A Deep learning library for neutrino telescopes
Code for calculating Coherent Elastic Neutrino-Nucleus Scattering (CEvNS) cross sections and recoil spectra. Also includes code for obtaining New Physics constraints from the COHERENT-2017 results.
Python tools for working with the IceCube public data.
A lightweight event generator for new physics in neutrino-nucleus scattering.
nuDoBe is a Python tool for neutrinoless double beta decay calculations based on an effective field theory approach.
Bayesian constraints on the astrophysical neutrino population from IceCube data
This repository contains the code used to perform the analysis described in the paper "A stacked search for spatial coincidences between IceCube neutrinos and radio pulsars" (https://arxiv.org/abs/2306.03427). The code is written in Python 3.10 and uses the following packages: numpy, scipy, matplotlib, pandas, numba, multiprocessing.
Magnetic moments of astrophysical neutrino (supernova and ultra high-energy neutrinos)
NuSD is a Geant4-based simulation framework developed to perform simulation studies on various segmented scintillation detectors.
Investigating coincident source-neutrino detections through simulations.
Rafelski, J., Birrell, J., Grayson, C., Steinmetz, A., Yang, C. T. Quarks to Cosmos: Particles and Plasma in Cosmological evolution. In press EPJ ST. arXiv:2409.19031 (2024).
Title: Convolution Neural Networks for the CHIPS Neutrino Detector R&D Project
Steinmetz, A. Modern topics in relativistic spin dynamics and magnetism. PhD dissertation. University of Arizona, 2023.
A Monte Carlo simulation of the electromagnetic cascade to compute the neutrino spectrum from cascade development.
A Convolutional Neural Network Implementation for the CHIPS Water Cherenkov R&D Project.
Rafelski, J., Steinmetz, A., & Yang, C. T. Dynamic fermion flavor mixing through transition dipole moments. International Journal of Modern Physics A 38.31 (2023): 2350163.
Contribution to the Harald Fritzsch Memorial Volume edited by Gerhard Buchalla, Dieter Lüst and Zhi-Zhong Xing.
UCL PHAS0056 (Machine Learning for Physicists) Final Project. Applying ML techniques to the binary classification and energy reconstruction of simulated neutrino events in LArTPCs
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