We propose an evolution of the Mu2e experiment, called Mu2e-II, that would leverage advances in detector technology and utilize the increased proton intensity provided by the Fermilab PIP-II upgrade to improve the sensitivity for neutrinoless muon-to-electron conversion by one order of magnitude beyond the Mu2e experiment, providing the deepest probe of charged lepton flavor violation in the foreseeable future. Mu2e-II will use as much of the Mu2e infrastructure as possible, providing, where required, improvements to the Mu2e apparatus to accommodate the increased beam intensity and cope with the accompanying increase in backgrounds.✝ Inquiries should be directed to Mu2e-II-contacts@fnal.gov

A new muon beamline, muon science innovative channel (MuSIC), was set up at , using the 392 MeV proton beam impinging on a target. The production of an intense muon beam relies on the efficient capture of pions, which subsequently decay to muons, using a novel superconducting solenoid magnet system. After the pion-capture solenoid the first 36 • of the curved muon transport line was commissioned and the muon flux was measured. In order to detect muons, a target of either copper or magnesium was placed to stop muons at the end of the muon beamline. Two stations of plastic scintillators located upstream and downstream from the muon target were used to reconstruct the decay spectrum of muons. In a complementary method to detect negatively-charged muons, the X-ray spectrum yielded by muonic atoms in the target were measured in a germanium detector. Measurements, at a proton beam current of 6 pA, yielded (10.4 ± 2.7) × 10 5 muons per Watt of proton beam power (µ + and µ − ), far in excess of other facilities. At full beam power (400 W), this implies a rate of muons of (4.2 ± 1.1) × 10 8 muons s −1 , amongst the highest in the world. The number of µ − measured was about a factor of 10 lower, again by far the most efficient muon beam produced. The set up is a prototype for future experiments requiring a high-intensity muon beam, such as a muon collider or neutrino factory, or the search for rare muon decays which would be a signature for phenomena beyond the Standard Model of particle physics. Such a muon beam can also be used in other branches of physics, nuclear and condensed matter, as well as other areas of scientific research.

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Background: Heavy charged particles after nuclear muon capture are an important nuclear physics background to the muon-to-electron conversion experiments Mu2e and COMET, which will search for charged lepton flavor violation at an unprecedented level of sensitivity. Purpose: The AlCap experiment aimed to measure the yield and energy spectra of protons, deuterons, tritons, and α particles emitted after the nuclear capture of muons stopped in Al, Si, and Ti in the low-energy range relevant for the muon-to-electron conversion experiments. Methods: Individual charged particle types were identified in layered silicon detector packages and their initial energy distributions were unfolded from the observed energy spectra. Results: The proton yields per muon capture were determined as Y p (Al) = 26.64(28 stat.)(77 syst.) × 10 −3 and Y p (Ti) = 26.48(35)(80) × 10 −3 in the energy range 3.5-20.0 MeV, and as Y p (Si) = 52.5(6)(18) × 10 −3 in the energy range 4.0-20.0 MeV. Detailed information on yields and energy spectra for all observed nuclei are presented in the paper. Conclusions: The yields in the candidate muon stopping targets, Al and Ti, are approximately half of that in Si, which was used in the past to estimate this background. The reduced background allows for less shielding and a better energy resolution in these experiments. It is anticipated that the comprehensive information presented in this paper will stimulate modern theoretical calculations of the rare process of muon capture with charged particle emission and inform the design of future muon-to-electron conversion experiments.

We describe the use of machine learning algorithms to select high-quality measurements for the Mu2e experiment. This technique is important for experiments with backgrounds that arise due to measurement errors. The algorithms use multiple pieces of ancillary information that are sensitive to measurement quality to separate high-quality and low-quality measurements.