J/A+A/691/A33 List of SNe Ia and II (Ramirez+, 2024)
A novel optimal transport-based approach for interpolating spectral time series.
Paving the way for photometric classification of supernovae.
Ramirez M., Pignata G., Forster F., Gonzalez-Gaitan S., Gutierrez C.P.,
Ayala B., Cabrera-Vives G., Catelan M., Munoz Arancibia A.M.,
Pineda-Garcia J.
<Astron. Astrophys. 691, A33 (2024)>
=2024A&A...691A..33R 2024A&A...691A..33R (SIMBAD/NED BibCode)
ADC_Keywords: Supernovae
Keywords: methods: data analysis - methods: statistical - supernovae: general
Abstract:
The Vera C. Rubin Observatory is set to discover 1 million supernovae
(SNe) within its first operational year. Given the impracticality of
spectroscopic classification at such scales, it is mandatory to
develop a reliable photometric classification framework.
This paper introduces a novel method for creating spectral time series
that can be used not only to generate synthetic light curves for
photometric classification, but also in applications such as
K-corrections and bolometric corrections. This approach is
particularly valuable in the era of large astronomical surveys, where
it can significantly enhance the analysis and understanding of an
increasing number of SNe, even in the absence of extensive
spectroscopic data.
By employing interpolations based on optimal transport theory,
starting from a spectroscopic sequence, we derive weighted average
spectra with high cadence. The weights incorporate an uncertainty
factor for penalizing interpolations between spectra that show
significant epoch differences and lead to a poor match between the
synthetic and observed photometry.
Our analysis reveals that even with a phase difference of up to 40
days between pairs of spectra, optical transport can generate
interpolated spectral time series that closely resemble the original
ones. Synthetic photometry extracted from these spectral time series
aligns well with observed photometry. The best results are achieved in
the V band, with relative residuals of less than 10% for 87% and 84%
of the data for type Ia and II, respectively. For the B, g, R, and r
bands, the relative residuals are between 65% and 87% within the
previously mentioned 10% threshold for both classes. The worse results
correspond to the i and I bands, where, in the case of SN Ia, the
values drop to 53% and 42%, respectively.
We introduce a new method for constructing spectral time series for
individual SNe starting from a sparse spectroscopic sequence, and
demonstrate its capability to produce reliable light curves that can
be used for photometric classification.
Description:
List of SNe included in this work.
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
tablec1.dat 75 141 SNe included in this work
refs.dat 225 195 References
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Byte-by-byte Description of file: tablec1.dat
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Bytes Format Units Label Explanations
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1- 11 A11 --- SN SN Name
13- 14 A2 --- Type
16- 17 I2 --- Nspec Number of spectra
19 A1 -- ---
20- 23 I4 --- Phasel Phase Range, lower value
25- 28 I4 --- Phaseu Phase Range, upper value
29 A1 --- ---
31- 40 A10 --- Phot Photometry data
43- 75 A33 --- Ref References in refs.dat file
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Byte-by-byte Description of file: refs.dat
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Bytes Format Units Label Explanations
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1- 3 I3 --- Ref Reference code
5- 23 A19 --- BibCode BibCode
25- 45 A21 --- Aut Author's name
47-225 A179 --- Com Comments
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History:
From electronic version of the journal
(End) Patricia Vannier [CDS] 07-Apr-2025