Abstract

Gamma-ray Bursts are the most luminous and energetic events in the universe. Understanding the intrinsic properties of GRBs allows for their use as standard candles (objects of known distance and luminosity) and for the study of extremely distant regions of space.

In this study we use the piecewise Willingale model to fit the X-ray flux vs. time curves (light curves) of 235 GRBs with detectable 'plateau' phase and known redshift using data from the swift repository. This sample is analyzed for consistency with a relation between the temporal decay and spectral indexes predicted by the Fireball model of GRB Emission. We also check the previously established Dainotti relation between plateau end time, plateau luminosity and peak luminosity. Data analysis was performed using Mathematica for producing non-linear regression fits and Python for data collection and processing.

We find that the predicted index relation does not hold, in agreement with previous studies . In our sample we find that 25% or less of GRBs in our sample fulfill this relation, even fewer than at least one previous study . The Dainotti relation is at least qualitatively confirmed in this sample; we find a positive correlation between both luminosity values along with anti-correlation between plateau end time and luminosity.

Additionally we fit 298 GRBs without known redshift and compare the index distributions between both GRBs with and without redshift values. Preliminarily, the similarity of these distributions suggests similar consistency with the predicted index relation.

Eleven gamma-ray light curves were fit using data from the Fermi telescope and four were found to have detectable plateaus. Future research will perform similar analysis in a comparable sample of gamma-ray light curves.

Disciplines

Other Astrophysics and Astronomy

Mentor

Dr. Maria Giovanna Dainotti

Lab site

SLAC National Accelerator Laboratory (SLAC)

Funding Acknowledgement

The 2018 STEM Teacher and Researcher Program and this project have been made possible through support from the National Science Foundation through the Robert Noyce Program under Grant No.1836335 and 1340110, the California State University Office of the Chancellor, and California Polytechnic State University in partnership with SLAC National Accelerator Laboratory. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funders.

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URL: https://digitalcommons.calpoly.edu/star/519

 

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