Exploring the Polarization of Axially Symmetric Supernovae with Unsupervised Deep Learning

TL;DR

This paper uses unsupervised deep learning and Monte Carlo simulations to analyze supernova polarization, revealing how different geometries influence observed spectral line profiles and proposing a method to infer supernova shapes from spectropolarimetric data.

Contribution

It introduces a novel machine learning framework combining VAEs and likelihood-free inference to determine supernova geometries from polarization spectra.

Findings

Identified key latent parameters describing polarization-line relationships.

Proposed the existence of conjugate geometries indistinguishable under rotation.

Applied the method to SN 2017gax to infer its possible geometry.

Abstract

The measurement of non-zero polarization can be used to infer the presence of departures from spherical symmetry in supernovae (SNe). The origin of the majority of the intrinsic polarization observed in SNe is in electron scattering, which induces a wavelength-independent continuum polarization that is generally observed to be low (<1%) for all SN types. The key indicator of asymmetry in SNe is the polarization observed across spectral lines, in particular the characteristic ``inverse P Cygni'' profile. The results of a suite of 900 Monte Carlo radiative transfer simulations are presented here. These simulations cover a range of possible axisymmetric structures (including unipolar, bipolar and equatorial enhancements) for the line forming region of the Ca II infrared triplet. Using a Variational Autoencoder, 7 key latent parameters are learned that describe the relationship between Stokes I and Stokes q, under the assumption of an axially symmetric line forming region and resonant scattering. Likelihood-free inference techniques are used to invert the Stokes I and q line profiles, in the latent space, to derive the underlying geometries. For axially symmetric structures, that yield an observable ``dominant axis'' on the Stokes $q-u$ plane, we propose the existence of a geometry ``conjugate" (which is indistinguishable under a rotation of $\pi /2$). Using this machine learning infrastructure, we attempt to identify possible geometries associated with spectropolarimetric observations of the Type Ib SN 2017gax.