Accelerating the dark matter signal search in astrophysical data with machine learning

Principal Investigator: Gabrijela Zaharijas

Areas:

  • Astrophysical Probes of Fundamental Interactions

Abstract: Over the past fifteen years, several satellites exploring the high-energy (HE) sky with gamma rays and charged cosmic rays gathered unprecedented data on the most energetic astrophysical processes in our Galaxy. These measurements resulted in a series of exciting discoveries in the field of HE astrophysics and provided an opportunity to search for a nature of Dark Matter (DM) particles in a completely new way. One of the limitations to taking the full advantage of this high-quality data is related to the challenges of thorough explorations of multi-dimensional parameter spaces. At the same time, machine-learning (ML) algorithms are being increasingly used to tackle the analyses of large data sets. They are now evolving to include elements of statistical analysis, which makes them suitable to address the science questions, as the one above and could bring to further breakthroughs in the field. Inspired in part by the arguments listed above, the scientific community joined together within the framework of darkmachines.org, which connects data scientists with physicists and astrophysicists, with a motivation to ‘accelerate search for dark matter with machine learning’. The community had already two face-to-face meeting, one in Leiden in 2018, and one organized also in part by IFPU, in Trieste in 2019. This program line focuses on an attempt to improve the analysis of the astrophysical data by adopting the ML techniques, with the final motivation to discern signals of particle DM. Some of the projects in this research program, currently under development (either as darkmachines challenges or as individual projects) include: - localization and classification of the gamma-ray point sources using a combination of supervised and unsupervised learning with the goal of looking for new source classes, including possibly the one from DM subhalos - application of the INFERNO (Inference-Aware Neural Optimisation) tool to the whole sky gamma-ray data, with the goal to provide a robust search for DM gamma-ray signals in dwarf satellite galaxies - analysis of the gravitational lensing or stellar data with the goal to constrain small scale DM clustering However, we anticipate that this line of research will likely expand to include more applications in the future.