Undergraduate

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.

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”.

WIDG Seminar: Will Tyndall, Yale, “Nearfield to Farfield Methods for Drone Beam Mapping”

Extracting cosmological 21 cm emission from the radio foregrounds which dominate requires precision calibration, including sub-percent measurements of the complex instrument beam. 21 cm cosmology experiments are typically driven to be compact transit interferometers with poor point-source sensitivity, and have found it difficult to constrain the beam shape to this precision with sky data alone. A technique that has been developed and demonstrated by multiple groups to address this is to transmit a calibrated RF signal from a drone into the telescope to measure the beam pattern.

WIDG Seminar: Evan Craft, Yale, “Beautiful and Charming Energy Correlators”

Understanding the detailed structure of energy flow within jets, a field known as jet substructure, plays a central role in searches for new physics, and precision studies of QCD. In this talk, I will discuss how reformulating jet susbtructure in terms of correlations of energy flow can be used to provide new insights into hadronization and intrinsic mass effects before confinement. In particular, I will show how energy correlators manifest the long-sought-after “dead-cone” effect of fundamental QCD.

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