Millions of Galaxies, Sparse Information: Reliable SED Inference for HETDEX with Neural Density Estimators

Thomas M. Menino Convention & Exhibition Center 

Interpreting galaxy imaging and spectroscopy is central to studies of galaxy formation and evolution. Widely used forward-modeling approaches infer galaxy parameters via MCMC but take hours per galaxy due to model-generation costs, making them prohibitive for modern astronomical surveys with millions of galaxies. Here, we present a simulation-based inference framework for fast, amortized inference of galaxy physical parameters in the HETDEX survey. A unique challenge is that astrophysical inference is not performed on raw measurements, but rather on processed observables with strongly heteroscedastic uncertainties, epistemic uncertainties, and patchy imaging coverage. We address these challenges using a neural posterior estimator trained on ~10^7 simulated galaxies generated from a 17-parameter model, paired with lin–log asinh magnitudes and a tailored uncertainty model. The resulting amortized approach recovers key galaxy properties, including redshift and stellar mass, in ~0.06 seconds per object, achieving a ~2.5 million-fold speedup over traditional nested sampling methods while preserving comparable accuracy and uncertainty quantification. Characterizing redshift posteriors for faint, poorly constrained galaxies is challenging because they can contain multiple well-separated modes which are difficult for a sampler to traverse. Our SBI framework avoids this traverse entirely by sampling directly from these modes, producing better-calibrated redshift posterior distributions than our state-of-the-art benchmark while maintaining comparable performance for bright objects. This demonstrates that improved posterior calibration does not come at the expense of predictive accuracy. We additionally introduce a realistic masking framework to accommodate missing data.