FIRST DETECTION OF COHERENT ELASTIC NEUTRINO-NUCLEUS SCATTERING ON AN ARGON TARGET

Thesis (Ph.D.) - Indiana University, Department of Physics, 2020Coherent elastic neutrino-nucleus scattering (CEvNS) was first proposed in 1974 but eluded detection for 40 years. The COHERENT collaboration made the first observation of CEvNS at the Oak Ridge National Laboratory Spallation Neutron Source (SNS) with a 14.6 kg CsI[Na] detector. One of the physics goals of the COHERENT experiment is to test the square of the neutron number dependence of the CEvNS cross section predicted in the Standard Model by observing CEvNS in multiple nuclei. To that end, the ~24 kg CENNS-10 liquid argon detector was deployed at the low-background Neutrino Alley at the SNS in early 2017. The detector was upgraded to allow for sensitivity to CEvNS in mid-2017. We analyzed 1.5 years of data taken after this upgrade to provide the first detection of CEvNS on an argon nucleus at > 3$\sigma$ significance. The measured CEvNS cross section of (2.3$\pm$0.7) x 10$^{39}$cm$^2$, averaged over the incident neutrino flux, is consistent with the Standard Model prediction. This result represents a detection of CEvNS on the lightest nuclei so far and improves bounds on beyond-the-standard-model physics in the form of non-standard neutrino interactions

TRANSLATE -- A Monte Carlo Simulation of Electron Transport in Liquid Argon

The microphysics of electron and photon propagation in liquid argon is a key component of detector design and calibrations needed to construct and perform measurements within a wide range of particle physics experiments. As experiments grow in scale and complexity, and as the precision of their intended measurements increases, the development of tools to investigate important microphysics effects impacting such detectors becomes necessary. In this paper we present a new time-domain Monte Carlo simulation of electron transport in liquid argon. The simulation models the TRANSport in Liquid Argon of near-Thermal Electrons (TRANSLATE) with the aim of providing a multi-purpose software package for the study and optimization of detector environments, with a particular focus on ongoing and next generation liquid argon neutrino experiments utilizing the time projection chamber technology. TRANSLATE builds on previous work of Wojcik and Tachiya, amongst others, introducing additional cross-section processes up to ionization, thus modeling the full range of drift electron scattering interactions. The simulation is validated by benchmarking its performance with swarm parameters from data collected in experimental setups operating in gas and liquid.Comment: 17 pages, 12 figure

Quantifying Adaptability in Pre-trained Language Models with 500 Tasks

When a neural language model (LM) is adapted to perform a new task, what aspects of the task predict the eventual performance of the model? In NLP, systematic features of LM generalization to individual examples are well characterized, but systematic aspects of LM adaptability to new tasks are not nearly as well understood. We present a large-scale empirical study of the features and limits of LM adaptability using a new benchmark, TaskBench500, built from 500 procedurally generated sequence modeling tasks. These tasks combine core aspects of language processing, including lexical semantics, sequence processing, memorization, logical reasoning, and world knowledge. Using TaskBench500, we evaluate three facets of adaptability, finding that: (1) adaptation procedures differ dramatically in their ability to memorize small datasets; (2) within a subset of task types, adaptation procedures exhibit compositional adaptability to complex tasks; and (3) failure to match training label distributions is explained by mismatches in the intrinsic difficulty of predicting individual labels. Our experiments show that adaptability to new tasks, like generalization to new examples, can be systematically described and understood, and we conclude with a discussion of additional aspects of adaptability that could be studied using the new benchmark.Comment: NAACL 2022; 20 pages, 6 figures, 8 table

Learning an Executable Neural Semantic Parser

This paper describes a neural semantic parser that maps natural language utterances onto logical forms which can be executed against a task-specific environment, such as a knowledge base or a database, to produce a response. The parser generates tree-structured logical forms with a transition-based approach which combines a generic tree-generation algorithm with domain-general operations defined by the logical language. The generation process is modeled by structured recurrent neural networks, which provide a rich encoding of the sentential context and generation history for making predictions. To tackle mismatches between natural language and logical form tokens, various attention mechanisms are explored. Finally, we consider different training settings for the neural semantic parser, including a fully supervised training where annotated logical forms are given, weakly-supervised training where denotations are provided, and distant supervision where only unlabeled sentences and a knowledge base are available. Experiments across a wide range of datasets demonstrate the effectiveness of our parser.Comment: In Journal of Computational Linguistic

Experiments and Facilities for Accelerator-Based Dark Sector Searches

This paper provides an overview of experiments and facilities for accelerator-based dark matter searches as part of the US Community Study on the Future of Particle Physics (Snowmass 2021). Companion white papers to this paper present the physics drivers: thermal dark matter, visible dark portals, and new flavors and rich dark sectors

Report of the Instrumentation Frontier Working Group for Snowmass 2021

Detector instrumentation is at the heart of scientific discoveries. Cutting edge technologies enable US particle physics to play a leading role worldwide. This report summarizes the current status of instrumentation for High Energy Physics (HEP), the challenges and needs of future experiments and indicates high priority research areas. The Snowmass Instrumentation Frontier studies detector technologies and Research and Development (R&D) needed for future experiments in collider physics, neutrino physics, rare and precision physics and at the cosmic frontier. It is divided into more or less diagonal areas with some overlap among a few of them. We lay out five high-level key messages that are geared towards ensuring the health and competitiveness of the US detector instrumentation community, and thus the entire particle physics landscape

Physics Opportunities for the Fermilab Booster Replacement

This white paper presents opportunities afforded by the Fermilab Booster Replacement and its various options. Its goal is to inform the design process of the Booster Replacement about the accelerator needs of the various options, allowing the design to be versatile and enable, or leave the door open to, as many options as possible. The physics themes covered by the paper include searches for dark sectors and new opportunities with muons