Training Deep 3D Convolutional Neural Networks to Extract BSM Physics Parameters Directly from HEP Data: a Proof-of-Concept Study Using Monte Carlo Simulations

We report on a novel application of computer vision techniques to extract beyond the Standard Model (BSM) parameters directly from high energy physics (HEP) flavor data. We develop a method of transforming angular and kinematic distributions into "quasi-images" that can be used to train a convolutional neural network to perform regression tasks, similar to fitting. This contrasts with the usual classification functions performed using ML/AI in HEP. As a proof-of-concept, we train a 34-layer Residual Neural Network to regress on these images and determine the Wilson Coefficient $C_{9}$ in MC (Monte Carlo) simulations of $B \rightarrow K^{*}\mu^{+}\mu^{-}$ decays. The technique described here can be generalized and may find applicability across various HEP experiments and elsewhere

Measurement of the e+e−→K+K−π+π−e^+e^- \to K^+K^-\pi^+\pi^- cross section with the CMD-3 detector at the VEPP-2000 collider

The process $e^+e^- \to K^+K^-\pi^+\pi^-$ has been studied in the center-of-mass energy range from 1500 to 2000\,MeV using a data sample of 23 pb$^{-1}$ collected with the CMD-3 detector at the VEPP-2000 $e^+e^-$ collider. Using about 24000 selected events, the $e^+e^- \to K^+K^-\pi^+\pi^-$ cross section has been measured with a systematic uncertainty decreasing from 11.7\% at 1500-1600\,MeV to 6.1\% above 1800\,MeV. A preliminary study of $K^+K^-\pi^+\pi^-$ production dynamics has been performed

Study of the process e+e−→ppˉe^+e^-\to p\bar{p} in the c.m. energy range from threshold to 2 GeV with the CMD-3 detector

Using a data sample of 6.8 pb$^{-1}$ collected with the CMD-3 detector at the VEPP-2000 $e^+e^-$ collider we select about 2700 events of the $e^+e^- \to p\bar{p}$ process and measure its cross section at 12 energy ponts with about 6\% systematic uncertainty. From the angular distribution of produced nucleons we obtain the ratio $|G_{E}/G_{M}| = 1.49 \pm 0.23 \pm 0.30$

Machine Learning for New Physics Searches in B → K*0µ+µ− Decays

We report the status of a neural network regression model trained to extract new physics (NP) parameters in Monte Carlo (MC) simulation data. We utilize a new EvtGen NP MC generator to generate B → K*0µ+µ− events according to the deviation of the Wilson Coefficient C9 from its SM value, δC9. We train a three-dimensional ResNet regression model, using images built from the angular observables and the invariant mass of the di-muon system, to extract values of δ C9 directly from the MC data samples. This work is intended for future analyses at the Belle II experiment but may also find applicability at other experiments

Measurement of the Pion Form Factor in the Energy Range 1.04-1.38 GeV with the CMD-2 Detector

The cross section for the process $e^+e^-\to\pi^+\pi^-$ is measured in the c.m. energy range 1.04-1.38 GeV from 995 000 selected collinear events including 860000 $e^+e^-$ events, 82000 $\mu^+\mu^-$ events, and 33000 $\pi^+\pi^-$ events. The systematic and statistical errors of measuring the pion form factor are equal to 1.2-4.2 and 5-13%, respectively.Comment: 5 pages, 2 figure

CsI(Tl) Pulse Shape Discrimination with the Belle II Electromagnetic Calorimeter as a Novel Method to Improve Particle Identification at Electron-Positron Colliders

This paper describes the implementation and performance of CsI(Tl) pulse shape discrimination for the Belle II electromagnetic calorimeter, representing the first application of CsI(Tl) pulse shape discrimination for particle identification at an electron-positron collider. The pulse shape characterization algorithms applied by the Belle II calorimeter are described. Control samples of $\gamma$, $\mu^+$, $\pi^\pm$, $K^\pm$ and $p/\bar{p}$ are used to demonstrate the significant insight into the secondary particle composition of calorimeter clusters that is provided by CsI(Tl) pulse shape discrimination. Comparisons with simulation are presented and provide further validation for newly developed CsI(Tl) scintillation response simulation techniques, which when incorporated with GEANT4 simulations allow the particle dependent scintillation response of CsI(Tl) to be modelled. Comparisons between data and simulation also demonstrate that pulse shape discrimination can be a new tool to identify sources of improvement in the simulation of hadronic interactions in materials. The $K_L^0$ efficiency and photon-as-hadron fake-rate of a multivariate classifier that is trained to use pulse shape discrimination is presented and comparisons are made to a shower-shape based approach. CsI(Tl) pulse shape discrimination is shown to reduce the photon-as-hadron fake-rate by over a factor of 3 at photon energies of 0.2 GeV and over a factor 10 at photon energies of 1 GeV

Precision Measurement of KS Meson Lifetime with the KLOE detector

Using a large sample of pure, slow, short lived K0 mesons collected with KLOE detector at DaFne, we have measured the KS lifetime. From a fit to the proper time distribution we find tau = (89.562 +- 0.029_stat +- 0.043_syst) ps. This is the most precise measurement today in good agreement with the world average derived from previous measurements. We observe no dependence of the lifetime on the direction of the Ks.Comment: 5 pages, 7 figure