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

TL;DR

This paper introduces a recurrent neural network approach, the Recurrent Inference Machine, for precise deconvolution of intrinsic X-ray spectra from instrumental responses, demonstrated on galaxy cluster data from Chandra.

Contribution

It presents a novel RNN-based method for high-precision spectral deconvolution, trained on advanced models and real data, improving spectral analysis accuracy.

Findings

Achieved sub-1-sigma accuracy in spectral reconstruction

Successfully applied to Chandra observations of galaxy cluster Abell 1550

Demonstrated potential for enhanced spectral insights in X-ray astronomy

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 high-precision 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 work marks a significant stride in the domain of X-ray spectral analysis, offering a promising avenue for unlocking hitherto concealed insights into spectra.