Spectral dataset of young type Ib supernovae and their time evolution

Due to high-cadence automated surveys, we can now detect and classify supernovae (SNe) within a few days after explosion, if not earlier. Early-time spectra of young SNe directly probe the outermost layers of the ejecta, providing insights into the extent of stripping in the progenitor star and the explosion mechanism in the case of core-collapse supernovae. However, many SNe show overlapping observational characteristics at early times, complicating the early-time classification. In this paper, we focus on the study and classification of type Ib supernovae (SNe Ib), which are a subclass of core-collapse SNe that lack strong hydrogen lines but show helium lines in their spectra. Here we present a spectral dataset of eight SNe Ib, chosen to have at least three pre-maximum spectra, which we call early spectra. Our dataset was obtained mainly by the Las Cumbres Observatory (LCO) and it consists of a total of 82 optical photospheric spectra, including 38 early spectra. This dataset increases the number of published SNe Ib with at least three early spectra by ∼60%. For our classification efforts, we used early spectra in addition to spectra taken around maximum light. We also converted our spectra into SN IDentification (SNID) templates and make them available to the community for easier identification of young SNe Ib. Our dataset increases the number of publicly available SNID templates of early spectra of SNe Ib by ∼43%. Half of our sample has SN types that change over time or are different from what is listed on the Transient Name Server (TNS). We discuss the implications of our dataset and our findings for current and upcoming SN surveys and their classification efforts.

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Publication Details

Journal
Astronomy and Astrophysics
Published
2024-12-19
DOI
https://doi.org/10.1051/0004-6361/202452214
Citations
4
Primary Topic
Gamma-ray bursts and supernovae
Type
article
Field-Weighted Citation Impact
1.33
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article

Spectral dataset of young type Ib supernovae and their time evolution

D. Hiramatsu, D.A. Howell, G. Terreran, Saurabh W. Jha et al.
4 citations
Astronomy and Astrophysics
Gamma-ray bursts and supernovae
1.33
article

Spectral dataset of young type Ib supernovae and their time evolution

D. Hiramatsu, D.A. Howell, G. Terreran, Saurabh W. Jha, C. Pellegrino, G. Hosseinzadeh, I. Arcavi, Estefania Padilla Gonzalez, M. Modjaz, Joseph Farah, C. McCully, Megan Newsome, N. Yesmin, Raphael Baer-Way
article en
4 citations

Abstract

Due to high-cadence automated surveys, we can now detect and classify supernovae (SNe) within a few days after explosion, if not earlier. Early-time spectra of young SNe directly probe the outermost layers of the ejecta, providing insights into the extent of stripping in the progenitor star and the explosion mechanism in the case of core-collapse supernovae. However, many SNe show overlapping observational characteristics at early times, complicating the early-time classification. In this paper, we focus on the study and classification of type Ib supernovae (SNe Ib), which are a subclass of core-collapse SNe that lack strong hydrogen lines but show helium lines in their spectra. Here we present a spectral dataset of eight SNe Ib, chosen to have at least three pre-maximum spectra, which we call early spectra. Our dataset was obtained mainly by the Las Cumbres Observatory (LCO) and it consists of a total of 82 optical photospheric spectra, including 38 early spectra. This dataset increases the number of published SNe Ib with at least three early spectra by ∼60%. For our classification efforts, we used early spectra in addition to spectra taken around maximum light. We also converted our spectra into SN IDentification (SNID) templates and make them available to the community for easier identification of young SNe Ib. Our dataset increases the number of publicly available SNID templates of early spectra of SNe Ib by ∼43%. Half of our sample has SN types that change over time or are different from what is listed on the Transient Name Server (TNS). We discuss the implications of our dataset and our findings for current and upcoming SN surveys and their classification efforts.

Astronomy and AstrophysicsVol. 693
Rutgers, The State University of New Jersey (US), Las Cumbres Observatory Global Telescope Network (US), University of Arizona (US), University of California, Santa Barbara (US), Tel Aviv University (IL), University of California San Diego (US), Université Bourgogne Franche-Comté (FR), Center for Astrophysics Harvard & Smithsonian (US), The NSF AI Institute for Artificial Intelligence and Fundamental Interactions (US), University of Virginia (US)
Openalex Percentile: Top 19%
Gamma-ray bursts and supernovae
1.33
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