Moller operators and Lippmann-Schwinger equations for step-like potentials

The Moller operators and the asociated Lippman-Schwinger equations obtained from different partitionings of the Hamiltonian for a step-like potential barrier are worked out, compared and related.Comment: 15 pages, 1 inlined figure, iopart.cl

High precision calculations of electroweak radiative corrections for polarized Moller scattering at one loop and beyond

Parity-violating Moller scattering measurements are a powerful probe of new physics effects, and the upcoming high-precision experiments will require a new level of accuracy for electroweak radiative corrections (EWC). First, we perform the updated calculations of one-loop EWC for Moller scattering asymmetry using two different approaches: semi-automatic, precise, with FeynArts and FormCalc as base languages, and "by hand", with reasonable approximations. In addition, we provide a tuned comparison between the one-loop results obtained in two different renormalization schemes: on-shell and constrained differential renormalization. As the last step, we discuss the two-loop EWC induced by squaring one-loop diagrams, and show that the significant size of this partial correction indicates a need for a complete study of the two-loop EWC in order to meet the precision goals of future experiments.Comment: PAVI11 Workshop Proceedings (September 5-9, 2011, Rome

Justice, Gender and the Family

A Review of Justice, Gender and the Family by Susan Moller Oki

Machine Learning and Electron Track Reconstruction for the MOLLER Experiment

This report presents the development of a machine learning algorithm (a neural net- work) for the purpose of track reconstruction in the MOLLER experiment. The MOLLER experiment is a collaboration at Jefferson Lab which plans on measuring the parity-violating asymmetry in high energy electron-electron (Møller) scattering. This measurement is an important test of the Standard Model and could potentially serve as evidence for new physics beyond the Standard Model. Reconstruction of electron trajectories provides important kinematic data about the electrons which is necessary for the asymmetry measurement. As such, the MOLLER experiment requires a track reconstruction tool which can efficiently handle large amounts of data. We created a recurrent neural network which models the trajectories of electrons in the detector system for MOLLER. We trained the network using data from the GEANT 4 Monte Carlo simulation for the experiment. At this stage, our network is able to correctly predict the locations where an electron hits the main detector given that electron’s previous positions in the detector system. We describe various studies we conducted to improve the accuracy and efficiency of our network, as well as present suggestions for any potential continuations of this project

Pion Identification through Machine Learning for the MOLLER Experiment at the Thomas Jefferson National Accelerator Facility

This report describes the implementation of a deep neural network for particle identification on the MOLLER experiment. The MOLLER experiment, currently in its early stages of design at the Thomas Jefferson National Accelerator Facility (JLab), will attempt to measure the parity-violating asymmetry present in the elastic electron-electron scattering, to a precision of 0.7 ppb. While the Standard Model precisely predicts this asymmetry, if the value measured by the MOLLER experiment were to differ significantly from the predicted value, then the experiment could provide laboratory-based evidence of physics beyhond the Standard Model (BSM) and point researchers in the right direction for its exploration. The high energy electron beam used in this experiment is predicted to generate scattered electrons as well as a background of roughly 0.13 percent pions. While the ratio of pions to electrons will be small, their presence may significantly affect the asymmetry measurement. The detected particles, predominantly pions and electrons, must thus be classified. Here, an algorithm is proposed to classify particles detected in the MOLLER experiment using deep neural networks (DNNs). Once the classification algorithm is successfully written and proven to work, the uncertainty in the classification of the particles as pions, electrons, or positrons will be determined. If successful, this classification algorithm may be used to optimize the design of the experiment hardware