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Abstract This paper presents a detailed characterization of the positron beam delivered by the Beam Test Facility at Laboratori Nazionali of Frascati to the PADME experiment during Run III, which took place from October to December 2022. It showcases the methodology used to measure the main beam parameters such as the position in space, the absolute momentum scale, the beam energy spread, and its intensity through a combination of data analysis and Monte Carlo simulations. The results achieved include an absolute precision in the momentum of the beam to within ~1–2 MeV/c, a relative beam energy spread below 0.25%, and an absolute precision in the intensity of the beam at the level of 2%.

Abstract The PADME experiment at the DAΦNE Beam-Test Facility (BTF) of the INFN Laboratory of Frascati is designed to search for invisible decays of dark sector particles produced in electron-positron annihilation events with a positron beam and a thin fixed target, by measuring the missing mass of single-photon final states. The presence of backgrounds originating from beam halo particles can significantly reduce the sensitivity of the experiment. To thoroughly understand the origin of the beam background contribution, a detailed Geant4-based Monte Carlo simulation has been developed, containing a full description of the detector together with the beam line and its optical elements. This simulation allows the full interactions of each particle to be described, both during beam line transport and during detection, a possibility which represents an innovative way to obtain reliable background predictions.

The PADME apparatus was built at the Frascati National Laboratory of INFN to search for a dark photon (A′) produced via the process e+e−→A′γ. The central component of the PADME detector is an electromagnetic calorimeter composed of 616 BGO crystals dedicated to the measurement of the energy and position of the final state photons. The high beam particle multiplicity over a short bunch duration requires reliable identification and measurement of overlapping signals. A regression machine-learning-based algorithm has been developed to disentangle with high efficiency close-in-time events and precisely reconstruct the amplitude of the hits and the time with sub-nanosecond resolution. The performance of the algorithm and the sequence of improvements leading to the achieved results are presented and discussed.