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Fermionic versus Bosonic Dark Matter in Neutron Stars: A Bayesian Study with Multi-Density Constraints

Payaswinee Arvikar, Sakshi Gautam, Anagh Venneti, Sarmistha Banik

TL;DR

The paper addresses whether fermionic or bosonic dark matter can be present in neutron stars without conflicting with current constraints. It adopts a unified Bayesian framework spanning multi-density information, including $πχ$EFT at low density, finite nuclei and heavy-ion data at intermediate density, and NICER plus GW170817 observations at high density, to constrain both the hadronic EoS and three dark-sector models (FDM and two bosonic models, BDM1 and BDM2). The results show DM fractions typically below $10\%$, with DM slightly softening the EoS and modestly reducing $M_{max}$, $R_{1.4}$, and $\,Λ_{1.4}$, yet remaining compatible with NICER and GW170817 data; log-evidence indicates no strong statistical preference among FDM, BDM1, and BDM2. This work provides a robust statistical framework to constrain DM properties using NS observables and highlights the need for future precision measurements to break degeneracies between DM scenarios.

Abstract

We perform a comparative Bayesian analysis of fermionic and bosonic dark matter admixed neutron stars (DMANS) by incorporating a comprehensive set of theoretical, experimental, and astrophysical constraints. The hadronic matter equation of state (EoS) is modeled using a relativistic mean-field approach, constrained by chiral effective field theory ($χ$EFT) calculations at low densities, finite nuclei and heavy-ion collision data at intermediate densities, and neutron star (NS) observations at high densities. For the dark sector, we consider fermionic dark matter (FDM) interacting via a dark vector meson, and two bosonic dark matter models (BDM1 and BDM2) characterized by self-interacting scalar fields. Bayesian inference is employed to constrain the model parameters, including the dark matter mass, coupling strength, and dark matter fraction within NSs. Our analysis finds that all models yield consistent nuclear matter parameters, allowing a small dark matter fraction under 10%. The presence of dark matter slightly softens the EoS, leading to a modest reduction in NS mass, radius, and tidal deformability, though all models remain compatible with NICER and GW170817 observations. The log-evidence and likelihood analyses reveal no statistical preference among the FDM and BDM models, indicating that current astrophysical data cannot decisively distinguish between fermionic and bosonic dark matter scenarios. This study provides a unified statistical framework to constrain dark matter properties using NS observables.

Fermionic versus Bosonic Dark Matter in Neutron Stars: A Bayesian Study with Multi-Density Constraints

TL;DR

The paper addresses whether fermionic or bosonic dark matter can be present in neutron stars without conflicting with current constraints. It adopts a unified Bayesian framework spanning multi-density information, including EFT at low density, finite nuclei and heavy-ion data at intermediate density, and NICER plus GW170817 observations at high density, to constrain both the hadronic EoS and three dark-sector models (FDM and two bosonic models, BDM1 and BDM2). The results show DM fractions typically below , with DM slightly softening the EoS and modestly reducing , , and , yet remaining compatible with NICER and GW170817 data; log-evidence indicates no strong statistical preference among FDM, BDM1, and BDM2. This work provides a robust statistical framework to constrain DM properties using NS observables and highlights the need for future precision measurements to break degeneracies between DM scenarios.

Abstract

We perform a comparative Bayesian analysis of fermionic and bosonic dark matter admixed neutron stars (DMANS) by incorporating a comprehensive set of theoretical, experimental, and astrophysical constraints. The hadronic matter equation of state (EoS) is modeled using a relativistic mean-field approach, constrained by chiral effective field theory (EFT) calculations at low densities, finite nuclei and heavy-ion collision data at intermediate densities, and neutron star (NS) observations at high densities. For the dark sector, we consider fermionic dark matter (FDM) interacting via a dark vector meson, and two bosonic dark matter models (BDM1 and BDM2) characterized by self-interacting scalar fields. Bayesian inference is employed to constrain the model parameters, including the dark matter mass, coupling strength, and dark matter fraction within NSs. Our analysis finds that all models yield consistent nuclear matter parameters, allowing a small dark matter fraction under 10%. The presence of dark matter slightly softens the EoS, leading to a modest reduction in NS mass, radius, and tidal deformability, though all models remain compatible with NICER and GW170817 observations. The log-evidence and likelihood analyses reveal no statistical preference among the FDM and BDM models, indicating that current astrophysical data cannot decisively distinguish between fermionic and bosonic dark matter scenarios. This study provides a unified statistical framework to constrain dark matter properties using NS observables.

Paper Structure

This paper contains 11 sections, 16 equations, 7 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Equation of state for nuclear matter and dark matter
  3. Hadronic Matter Equation of State
  4. Fermionic Dark Matter Equation of State
  5. Bosonic Dark Matter Equation of State
  6. Methodology
  7. Two-fluid formalism
  8. Constraints on the Equation of State
  9. Bayesian Inference
  10. Results and Discussions
  11. Conclusion

Figures (7)

  • Figure 1: Posterior distributions of nuclear matter parameters (NMPs) for No DM case. The $1 \sigma$ CI is displayed as vertical dashed lines and median as dotted lines in the marginalized posterior distribution for the NMPs $\rho_0, E_0, K_0, \frac{M^*}{M},J_0,L_0$. Off-diagonal panels show the contours enclosing 1$\sigma$ and 2$\sigma$ CIs. All are in units of MeV, except for the saturation density $\rho_0$(fm$^{-3}$), and $M^*/M$ (dimensionless).
  • Figure 2: Posterior distributions of NMPs and DM parameters, $\rho_0, E_0, K_0, \frac{M^*}{M},J_0,L_0,C_{vd}, M_D, f_{DM}$, for fermionic DM model (FDM). Units of DM parameters are $C_{vd}$ (MeV)$^{-1}$ and $M_D$ (MeV). Contours and vertical lines represent CIs similar to those in Fig. \ref{['fig:corner_NoDM']}.
  • Figure 3: Posterior distributions of NMPs and DM parameters, $\rho_0, E_0, K_0, \frac{M^*}{M},J_0,L_0,\lambda, M_D, f_{DM}$, for first bosonic DM model (BDM1). Unit of $M_D$ is MeV and $\lambda$ is dimensionless. Contours and vertical lines represent CIs similar to those in Fig. \ref{['fig:corner_NoDM']}.
  • Figure 4: Posterior distributions of NMPs and DM parameters, $\rho_0, E_0, K_0, \frac{M^*}{M},J_0,L_0,l_D, M_D, f_{DM}$, for second bosonic DM model (BDM2). Units of DM parameters are $l_D$ (fm) and $M_D$ (MeV). Contours and vertical lines represent CIs similar to those in Fig. \ref{['fig:corner_NoDM']}.
  • Figure 5: M-R distribution plots for pure NS [upper left panel] and DMANS with the three DM models [other three panels]. Red lines show all the M-R$_H$ curves obtained from the posteriors samples of Bayesian analysis. Blue hatched region is the 2$\sigma$ spread of the M-R distribution from the posteriors for total radius $R_T$ (hadronic + dark). Brown and blue dashed lines represent the corresponding medians for $R_H$ and $R_T$. NICER X-ray observations are shown as Pink and Green contours for PSR J0740+6620 )high mass) Miller_2021Riley_2021 and PSR J0030+045 Miller_2019Riley_2019, respectively. Orange and Yellow contours correspond to the NICER data of pulsars PSR J0437-4715 Choudhury:2024xbk and PSR J0614-3329 Mauviard:2025 respectively. The grey shaded M-R regions show 90%(light) and 50%(dark) CI for the LIGO/Virgo constraints derived from the binary components of GW170817 event.
  • ...and 2 more figures