Deconvolving X-ray Galaxy Cluster Spectra Using a Recurrent Inference Machine

TL;DR

This paper introduces a recurrent inference machine that deconvolves galaxy cluster X-ray spectra from instrumental effects, achieving high accuracy on mock data and promising applications for real astronomical observations.

Contribution

It presents a novel machine learning approach using RIM to accurately recover intrinsic X-ray spectra from observed data, improving spectral analysis in astronomy.

Findings

RIM recovers mock spectra below 1-sigma error threshold

Reconstructed spectra match observations indistinguishably

Discrepancies with models suggest implicit prior encoding

Abstract

Recent advances in machine learning algorithms have unlocked new insights in observational astronomy by allowing astronomers to probe new frontiers. In this article, we present a methodology to disentangle the intrinsic X-ray spectrum of galaxy clusters from the instrumental response function. Employing state-of-the-art modeling software and data mining techniques of the Chandra data archive, we construct a set of 100,000 mock Chandra spectra. We train a recurrent inference machine (RIM) to take in the instrumental response and mock observation and output the intrinsic X-ray spectrum. The RIM can recover the mock intrinsic spectrum below the 1-$\sigma$ error threshold; moreover, the RIM reconstruction of the mock observations are indistinguishable from the observations themselves. To further test the algorithm, we deconvolve extracted spectra from the central regions of the galaxy group NGC 1550, known to have a rich X-ray spectrum, and the massive galaxy clusters Abell 1795. Despite the RIM reconstructions consistently remaining below the 1-$\sigma$ noise level, the recovered intrinsic spectra did not align with modeled expectations. This discrepancy is likely attributable to the RIM's method of implicitly encoding prior information within the neural network. This approach holds promise for unlocking new possibilities in accurate spectral reconstructions and advancing our understanding of complex X-ray cosmic phenomena.