Bulletin of the American Physical Society
Annual Meeting of the APS Four Corners Section
Volume 62, Number 17
Friday–Saturday, October 20–21, 2017; Fort Collins, CO
Session J1: Lustig Award Presentations |
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Chair: Kathrin Spendier, University of Colorado Springs Room: Lory Student Center Theate |
Saturday, October 21, 2017 8:00AM - 8:03AM |
J1.00001: Introduction Kathrin Spendier |
Saturday, October 21, 2017 8:03AM - 8:27AM |
J1.00002: Materials prediction using high-throughput and machine learning techniques Invited Speaker: Chandramouli Nyshadham The importance of designing new materials with enhanced properties is vital for mankind to prosper and meet their ever-increasing necessities. The task of searching for new and advanced materials is colossal because of the innumerable combinations of different elements. Material scientists have developed large databases of known materials over the last century. The challenge now is to use data from computer simulations to discover new materials. Here at Brigham Young University (BYU) we have built a large database of alloy simulations. High-throughput and machine learning techniques can be used to leverage the database and discover materials at a faster pace. The high-throughput technique is an intelligent way to interrogate a database for inventing new materials. Machine learning models give a computer the ability to learn about materials without being programmed explicitly. In this talk I’ll give a brief overview of my three year work as a PhD student here at BYU. The talk will focus on two important topics: 1) A high-throughput technique we used to invent new materials called superalloys[1], and 2) a few machine learning techniques we are currently pursing for faster prediction of new materials. \\ \\ \Small{[1]Chandramouli Nyshadham, Corey Oses, Jacob E. Hansen, Ichiro Takeuchi, Stefano Curtarolo and Gus L. W. Hart,“A computational high-throughput search for new ternary superalloys.” Acta Materialia 122 (2017): 438-447. } [Preview Abstract] |
Saturday, October 21, 2017 8:27AM - 8:51AM |
J1.00003: Probing Many-Body Physics in an Optical Lattice Clock Invited Speaker: Sarah Bromley My graduate research has focused on experiments with atomic clocks. Advances in atomic, molecular, and optical (AMO) physics push the frontiers of atomic clock research and offer exciting research opportunities. At the same time, atomic clocks now provide us with sensitive measurement tools, accurate navigation through the Global Positioning System (GPS), and are vital for many internet based applications. The base unit of time, the second, is now derived from a microwave transition frequency in cesium. However, the systematic uncertainty of the most advanced clocks based on optical transitions now surpass those of the cesium atomic standards. These transition frequencies are effected by environmental perturbations which include, for example, the local electric and magnetic field environment. For the case of optical lattice atomic clocks, multiple atoms are confined together within a standing wave of light and the atom-atom and atom-light interactions both need to be considered. My PhD research focuses on studies of these interactions. These studies not only help to understand the systematic shifts these clocks experience but also allow the simulation of many-body Hamiltonians and dipolar interactions and are therefore pushing the frontiers of AMO physics. [Preview Abstract] |
Saturday, October 21, 2017 8:51AM - 9:15AM |
J1.00004: Neutrino Oscillation Measurements Using A Maximum Likelihood Event Reconstruction Algorithm Invited Speaker: Andrew Missert A new maximum likelihood event reconstruction algorithm is introduced for the Super-Kamiokande (SK) detector. This new algorithm improves the expected particle identification and kinematic resolution for SK neutrino events from the T2K beam. Event selections that exclusively use the new reconstruction methods are then optimized. A particular emphasis is placed on the SK fiducial volume cuts, which must be optimized in the presence of systematic uncertainties. To estimate the SK detector systematic uncertainty, a Markov chain Monte Carlo fit is performed using SK atmospheric data as a control sample. Applying the new reconstruction methods with the optimized fiducial volume cuts increases the statistical size of the T2K event samples by as much as 20\%, which improves the sensitivity to the neutrino mixing parameters $\theta_{23}$, $\theta_{13}$, $\Delta m^{2}_{23}$, and the CP-violating phase $\delta_{CP}$. [Preview Abstract] |
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