J/ApJS/276/3 PS1-MDS supernovae classifications (Villar+, 2025)
The impact of host-galaxy properties on supernova classification with
hierarchical labels.
Villar V.A., Gomez S., Berger E., Gagliano A.
<Astrophys. J. Suppl. Ser., 276, 3 (2025)>
=2025ApJS..276....3V 2025ApJS..276....3V
ADC_Keywords: Supernovae; Photometry; Spectral types
Keywords: Supernovae ; Light curve classification ; Neural networks
Abstract:
With the advent of the Vera C. Rubin Observatory, the discovery rate
of supernovae (SNe) will surpass the rate of SNe with real time
spectroscopic follow-up by 3 orders of magnitude. Accurate photometric
classifiers are essential to both select interesting events for
follow-up in real time and for archival population-level studies. In
this work, we investigate the impact of observable host-galaxy
information on the classification of SNe, both with and without
additional light-curve and redshift information. We find that
host-galaxy information alone can successfully isolate relatively pure
(>90%) samples of Type Ia SNe with or without redshift information.
With redshift information, we can additionally produce somewhat pure
(>70%) samples of Type II SNe and superluminous SNe. Additionally with
redshift information, host- galaxy properties do not significantly
improve the accuracy of SN classification when paired with complete
light curves. In the absence of redshift information, however, galaxy
properties significantly increase the accuracy of photometric
classification. As a part of this analysis, we present the first
formal application of a new objective function, the weighted
hierarchical cross entropy, to the problem of SN classification. This
objective function more naturally accounts for the hierarchical nature
of SN classes and, more broadly, transients. Finally, we present a new
set of SN classifications for the Pan-STARRS Medium Deep Survey of SNe
that lack spectroscopic redshift, increasing the full photometric
sample to >4400 events.
Description:
We utilize the Pan-STARRS Medium Deep Survey (PS1-MDS) catalog of
SN-like light curves published by Villar+ (2020, J/ApJ/905/94) and
Hosseinzadeh+ (2020, J/ApJ/905/93). In total, our sample includes
557 spectroscopically identified SNe, in five classes: Type Ia SNe
(404 objects), Type II SNe (93 objects), Type IIn SNe (24 objects),
Type Ib/c SNe (19 objects), and Type I SLSNe (17 objects).
We use the full set of light-curve features available from SuperRAENN.
See Section 2.
Finally, we use our redshift-independent classifier, which uses both
host and light-curve information, to classify the full set of
4407 SN-like transients from the PS1-MDS originally presented in
Villar+ (2020) and Hosseinzadeh+ (2020). This sample includes all
objects that are not spectroscopically classified and not identified
as variables or otherwise "bad" objects.
See Section 5.
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
table2.dat 89 4406 Classification probabilities of all SN-like objects
in PS1-MDS
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See also:
II/317 : The CFHTLS Survey (T0007 release) (Hudelot+ 2012)
J/MNRAS/412/1419 : Nearby supernova rates (Leaman+, 2011)
J/MNRAS/424/2841 : Morphology of supernova host galaxies (Habergham+, 2012)
J/A+A/544/A81 : Supernovae and their hosts in the SDSS DR8 (Hakobyan+, 2012)
J/ApJ/759/107 : Core-collapse SNe and host galaxies (Kelly+, 2012)
J/ApJ/7/107 : Host galaxies of SNIa from SNfactory (Childress+, 2013)
J/ApJ/905/93 : Classif. of PS1-MDS SNe with Superphot (Hosseinzadeh+, 2020)
J/ApJ/905/94 : Classif. for PS1-MDS SNe with SuperRAENN (Villar+, 2020)
J/ApJ/912/46 : ZTF Type II supernovae with follow-up obs. (Bruch+, 2021)
J/ApJS/255/29 : PTF core-collapse SN host-galaxy sample. I. (Schulze+, 2021)
Byte-by-byte Description of file: table2.dat
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Bytes Format Units Label Explanations
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1- 9 A9 --- Name Supernova name
11- 14 F4.2 --- p(Ia) [0/1] Probability of Type Ia classification
16- 19 F4.2 --- e_p(Ia) [0/0.51] Uncertainty in p(Ia)
21- 24 F4.2 --- p(CC) [0/1] Probability of core-collapse
classification
26- 29 F4.2 --- e_p(CC) [0/0.51] Uncertainty in p(CC)
31- 34 F4.2 --- p(H-rich) [0/1] Probability of hydrogen-rich
classification
36- 39 F4.2 --- e_p(H-rich) [0/0.47] Uncertainty in p(H-rich)
41- 44 F4.2 --- p(H-poor) [0/1] Probability of hydrogen-poor
classification
46- 49 F4.2 --- e_p(H-poor) [0/0.48] Uncertainty in p(H-poor)
51- 54 F4.2 --- p(SLSN) [0/1] Probability of superluminous supernova
classification
56- 59 F4.2 --- e_p(SLSN) [0/0.45] Uncertainty in p(SLSN)
61- 64 F4.2 --- p(II) [0/0.91] Probability of Type II
classification
66- 69 F4.2 --- e_p(II) [0/0.39] Uncertainty in p(II)
71- 74 F4.2 --- p(IIn) [0/0.86] Probability of Type IIn
classification
76- 79 F4.2 --- e_p(IIn) [0/0.41] Uncertainty in p(IIn)
81- 84 F4.2 --- p(Ibc) [0/0.92] Probability of Type Ibc
classification
86- 89 F4.2 --- e_p(Ibc) [0/0.42] Uncertainty in p(Ibc)
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History:
From electronic version of the journal
(End) Prepared by [AAS], Emmanuelle Perret [CDS] 22-Jan-2026