Search for low mass dark matter in DarkSide-50: the bayesian network approach

Journal article

This study explores a minimal renormalizable dark matter (DM) model, incorporating a sub-GeV Majorana DM and a singlet scalar particle $\phi$. Using scalar and pseudo-scalar interactions (couplings $c_s$ and $c_p$), we investigate implications for DM detection, considering $s$-wave, $p$-wave, and combined ($s$+$p$ wave) contributions in DM annihilation cross-section, as well as loop-correction contributions to DM-nucleon elastic scattering. Identifying a broad parameter space ($10 \,\rm{MeV} < m_\chi \lesssim m_\phi$) within the $2\sigma$ allowed region, we explore scenarios ($\left|c_s\right|\gg \left|c_p\right|$, $\left|c_s\right|\ll \left|c_p\right|$, and $\left|c_s\right|\approx \left|c_p\right|$). We find that (i) a non-zero pseudo-scalar coupling alleviates direct detection constraints as a comparison with the previous pure scalar coupling case; (ii) CMB observations set stringent limits on pseudo-scalar interaction dominant cases, making $s$-wave annihilation viable only for $m_\chi>1\,\rm{GeV}$; (iii) the preferred $\phi$-resonance region can be tested in the future indirect detection experiments, such as e-ASTROGAM.Comment: 35 pages, 4 figure

Authors: Agnes, P; Albuquerque, IFM; Alexander, T; Alton, AK; Ave, M

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: SpringerOpen
• Journal: The European Physical Journal C
• Volume: 83
• Issue: 4
• Publication date: 2023-04-24
• DOI: 10.1140/epjc/s10052-023-11410-4
• EISSN: 1434-6052
• ISSN: 1434-6044
• Language: English
• Keywords: Parametric model; Artificial intelligence; Algorithm; Posterior probability; Nuisance parameter; Bayesian inference; Estimation theory; Particle physics; Sensitivity (control systems); Markov chain Monte Carlo; Parametric statistics; Bayesian probability; Computer science; Inference; Mathematics; Detector; Dark matter; Statistics; Likelihood function; Physics; Statistical physics