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Events During the Week of May 5th through May 12th, 2024

Monday, May 6th, 2024

No events scheduled

Tuesday, May 7th, 2024

Flow-based Nonperturbative Simulation of First-order Phase Transitions
Time: 2:00 pm - 4:00 pm
Place: B343, Sterling Hall
Speaker: Dean Chen, Physics PhD Graduate Student
Abstract: In this talk, I will introduce a flow-based nonperturbative method to study the first-order phase transition (FOPT) of a scalar field theory on a lattice. Motivated by possible early-universe first-order electroweak and QCD phase transitions and recent developments in machine learning tools, including normalizing flows (NFs) for lattice field theory, we have developed a simulation algorithm to efficiently calculate bubble nucleation rates. We propose the ``partitioning flow-based (PF) sampling" method to overcome two challenges in the application of NFs for lattice field theory: the "mode-collapse" and "rare-event sampling" problems. Using a (2+1)-dimensional real scalar model as an example, we demonstrate the capability of our PF method to calculate the nucleation rates for the thermal FOPT. This method could be applied to (3+1)-dimensional theories and used to study realistic cosmological phase transitions.
Host: Yang Bai
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Wednesday, May 8th, 2024

No events scheduled

Thursday, May 9th, 2024

No events scheduled

Friday, May 10th, 2024

Application of geometric deeping learning in charged-particle track reconstruction in the ATLAS ITk
Time: 9:00 am - 11:00 am
Speaker: Tuan Pham, Physics Graduate Student
Abstract: The reconstruction of charged-particle trajectories, ranking amongst the most computationally demanding tasks in particle collider experiments, such as the ATLAS experiment at CERN, plays an essential role in any High-Energy Physics program, as it determines the quality of particle identification, kinematic measurement, vertex finding, lepton reconstruction, jet flavor tagging, and other downstream tasks. The upcoming High Luminosity phase of the Large Hadron Collider (HL-LHC) represents a steep increase in the average number of proton-proton interactions and hence in the computing resources required for offline track reconstruction of the ATLAS Inner Tracker (ITk). As such, track pattern recognition algorithms based on Graph Neural Networks (GNNs) have been demonstrated as a promising approach to these challenges. We present a novel algorithm developed for track reconstruction in silicon detectors based on a number of deep learning techniques including GNN architectures. Using detector simulation of collision events associated with the production of a top quark pair on the latest version of ITk geometry under HL-LHC conditions, we demonstrate the performance of our algorithm, and compare to that of the tracking algorithm currently used in ATLAS on a range of important physics metrics, including reconstruction efficiency, and track parameter resolution. Finally, we discuss the algorithm's computational performance and optimisations that reduce computing costs, as well as our effort to integrate into the ATLAS analysis software for full-chain testing and production.
Host: Sau Lan Wu
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