A dual-encoder contrastive model trained on star-instrument triplets learns separate stellar and instrumental latent spaces, improving few-shot prediction of stellar parameters in simulated TESS-like light curves.
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Causal Foundation Models: Disentangling Physics from Instrument Properties
A dual-encoder contrastive model trained on star-instrument triplets learns separate stellar and instrumental latent spaces, improving few-shot prediction of stellar parameters in simulated TESS-like light curves.