Measurement of proton, deuteron, triton, and alpha particle emission after nuclear muon capture on Al, Si, and Ti with the AlCap experiment

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

The FAST-HEP toolset: Using YAML to make tables out of trees

The Faster Analysis Software Taskforce (FAST) is a small, European group of HEP researchers that have been investigating and developing modern software approaches to improve HEP analyses. We present here an overview of the key product of this effort: a set of packages that allows a complete implementation of an analysis using almost exclusively YAML files. Serving as an analysis description language (ADL), this toolset builds on top of the evolving technologies from the Scikit-HEP and IRIS-HEP projects as well as industry-standard libraries such as Pandas and Matplotlib. Data processing starts with event-level data (the trees) and can proceed by adding variables, selecting events, performing complex user-defined operations and binning data, as defined in the YAML description. The resulting outputs (the tables) are stored as Pandas dataframes which can be programmatically manipulated and converted to plots or inputs for fitting frameworks. No longer just a proof-of-principle, these tools are now being used in CMS analyses, the LUX-ZEPLIN experiment, and by students on several other experiments. In this talk we will showcase these tools through examples, highlighting how they address the different experiments’ needs, and compare them to other similar approaches