Majorana Demonstrator Data Release for AI/ML Applications

I. J. Arnquist,F. T. Avignone III，C. -H. Yu,B. X. Zhu

Computing Research Repository (CoRR)（2023）

Pacific Northwest National Laboratory | Oak Ridge National Laboratory Department of Physics and Astronomy | National Research Center "Kurchatov Institute" Kurchatov Complex of Theoretical and Experimental Physics | University of South Dakota Department of Physics | Triangle Universities Nuclear Laboratory Department of Physics and Astronomy | North Carolina State University Department of Physics | Duke University Department of Physics | University of Washington Department of Physics | Lawrence Berkeley National Laboratory Nuclear Science Division | South Dakota Mines | Los Alamos National Laboratory | Medioambientales y Tecnológicas Centro de Investigaciones Energéticas | University of Tennessee Department of Physics and Astronomy | Osaka University Research Center for Nuclear Physics | Indiana University IU Center for Exploration of Energy and Matter | Williams College Physics Department | Oak Ridge National Laboratory | Tennessee Tech University | Queen's University Department of Physics | Technische Universität Physik Department and Excellence Cluster Universe | University of South Carolina Department of Physics and Astronomy | Joint Institute for Nuclear Research

Cited 0|Views50

Abstract

The enclosed data release consists of a subset of the calibration data from the Majorana Demonstrator experiment. Each Majorana event is accompanied by raw Germanium detector waveforms, pulse shape discrimination cuts, and calibrated final energies, all shared in an HDF5 file format along with relevant metadata. This release is specifically designed to support the training and testing of Artificial Intelligence (AI) and Machine Learning (ML) algorithms upon our data. This document is structured as follows. Section I provides an overview of the dataset's content and format; Section II outlines the location of this dataset and the method for accessing it; Section III presents the NPML Machine Learning Challenge associated with this dataset; Section IV contains a disclaimer from the Majorana collaboration regarding the use of this dataset; Appendix A contains technical details of this data release. Please direct questions about the material provided within this release to liaobo77@ucsd.edu (A. Li).

Translated text

Key words

Detector Performance

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Try using models to generate summary,it takes about 60s

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper