Undergraduate

NPA Seminar: Prakhar Garg, Stony Brook University, “Gaseous detectors for upcoming and future experiments”

Gaseous detectors are one of the most versatile concepts used in a wide range of physics experiments.
In this seminar, I would discuss some of their flavours in nuclear and high energy physics experiments. Particular emphasis will be on Time Projection Chamber for sPHENIX experiment, GEM Trackers for MOLLER experiment, Cylindrical \mu-RWELL for PIONEER Experiment and Generic ongoing R&D plans of GridPix detector for EIC. I would try to incorporate mostly the key features of these detector concepts and what makes them interesting to us.
Host: Helen Caines

Dissertation Defense: London Cooper-Troendle, Yale University, "First Measurement of Inclusive Muon Neutrino Charged Current Triple Differential Cross Section on Argon"

The field of accelerator neutrino experiments is entering an era of precision oscillation measurements where the remaining unknown neutrino measurements will be determined. The upcoming DUNE and Hyper-K experiments aim to determine the neutrino mass hierarchy and degree of Charge-Parity (CP) violation in the neutrino sector, providing potential insight on the matter-antimatter imbalance observed in the universe. However, these experiments require highly accurate measurements, and neutrino cross section modeling uncertainties may limit their capabilities.

Inference Project Virtual Talk: Inference in a Nonconceptual World

Classical models of inference, such as those based on logic, take inference to be *conceptual* – i.e., to involve representations formed of terms, predicates, relation symbols, and the like. Conceptual representation of this sort is assumed to reflect the structure of the world: objects of various types, exemplifying properties, standing in relations, grouped together in sets, etc. These paired roughly algebraic assumptions (one epistemic, the other ontological) form the basis of classical logic and traditional AI (GOFAI).

Dissertation Defense: Kaicheng Li, Yale University, "Searching for the Electron Neutrino Anomaly with the MicroBooNE Experiment Using Wire-Cell Reconstruction"

The Micro Booster Neutrino Experiment (MicroBooNE) is a leading large-scale Liquid Argon Time Projection Chamber (LArTPC) experiment, designed for precision neutrino physics. The main scientific objectives of MicroBooNE include the investigation of the Low Energy Excess (LEE) observed by the MiniBooNE Experiment between 2002-2019 in the Booster Neutrino Beam (BNB) at Fermilab, the measurements of neutrino-argon interactions, and the research and development of LArTPC technology. This thesis focuses on understanding the MiniBooNE LEE through charged-current electron neutrino interactions.

Introduction to Containerized Workloads Workshop

Reproducibility is important to scientific research. This should extend not only to the data and methods, but also to the programs and the execution environment of the programs. Traditional workloads require compilation, configuration, and installation of software to the computing environment. Containers encapsulate the necessary runtime dependencies for the software and allow them the software to run on different hosts with no modification of the underlying node. Automated builds ensure that software behaves predictably.

NPA Seminar: Andrew Mastbaum, Rutgers University, “Xenon-doped liquid argon TPCs as a neutrinoless double beta decay platform”

Searches for neutrinoless double-beta decay (NLDBD) continue to expand our understanding of the lepton sector, with several promising experimental paths toward increased sensitivity. We have considered the possible reach of a large-scale deep-underground LArTPC experiment doped with NLDBD candidate isotope xenon, and the challenges this approach would entail. In this talk, we will review the essential design requirements, background mitigations, and several open R&D questions relevant to such a detector, and discuss the potential sensitivity.

Introduction to Data Analysis with Python Workshop

Python is general purpose, interpreted programming language with a rich set of scientific and mathematic modules. As an interpreted language, it trades computational speed for iterative agility. It lends itself particularly well to the task of preparing raw data and performing exploratory analysis. This workshop will introduce participants to data analysis using Jupiter and Python, Numpy, and Pandas. Prior experience with Python is useful but not essential.
Led by Vincent Balbarin, Research Computing Specialist, Wright Lab & YCRC

Nobel Prize in Physics 2022 Lecture: A. Douglas Stone, Yale University, "Spooky Action at a Distance wins the Nobel Prize"

This talk will explain at a relatively non-technical level the significance of the experiments on quantum entanglement which were very recently recognized by the award of the 2022 Nobel Prize in Physics, jointly to Alain Aspect, John Clauser and Anton Zeilinger. The fact that a phenomenon we now call entanglement was inherent in quantum theory was first recognized by Albert Einstein in the 1930s, who disparagingly referred to it as “spooky action at a distance”.

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