Spectral dataset of young type Ib supernovae and their time evolution

¹ Department of Astronomy, University of Virginia, Charlottesville, VA 22904, USA
² Las Cumbres Observatory, 6740 Cortona Drive, Suite 102, Goleta, CA 93117-5575, USA
³ Department of Physics, University of California, Santa Barbara, CA 93106-9530, USA
⁴ School of Physics and Astronomy, Tel Aviv University, Tel Aviv 69978, Israel
⁵ Center for Astrophysics, Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138-1516, USA
⁶ The NSF AI Institute for Artificial Intelligence and Fundamental Interactions, 77 Massachusetts Ave, Cambridge, MA 02139, USA
⁷ Steward Observatory, University of Arizona, 933 North Cherry Avenue, Tucson, AZ 85721-0065, USA
⁸ Department of Astronomy & Astrophysics, University of California, San Diego, 9500 Gilman Drive, MC 0424, La Jolla, CA 92093-0424, USA
⁹ Department of Physics and Astronomy, Rutgers, The State University of New Jersey, 136 Frelinghuysen Road, Piscataway, NJ 08854-8019, USA

^⋆ Corresponding authors; pqt7tv@virginia.edu, vru7qe@virginia.edu

Received: 12 September 2024
Accepted: 7 December 2024

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.