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

Unraveling the Mysteries of Galaxy Clusters: Recurrent Inference Deconvolution of X-ray Spectra

Carter Rhea · Julie Hlavacek-Larrondo · Ralph Kraft · Akos Bogdan · Laurence Perreault-Levasseur · Alexandre Adam · John Zuhone

Abstract: In the realm of X-ray spectral analysis, the true nature of spectra has remained elusive, as observed spectra have long been the outcome of convolution between instrumental response functions and intrinsic spectra. In this study, we employ a recurrent neural network framework, the Recurrent Inference Machine (RIM), to achieve the unprecedented deconvolution of intrinsic spectra from instrumental response functions. Our RIM model is meticulously trained on cutting-edge thermodynamic models and authentic response matrices sourced from the Chandra X-ray Observatory archive. Demonstrating remarkable accuracy, our model successfully reconstructs intrinsic spectra well below the 1-$\sigma$ error level. We showcase the practical application of this novel approach through real Chandra observations of the galaxy cluster Abell 1550—a vital calibration target for the recently launched X-ray telescope, XRISM This pioneering work marks a significant stride in the domain of X-ray spectral analysis, offering a promising avenue for unlocking hitherto concealed insights into spectra.

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