AstroAI Lunch Talks - April 13, 2026 - Aryana Haghjoo
13 Apr 2026 - Joshua Wing
The video can be found here: https://www.youtube.com/watch?v=M9KbXy-63ls
Speaker: Aryana Haghjoo (UC Riverside)
Title: Learning to See Sharper: A Physics-Informed Artificial Intelligence Framework for Super-Resolving Galaxy Spectra
Abstract: The information recoverable from galaxy spectra depends fundamentally on spectral resolution, yet assembling large samples at high resolution remains observationally expensive. We present a deep-learning framework for spectral super-resolution that enhances low-resolution galaxy spectra by a factor of ∼10 in resolving power (R ∼ 100 to R ∼ 1000). The model is trained on 1,187 paired JWST/NIRSpec observations from the JADES program, where low-resolution prism spectra are matched with medium-resolution grating spectra (G140M, G235M, G395M) combined into a unified reference covering 1–5μm. Our three-stage architecture performs an initial super-resolution, infers the redshift from the coarse reconstruction, and then applies a physics-informed residual refinement that uses attention across emission-line tokens to learn inter-line relationships and predict parametric line profiles, alongside a convolutional branch for continuum corrections. Evaluated on a 20% held-out sample, the model achieves noise-limited residuals over most of the spectral range and systematically improves the signal-to-noise ratio of key diagnostic lines including [O II], Hβ, [O III], and Hα, often by factors of several. The super-resolved spectra successfully deblend features that are entirely unresolved at prism resolution, such as the [O III] λλ4959, 5007 doublet and Hβ. As a proof of concept using JWST data, this approach is readily extensible to the low-resolution grism spectroscopy that will be delivered by Euclid and the Roman Space Telescope, potentially enabling population-level diagnostics across millions of galaxy spectra that would otherwise be inaccessible at low-resolution.