Engineering topics

Fantechi, R.

Publications and source records attributed to Fantechi, R..

3 records

DOE OSTI2024

Workshop summary: Kaons@CERN 2023

Kaon physics is at a turning point – while the rare-kaon experiments NA62 and KOTO are in full swing, the end of their lifetime is approaching and the future experimental landscape needs to be defined. With HIKE, KOTO-II and LHCb-Phase-II on the table and under scrutiny, it is a very good moment in time to take stock and contemplate about the opportunities these experiments and theoretical developments provide for particle physics in the coming decade and beyond. This paper provides a compact summary of talks and discussions from the Kaons@CERN 2023 workshop, held in September 2023 at CERN.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

First observation and study of the $K^± → π^0π^0μ^±ν$ decay

The NA48/2 experiment at CERN reports the first observation of the $K^± → π^0π^0μ^±$ν decay based on a sample of 2437 candidates with 15% background contamination collected in 2003–2004. The decay branching ratio in the kinematic region of the squared dilepton mass above 0.03 GeV 2 /c 4 is measured to be (0.65 ± 0.03) × 10 –6 . The extrapolation to the full kinematic space, using a specific model, is found to be (3.45 ± 0.16) × 10 –6 , in agreement with chiral perturbation theory predictions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Improved calorimetric particle identification in NA62 using machine learning techniques

Measurement of the ultra-rare $K^+$ → $π$ +$ν$$\overline{ν}$ decay at the NA62 experiment at CERN requires high-performance particle identification to distinguish muons from pions. Calorimetric identification currently in use, based on a boosted decision tree algorithm, achieves a muon misidentification probability of 1.2×10 -5 for a pion identification efficiency of 75% in the momentum range of 15–40 GeV/c. In this work, calorimetric identification performance is improved by developing an algorithm based on a convolutional neural network classifier augmented by a filter. Muon misidentification probability is reduced by a factor of six with respect to the current value for a fixed pion-identification efficiency of 75%. Alternatively, pion identification efficiency is improved from 72% to 91% for a fixed muon misidentification probability of 10 -5 .

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗