Impact of astrophysical effects on the dark matter mass constraint with 21 cm intensity mapping

Abstract

We present an innovative approach to constraining the non-cold dark matter model using a convolutional neural network (CNN). We perform a suite of hydrodynamic simulations with varying dark matter particle masses and generate mock 21cm radio intensity maps to trace the dark matter distribution at z = 3 in the post-reionization epoch. Our proposed method complements the traditional power spectrum analysis. We compare the results of the CNN classification between the mock maps with different dark matter masses with those from the 2D power spectrum of the differential brightness temperature map of 21cm radiation. We find that the CNN outperforms the power spectrum. Moreover, we investigate the impact of baryonic physics on the dark matter model constraint, including star formation, self-shielding of HI gas, and UV background model. We find that these effects may introduce some contamination in the dark matter constraint, but they are insignificant compared to the system noise of the SKA instruments.