{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/improving-statistical-sensitivity-of-x-ray","title":"Improving Statistical Sensitivity of X-ray Searches for Axion-Like Particles","arxiv_id":"1808.05916","date":"2018-08-17","proceeding":null,"authors":["Joseph P. Conlon","Markus Rummel"],"abstract":"X-ray observations of bright AGNs in or behind galaxy clusters offer unique capabilities to constrain axion-like particles (ALPs). Existing analysis technique rely on measurements of the global goodness-of-fit. We develop a new analysis methodology that improves the statistical sensitivity to ALP-photon oscillations by isolating the characteristic quasi-sinusoidal modulations induced by ALPs. This involves analysing residuals in wavelength space allowing the Fourier structure to be made manifest as well as a machine learning approach. For telescopes with microcalorimeter resolution, simulations suggest these methods give an additional factor of two in sensitivity to ALPs compared to previous approaches.","url_abs":"http://arxiv.org/abs/1808.05916v1","url_pdf":"http://arxiv.org/pdf/1808.05916v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"improving-statistical-sensitivity-of-x-ray","repo_url":"https://github.com/mrummphys/axion_statistics","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}