Unraveling particle dark matter with Physics-Informed Neural Networks
This work addresses the challenge of modeling dark matter in non-standard cosmological scenarios for physicists, though it appears incremental as it applies an existing PINN method to a specific domain problem.
The researchers tackled the problem of determining physical attributes of freeze-in dark matter in alternative cosmologies by using Physics-Informed Neural Networks (PINNs) to solve Boltzmann equations, predicting a distinct relationship between power-law exponents and particle interaction cross sections based on observed relic density data.
We parametrically solve the Boltzmann equations governing freeze-in dark matter (DM) in alternative cosmologies with Physics-Informed Neural Networks (PINNs), a mesh-free method. Through inverse PINNs, using a single DM experimental point -- observed relic density -- we determine the physical attributes of the theory, namely power-law cosmologies, inspired by braneworld scenarios, and particle interaction cross sections. The expansion of the Universe in such alternative cosmologies has been parameterized through a switch-like function reproducing the Hubble law at later times. Without loss of generality, we model more realistically this transition with a smooth function. We predict a distinct pair-wise relationship between power-law exponent and particle interactions: for a given cosmology with negative (positive) exponent, smaller (larger) cross sections are required to reproduce the data. Lastly, via Bayesian methods, we quantify the epistemic uncertainty of theoretical parameters found in inverse problems.