Sensitivity toward dark matter annihilation imprints on 21-cm signal with SKA-Low: A convolutional neural network approach

Abstract

This study investigates the sensitivity of the radio interferometers to identify imprints of spatially inhomogeneous dark matter annihilation signatures in the 21-cm signal during the pre-reionization era. We focus on the upcoming low-mode survey of the Square Kilometre Array (SKA-Low) telescope. Using CNNs, we analyze simulated 3D 21-cm differential brightness temperature maps generated via the DM21cm code, which is based on 21cmFAST and DarkHistory, to distinguish between spatially homogeneous and inhomogeneous energy injection/deposition scenarios arising from dark matter annihilation. The inhomogeneous case accounts for local dark matter density contrasts and gas properties, such as thermal and ionization states, while the homogeneous model assumes uniform energy deposition. Our study focuses on two primary annihilation channels to electron-positron pairs ($e^{+}{e}^{-}$) and photons ($\gamma \gamma$), exploring dark matter masses from 1 MeV to 100 MeV and a range of annihilation cross-sections. For $\gamma \gamma$ channel, the distinction across dark matter models is less pronounced due to the larger mean free path of the emitted photons, resulting in a more uniform energy deposition. For $e^{+}{e}^{-}$ channel, the results indicate that the CNNs can effectively differentiate between the inhomogeneous and homogeneous cases. Despite observational challenges, the results demonstrate that these effects remain detectable even after incorporating noise from next-generation radio interferometers, such as the SKA. We find that the inhomogeneous dark matter annihilation models can leave measurable imprints on the 21-cm signal maps distinguishable from the homogeneous scenarios for the dark matter masses $m_{\mathrm{DM}}=1$ MeV and the annihilation cross-sections of $\ge 5\times 10^{-30}\mathrm{c}{\mathrm{m}}^3/sec$ ($\ge 5\times 10^{-29}\mathrm{c}{\mathrm{m}}^3/sec$ for $m_{\mathrm{DM}}=100$ MeV) for moderate SKA-Low noise.

19 pages, 7 figures. Updated to match the published version

References in corpus (21)

• Dark Matter Candidates from Particle Physics and Methods of Detection
• How accurately can 21 cm tomography constrain cosmology?
• Improved Constraints on the 21 cm EoR Power Spectrum and the X-Ray Heating of the IGM with HERA Phase I Observations
• HERA Phase I Limits on the Cosmic 21-cm Signal: Constraints on Astrophysics and Cosmology During the Epoch of Reionization
• 21cmFAST v3: A Python-integrated C code forgenerating 3D realizations of the cosmic 21cm signal
• BIGHORNS - Broadband Instrument for Global HydrOgen ReioNisation Signal
• The Impact of the First Galaxies on Cosmic Dawn and Reionization
• Galaxy Morphological Classification Catalogue of the Dark Energy Survey Year 3 data with Convolutional Neural Networks
• A Space-Based Observational Strategy for Characterizing the First Stars and Galaxies Using the Redshifted 21-cm Global Spectrum
• SARAS 3 CD/EoR Radiometer: Design and Performance of the Receiver
• Detecting Gravitational-waves from Extreme Mass Ratio Inspirals using Convolutional Neural Networks
• Convolutional neural networks for the detection of the early inspiral of a gravitational-wave signal
• Improved deep learning techniques in gravitational-wave data analysis
• Antenna beam characterisation for the global 21cm experiment LEDA and its impact on signal model parameter reconstruction
• Square Kilometre Array Science Data Challenge 3a: foreground removal for an EoR experiment
• Probing Ultra-light Axion Dark Matter from 21cm Tomography using Convolutional Neural Networks
• Constraints on freeze-in dark matter from Lyman-$\alpha$ forest and 21-cm signal : single-field models
• Exploring symbolic regression and genetic algorithms for astronomical object classification
• Non-Linearity-Free prediction of the growth-rate $f{\sigma }_8$ using Convolutional Neural Networks
• Prospects for probing dark matter particles and primordial black holes with the Square Kilometre Array using the 21 cm power spectrum at cosmic dawn
• Differentiating Warm Dark Matter Models through 21cm Line Intensity Mapping: A Convolutional Neural Network Approach

Cited by in corpus (1)

• Astrophysical uncertainties challenge 21-cm forecasts: A primordial black hole case study