J/ApJ/896/L20 Swift BAT gamma-ray burst durations (Jespersen+, 2020)
An unambiguous separation of gamma-ray bursts into two classes from prompt
emission alone.
Jespersen C.K., Severin J.B., Steinhardt C.L., Vinther J., Fynbo J.P.U.,
Selsing J., Watson D.
<Astrophys. J., 896, L20 (2020)>
=2020ApJ...896L..20J 2020ApJ...896L..20J
ADC_Keywords: GRB
Keywords: Gamma-ray bursts ; Astronomy data analysis ;
Astronomy data visualization ; Light curve classification ;
Supernovae ; High energy astrophysics
Abstract:
The duration of a gamma-ray burst (GRB) is a key indicator of its
physical origin, with long bursts perhaps associated with the collapse
of massive stars and short bursts with mergers of neutron stars.
However, there is substantial overlap in the properties of both short
and long GRBs and neither duration nor any other parameter so far
considered completely separates the two groups. Here we unambiguously
classify every GRB using a machine-learning dimensionality reduction
algorithm, t-distributed stochastic neighborhood embedding, providing
a catalog separating all Swift GRBs into two groups. Although the
classification takes place only using prompt emission light curves,
every burst with an associated supernova is found in the longer group
and bursts with kilonovae in the short, suggesting along with the
duration distributions that these two groups are truly long and short
GRBs. Two bursts with a clear absence of a supernova belong to the
longer class, indicating that these might have been direct-collapse
black holes, a proposed phenomenon that may occur in the deaths of
more massive stars.
Description:
The classification proposed here takes the entirety of the normalized
Swift light curves from prompt emission and in an unsupervised way
determines which GRBs should be considered similar based on the prompt
data alone. This is done using t-SNE (t-distributed Stochastic
Neighbor Embedding algorithm; Maaten & Hinton 2008 Journal of Machine
Learning Research 9 2579; van der Maaten 2014 Journal of Machine
Learning Research 15 3221), a dimensionality reduction algorithm that
can take complex, high-dimensional data and produce a faithful
representation of that data in a low-dimensional space.
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
table1.dat 29 1318 Classification of Swift GRBs
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See also:
B/swift : Swift Master Catalog (HEASARC, 2004-)
IX/20 : The Fourth BATSE Burst Revised Catalog (Paciesas+ 1999)
J/ApJ/711/495 : Durations of Swift/BAT GRBs (Butler+, 2010)
J/ApJ/731/103 : Redshift catalog for Swift long GRBs (Xiao+, 2011)
J/A+A/568/A19 : Photometry of 3 γ-ray burst supernovae (Cano+, 2014)
J/ApJ/818/110 : Short GRBs with Fermi GBM and Swift BAT (Burns+, 2016)
J/ApJ/828/36 : GRB light-curve decay indices with Swift (Del Vecchio+, 2016)
J/ApJ/829/7 : 3rd Swift/BAT GRB catalog (past ∼11yrs) (BAT3) (Lien+, 2016)
J/A+A/609/A112 : Bulk Lorentz factors of gamma-ray bursts (Ghirlanda+, 2018)
J/ApJS/248/21 : Swift long gamma-ray bursts (Hao+, 2020)
http://swift.gsfc.nasa.gov/results/batgrbcat/ : Online Swift BAT GRB catalog
Byte-by-byte Description of file: table1.dat
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Bytes Format Units Label Explanations
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1- 3 A3 --- --- [GRB]
4- 10 A7 --- GRB Gamma-ray burst given name
11 A1 --- --- [-]
12 I1 --- m_GRB [1/2]? Multiplicity number for GRB140716A
14- 19 F6.2 s T90 [0.01/811]? Duration for 90% of measured fluence
21- 29 A9 --- Type Type of GRB using our classification scheme (1)
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Note (1): Type of GRB as follows:
L = type-L (∼long) GRB (1145 occurrences)
S = type-S (∼short) GRB (109 occurrences)
Discarded = 64 occurrences
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History:
From electronic version of the journal
(End) Prepared by [AAS], Emmanuelle Perret [CDS] 03-Nov-2021