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Book ; Online: Automated Segmentation of Computed Tomography Images with Submanifold Sparse Convolutional Networks

Alonso-Monsalve, Saúl / Whitehead, Leigh H. / Aurisano, Adam / Sanchez, Lorena Escudero

2022

Abstract: Quantitative cancer image analysis relies on the accurate delineation of tumours, a very specialised and time-consuming task. For this reason, methods for automated segmentation of tumours in medical imaging have been extensively developed in recent ... ...

Abstract	Quantitative cancer image analysis relies on the accurate delineation of tumours, a very specialised and time-consuming task. For this reason, methods for automated segmentation of tumours in medical imaging have been extensively developed in recent years, being Computed Tomography one of the most popular imaging modalities explored. However, the large amount of 3D voxels in a typical scan is prohibitive for the entire volume to be analysed at once in conventional hardware. To overcome this issue, the processes of downsampling and/or resampling are generally implemented when using traditional convolutional neural networks in medical imaging. In this paper, we propose a new methodology that introduces a process of sparsification of the input images and submanifold sparse convolutional networks as an alternative to downsampling. As a proof of concept, we applied this new methodology to Computed Tomography images of renal cancer patients, obtaining performances of segmentations of kidneys and tumours competitive with previous methods (~84.6% Dice similarity coefficient), while achieving a significant improvement in computation time (2-3 min per training epoch).
Keywords	Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition
Subject code	004
Publishing date	2022-12-06
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Machine learning at the energy and intensity frontiers of particle physics.

Radovic, Alexander / Williams, Mike / Rousseau, David / Kagan, Michael / Bonacorsi, Daniele / Himmel, Alexander / Aurisano, Adam / Terao, Kazuhiro / Wongjirad, Taritree

Nature

2018 Volume 560, Issue 7716, Page(s) 41–48

Abstract: Our knowledge of the fundamental particles of nature and their interactions is summarized by the standard model of particle physics. Advancing our understanding in this field has required experiments that operate at ever higher energies and intensities, ... ...

Abstract	Our knowledge of the fundamental particles of nature and their interactions is summarized by the standard model of particle physics. Advancing our understanding in this field has required experiments that operate at ever higher energies and intensities, which produce extremely large and information-rich data samples. The use of machine-learning techniques is revolutionizing how we interpret these data samples, greatly increasing the discovery potential of present and future experiments. Here we summarize the challenges and opportunities that come with the use of machine learning at the frontiers of particle physics.
Language	English
Publishing date	2018-08-01
Publishing country	England
Document type	Journal Article ; Review
ZDB-ID	120714-3
ISSN	1476-4687 ; 0028-0836
ISSN (online)	1476-4687
ISSN	0028-0836
DOI	10.1038/s41586-018-0361-2
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Book ; Online: Track Seeding and Labelling with Embedded-space Graph Neural Networks

Choma, Nicholas / Murnane, Daniel / Ju, Xiangyang / Calafiura, Paolo / Conlon, Sean / Farrell, Steven / Prabhat / Cerati, Giuseppe / Gray, Lindsey / Klijnsma, Thomas / Kowalkowski, Jim / Spentzouris, Panagiotis / Vlimant, Jean-Roch / Spiropulu, Maria / Aurisano, Adam / Hewes, V / Tsaris, Aristeidis / Terao, Kazuhiro / Usher, Tracy

2020

Abstract: To address the unprecedented scale of HL-LHC data, the Exa.TrkX project is investigating a variety of machine learning approaches to particle track reconstruction. The most promising of these solutions, graph neural networks (GNN), process the event as a ...

Abstract	To address the unprecedented scale of HL-LHC data, the Exa.TrkX project is investigating a variety of machine learning approaches to particle track reconstruction. The most promising of these solutions, graph neural networks (GNN), process the event as a graph that connects track measurements (detector hits corresponding to nodes) with candidate line segments between the hits (corresponding to edges). Detector information can be associated with nodes and edges, enabling a GNN to propagate the embedded parameters around the graph and predict node-, edge- and graph-level observables. Previously, message-passing GNNs have shown success in predicting doublet likelihood, and we here report updates on the state-of-the-art architectures for this task. In addition, the Exa.TrkX project has investigated innovations in both graph construction, and embedded representations, in an effort to achieve fully learned end-to-end track finding. Hence, we present a suite of extensions to the original model, with encouraging results for hitgraph classification. In addition, we explore increased performance by constructing graphs from learned representations which contain non-linear metric structure, allowing for efficient clustering and neighborhood queries of data points. We demonstrate how this framework fits in with both traditional clustering pipelines, and GNN approaches. The embedded graphs feed into high-accuracy doublet and triplet classifiers, or can be used as an end-to-end track classifier by clustering in an embedded space. A set of post-processing methods improve performance with knowledge of the detector physics. Finally, we present numerical results on the TrackML particle tracking challenge dataset, where our framework shows favorable results in both seeding and track finding. Comment: Proceedings submission in Connecting the Dots Workshop 2020, 10 pages
Keywords	Physics - Instrumentation and Detectors ; Computer Science - Machine Learning ; High Energy Physics - Experiment ; Physics - Computational Physics
Subject code	006
Publishing date	2020-06-30
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Performance of a Geometric Deep Learning Pipeline for HL-LHC Particle Tracking

2021

Abstract: The Exa.TrkX project has applied geometric learning concepts such as metric learning and graph neural networks to HEP particle tracking. Exa.TrkX's tracking pipeline groups detector measurements to form track candidates and filters them. The pipeline, ... ...

Abstract	The Exa.TrkX project has applied geometric learning concepts such as metric learning and graph neural networks to HEP particle tracking. Exa.TrkX's tracking pipeline groups detector measurements to form track candidates and filters them. The pipeline, originally developed using the TrackML dataset (a simulation of an LHC-inspired tracking detector), has been demonstrated on other detectors, including DUNE Liquid Argon TPC and CMS High-Granularity Calorimeter. This paper documents new developments needed to study the physics and computing performance of the Exa.TrkX pipeline on the full TrackML dataset, a first step towards validating the pipeline using ATLAS and CMS data. The pipeline achieves tracking efficiency and purity similar to production tracking algorithms. Crucially for future HEP applications, the pipeline benefits significantly from GPU acceleration, and its computational requirements scale close to linearly with the number of particles in the event.
Keywords	Physics - Data Analysis ; Statistics and Probability ; Computer Science - Machine Learning ; High Energy Physics - Experiment
Subject code	669
Publishing date	2021-03-11
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Precision Constraints for Three-Flavor Neutrino Oscillations from the Full MINOS+ and MINOS Dataset.

Adamson, P / Anghel, I / Aurisano, A / Barr, G / Blake, A / Cao, S V / Carroll, T J / Castromonte, C M / Chen, R / Childress, S / Coelho, J A B / De Rijck, S / Evans, J J / Feldman, G J / Flanagan, W / Gabrielyan, M / Germani, S / Gomes, R A / Gouffon, P /

Graf, N / Grzelak, K / Habig, A / Hahn, S R / Hartnell, J / Hatcher, R / Holin, A / Huang, J / Koerner, L W / Kordosky, M / Kreymer, A / Lang, K / Lucas, P / Mann, W A / Marshak, M L / Mayer, N / Mehdiyev, R / Meier, J R / Miller, W H / Mills, G / Naples, D / Nelson, J K / Nichol, R J / O'Connor, J / Pahlka, R B / Pavlović, Ž / Pawloski, G / Perch, A / Pfützner, M M / Phan, D D / Plunkett, R K / Poonthottathil, N / Qiu, X / Radovic, A / Sail, P / Sanchez, M C / Schneps, J / Schreckenberger, A / Sharma, R / Sousa, A / Tagg, N / Thomas, J / Thomson, M A / Timmons, A / Todd, J / Tognini, S C / Toner, R / Torretta, D / Vahle, P / Weber, A / Whitehead, L H / Wojcicki, S G

Physical review letters

2020 Volume 125, Issue 13, Page(s) 131802

Abstract: We report the final measurement of the neutrino oscillation parameters Δm_{32}^{2} and sin^{2}θ_{23} using all data from the MINOS and MINOS+ experiments. These data were collected using a total exposure of 23.76×10^{20} protons on target producing ν_{μ} ...

Abstract	We report the final measurement of the neutrino oscillation parameters Δm_{32}^{2} and sin^{2}θ_{23} using all data from the MINOS and MINOS+ experiments. These data were collected using a total exposure of 23.76×10^{20} protons on target producing ν_{μ} and ν[over ¯]_{μ} beams and 60.75 kt yr exposure to atmospheric neutrinos. The measurement of the disappearance of ν_{μ} and the appearance of ν_{e} events between the Near and Far detectors yields \|Δm_{32}^{2}\|=2.40_{-0.09}^{+0.08}(2.45_{-0.08}^{+0.07})×10^{-3} eV^{2} and sin^{2}θ_{23}=0.43_{-0.04}^{+0.20}(0.42_{-0.03}^{+0.07}) at 68% C.L. for normal (inverted) hierarchy.
Language	English
Publishing date	2020-10-09
Publishing country	United States
Document type	Journal Article
ZDB-ID	208853-8
ISSN	1079-7114 ; 0031-9007
ISSN (online)	1079-7114
ISSN	0031-9007
DOI	10.1103/PhysRevLett.125.131802
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Book ; Online: Deep Underground Neutrino Experiment (DUNE), Far Detector Technical Design Report, Volume IV

Abi, B. / Acciarri, R. / Acero, Mario A. / Adamov, G. / Adams, D. / Adinolfi, M. / Ahmad, Z. / Ahmed, J. / Alion, T. / Monsalve, S. Alonso / Alt, C. / Anderson, J. / Andreopoulos, C. / Andrews, M. P. / Andrianala, F. / Andringa, S. / Ankowski, A. / Anthony, J. / Antonova, M. /

Antusch, S. / Fernandez, A. Aranda / Ariga, A. / Arnold, L. O. / Arroyave, M. A. / Asaadi, J. / Aurisano, A. / Aushev, V. / Autiero, D. / Azfar, F. / Back, H. / Back, J. J. / Backhouse, C. / Baesso, P. / Bagby, L. / Bajou, R. / Balasubramanian, S. / Baldi, P. / Bambah, B. / Barao, F. / Barenboim, G. / Barker, G. J. / Barkhouse, W. / Barnes, C. / Barr, G. / Monarca, J. Barranco / Barros, N. / Barrow, J. L. / Bashyal, A. / Basque, V. / Bay, F.

Far Detector Single-phase Technology

2020

Abstract: The preponderance of matter over antimatter in the early universe, the dynamics of the supernovae that produced the heavy elements necessary for life, and whether protons eventually decay -- these mysteries at the forefront of particle physics and ... ...

Abstract	The preponderance of matter over antimatter in the early universe, the dynamics of the supernovae that produced the heavy elements necessary for life, and whether protons eventually decay -- these mysteries at the forefront of particle physics and astrophysics are key to understanding the early evolution of our universe, its current state, and its eventual fate. DUNE is an international world-class experiment dedicated to addressing these questions as it searches for leptonic charge-parity symmetry violation, stands ready to capture supernova neutrino bursts, and seeks to observe nucleon decay as a signature of a grand unified theory underlying the standard model. Central to achieving DUNE's physics program is a far detector that combines the many tens-of-kiloton fiducial mass necessary for rare event searches with sub-centimeter spatial resolution in its ability to image those events, allowing identification of the physics signatures among the numerous backgrounds. In the single-phase liquid argon time-projection chamber (LArTPC) technology, ionization charges drift horizontally in the liquid argon under the influence of an electric field towards a vertical anode, where they are read out with fine granularity. A photon detection system supplements the TPC, directly enhancing physics capabilities for all three DUNE physics drivers and opening up prospects for further physics explorations. The DUNE far detector technical design report (TDR) describes the DUNE physics program and the technical designs of the single- and dual-phase DUNE liquid argon TPC far detector modules. Volume IV presents an overview of the basic operating principles of a single-phase LArTPC, followed by a description of the DUNE implementation. Each of the subsystems is described in detail, connecting the high-level design requirements and decisions to the overriding physics goals of DUNE. Comment: Minor corrections made for JINST submission, 673 pages, 312 figures (corrected errors in author list)
Keywords	Physics - Instrumentation and Detectors ; High Energy Physics - Experiment
Subject code	910
Publishing date	2020-02-07
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Deep Underground Neutrino Experiment (DUNE), Far Detector Technical Design Report, Volume III

DUNE Far Detector Technical Coordination

2020

Abstract	The preponderance of matter over antimatter in the early universe, the dynamics of the supernovae that produced the heavy elements necessary for life, and whether protons eventually decay -- these mysteries at the forefront of particle physics and astrophysics are key to understanding the early evolution of our universe, its current state, and its eventual fate. The Deep Underground Neutrino Experiment (DUNE) is an international world-class experiment dedicated to addressing these questions as it searches for leptonic charge-parity symmetry violation, stands ready to capture supernova neutrino bursts, and seeks to observe nucleon decay as a signature of a grand unified theory underlying the standard model. The DUNE far detector technical design report (TDR) describes the DUNE physics program and the technical designs of the single- and dual-phase DUNE liquid argon TPC far detector modules. Volume III of this TDR describes how the activities required to design, construct, fabricate, install, and commission the DUNE far detector modules are organized and managed. This volume details the organizational structures that will carry out and/or oversee the planned far detector activities safely, successfully, on time, and on budget. It presents overviews of the facilities, supporting infrastructure, and detectors for context, and it outlines the project-related functions and methodologies used by the DUNE technical coordination organization, focusing on the areas of integration engineering, technical reviews, quality assurance and control, and safety oversight. Because of its more advanced stage of development, functional examples presented in this volume focus primarily on the single-phase (SP) detector module. Comment: Minor corrections made for JINST submission, 209 pages, 55 figures (updated typos in Table A.5; corrected errors in author list)
Keywords	Physics - Instrumentation and Detectors ; High Energy Physics - Experiment
Subject code	910
Publishing date	2020-02-07
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Deep Underground Neutrino Experiment (DUNE), Far Detector Technical Design Report, Volume I

Introduction to DUNE

2020

Abstract	The preponderance of matter over antimatter in the early universe, the dynamics of the supernovae that produced the heavy elements necessary for life, and whether protons eventually decay -- these mysteries at the forefront of particle physics and astrophysics are key to understanding the early evolution of our universe, its current state, and its eventual fate. The Deep Underground Neutrino Experiment (DUNE) is an international world-class experiment dedicated to addressing these questions as it searches for leptonic charge-parity symmetry violation, stands ready to capture supernova neutrino bursts, and seeks to observe nucleon decay as a signature of a grand unified theory underlying the standard model. The DUNE far detector technical design report (TDR) describes the DUNE physics program and the technical designs of the single- and dual-phase DUNE liquid argon TPC far detector modules. This TDR is intended to justify the technical choices for the far detector that flow down from the high-level physics goals through requirements at all levels of the Project. Volume I contains an executive summary that introduces the DUNE science program, the far detector and the strategy for its modular designs, and the organization and management of the Project. The remainder of Volume I provides more detail on the science program that drives the choice of detector technologies and on the technologies themselves. It also introduces the designs for the DUNE near detector and the DUNE computing model, for which DUNE is planning design reports. Volume II of this TDR describes DUNE's physics program in detail. Volume III describes the technical coordination required for the far detector design, construction, installation, and integration, and its organizational structure. Volume IV describes the single-phase far detector technology. A planned Volume V will describe the dual-phase technology. Comment: Minor corrections made for JINST submission; 244 pages, 114 figures
Keywords	Physics - Instrumentation and Detectors ; High Energy Physics - Experiment
Subject code	910
Publishing date	2020-02-07
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Deep Underground Neutrino Experiment (DUNE), Far Detector Technical Design Report, Volume II

DUNE Physics

2020

Abstract	The preponderance of matter over antimatter in the early universe, the dynamics of the supernovae that produced the heavy elements necessary for life, and whether protons eventually decay -- these mysteries at the forefront of particle physics and astrophysics are key to understanding the early evolution of our universe, its current state, and its eventual fate. DUNE is an international world-class experiment dedicated to addressing these questions as it searches for leptonic charge-parity symmetry violation, stands ready to capture supernova neutrino bursts, and seeks to observe nucleon decay as a signature of a grand unified theory underlying the standard model. The DUNE far detector technical design report (TDR) describes the DUNE physics program and the technical designs of the single- and dual-phase DUNE liquid argon TPC far detector modules. Volume II of this TDR, DUNE Physics, describes the array of identified scientific opportunities and key goals. Crucially, we also report our best current understanding of the capability of DUNE to realize these goals, along with the detailed arguments and investigations on which this understanding is based. This TDR volume documents the scientific basis underlying the conception and design of the LBNF/DUNE experimental configurations. As a result, the description of DUNE's experimental capabilities constitutes the bulk of the document. Key linkages between requirements for successful execution of the physics program and primary specifications of the experimental configurations are drawn and summarized. This document also serves a wider purpose as a statement on the scientific potential of DUNE as a central component within a global program of frontier theoretical and experimental particle physics research. Thus, the presentation also aims to serve as a resource for the particle physics community at large. Comment: 357 pages, 165 figures (updated typos in Table 6.1 and corrected errors in author list)
Keywords	High Energy Physics - Experiment ; Physics - Instrumentation and Detectors
Subject code	910
Publishing date	2020-02-07
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Measurement of the ν_{e}-Nucleus Charged-Current Double-Differential Cross Section at ⟨E_{ν}⟩=2.4 GeV Using NOvA.

Acero, M A / Adamson, P / Aliaga, L / Anfimov, N / Antoshkin, A / Arrieta-Diaz, E / Asquith, L / Aurisano, A / Back, A / Baird, M / Balashov, N / Baldi, P / Bambah, B A / Bashar, S / Bays, K / Bernstein, R / Bhatnagar, V / Bhattarai, D / Bhuyan, B /

Bian, J / Booth, A C / Bowles, R / Brahma, B / Bromberg, C / Buchanan, N / Butkevich, A / Calvez, S / Carroll, T J / Catano-Mur, E / Childress, S / Chatla, A / Chirco, R / Choudhary, B C / Christensen, A / Coan, T E / Colo, M / Cremonesi, L / Davies, G S / Derwent, P F / Ding, P / Djurcic, Z / Dolce, M / Doyle, D / Dueñas Tonguino, D / Dukes, E C / Ehrlich, R / Elkins, M / Ewart, E / Feldman, G J / Filip, P / Franc, J / Frank, M J / Gallagher, H R / Gandrajula, R / Gao, F / Giri, A / Gomes, R A / Goodman, M C / Grichine, V / Groh, M / Group, R / Guo, B / Habig, A / Hakl, F / Hall, A / Hartnell, J / Hatcher, R / Hausner, H / He, M / Heller, K / Hewes, V / Himmel, A / Jargowsky, B / Jarosz, J / Jediny, F / Johnson, C / Judah, M / Kakorin, I / Kaplan, D M / Kalitkina, A / Keloth, R / Klimov, O / Koerner, L W / Kolupaeva, L / Kotelnikov, S / Kralik, R / Kullenberg, Ch / Kubu, M / Kumar, A / Kuruppu, C D / Kus, V / Lackey, T / Lang, K / Lasorak, P / Lesmeister, J / Lin, S / Lister, A / Liu, J / Lokajicek, M / Lopez, J M C / Mahji, R / Magill, S / Manrique Plata, M / Mann, W A / Manoharan, M T / Marshak, M L / Martinez-Casales, M / Matveev, V / Mayes, B / Messier, M D / Meyer, H / Miao, T / Mikola, V / Miller, W H / Mishra, S / Mishra, S R / Mislivec, A / Mohanta, R / Moren, A / Morozova, A / Mu, W / Mualem, L / Muether, M / Mulder, K / Naples, D / Nath, A / Nayak, N / Nelleri, S / Nelson, J K / Nichol, R / Niner, E / Norman, A / Norrick, A / Nosek, T / Oh, H / Olshevskiy, A / Olson, T / Ott, J / Pal, A / Paley, J / Panda, L / Patterson, R B / Pawloski, G / Petrova, O / Petti, R / Phan, D D / Plunkett, R K / Pobedimov, A / Porter, J C C / Rafique, A / Prais, L R / Raj, V / Rajaoalisoa, M / Ramson, B / Rebel, B / Rojas, P / Roy, P / Ryabov, V / Samoylov, O / Sanchez, M C / Sánchez Falero, S / Shanahan, P / Shukla, S / Sheshukov, A / Singh, I / Singh, P / Singh, V / Smith, E / Smolik, J / Snopok, P / Solomey, N / Sousa, A / Soustruznik, K / Strait, M / Suter, L / Sutton, A / Swain, S / Sweeney, C / Sztuc, A / Talaga, R L / Tapia Oregui, B / Tas, P / Temizel, B N / Thakore, T / Thayyullathil, R B / Thomas, J / Tiras, E / Tripathi, J / Trokan-Tenorio, J / Torun, Y / Urheim, J / Vahle, P / Vallari, Z / Vasel, J / Vrba, T / Wallbank, M / Warburton, T K / Wetstein, M / Whittington, D / Wickremasinghe, D A / Wieber, T / Wolcott, J / Wu, W / Xiao, Y / Yaeggy, B / Yallappa Dombara, A / Yankelevich, A / Yonehara, K / Yu, S / Yu, Y / Zadorozhnyy, S / Zalesak, J / Zhang, Y / Zwaska, R

Physical review letters

2023 Volume 130, Issue 5, Page(s) 51802

Abstract: The inclusive electron neutrino charged-current cross section is measured in the NOvA near detector using 8.02×10^{20} protons-on-target in the NuMI beam. The sample of GeV electron neutrino interactions is the largest analyzed to date and is limited by ≃ ...

Abstract	The inclusive electron neutrino charged-current cross section is measured in the NOvA near detector using 8.02×10^{20} protons-on-target in the NuMI beam. The sample of GeV electron neutrino interactions is the largest analyzed to date and is limited by ≃17% systematic rather than the ≃7.4% statistical uncertainties. The double-differential cross section in final-state electron energy and angle is presented for the first time, together with the single-differential dependence on Q^{2} (squared four-momentum transfer) and energy, in the range 1 GeV≤E_{ν}<6 GeV. Detailed comparisons are made to the predictions of the GENIE, GiBUU, NEUT, and NuWro neutrino event generators. The data do not strongly favor a model over the others consistently across all three cross sections measured, though some models have especially good or poor agreement in the single differential cross section vs Q^{2}.
Language	English
Publishing date	2023-02-17
Publishing country	United States
Document type	Journal Article
ZDB-ID	208853-8
ISSN	1079-7114 ; 0031-9007
ISSN (online)	1079-7114
ISSN	0031-9007
DOI	10.1103/PhysRevLett.130.051802
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