Mixture-of-Expert Variational Autoencoders for Cross-Modality Embedding of Type Ia Supernova Data

TL;DR

This paper introduces a multi-modal mixture-of-expert variational autoencoder that effectively learns joint embeddings of supernova light curves and spectra, improving cross-modality reconstruction in astrophysics.

Contribution

The work presents a novel architecture inspired by Perceiver that handles variable-length, irregular supernova data for joint embedding and cross-modality reconstruction.

Findings

Outperforms nearest-neighbor searches in latent space

Successfully reconstructs supernova spectra and parameters

Demonstrates effectiveness on simulated data

Abstract

Time-domain astrophysics relies on heterogeneous and multi-modal data. Specialized models are often constructed to extract information from a single modality, but this approach ignores the wealth of cross-modality information that may be relevant for the tasks to which the model is applied. In this work, we propose a multi-modal, mixture-of-expert variational autoencoder to learn a joint embedding for supernova light curves and spectra. Our method, which is inspired by the Perceiver architecture, natively accommodates variable-length inputs and the irregular temporal sampling inherent to supernova light curves. We train our model on radiative transfer simulations and validate its performance on cross-modality reconstruction of supernova spectra and physical parameters from the simulation. Our model achieves superior performance in cross-modality generation to nearest-neighbor searches in a contrastively-trained latent space, showing its promise for constructing informative latent representations of multi-modal astronomical datasets.