Maven: A Multimodal Foundation Model for Supernova Science

• URL: http://arxiv.org/abs/2408.16829v1
• Date: Thu, 29 Aug 2024 18:00:05 GMT
• Title: Maven: A Multimodal Foundation Model for Supernova Science
• Authors: Gemma Zhang, Thomas Helfer, Alexander T. Gagliano, Siddharth Mishra-Sharma, V. Ashley Villar,
• Abstract summary: We present Maven, the first foundation model for supernova science. We first pre-train our model to align photometry and spectroscopy from 0.5M synthetic supernovae. We then fine-tune the model on 4,702 observed supernovae from the Zwicky Transient Facility.
• Score: 40.20166238855543
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A common setting in astronomy is the availability of a small number of high-quality observations, and larger amounts of either lower-quality observations or synthetic data from simplified models. Time-domain astrophysics is a canonical example of this imbalance, with the number of supernovae observed photometrically outpacing the number observed spectroscopically by multiple orders of magnitude. At the same time, no data-driven models exist to understand these photometric and spectroscopic observables in a common context. Contrastive learning objectives, which have grown in popularity for aligning distinct data modalities in a shared embedding space, provide a potential solution to extract information from these modalities. We present Maven, the first foundation model for supernova science. To construct Maven, we first pre-train our model to align photometry and spectroscopy from 0.5M synthetic supernovae using a constrastive objective. We then fine-tune the model on 4,702 observed supernovae from the Zwicky Transient Facility. Maven reaches state-of-the-art performance on both classification and redshift estimation, despite the embeddings not being explicitly optimized for these tasks. Through ablation studies, we show that pre-training with synthetic data improves overall performance. In the upcoming era of the Vera C. Rubin Observatory, Maven serves as a Rosetta Stone for leveraging large, unlabeled and multimodal time-domain datasets.

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