Differentiating Warm Dark Matter Models through 21cm Line Intensity Mapping: A Convolutional Neural Network Approach

Abstract: We apply the convolutional neural networks (CNNs) to the mock 21cm maps from the post-reionization epoch to show that the $\Lambda$ cold dark matter and warm dark matter (WDM) model can be distinguished for WDM particle masses $m_{FD}<3$\,keV, under the assumption of thermal production of WDM following the Fermi-Dirac (FD) distribution. We demonstrate that the CNN is a potent tool in distinguishing the dark matter masses, highlighting its sensitivity to the subtle differences in the 21cm maps produced by varying dark matter masses. Furthermore, we extend our analysis to encompass different WDM production mechanisms, recognizing that the dark matter production mechanism in the early Universe is among the sources of the most significant uncertainty for the dark matter model building. In this work, given the mass of the dark matter, we discuss the feasibility of discriminating four different WDM models: Fermi-Dirac (FD) distribution model, neutrino minimal Standard Model ($\nu$MSM), Dodelson-Widrow (DW), and Shi-Fuller (SF) model. For instance, when the WDM mass is 2\,keV, we show that one can differentiate between CDM, FD, $\nu$MSM, and DW models while discerning between the DW and SF models turns out to be challenging. Our results reinforce the viability of the CNN as a robust analysis for 21cm maps and shed light on its potential to unravel the features associated with different dark matter production mechanisms.