Accelerating the search for Axion-Like Particles with machine learning

Abstract

Machine learning (ML) techniques have been applied with tremendous success in many areas of physics. In this work, we use ML to place bounds on the coupling between photons and axion-like particles (ALPs). This coupling causes ALPs and photons to interconvert in the presence of a background magnetic field. This would lead to modulations in the spectra of point sources shining through the magnetic fields of galaxy clusters. This effect has already been used to place world-leading bounds on the ALP-photon coupling using conventional statistical methods. We train ML classification algorithms on simulated spectra from the Chandra X-ray telescope for a range of point sources and ALP-photon couplings. We then use the response of these algorithms to the real Chandra spectra to place bounds on ALP-photon interactions. We obtain bounds at a similar level to those based on other techniques, but find improvements on an individual source basis. We expect such search techniques to become increasingly important for ALP searches with future telescopes that will offer substantially higher energy resolution.

Version accepted for publication in JCAP

References in corpus (28)

• The Chandra Source Catalog
• Sherpa: a Mission-Independent Data Analysis Application
• XMM-Newton observations of the Perseus Cluster I: The temperature and surface brightness structure
• The Coma cluster magnetic field from Faraday rotation measures
• Search for Spectral Irregularities due to Photon-Axionlike-Particle Oscillations With the Fermi Large Area Telescope
• Chandra Observation of the Cooling Flow Cluster Abell 2052
• A New Bound on Axion-Like Particles
• Astrophysical limits on very light axion-like particles from Chandra grating spectroscopy of NGC 1275
• A thermally stable heating mechanism for the intracluster medium: turbulence, magnetic fields and plasma instabilities
• Guiding New Physics Searches with Unsupervised Learning
• Magnetic Fields in the Center of the Perseus Cluster
• Uncovering latent jet substructure
• Transfer learning for galaxy morphology from one survey to another
• Sardinia Radio Telescope observations of Abell 194 - the intra-cluster magnetic field power spectrum
• The intracluster magnetic field power spectrum in A2199
• Constraints on axion-like particles from X-ray observations of Hydra galaxy cluster
• Magnetic power spectra from Faraday rotation maps - REALMAF and its use on Hydra A
• Constraints on Axion-Like Particles from X-ray Observations of NGC1275
• Constraints on Axion-Like Particles from Non-Observation of Spectral Modulations for X-ray Point Sources
• FPGA-accelerated machine learning inference as a service for particle physics computing
• Soft X-ray Excess in the Coma Cluster from a Cosmic Axion Background
• Deep Learning at Scale for the Construction of Galaxy Catalogs in the Dark Energy Survey
• Projected bounds on ALPs from Athena
• Simulations of gas sloshing in galaxy cluster Abell 2052
• Searching for Axion-Like Particles with X-ray Polarimeters
• Neural network-based anomaly detection for high-resolution X-ray spectroscopy
• Improving Statistical Sensitivity of X-ray Searches for Axion-Like Particles
• Constraints on Massive Axion-Like Particles from X-ray Observations of NGC1275

Cited by in corpus (9)

• Detecting an axion-like particle with machine learning at the LHC
• Updated Bounds on Axion-Like Particles from X-ray Observations
• Back to the phase space: thermal axion dark radiation via couplings to standard model fermions
• Physics Beyond the Standard Model with Future X-ray Observatories: Projected Constraints on Very-Light Axion-Like Particles with $Athena$ and $AXIS$
• Magnetohydrodynamics predicts heavy-tailed distributions of axion-photon conversion
• A first application of machine and deep learning for background rejection in the ALPS II TES detector
• Ultralight Dark Matter from the Edge of Field Space
• How do Magnetic Field Models Affect Astrophysical Limits on Light Axion-like Particles? An X-ray Case Study with NGC 1275
• Projected sensitivity of CTAO to axion-like particles from blazars with a machine learning approach