Lights, Camera, Axion: Tracing Axions from Supernovae in the Diffuse $γ$-ray Sky

Abstract

Axions produced copiously in core-collapse supernovae can convert into photons as they propagate through various astrophysical magnetic fields. The cumulative emission from the cosmic population of supernovae can therefore generate a diffuse gamma-ray signal through axion-photon conversion. In this work, we develop a comprehensive framework to compute the diffuse gamma-ray flux by modeling axion production in supernovae and, \textit{for the first time}, consistently accounting for their conversion into photons across all relevant magnetic field environments - progenitor, host galaxy, intergalactic medium, and the Milky Way - together with an updated cosmic star formation rate. Using measurements of the diffuse gamma-ray sky from COMPTEL, EGRET, and \textit{Fermi}-LAT, we derive competitive constraints on the axion-photon coupling over a wide range of axion masses. We further forecast the sensitivity of upcoming MeV gamma-ray telescopes to this diffuse signal using a Fisher forecast analysis.

Figures (10)

• Figure 1: Time-integrated axion spectra for the supernova progenitor profiles SFHo-18.6 (blue), SFHo-18.8 (red), and SFHo-20.0 (green). The spectra are shown for different production channels: the Primakoff process (left), nucleon–nucleon bremsstrahlung (middle), and pion conversion (right). Solid (dashed) lines correspond to the KSVZ (ALP) case with nucleon couplings $C_{app} \approx -0.47$, $C_{ann} \approx -0.02$ ($C_{app} \approx C_{ann} \approx 10^{-4}$) Manzari:2024jns. The axion–photon coupling is fixed at $g_{a\gamma\gamma}=10^{-12} \, \mathrm{GeV}^{-1}$. All spectra are shown in the massless axion limit.
• Figure 2: Axion–photon conversion probabilities in various magnetic field environments: (a) Progenitor star (upper left), where red and blue bands indicate uncertainties from magnetic field modeling for RSG and BSG, respectively; (b) Host galaxy (upper right), showing results for $z=0.01$ (red) and $z=5$ (blue). The bands represent the uncertainties arising from the host galaxy magnetic field configurations; (c) Intergalactic medium (lower left), with the shaded area represents uncertainties corresponding to the allowed range of IGM magnetic field strengths for sources at $z=0.01$ (red) and $z=5$ (blue); (d) Galactic magnetic field (lower right), where eight curves represent UF23 models. The red (blue) line marks the maximum (minimum) conversion probability defining the uncertainty band. A zoomed version is shown to highlight the eight different UF23 models. The wiggles in some of the curves are numerical artifacts.
• Figure 3: Fits to the cosmic SFRD in two regimes: a low redshift fit (green solid curve) to the weighted SFRD compilation of Ref. Ekanger:2023qzw (applied for $z \leq 1$), and a high redshift fit (blue solid curve) to the total (IR$+$UV, without dust correction) SFRD evolution from Ref. Fujimoto:2023vfa (applied for $z > 1$). The purple and red data points with error bars correspond to the datasets used for the low redshift and high redshift fits, respectively. Shaded regions show the propagated $1\sigma$ fit uncertainties in each regime.
• Figure 4: Diffuse axion-induced photon flux as a function of energy for the SFHo-18.6, together with the COMPTEL, EGRET, and Fermi-LAT diffuse-background measurements. The photon flux is shown for $m_a=10^{-12}\,\rm eV$ and $g_{a\gamma\gamma}=10^{-12}\,\rm GeV$ in the KSVZ scenario. The blue-shaded region denotes the uncertainty enevelope obtained by propagating uncertainties in the CCSN rate and conversion probabilities across different astrophysical environments. The solid lines show the best-fit power-law background together with the data points of COMPTEL (purple), EGRET (red), and Fermi-LAT(teal).
• Figure 5: Constraints on $g_{a\gamma\gamma}$ with 95% CL for SFHo-18.6 model in the KSVZ (left) and ALP (right) scenarios. The purple, red, and teal bands show the exclusion limits from COMPTEL, EGRET, and Fermi-LAT, respectively, incorporating astrophysical and conversion uncertainties. Existing constraints from astrophysical observations Wouters:2013huaHESS:2013udxMarsh:2017yvcKohri:2017ljtReynolds:2019uqtBuen-Abad:2020zbdCalore:2020tjwDessert:2020lilLi:2020pcnMeyer:2020vzyXiao:2020praCalore:2021hhnChan:2021gjlDessert:2021bkvLi:2021gxsKeller:2021zblReynes:2021bpeDessert:2022yqqDolan:2022kulHoof:2022xbeJacobsen:2022swaFoster:2022fxnNoordhuis:2022ljwEscudero:2023vgvBattye:2023oacLi:2024zstCyr:2024sbdManzari:2024jnsNing:2024ekyRuz:2024gklMAGIC:2024arqBenabou:2025jcv, as well as laboratory searches including haloscope experiments DePanfilis:1987dkWuensch:1989saHagmann:1990tjHagmann:1996qd2010PhRvL.104d1301ABrubaker:2016ktlMcAllister:2017lkbOuellet:2018beuHAYSTAC:2018rwyADMX:2018ghoADMX:2018ogsADMX:2019uokAlesini:2019ajtLee:2020cfjCAPP:2020utbAlesini:2020vnyHAYSTAC:2020kwvCAST:2020rlfGramolin:2020ictJeong:2020cwzDevlin:2021fpqSalemi:2021gckGrenet:2021vbbThomson:2021zvqADMX:2021nhdAdair:2022rtwKim:2022hmgYoon:2022gzpLee:2022mncAlesini:2022lnpQuiskamp:2022pksTASEH:2022vvuKim:2023vpoYang:2023yryThomson:2023mocQuiskamp:2023ehrQUAX:2023gopHAYSTAC:2023camHeinze:2023nfbBae:2024kmyAhyoune:2024kltQUAX:2024futQuiskamp:2024oetHAYSTAC:2024jchMADMAX:2024sxsADMX:2024xbvPandey:2024dcdGigaBREAD:2025lzqADMX:2025vom and helioscope experiments CAST:2007jpsEhret:2010mhBetz:2013dzaOSQAR:2015qdvCAST:2017uphCAST:2024eil, are overlaid as a grey-shaded region for comparison.