Speaker
Description
The LEM-X observatory is a proposed lunar coded-mask telescope for wide-field monitoring of steady and transient sources in the (2–50 keV) range, with a pathfinder under development with slated launch in the early 2030s [1]. Coded-mask imagers represent essential tools in high energy astrophysics, providing all-sky cover with respect to narrow FoV free-flier telescopes. However, these systems require robust sky decoding techniques to efficiently extract and analyse the collected data.
The Iterative Removal Of Sources (IROS) [4] is a reconstruction method used to enhance the imaging performance of coded-mask cameras, allowing for efficient localisation and detection in all-sky surveys while ensuring affordable computational costs.
IROS relies on the iterative identification and subtraction of statistically significant sources. To do this effectively, the procedure demands highly accurate analytical modelling of the source projection on the detector plane, which must account for the system design and instrumental effects [2]. Generating high-fidelity models can be computationally expensive and difficult to achieve due to photons energy statistics, creating a severe bottleneck when processing crowded sky-fields and/or searching for transient signals [3].
To overcome these computational barriers, we propose a novel, data-driven approach utilising generative deep learning models to identify a source and simulate its footprint. By training a diffusion model directly on ad-hoc simulated data, our method inherently captures the instrumental effects without requiring relatively computationally heavy, first-principles physics simulations and design considerations. This allows for the rapid and accurate generation of source templates for the subtraction phase, significantly reducing the computational cost of sky reconstruction while maintaining or improving decoding efficiency.
To our knowledge, there is currently no literature exploring a machine-learning-based IROS implementation, nor has this specific type of generative analysis been applied to coded-mask data. The whole framework acts as groundwork for faster, highly efficient analysis pipelines for current and future high energy and multi-messenger astrophysics analyses.
References
[1] E. Del Monte et al., 2024. “Status of the Lunar Electromagnetic Monitor in X-rays (LEM-X)”, Space Telescopes and Instrumentation 2024. doi:10.1117/12.3018838
[2] Y. Evangelista et al., 2026. “Design and performance of the coded mask for the Lunar Electromagnetic Monitor in X-rays (LEM-X)”, Experimental Astronomy 61, 7. doi:10.1007/s10686-026-10047-x, arXiv:2603.10752
[3] E Giancarli et al., 2026. “Enhancing LEM-X Imaging with IROS”, in preparation
[4] A. Hammersley et al., 1992. “Reconstruction of images from a coded-aperture box camera”. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 311(3):585–594