Multiband Statistical Study Of Gamma-ray Bursts

Gamma-ray bursts(GRBs)is a high-energy burst phenomenon in which gamma-ray from cosmic space are suddenly enhanced in a short period of time.Observations show that GRBs are associated with supernova,gravitational wave,and kilonova,and are one of the hot areas of current astrophysical research.GRBs originate from cosmological distances and are one of the important tools for studying cosmology.Although the study of GRBs has made great progress,some fundamental issues such as:classification,progenitor,radiation mechanism,and the com-position of the outflow are not solved.These urgently requires us to deeply mine the multi-band observations to further study the statistical properties of GRBs and elucidate the fundamental physics of GRBs.The research results will be helpful to the problems of gravitational wave,the origin and evolution of the universe,etc.This paper focus on the classification of GRBs,the properties of short duration bursts and X-ray flares using multi-band observations.The full text is divided into five chapters.A brief overview of the progress,observational properties and theoretical models of GRBs are described in Chapter 1.In Chapter 2,we focus on the intrinsic statistical properties of short bursts.In Chapter 3,we study the statistical properties of single X-ray flare GRBs and the relation between flares and prompt emission.In Chapter 4,we inves-tigate the application of machine learning to the classification of GRBs.And we conclude with an outlook in Chapter 5.In Chapter 2,we collect the latest observations of short burst samples and their host galax-ies,and study the intrinsic statistical properties of short bursts and the relation with their host galaxies.We update the spectrum-energy relations of short bursts(Ep,z-Eiso and Ep,z-Liso rela-tions),and further confirm that short bursts have different Ep,z-Eiso relations from long bursts.We find for the first time a positive correlation between the isotropic energy of short bursts and the specific star formation rate of their host galaxies,which suggests that host galaxies with energetic short bursts have high star formation rates.Using a sample of 11 short bursts with observed jet break,we first find a strong correlation between the isotropic energy,peak energy,and jet break time of short bursts,and use this relation to estimate the jet opening angle of other short bursts,finding that the typical value of the jet opening angle of short bursts is 7.5°which is larger than long bursts.The relations between the flare and the prompt emission of GRBs with observed single X-ray flare are investigated in the rest frame and described in Chapter 3.The relations between the X-ray flare and the prompt emission characteristics are analyzed by the Markov Chain-Monte Carlo(MCMC)method,and we find a tight correlation among the peak luminosity of flare,peak time of flare and the isotropic energy of prompt emission,which indicates that the central engine of the GRBs will be active again and produce X-ray flares at a later stage.We also first find that the strong energy-spectrum evolution during the flare phase leads to pseudo X-ray flare phenomena in the observer frame,which suggests that part of the X-ray flares may not be real flares but pseudo-phenomena due to energy spectrum evolution.In Chapter 4,we use the unsupervised machine learning algorithm(t-SNE and UMAP algorithms)to clearly classify GRBs into two clusters,GRBs-I and GRBs-II,based on the catalog of Fermi,Swift,and BATSE.And by comparing certified supernovae and kilonovae,it shows that the two classes correspond to two different progenitor models,respectively.We find that the classification method using machine learning is more reasonable than the traditional method of classification by duration.