Kavli Affiliate: Salman Habib
| First 5 Authors: Bin Xia, Bin Xia, , ,
| Summary:
We present a multi-modal foundation model for astrophysical galaxy data,
designed to map between simulation- and observation-based galactic features.
Our encoder-only transformer flexibly ingests scalar quantities (e.g.,
redshifts, galaxy masses) and vectors (e.g., star formation histories,
spectra), supporting multi-task training that includes within-modality
reconstruction and cross-modality prediction. With a dynamic masking strategy,
the model can query arbitrary galaxy properties from partial inputs —
including predicting spectra from redshift and mass, or estimating photometric
redshifts from broadband magnitudes — while also recovering missing segments
within a modality. Trained on 185,000 simulated galaxies from a
gigaparsec-scale Cosmology simulation, the model yields a 50% improvement in
redshift estimation when combining LSST and SPHEREx photometry over LSST
photometry alone, and a 63% improvement in stellar mass inference when
combining late-time SFH with LSST photometry over early-time SFH with LSST
photometry. The model demonstrates strong generalization across multi-modal
tasks and lays the groundwork for future integration of higher-dimensional and
structured data such as images, merger trees, and 3D fields. This approach
provides a unified framework for connecting simulations and observations,
advancing the development of generalizable astrophysical foundation models.
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