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Workshop: NeurIPS 2023 Workshop: Machine Learning and the Physical Sciences

Simulation-Based Inference for Detecting Blending in Spectra

Declan McNamara · Jeffrey Regier


Many galaxies overlap visually from the vantage point of Earth; these galaxies are known as ``blends''. Undetected blends can lead to errors in the estimation of quantities of scientific interest, such as cosmological parameters and redshift. We propose a generative model based on a state-of-the-art simulator of galaxy spectra, and develop a likelihood-free inference method to detect unrecognized blends. Our inference routine simulates both blended and unblended spectra with which it trains an inference network to solve the inverse problem, that is, to map spectra to a Bernoulli distribution indicating the presence or absence of blendedness. Our experiments demonstrate the potential of our method to detect unrecognized blends in high-resolution spectral data from the Dark Energy Spectroscopic Instrument (DESI).

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