Determining the Type, Redshift, and Phase of a Supernova Spectrum

Blondin, S.; Tonry, J.

doi:10.1063/1.2774875

Cited by 6 publications

(6 citation statements)

References 19 publications

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“…ling et al 2020), which is consistent with what was found for higher resolution spectra by Blondin & Tonry (2007). As such, we conclude that the performance of SNIascore is consistent with what can be done with SNID when each result and spectrum is manually inspected.…”

Section: Redshiftssupporting

confidence: 91%

“…In order to evaluate the classification performance of SNIascore we have performed comparisons to SNID (Blondin & Tonry 2007) and DASH (Muthukrishna et al 2019b) (Section 5.1). We also evaluate the accuracy of the associated redshift predictions for the SNe Ia that are identified by SNIascore by comparison to host galaxy redshifts and redshifts determined after manual inspection with SNID (Section 5.2).…”

Section: Performancementioning

confidence: 99%

“…The classifications from the BTS are made public on a daily basis via the Transient Name Server (TNS 1 ). These classifications have up until now been based on manual matching of observed spectra to spectral templates using mainly the SuperNova IDentification (SNID; Blondin & Tonry 2007) code, along with careful inspection of each obtained spectrum. This makes classification of thousands of SNe a very time-consuming endeavor.…”

Section: Introductionmentioning

confidence: 99%

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SNIascore: Deep-learning Classification of Low-resolution Supernova Spectra

Fremling

Hall

Coughlin

et al. 2021

ApJL

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We present SNIascore, a deep-learning based method for spectroscopic classification of thermonuclear supernovae (SNe Ia) based on very low-resolution (R∼ 100) data. The goal of SNIascore is fully automated classification of SNe Ia with a very low false-positive rate (FPR) so that human intervention can be greatly reduced in large-scale SN classification efforts, such as that undertaken by the public Zwicky Transient Facility (ZTF) Bright Transient Survey (BTS). We utilize a recurrent neural network (RNN) architecture with a combination of bidirectional long short-term memory and gated recurrent unit layers. SNIascore achieves a < 0.6% FPR while classifying up to 90% of the low-resolution SN Ia spectra obtained by the BTS. SNIascore simultaneously performs binary classification and predicts the redshifts of secure SNe Ia via regression (with a typical uncertainty of < 0.005 in the range from z = 0.01 to z = 0.12). For the magnitude-limited ZTF BTS survey (≈ 70% SNe Ia), deploying SNIascore reduces the amount of spectra in need of human classification or confirmation by ≈ 60%. Furthermore, SNIascore allows SN Ia classifications to be automatically announced in real-time to the public immediately following a finished observation during the night.

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Section: Redshiftssupporting

confidence: 91%

Section: Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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SNIascore: Deep-learning Classification of Low-resolution Supernova Spectra

Fremling

Hall

Coughlin

et al. 2021

ApJL

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“…The spectrum, discussed in Sect. 7, was obtained soon after the discovery with the Las Campanas 2.5-m du Pont telescope (+WFCCD), and crosscorrelated with a library of SN spectra using the "Supernova Identification" code (SNID; Blondin & Tonry 2007). SN 2013gc appeared as a type IIn SN similar to SN 1996L (Benetti et al 1999) at about two months after the explosion.…”

Section: Introductionmentioning

confidence: 99%

Signatures of an eruptive phase before the explosion of the peculiar core-collapse SN 2013gc

Reguitti

Pastorello

Pignata

et al. 2018

Monthly Notices of the Royal Astronomical Society

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We present photometric and spectroscopic analysis of the peculiar core-collapse SN 2013gc, spanning seven years of observations. The light curve shows an early maximum followed by a fast decline and a phase of almost constant luminosity. At +200 days from maximum, a brightening of 1 mag is observed in all bands, followed by a steep linear luminosity decline after +300 d. In archival images taken between 1.5 and 2.5 years before the explosion, a weak source is visible at the supernova location, with mag≈20. The early supernova spectra show Balmer lines, with a narrow (∼560 km s −1 ) P-Cygni absorption superimposed on a broad (∼3400 km s −1 ) component, typical of type IIn events. Through a comparison of colour curves, absolute light curves and spectra of SN 2013gc with a sample of supernovae IIn, we conclude that SN 2013gc is a member of the so-called type IId subgroup. The complex profile of the Hα line suggests a composite circumstellar medium geometry, with a combination of lower velocity, spherically symmetric gas and a more rapidly expanding bilobed feature. This circumstellar medium distribution has been likely formed through major massloss events, that we directly observed from 3 years before the explosio. The modest luminosity (M I ∼ −16.5 near maximum) of SN 2013gc at all phases, the very small amount of ejected 56 Ni (of the order of 10 −3 M ), the major pre-supernova stellar activity and the lack of prominent [O i] lines in late-time spectra support a fall-back core-collapse scenario for the massive progenitor of SN 2013gc.

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“…One is to set it as the midpoint between the last nondetection date (MJD nondet ) and the discovery date (MJD disc ), along with the representative uncertainty determined by (MJD disc − MJD nondet )/2. Another is to perform a comparison between the observed spectra at early times and SNID templates (Blondin & Tonry 2011) to find a best-fit spectral phase. Applying these two methods to their SN II sample, Gutiérrez et al (2017) find a mean offset of 0.5 d between them.…”

Section: Photometric Evolutionmentioning

confidence: 99%

SN 2018hfm : A Low-Energy Type II Supernova with Prominent Signatures of Circumstellar Interaction and Dust Formation

Zhang,

Wang,

Sai

et al. 2021

Preprint

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We present multiband optical photometric and spectroscopic observations of an unusual Type II supernova, SN 2018hfm, which exploded in the nearby (𝑑 ≈ 34.67 Mpc) dwarf galaxy PGC 1297331 with a very low star-formation rate (0.0270 M yr −1 ) and a subsolar metallicity environment (∼ 0.5 Z ). The 𝑉-band light curve of SN 2018hfm reaches a peak with value of −18.69 ± 0.64 mag, followed by a fast decline (4.42 ± 0.13 mag (100 d) −1 ). After about 50 days, it is found to experience a large flux drop (∼ 3.0 mag in 𝑉), and then enters into an unusually faint tail, which indicates a relatively small amount of 56 Ni synthesised during the explosion. From the bolometric light curve, SN 2018hfm is estimated to have low ejecta mass (∼ 1.3 M ) and low explosion energy (∼ 10 50 erg) compared with typical SNe II. The photospheric spectra of SN 2018hfm are similar to those of other SNe II, with P Cygni profiles of the Balmer series and metal lines, while at late phases the spectra are characterised by box-like profiles of H𝛼 emission, suggesting significant interaction between the SN ejecta and circumstellar matter. These box-like emission features are found to show increasing asymmetry with time, with the red-side component becoming gradually weaker, indicating that dust is continuously formed in the ejecta. Based on the dust-estimation tool , we find that the dust increases from ∼ 10 −6 M to 10 −4 -10 −3 M between +66.7 d and +389.4 d after explosion.

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Determining the Type, Redshift, and Phase of a Supernova Spectrum

Cited by 6 publications

References 19 publications

SNIascore: Deep-learning Classification of Low-resolution Supernova Spectra

SNIascore: Deep-learning Classification of Low-resolution Supernova Spectra

Signatures of an eruptive phase before the explosion of the peculiar core-collapse SN 2013gc

SN 2018hfm : A Low-Energy Type II Supernova with Prominent Signatures of Circumstellar Interaction and Dust Formation

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