Light Dark Matter Discovery Potential and Model Selection at LDMX

Abstract

Light dark matter (DM) is a compelling scenario for the observed relic abundance, with accelerator-based searches as a powerful discovery strategy. The upcoming Light Dark Matter eXperiment (LDMX) is designed to probe light DM by measuring the energy and transverse momentum of recoil electrons in high-intensity electron-nucleus collisions. We evaluate the discovery potential of LDMX to light DM at four benchmark points along the thermal target for complex scalar DM mediated by a dark photon, for different background assumptions, and assess its model selection power at a representative benchmark. We find that LDMX has strong projected 5$\sigma$ discovery potential along the relic target across both background scenarios for certain benchmarks, and we further compute projected 90% C.L. exclusion limits assuming no measured signal events. The normalization and shape of the two-dimensional recoil electron distribution encodes the coupling and dark photon mass, respectively, enabling parameter inference in the event of a signal excess. We perform parameter estimation on simulated data and find that both parameters are recovered within their uncertainties along the relic target. We assess whether the data can distinguish between competing dark sector hypotheses, in particular, dark photons with additional higher electromagnetic moment interactions. We demonstrate that model comparison using the Bayes factor allows dark sector hypotheses to be statistically distinguished, with the two-dimensional analysis affording substantially greater discriminating power than the one-dimensional analysis. These results are obtained within a likelihood-based statistical framework, incorporating signal and background modelling with their associated systematic uncertainties and employing both frequentist and Bayesian methods. The framework is designed for direct application to real LDMX data.