Traces of Helium Detected in Type Ic Supernova 2014L

TL;DR

This study uses advanced spectral modeling and Bayesian inference to detect and quantify helium in the ejecta of Type Ic supernova 2014L, challenging the assumption of helium absence.

Contribution

It introduces a deep-learning emulator integrated with Bayesian inference for rapid, accurate spectral analysis of supernovae to constrain helium presence.

Findings

Detected 0.018 to 0.020 solar masses of helium in SN 2014L

Spectra are inconsistent with a helium-free model

Posterior density exponent aligns with theoretical explosion models

Abstract

The absence of helium features in optical spectra is one of the classification criteria for Type Ic supernovae (SNe Ic). However, it is highly debated whether helium is truly absent in ejecta or spectroscopically undetectable in the optical region. The near-infrared (NIR) region contains cleaner He lines that are less blended with other common ions in SNe Ic ejecta. We perform full spectral modeling on the near-peak-light optical and NIR spectra of the SN Ic 2014L to quantitatively constrain helium and other outer-ejecta properties, using the radiative transfer code TARDIS. We employ a deep-learning emulator for SNe Ic spectra that serves as a fast surrogate for TARDIS simulations. We then integrate the emulator within the Bayesian inference framework to infer the ejecta properties. The emulator achieves a mean fractional error of 1% between the emulated and TARDIS fluxes across all wavelengths and all samples in the test dataset. We constrain 0.018 to 0.020 M_sun (16% to 84% posterior percentile) of He above the photosphere near peak light in SN 2014L, inferred from the observed spectra covering 3500A to 24000A. A Bayesian statistical test shows that the observed spectra are inconsistent with no helium. Furthermore, the posterior favors a power-law density exponent of -7.04 to -6.88 (16% to 84% credible interval), consistent with theoretical calculations of radiation-dominated explosions. This work demonstrates that Bayesian radiative-transfer inference over a wide wavelength range provides a powerful path toward systematic constraints on He in SNe Ic.