
2.1 Scientific motivation
2.2 GRBs in the domain
2.2.1 Past, current (and future) missions
2.2.2 Distance
2.2.3 GRB spectrum
2.2.4 Fluxes in the domain
2.2.5 Fluence
2.3 Scientific landscape for GRB population models
3 Constructing an LGRB population
3.1 Forward modeling approach
3.2 Parametrization of the intrinsic long GRB population
3.2.1 Peak Luminosity Function
3.2.2 Peak Energy distribution
3.2.3 Spectral slopes distribution
3.2.4 Redshift distribution and LGRB efficiency
3.2.5 Parametrization of the LGRB comoving rate
3.3 Creating mock samples
3.3.1 From the source frame to the observer frame
3.3.2 CGRO/BATSE
3.3.3 Fermi/GBM and Swift/BAT
3.3.4 HETE2/FREGATE and WXM
3.4 Summary
4 Observational constraints
4.1 Intensity Constraint
4.1.1 logN-logP
4.1.2 Efficiency correction
4.1.3 Normalization of the LGRB population
4.2 Spectral Constraint
4.2.1 Observed Ep distribution
4.3 Redshift Constraint
4.3.1 Biases in redshift distributions
4.3.2 BAT6: a well-controlled, complete sample
4.4 Additional observables for crosschecking and complementary studies
4.4.1 The eBAT6 Ep-L plane
4.4.2 The SHOALS redshift distribution
4.5 Summary
5 Parameter space exploration
5.1 Statistical tools
5.1.1 Bayesian inference
5.1.2 Goodness of fit estimator
5.2 Exploring the parameter space
5.2.1 Brute Force
5.2.2 Markov Chain Mont Carlo
6 Results and discussion
6.1 Introduction
6.2 Methodological check
6.3 Scenario without intrinsic spectrum-luminosity correlation
6.3.1 Scenarios with a constant LGRB efficiency (n˙ GRB / SFR)
6.3.2 Scenarios with a redshift distribution free to vary
6.3.3 Summary for LN-Ep models
6.4 Scenario with intrinsic spectrum-luminosity correlation
6.4.1 Scenarios with a constant LGRB efficiency (n˙ GRB / SFR)
6.4.2 Scenarios with an LGRB efficiency free to vary
6.4.3 Summary for A-Ep models
6.5 Discussion
6.5.1 Distinguishing between well-fitting scenarios
6.5.2 The intrinsic redshift distribution and the production efficiency of LGRBs
6.5.3 What is the luminosity function?
6.5.4 What is the true LGRB rate?
6.5.5 Is there an intrinsic « Amati-like » correlation?
6.6 Conclusion
Appendix A Results from population model MCMC exploration
Appendix B Fits to the observational constraints from our population model
Appendix C Additional cross-checks
Appendix D The SVOM/ECLAIRs sample
III The environment of Long Gamma-Ray Bursts revealed by their host galaxies
7 Introduction
7.1 Why study the host galaxies of LGRBs?
7.2 The BAT6 LGRB host sample
7.3 The X-Shooter instrument
8 Are LGRBs biased tracers of star formation? Clues from the host galaxies of the Swift/BAT6 complete sample of bright LGRBs (Palmerio, Vergani et al. 2018)
8.1 Abstract
8.2 Introduction
8.3 The BAT6 sample of LGRB host galaxies at z > 1
8.3.1 Selection
8.3.2 Stellar mass
8.3.3 Star Formation Rate and Metallicity
8.4 Comparison with the star-forming galaxy population
8.4.1 Comparison samples
8.4.2 Bayesian framework
8.4.3 Stellar Mass
8.4.4 Star Formation Rate
8.4.5 Metallicity
8.5 Discussion
8.6 Conclusions
Appendix E Spectral Energy Distribution fitting with BEAGLE
Appendix F LGRB host galaxies: magnitudes and emission line fluxes
IV General conclusion
9 General conclusion
9.1 Summary of our main results
9.1.1 Population model for LGRBs
9.1.2 Host galaxies of LGRBs
9.2 Consequences for LGRB production efficiency
9.3 Perspectives
9.3.1 Extending the host galaxy study
9.3.2 Predictions for SVOM/ECLAIRs
9.3.3 Parametrization of the redshift distribution of the intrinsic LGRB population
9.3.4 Is our model representative of the whole intrinsic GRB population?
9.3.5 Can we extend the model to other GRB properties?
Bibliography