J/ApJ/828/111 Unsupervised clustering of type II SNe LCs (Rubin+, 2016)
Unsupervised clustering of type II supernova light curves.
Rubin A., Gal-Yam A.
<Astrophys. J., 828, 111-111 (2016)>
=2016ApJ...828..111R 2016ApJ...828..111R (SIMBAD/NED BibCode)
ADC_Keywords: Supernovae ; Photometry ; Models
Keywords: supernovae: general
Abstract:
As new facilities come online, the astronomical community will be
provided with extremely large data sets of well-sampled light curves
(LCs) of transients. This motivates systematic studies of the LCs of
supernovae (SNe) of all types, including the early rising phase. We
performed unsupervised k-means clustering on a sample of 59 R-band
SNII LCs and find that the rise to peak plays an important role in
classifying LCs. Our sample can be divided into three classes: slowly
rising (II-S), fast rise/slow decline (II-FS), and fast rise/fast
decline (II-FF). We also identify three outliers based on the
algorithm. The II-FF and II-FS classes are disjoint in their decline
rates, while the II-S class is intermediate and "bridges the gap."
This may explain recent conflicting results regarding II-P/II-L
populations. The II-FS class is also significantly less luminous than
the other two classes. Performing clustering on the first two
principal component analysis components gives equivalent results to
using the full LC morphologies. This indicates that Type II LCs could
possibly be reduced to two parameters. We present several important
caveats to the technique, and find that the division into these
classes is not fully robust. Moreover, these classes have some
overlap, and are defined in the R band only. It is currently unclear
if they represent distinct physical classes, and more data is needed
to study these issues. However, we show that the outliers are actually
composed of slowly evolving SN IIb, demonstrating the potential of
such methods. The slowly evolving SNe IIb may arise from single
massive progenitors.
Description:
Here we attempt to divide the sample of 57 light curves (LCs)
presented in Rubin et al. (2016, J/ApJ/820/33) --in addition to two
supernovae (SNe) LCs that were originally included in that sample but
subsequently removed (iPTF13blq and iPTF14bas; I. Arcavi et al. 2016,
in preparation)-- into classes of similar LC shape by using the
unsupervised clustering algorithm K-Means on the LCs directly (as
opposed to extracted parameters).
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
table1.dat 43 291 Mean and standard deviation of each cluster
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See also:
J/AJ/151/33 : UBVRIz light curves of 51 Type II SNe (Galbany+, 2016)
J/ApJ/820/33 : R-band light curves of type II supernovae (Rubin+, 2016)
J/ApJ/799/215 : 26 type II-Plateau supernovae parameters (Pejcha+, 2015)
J/ApJ/799/208 : Type IIP supernovae from Pan-STARRS1 (Sanders+, 2015)
J/MNRAS/442/844 : BVRI light curves of type II-P supernovae (Faran+, 2014)
J/A+A/555/A10 : Light curves of 5 type IIn supernovae (Taddia+, 2013)
J/MNRAS/412/1441 : SNe luminosity functions (Li+, 2011)
Byte-by-byte Description of file: table1.dat
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Bytes Format Units Label Explanations
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1- 4 F4.1 d Phase Phase from estimated date of explosion
6- 11 F6.3 mag II-FSMean [-0.06/0.7] Mean of fast rise/slow decline
SN II LC
13- 17 F5.3 mag II-FSstd [0.02/0.3] Standard deviation of fast
rise/slow decline SN II LC
19- 24 F6.3 mag II-FFMean [-0.03/0.7] Mean of fast rise/fast decline
SN II LC
26- 30 F5.3 mag II-FFstd [0.01/0.3] Standard deviation of fast
rise/fast decline SN II LC
32- 37 F6.3 mag II-SMean [-0.007/1.6] Mean of slow rise SN II LC
39- 43 F5.3 mag II-Sstd [0.03/0.4] Standard deviation of slow rise
SN II LC
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History:
From electronic version of the journal
(End) Prepared by [AAS], Emmanuelle Perret [CDS] 06-Dec-2016