Inferring the Redshift of More than 150 GRBs with a Machine-learning Ensemble Model
Journal Article
·
· The Astrophysical Journal. Supplement Series
- National Astronomical Observatory of Japan, Tokyo (Japan); Graduate Univ. for Advanced Studies, Hayama (Japan); Space Science Institute, Boulder, CO (United States); Univ. of Nevada, Las Vegas, NV (United States); Bay Area Environmental Research Institute, Moffett Field, Mountain View, CA (United States); SLAC
- Michigan State Univ., East Lansing, MI (United States)
- Yale Univ., New Haven, CT (United States)
- Univ. of California, Berkeley, CA (United States)
- Jagiellonian Univ., Krakow (Poland)
- Jagiellonian Univ., Krakow (Poland); National Center for Nuclear Physics (NCB), Warsaw (Poland)
- SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States); Stanford Univ., CA (United States)
- Univ. of Wroclaw (Poland); Lund Univ. (Sweden)
- Stanford Univ., CA (United States)
- National Astronomical Observatory of Japan, Tokyo (Japan); Graduate Univ. for Advanced Studies, Hayama (Japan)
Gamma-ray bursts (GRBs), due to their high luminosities, are detected up to a redshift of 10, and thus have the potential to be vital cosmological probes of early processes in the Universe. Fulfilling this potential requires a large sample of GRBs with known redshifts, but due to observational limitations, only 11% have known redshifts (z). There have been numerous attempts to estimate redshifts via correlation studies, most of which have led to inaccurate predictions. To overcome this, we estimated GRB redshift via an ensemble-supervised machine-learning (ML) model that uses X-ray afterglows of long-duration GRBs observed by the Neil Gehrels Swift Observatory. The estimated redshifts are strongly correlated (a Pearson coefficient of 0.93) and have an rms error, namely, the square root of the average squared error $$\langle$$Δz2$$\rangle$$, of 0.46 with the observed redshifts showing the reliability of this method. The addition of GRB afterglow parameters improves the predictions considerably by 63% compared to previous results in peer-reviewed literature. Finally, we use our ML model to infer the redshifts of 154 GRBs, which increase the known redshifts of long GRBs with plateaus by 94%, a significant milestone for enhancing GRB population studies that require large samples with redshift.
- Research Organization:
- SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)
- Sponsoring Organization:
- Ministry of Science and Higher Education; National Aeronautics and Space Administration (NASA); National Science Center (NCN); USDOE Office of Science (SC)
- Grant/Contract Number:
- AC02-76SF00515
- OSTI ID:
- 2403400
- Journal Information:
- The Astrophysical Journal. Supplement Series, Journal Name: The Astrophysical Journal. Supplement Series Journal Issue: 1 Vol. 271; ISSN 0067-0049
- Publisher:
- IOP PublishingCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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