Mixture-of-Expert Variational Autoencoders for Cross-Modality Embedding of Type Ia Supernova Data
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916857292783616 |
|---|---|
| author | Shen, Yunyi Gagliano, Alexander T. |
| author_facet | Shen, Yunyi Gagliano, Alexander T. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_16817 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Mixture-of-Expert Variational Autoencoders for Cross-Modality Embedding of Type Ia Supernova Data Shen, Yunyi Gagliano, Alexander T. Instrumentation and Methods for Astrophysics High Energy Astrophysical Phenomena 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. |
| title | Mixture-of-Expert Variational Autoencoders for Cross-Modality Embedding of Type Ia Supernova Data |
| topic | Instrumentation and Methods for Astrophysics High Energy Astrophysical Phenomena |
| url | https://arxiv.org/abs/2507.16817 |