Calorimetric classification of track-like signatures in liquid argon TPCs using MicroBooNE data

(2021)

Authors:

MicroBooNE collaboration, P Abratenko, R An, J Anthony, J Asaadi, A Ashkenazi, S Balasubramanian, B Baller, C Barnes, G Barr, V Basque, L Bathe-Peters, O Benevides Rodrigues, S Berkman, A Bhanderi, A Bhat, M Bishai, A Blake, T Bolton, L Camilleri, D Caratelli, I Caro Terrazas, R Castillo Fernandez, F Cavanna, G Cerati, Y Chen, E Church, D Cianci, JM Conrad, M Convery, L Cooper-Troendle, JI Crespo-Anadon, M Del Tutto, SR Dennis, D Devitt, R Diurba, R Dorrill, K Duffy, S Dytman, B Eberly, A Ereditato, JJ Evans, R Fine, GA Fiorentini Aguirre, RS Fitzpatrick, BT Fleming, N Foppiani, D Franco, AP Furmanski, D Garcia-Gamez, S Gardiner, G Ge, S Gollapinni, O Goodwin, E Gramellini, P Green, H Greenlee, W Gu, R Guenette, P Guzowski, L Hagaman, E Hall, P Hamilton, O Hen, GA Horton-Smith, A Hourlier, R Itay, C James, X Ji, L Jiang, JH Jo, RA Johnson, YJ Jwa, N Kamp, N Kaneshige, G Karagiorgi, W Ketchum, M Kirby, T Kobilarcik, I Kreslo, R LaZur, I Lepetic, K Li, Y Li, K Lin, BR Littlejohn, WC Louis, X Luo, K Manivannan, C Mariani, D Marsden, J Marshall, DA Martinez Caicedo, K Mason, A Mastbaum, N McConkey, V Meddage, T Mettler, K Miller, J Mills, K Mistry, T Mohayai, A Mogan, J Moon, M Mooney, AF Moor, CD Moore, L Mora Lepin, J Mousseau, M Murphy, D Naples, A Navrer-Agasson, RK Neely, J Nowak, M Nunes, O Palamara, V Paolone, A Papadopoulou, V Papavassiliou, SF Pate, A Paudel, Z Pavlovic, E Piasetzky, I Ponce-Pinto, S Prince, X Qian, JL Raaf, V Radeka, A Rafique, M Reggiani-Guzzo, L Ren, LCJ Rice, L Rochester, J Rodriguez Rondon, HE Rogers, M Rosenberg, M Ross-Lonergan, G Scanavini, DW Schmitz, A Schukraft, W Seligman, MH Shaevitz, R Sharankova, J Sinclair, A Smith, EL Snider, M Soderberg, S Soldner-Rembold, P Spentzouris, J Spitz, M Stancari, J St John, T Strauss, K Sutton, S Sword-Fehlberg, AM Szelc, N Tagg, W Tang, K Terao, C Thorpe, D Totani, M Toups, Y-T Tsai, MA Uchida, T Usher, W Van De Pontseele, B Viren, M Weber, H Wei, Z Williams, S Wolbers, T Wongjirad, M Wospakrik, N Wright, W Wu, E Yandel, T Yang, G Yarbrough, LE Yates, GP Zeller, J Zennamo, C Zhang

Cosmic Ray Background Removal With Deep Neural Networks in SBND

Frontiers in Artificial Intelligence Frontiers 4 (2021) 649917

Authors:

R Acciarri, C Adams, C Andreopoulos, J Asaadi, M Babicz, C Backhouse, W Badgett, L Bagby, D Barker, V Basque, MCQ Bazetto, M Betancourt, A Bhanderi, A Bhat, C Bonifazi, D Brailsford, AG Brandt, T Brooks, MF Carneiro, Y Chen, H Chen, G Chisnall, JI Crespo-Anadón, E Cristaldo, C Cuesta, IL de Icaza Astiz, A De Roeck, G de Sá Pereira, M Del Tutto, V Di Benedetto, A Ereditato, JJ Evans, AC Ezeribe, RS Fitzpatrick, BT Fleming, W Foreman, D Franco, I Furic, AP Furmanski, S Gao, D Garcia-Gamez, H Frandini, G Ge, I Gil-Botella, S Gollapinni, O Goodwin, P Green, WC Griffith, R Guenette, P Guzowski, T Ham, J Henzerling, A Holin, B Howard, RS Jones, D Kalra, G Karagiorgi, L Kashur, W Ketchum, MJ Kim, VA Kudryavtsev, J Larkin, H Lay, I Lepetic, BR Littlejohn, WC Louis, AA Machado, M Malek, D Mardsen, C Mariani, F Marinho, A Mastbaum, K Mavrokoridis, N McConkey, V Meddage, DP Méndez, T Mettler, K Mistry, A Mogan, J Molina, M Mooney, L Mora, CA Moura, J Mousseau, A Navrer-Agasson, FJ Nicolas-Arnaldos, JA Nowak, O Palamara, V Pandey, J Pater, L Paulucci, VL Pimentel, F Psihas, G Putnam, X Qian, E Raguzin, H Ray, M Reggiani-Guzzo, D Rivera, M Roda, M Ross-Lonergan, G Scanavini, A Scarff, DW Schmitz, A Schukraft, E Segreto, M Soares Nunes, M Soderberg, S Söldner-Rembold, J Spitz, NJC Spooner, M Stancari, GV Stenico, A Szelc, W Tang, J Tena Vidal, D Torretta, M Toups, C Touramanis, M Tripathi, S Tufanli, E Tyley, GA Valdiviesso, E Worcester, M Worcester, G Yarbrough, J Yu, B Zamorano, J Zennamo, A Zglam

An error analysis toolkit for binned counting experiments

EPJ Web of Conferences EDP Sciences 251 (2021)

Authors:

Ben Messerly, Rob Fine, Andrew Olivier, X-G Lu, Kang Yang

Abstract:

We introduce the MINERvA Analysis Toolkit (MAT), a utility for centralizing the handling of systematic uncertainties in HEP analyses. The fundamental utilities of the toolkit are the MnvHnD, a powerful histogram container class, and the systematic Universe classes, which provide a modular implementation of the many universe error analysis approach. These products can be used stand-alone or as part of a complete error analysis prescription. They support the propagation of systematic uncertainty through all stages of analysis, and provide flexibility for an arbitrary level of user customization. This extensible solution to error analysis enables the standardization of systematic uncertainty definitions across an experiment and a transparent user interface to lower the barrier to entry for new analyzers.

Measurements of the inclusive and differential production cross sections of a top-quark–antiquark pair in association with a Z boson at $$\sqrt{s} = 13$$ TeV with the ATLAS detector

The European Physical Journal C SpringerOpen 81:8 (2021) 737

Authors:

G Aad, B Abbott, DC Abbott, A Abed Abud, K Abeling, DK Abhayasinghe, SH Abidi, OS AbouZeid, NL Abraham, H Abramowicz, H Abreu, Y Abulaiti, BS Acharya, B Achkar, L Adam, C Adam Bourdarios, L Adamczyk, L Adamek, J Adelman, A Adiguzel, S Adorni, T Adye, AA Affolder, Y Afik, C Agapopoulou

Abstract:

Abstract Measurements of both the inclusive and differential production cross sections of a top-quark–antiquark pair in association with a Z boson ( $$t{\bar{t}}Z$$ t t ¯ Z ) are presented. The measurements are performed by targeting final states with three or four isolated leptons (electrons or muons) and are based on $$\sqrt{s} = 13$$ s = 13 TeV proton–proton collision data with an integrated luminosity of 139 $$\hbox {fb}^{-1}$$ fb - 1 , recorded from 2015 to 2018 with the ATLAS detector at the CERN Large Hadron Collider. The inclusive cross section is measured to be $$\sigma _{t{\bar{t}}Z} = 0.99 \pm 0.05$$ σ t t ¯ Z = 0.99 ± 0.05 (stat.) $$\pm \, 0.08$$ ± 0.08 (syst.) pb, in agreement with the most precise theoretical predictions. The differential measurements are presented as a function of a number of kinematic variables which probe the kinematics of the $$t{\bar{t}}Z$$ t t ¯ Z system. Both absolute and normalised differential cross-section measurements are performed at particle and parton levels for specific fiducial volumes and are compared with theoretical predictions at different levels of precision, based on a $$\chi ^{2}/$$ χ 2 / ndf and p value computation. Overall, good agreement is observed between the unfolded data and the predictions.

Measurements of sensor radiation damage in the ATLAS inner detector using leakage currents

Journal of Instrumentation IOP Publishing 16:08 (2021) P08025-P08025

Authors:

G Aad, B Abbott, DC Abbott, A Abed Abud, K Abeling, DK Abhayasinghe, SH Abidi, OS AbouZeid, NL Abraham, H Abramowicz, H Abreu, Y Abulaiti, AC Abusleme Hoffman, BS Acharya, B Achkar, L Adam, C Adam Bourdarios, L Adamczyk, L Adamek, J Adelman, A Adiguzel, S Adorni, T Adye, AA Affolder, Y Afik

Abstract:

Abstract Non-ionizing energy loss causes bulk damage to the silicon sensors of the ATLAS pixel and strip detectors. This damage has important implications for data-taking operations, charged-particle track reconstruction, detector simulations, and physics analysis. This paper presents simulations and measurements of the leakage current in the ATLAS pixel detector and semiconductor tracker as a function of location in the detector and time, using data collected in Run 1 (2010–2012) and Run 2 (2015–2018) of the Large Hadron Collider. The extracted fluence shows a much stronger | z |-dependence in the innermost layers than is seen in simulation. Furthermore, the overall fluence on the second innermost layer is significantly higher than in simulation, with better agreement in layers at higher radii. These measurements are important for validating the simulation models and can be used in part to justify safety factors for future detector designs and interventions.