  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.
