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Learn.: Sci. Technol."],"published-print":{"date-parts":[[2024,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>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 0.5\u2009M synthetic supernovae using a contrastive objective. We then fine-tune the model on 4702 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 will serve as a valuable tool for leveraging large, unlabeled and multimodal time-domain datasets.<\/jats:p>","DOI":"10.1088\/2632-2153\/ad990d","type":"journal-article","created":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T11:47:53Z","timestamp":1733140073000},"page":"045069","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Maven: a multimodal foundation model for supernova science"],"prefix":"10.1088","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8019-8082","authenticated-orcid":true,"given":"Gemma","family":"Zhang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6880-1005","authenticated-orcid":true,"given":"Thomas","family":"Helfer","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4906-8447","authenticated-orcid":true,"given":"Alexander T","family":"Gagliano","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9088-7845","authenticated-orcid":true,"given":"Siddharth","family":"Mishra-Sharma","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5814-4061","authenticated-orcid":true,"given":"V","family":"Ashley Villar","sequence":"additional","affiliation":[]}],"member":"266","published-online":{"date-parts":[[2024,12,19]]},"reference":[{"key":"mlstad990dbib1","doi-asserted-by":"publisher","first-page":"9","DOI":"10.3847\/1538-4365\/acbfba","article-title":"The young supernova experiment data release 1 (YSE DR1): light curves and photometric classification of 1975 supernovae","volume":"266","author":"Aleo","year":"2023","journal-title":"Astrophys. 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