Bayesian analysis of the break in DAMPE lepton spectra

TL;DR

This paper performs a Bayesian analysis of DAMPE cosmic-ray lepton data, fitting models involving pulsars and dark matter to explain the observed spectral break at 1 TeV and excess features.

Contribution

It introduces a comprehensive Bayesian framework to simultaneously fit cosmic-ray data, constraining parameters for pulsar and dark matter scenarios explaining the spectral features.

Findings

Pulsar scenario: spectral index ~0.65, cutoff ~650 GV.

Dark matter: mass ~1208 GeV, cross section ~1.48e-23 cm^3/s.

Dark matter annihilation via tau channels is suppressed.

Abstract

Recently, DAMPE has released its first results on the high-energy cosmic-ray electrons and positrons (CREs) from about $25$ GeV to $4.6$ TeV, which directly detect a break at $\sim 1$ TeV. This result gives us an excellent opportunity to study the source of the CREs excess. In this work, we used the data fo proton and helium flux (from AMS-02 and CREAM), $\bar{\mathrm{p}}/\mathrm{p}$ ratio (from AMS-02), positron flux (from AMS-02) and CREs flux (from DAMPE without the peak signal point at $\sim 1.4$ TeV) to do global fitting simultaneously, which can account the influence from the propagation model, the nuclei and electron primary source injection and the secondary lepton production precisely. For extra source to interpret the excess in lepton spectrum, we consider two separate scenarios (pulsar and dark matter annihilation via leptonic channels) to construct the bump ($\gtrsim 100$ GeV) and the break at $\sim 1$ TeV. The result shows: (i) in pulsar scenario, the spectral index of the injection should be $\nu_{\mathrm{psr}} \sim 0.65$ and the cut-off should be $R_{c} \sim 650$ GV; (ii) in dark matter scenario, the dark matter particle's mass is $m_{\chi} \sim 1208$ GeV and the cross section is $\langle \sigma v \rangle \sim 1.48 \times 10^{-23} \mathrm{cm}^{3} \mathrm{s}^{-1}$. Moreover, in the dark matter scenario, the $\tau \bar{\tau}$ annihilation channel is highly suppressed, and a DM model is built to satisfy the fitting results.