arXiv:2606.10197v1 Announce Type: cross Abstract: Integral field unit (IFU) spectroscopy provides spatially resolved spectra across galaxies, offering crucial insights into their evolution. However, its high observational cost limits current IFU datasets to $\sim 10^4$ objects. We present a multi-modal, probabilistic foundation model that predicts high-resolution spectra with calibrated uncertainties at arbitrary spatial locations within a galaxy directly from broadband images. Built on a masked autoencoder framework, our architecture injects fiber positional encodings and redshift aware wavel
The proliferation of advanced AI models, particularly foundation models, is enabling new paradigms in scientific data analysis that were previously computationally prohibitive or required extensive human intervention.
This development significantly reduces the observational cost and data scarcity issues in complex astronomical research, accelerating our understanding of galaxy evolution.
Astronomers can now derive high-resolution spectroscopic data from broadband images at arbitrary spatial locations, overcoming limitations of current IFU datasets and potentially expanding the scope of cosmic surveys.
- · Astronomical research institutions
- · Astrophysicists
- · AI model developers
- · Space agencies
The ability to generate synthetic high-resolution spectra will lead to a rapid increase in the effective astronomical IFU dataset size.
This expanded dataset will enable the discovery of new phenomena and more precise models of galaxy formation and evolution.
These advancements could inform future telescope design and observation strategies, prioritizing broadband imaging capabilities over extensive spectroscopic instrumentation for certain applications.
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Read at arXiv cs.AI