Speaker
Description
Millisecond pulsars (MSPs) are widely believed to be responsible for the gamma-ray emission of globular clusters (GCs), yet the underlying MSP gamma-ray luminosity function remains uncertain. Existing GC-based determinations of the latter often rely on external prescriptions for the number of MSPs in each cluster.
In this work, we constrain the MSP gamma-ray luminosity function in Milky-Way GCs directly from gamma-ray spectral energy distributions (SEDs), establishing an inference strategy that remains computationally viable for joint analyses of large GC samples.
We construct a forward model in which each GC hosts a population of MSPs whose luminosities are drawn from a universal log-normal luminosity function. Synthetic GC SEDs are generated by summing individual MSP spectra, and we infer posterior distributions using first a likelihood-based nested sampling and then a Bayesian implicit-likelihood inference in the neural ratio estimation (NRE) approach.
We show how the likelihood-based nested sampling approach becomes rapidly impractical as the number of jointly modelled clusters (and cluster-specific latent parameters) increases, whereas the NRE approach scales mildly with sample size and reproduces the likelihood-based posteriors in regimes where direct sampling is feasible.
We then apply the implicit-likelihood pipeline to Fermi-LAT SEDs from the 4FGL-DR4 catalog for a sample of 36 GC-associated sources, augmented by dedicated phase-resolved ON/OFF SEDs for two clusters hosting individually detected MSPs.
For the real data sample, we obtain informative constraints on the luminosity-function parameters consistent with previous analyses that constrain the cluster-specific MSP counts via complementary multi-wavelength information.