Bulletin of the American Physical Society
APS April Meeting 2021
Volume 66, Number 5
Saturday–Tuesday, April 17–20, 2021; Virtual; Time Zone: Central Daylight Time, USA
Session Z19: Innovative Algorithms for HEP Data ProcessingLive
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Sponsoring Units: DPF Chair: David Brown, LBNL |
Tuesday, April 20, 2021 3:45PM - 3:57PM Live |
Z19.00001: Wire-Cell Pattern Recognition for Liquid Argon Time Projection Chambers Haiwang Yu Liquid argon time projection chambers (LArTPCs) are widely used in current and future neutrino experiments to answer some of the key questions about neutrino physics. Event reconstruction is a critical but challenging task in analyzing the data from LArTPCs. Following the principle of LArTPC, we developed a new tomographic event reconstruction paradigm, Wire-Cell. Wire-Cell achieved fully automated LArTPC reconstruction with multiple components including noise filtering, signal processing, 3D charge reconstruction and clustering, light-charge matching and higher-level pattern recognition. In this talk, we present the Wire-Cell pattern recognition algorithms incorporating both traditional and deep learning techniques. [Preview Abstract] |
Tuesday, April 20, 2021 3:57PM - 4:09PM Live |
Z19.00002: DeXTer: Deep Sets based Neural Networks for Low-$p_{\text{T}}$ $X \rightarrow$ $b\bar{b}$ identification in ATLAS Yuan-Tang Chou This work presents algorithms for flavor tagging identification of jets that are initiated by one or two independent heavy-flavor hadrons. Algorithms in ATLAS for hadronic jets typically focus on high transverse momentum, above 200 GeV. This work describes the first implementation of a double-b tagger for low transverse momentum jets, below 200 GeV. This algorithm relies on large radius track-jets which can be defined at low transverse momenta and implements a DeepSets neural network that uses displaced tracks, secondary vertices, and substructure information to identify the presence of multiple heavy-flavored hadrons. [Preview Abstract] |
Tuesday, April 20, 2021 4:09PM - 4:21PM Live |
Z19.00003: Deep Learning for Pion Classification with the ATLAS Detector Nicholas Luongo Hadronic signatures, and pions specifically, are ubiquitous in events captured by the ATLAS detector. Their classification as electromagnetically showering neutral pions or hadronically showering charged pions is therefore extremely important for particle and eventual jet reconstruction. Various deep learning models are trained on the energy deposits of pions in the calorimeter system to predict their shower profile. The deposits for a pion are represented as a group of images each corresponding to a different layer of the calorimeter. These trained models achieve more accurate classification when compared to current baseline methods. See ATLAS PUB note here: https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-PHYS-PUB-2020-018/ [Preview Abstract] |
Tuesday, April 20, 2021 4:21PM - 4:33PM Live |
Z19.00004: Particle identification with the cluster counting technique for a drift chamber at CEPC Shuiting Xin The Circular Electron Positron Collider(CEPC) is designed to operate at center-of-mass energies of 240 GeV as a Higgs factory, as well as at the Z-pole and the WW production threshold for electroweak precision measurements and study of flavor physics. A good identification of charged kaons is essential for the flavor physics and benefits the determination of jet flavor and jet charge. To achieve these physics goals, a design of tracking system combining a silicon tracker and a drift chamber is proposed. The silicon tracker provides excellent spatial resolution and granularity to cope with track separation in dense jets. The drift chamber could provide dN/dx measurements with cluster counting technique, as well as those of dE/dx. A simulation study on the cluster counting technique has been performed with the Garfield++ program, and the primary ionization, avalanche processes, and peak finding on the induction signals have been carefully investigated. The effects of the gas mixture, sampling frequency, and the noises are also taken into account. The study shows that the resolution and separation power with dN/dx method are significantly better than those of the traditional dE/dx method. A prototype system is being prepared to study feasibility of cluster counting technique. [Preview Abstract] |
Tuesday, April 20, 2021 4:33PM - 4:45PM Not Participating |
Z19.00005: Fast pattern recognition for ATLAS track triggers in HL-LHC Charles Kalderon, Viviana Cavaliere Fast tracking systems are being developed in ATLAS for the High Luminosity upgrade of the Large Hadron Collider (HL-LHC). The goal is to provide the high-level trigger with full-scan tracking at 100 kHz and regional tracking at 1 MHz, in the high pile-up conditions of the HL-LHC (in $pp$ collisions at $\sqrt{s}=$ 14 TeV with the ATLAS detector). Here, methods for fast hit filtering and track seeding are investigated. In the filtering method, known as stub-finding, hit pairs in closely-spaced silicon strip layers are accepted or rejected based on their azimuthal separation. In the track-seeding method, known as the Hough Transform, detector hits are mapped onto a 2D parameter space with one parameter related to the transverse momentum and one to the initial track direction. The performance of these methods is studied at different pile-up values (140 and 200) and compared, using full event simulation, to the currently-used CPU track reconstruction, as well as with a method based on matching detector hits to pattern banks of simulated tracks stored in custom made Associative Memory ASICs. A discussion of the hit reduction from stub-finding and associated tracking speedup, and a comparison of the overall tracking performance of the methods, will be presented. [Preview Abstract] |
Tuesday, April 20, 2021 4:45PM - 4:57PM Live |
Z19.00006: Algorithm design and expected performance for ATLAS Run-3 Level-1 calorimeter trigger system Ava Myers, Tae Min Hong, Benjamin Carlson In Run 3 of the LHC (2021-2024), the Level-1 trigger system of the ATLAS Experiment will introduce three feature extractors (FEX): eFEX for electron/photon, jFEX for jets/MET, and gFEX for global quantities. The increased calorimeter granularity is useful for all physics channels that deposit energy in the calorimeter, from high-bandwidth items like electrons to MET (missing transverse momentum). An overview of the hardware implementation will be discussed. Details of the algorithm design will be presented, along with the projected performance for electron/photon, jet, and MET triggers. [Preview Abstract] |
Tuesday, April 20, 2021 4:57PM - 5:09PM Live |
Z19.00007: Integral Calculations on numerical data using a Mathematical Transform Equation. Brette Delahoussaye, Mathematics Teacher, LAUSD. Brette De La Houssaye A Mathematical Transform Equation can be used with regression modeling of data to determine integral calculations. The Transform equation is: The area under the curve for any integrand variable \begin{figure}[htbp] \centerline{\includegraphics[width=0.32in,height=0.20in]{231220201.eps}} \label{fig1} \end{figure} as it would vary in terms of some differential variable \begin{figure}[htbp] \centerline{\includegraphics[width=0.32in,height=0.18in]{231220202.eps}} \label{fig2} \end{figure} exactly equals the time integral of the product of the integrand and first derivative of the differential. Other domains besides that of time, are applicable as well. [Preview Abstract] |
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