A Bayesian Inference Framework for Gamma-ray Burst Afterglow Properties
In the field of multi-messenger astronomy, Bayesian inference is commonly adopted to compare the compatibility of models given the observed data. However, to describe a physical system like neutron star mergers and their associated gamma-ray burst (GRB) events, usually more than ten physical paramet...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-09-01
|
| Series: | Universe |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2218-1997/7/9/349 |
| _version_ | 1827681126498435072 |
|---|---|
| author | En-Tzu Lin Fergus Hayes Gavin P. Lamb Ik Siong Heng Albert K. H. Kong Michael J. Williams Surojit Saha John Veitch |
| author_facet | En-Tzu Lin Fergus Hayes Gavin P. Lamb Ik Siong Heng Albert K. H. Kong Michael J. Williams Surojit Saha John Veitch |
| author_sort | En-Tzu Lin |
| collection | DOAJ |
| description | In the field of multi-messenger astronomy, Bayesian inference is commonly adopted to compare the compatibility of models given the observed data. However, to describe a physical system like neutron star mergers and their associated gamma-ray burst (GRB) events, usually more than ten physical parameters are incorporated in the model. With such a complex model, likelihood evaluation for each Monte Carlo sampling point becomes a massive task and requires a significant amount of computational power. In this work, we perform quick parameter estimation on simulated GRB X-ray light curves using an interpolated physical GRB model. This is achieved by generating a grid of GRB afterglow light curves across the parameter space and replacing the likelihood with a simple interpolation function in the high-dimensional grid that stores all light curves. This framework, compared to the original method, leads to a ∼90× speedup per likelihood estimation. It will allow us to explore different jet models and enable fast model comparison in the future. |
| first_indexed | 2024-03-10T07:09:56Z |
| format | Article |
| id | doaj.art-b98f3982b10c4c8da421e7dfe5c5a8e7 |
| institution | Directory Open Access Journal |
| issn | 2218-1997 |
| language | English |
| last_indexed | 2024-03-10T07:09:56Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Universe |
| spelling | doaj.art-b98f3982b10c4c8da421e7dfe5c5a8e72023-11-22T15:33:15ZengMDPI AGUniverse2218-19972021-09-017934910.3390/universe7090349A Bayesian Inference Framework for Gamma-ray Burst Afterglow PropertiesEn-Tzu Lin0Fergus Hayes1Gavin P. Lamb2Ik Siong Heng3Albert K. H. Kong4Michael J. Williams5Surojit Saha6John Veitch7Institute of Astronomy, National Tsing Hua University, Hsinchu 300044, TaiwanSUPA, School of Physics and Astronomy, University of Glasgow, Glasgow G12 8QQ, UKSchool of Physics and Astronomy, University of Leicester, Leicester LE1 7RH, UKSUPA, School of Physics and Astronomy, University of Glasgow, Glasgow G12 8QQ, UKInstitute of Astronomy, National Tsing Hua University, Hsinchu 300044, TaiwanSUPA, School of Physics and Astronomy, University of Glasgow, Glasgow G12 8QQ, UKInstitute of Astronomy, National Tsing Hua University, Hsinchu 300044, TaiwanSUPA, School of Physics and Astronomy, University of Glasgow, Glasgow G12 8QQ, UKIn the field of multi-messenger astronomy, Bayesian inference is commonly adopted to compare the compatibility of models given the observed data. However, to describe a physical system like neutron star mergers and their associated gamma-ray burst (GRB) events, usually more than ten physical parameters are incorporated in the model. With such a complex model, likelihood evaluation for each Monte Carlo sampling point becomes a massive task and requires a significant amount of computational power. In this work, we perform quick parameter estimation on simulated GRB X-ray light curves using an interpolated physical GRB model. This is achieved by generating a grid of GRB afterglow light curves across the parameter space and replacing the likelihood with a simple interpolation function in the high-dimensional grid that stores all light curves. This framework, compared to the original method, leads to a ∼90× speedup per likelihood estimation. It will allow us to explore different jet models and enable fast model comparison in the future.https://www.mdpi.com/2218-1997/7/9/349Bayesian inferencemulti-messenger astronomyGRB afterglows |
| spellingShingle | En-Tzu Lin Fergus Hayes Gavin P. Lamb Ik Siong Heng Albert K. H. Kong Michael J. Williams Surojit Saha John Veitch A Bayesian Inference Framework for Gamma-ray Burst Afterglow Properties Universe Bayesian inference multi-messenger astronomy GRB afterglows |
| title | A Bayesian Inference Framework for Gamma-ray Burst Afterglow Properties |
| title_full | A Bayesian Inference Framework for Gamma-ray Burst Afterglow Properties |
| title_fullStr | A Bayesian Inference Framework for Gamma-ray Burst Afterglow Properties |
| title_full_unstemmed | A Bayesian Inference Framework for Gamma-ray Burst Afterglow Properties |
| title_short | A Bayesian Inference Framework for Gamma-ray Burst Afterglow Properties |
| title_sort | bayesian inference framework for gamma ray burst afterglow properties |
| topic | Bayesian inference multi-messenger astronomy GRB afterglows |
| url | https://www.mdpi.com/2218-1997/7/9/349 |
| work_keys_str_mv | AT entzulin abayesianinferenceframeworkforgammarayburstafterglowproperties AT fergushayes abayesianinferenceframeworkforgammarayburstafterglowproperties AT gavinplamb abayesianinferenceframeworkforgammarayburstafterglowproperties AT iksiongheng abayesianinferenceframeworkforgammarayburstafterglowproperties AT albertkhkong abayesianinferenceframeworkforgammarayburstafterglowproperties AT michaeljwilliams abayesianinferenceframeworkforgammarayburstafterglowproperties AT surojitsaha abayesianinferenceframeworkforgammarayburstafterglowproperties AT johnveitch abayesianinferenceframeworkforgammarayburstafterglowproperties AT entzulin bayesianinferenceframeworkforgammarayburstafterglowproperties AT fergushayes bayesianinferenceframeworkforgammarayburstafterglowproperties AT gavinplamb bayesianinferenceframeworkforgammarayburstafterglowproperties AT iksiongheng bayesianinferenceframeworkforgammarayburstafterglowproperties AT albertkhkong bayesianinferenceframeworkforgammarayburstafterglowproperties AT michaeljwilliams bayesianinferenceframeworkforgammarayburstafterglowproperties AT surojitsaha bayesianinferenceframeworkforgammarayburstafterglowproperties AT johnveitch bayesianinferenceframeworkforgammarayburstafterglowproperties |