Constraining dark matter model using 21cm line intensity mapping
arxiv(2024)
摘要
We apply the Convolutional Neural Networks (CNNs) to the mock 21cm maps from
the post-reionization epoch to show that the ΛCDM 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 (νMSM), 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, νMSM, 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.
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