Speaker
Description
Axion-like particles (ALPs) are promising candidates from theories beyond the Standard Model, possibly linked to dark matter. When subjected to external magnetic fields, ALPs can convert to photons and vice versa, rendering them observable. The ALP-photon mixing distorts the gamma-ray blazar spectra with measurable effects, albeit tiny. The description of blazar jets varies per target and demands multi-wavelength data but a global study of the jet in the ALP scenario is still missing. The primary part of my project aims to target this issue by performing a structural study of blazar jets with open-source modeling tools, including polarization measurement. Additionally, we plan to harness the power of the supervised Machine Learning (ML) approach to investigate the interplay between photons and ALPs, leveraging gamma-ray data from the upcoming Cherenkov Telescope Array (CTA) and the Large Size Telescope (LST). By training the ML network with a large set of simulated data from CTA and LST for a diverse set of magnetic fields and coupling constants, we seek to yield robust constraints on ALP-photon interactions and make substantial advancements in this field.
