Astro-MoE: Mixture of Experts for Multiband Astronomical Time Series
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909692424355840 |
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| author | Cádiz-Leyton, Martina Cabrera-Vives, Guillermo Protopapas, Pavlos Moreno-Cartagena, Daniel Becker, Ignacio |
| author_facet | Cádiz-Leyton, Martina Cabrera-Vives, Guillermo Protopapas, Pavlos Moreno-Cartagena, Daniel Becker, Ignacio |
| contents | Multiband astronomical time series exhibit heterogeneous variability patterns, sampling cadences, and signal characteristics across bands. Standard transformers apply shared parameters to all bands, potentially limiting their ability to model this rich structure. In this work, we introduce Astro-MoE, a foundational transformer architecture that enables dynamic processing via a Mixture of Experts module. We validate our model on both simulated (ELAsTiCC-1) and real-world datasets (Pan-STARRS1). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_12611 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Astro-MoE: Mixture of Experts for Multiband Astronomical Time Series Cádiz-Leyton, Martina Cabrera-Vives, Guillermo Protopapas, Pavlos Moreno-Cartagena, Daniel Becker, Ignacio Instrumentation and Methods for Astrophysics Multiband astronomical time series exhibit heterogeneous variability patterns, sampling cadences, and signal characteristics across bands. Standard transformers apply shared parameters to all bands, potentially limiting their ability to model this rich structure. In this work, we introduce Astro-MoE, a foundational transformer architecture that enables dynamic processing via a Mixture of Experts module. We validate our model on both simulated (ELAsTiCC-1) and real-world datasets (Pan-STARRS1). |
| title | Astro-MoE: Mixture of Experts for Multiband Astronomical Time Series |
| topic | Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2507.12611 |