With Yale’s Office of Career Strategy, Wright Lab is co-hosting the preliminary round for Yale’s annual 3MT competition, physical sciences division. Read more at https://ocs.yale.edu/channels/3-minute-thesis-competition/
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.
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).
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.
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.
Please RSVP here by Wednesday, November 9th: https://forms.gle/oCD55BkNV255fpVi7
More information coming soon.
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.
The Wright Lab community is invited to join former Wright Lab Artist-in-Residence Emily Coates to screen the film “Invisible Universe,” which was developed during her residency at Wright Lab.
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
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.
