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

Abi, B.; Acciarri, R.; Acero, M. A.; Adamov, G.; Adams, D. L.; Adinolfi, M.; Ahmad, Zahid; Ahmed, Jamal; Alion, T.; Monsalve, S. Alonso; Alt, C.; Anderson, J.; Andreopoulos, C.; Andrews, M.; Andrianala, F.; Andringa, S.; Ankowski, A.; Anthony, J.; Antonova, M.; Antusch, Stefan; Fernandez, A. Aranda; Ariga, A.; Arnold, L.; Arroyave, M. A.; Asaadi, J.; Aurisano, A.; Aushev, V.; Autiero, D.; Azfar, F.; Back, H. O.; Back, J. J.; Backhouse, C.; Baesso, P.; Bagby, L.; Bajou, R.; Balasubramanian, S.; Baldi, Pierre; Bambah, Bindu A.; Barão, F.; Barenboim, Gabriela; Barker, G. J.; Barkhouse, W. A.; Barnes, C.; Barr, G.; Monarca, J. Barranco; Barros, N.; Barrow, J. L.; Bashyal, A.; Basque, V.; Bay, F.; Alba, J. L. Bazo; Beacom, J. F.; Bechetoille, E.; Behera, B. R.; Bellantoni, L.; Bellettini, G.; Bellini, Vincenzo; Beltramello, O.; Belver, D.; Benekos, N.; Neves, F.; Berger, Joshua; Berkman, S.; Bernardini, P.; Berner, R. M.; Berns, H.; Bertolucci, S.; Betancourt, M.; Bezawada, Y.; Bhattacharjee, M.; Bhuyan, B.; Biagi, S.; Bian, J. M.; Biassoni, M.; Biery, K.; Bilki, B.; Bishai, M.; Bitadze, A.; Blake, A.; Siffert, B. Blanco; Blaszczyk, F.; Blazey, G.; Blucher, E.; Boissevain, J.; Bolognesi, S.; Bolton, T.; Bonesini, M.; Bongrand, M.; Bonini, F.; Booth, Alexander; Booth, C.N.; Bordoni, S.; Borkum, A.; Boschi, T.; Bostan, N.; Bour, P.; Boyd, S.; Boyden, D.; Braciník, J.; Braga, D.; Brailsford, D.; Brandt, A.; Bremer, J.; Brew, C.; Brianne, Eldwan; Brice, S. J.; Brizzolari, C.; Bromberg, C.; Brooijmans, G.; Brooke, J. J.; Bross, A.; Brunetti, G.; Buchanan, N. J.; Budd, H. S.; Caiulo, D.; Calafiura, P.; Calcutt, J.; Călin, M.; Calvez, S.; Calvo, E.; Camilleri, L.; Caminata, A.; Campanelli, M.; Caratelli, D.; Carini, G.; Carlus, B.; Carniti, P.; Terrazas, I. Caro; Carranza, H.; Castillo, A.; Castromonte, C.; Cattadori, C.; Cavalier, F.; Cavanna, F.; Centro, S.; Cerati, G. B.; Cervelli, A.; Villanueva, A. Cervera; Chalifour, M.; Chang, C.; Chardonnet, Etienne; Chatterjee, A.; Chattopadhyay, S.; Chaves, J.; Chen, H.; Chen, M.; Chen, Y.; Cherdack, D.; Chi, C.Y.; Childress, S.; Chiriacescu, A.; Cho, K.; Choubey, Sandhya; Christensen, A.; Christian, David; Christodoulou, G.; Church, E.; Clarke, P. E. L.; Coan, T. E.; Cocco, A. G.; Coelho, J. A. B.; Conley, E.; Conrad, J. M.; Convery, M. R.; Corwin, L. A.; Cotte, P.; Cremaldi, L.; Cremonesi, L.; Crespo-Anadón, J. I.; Cristaldo, E.; Cross, R.; Cuesta, C.; Cui, Yonggang; Cussans, D.; Dabrowski, M.; Motta, H. da; Peres, L. Da Silva; David, Q.; Davies, G. S.; Davini, S.; Dawson, J.; De, K.; Almeida, R. M. de; Debbins, P.; Bonis, I. De; Decowski, M. P.; Gouvêa, A. de; Holanda, P. C. de; Astiz, I. L. De Icaza; Deisting, A.; Jong, P. de; Delbart, A.; Delépine, D.; Dell’Acqua, A.; Lurgio, P. De; Neto, J. R. T. de Mello; DeMuth, D. M.; Dennis, S. R.; Densham, C.; Deptuch, G.; Roeck, A. De; Romeri, Valentina De; Vries, J. 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T.; Macier, J. R.; Maddalena, A.; Madigan, P.; Magill, S.; Mahn, Kendall; Maio, A.; Maloney, J.A.; Mandrioli, G.; Maneira, J.; Manenti, L.; Manly, S.; Mann, A.; Manolopoulos, K.; Plata, M. Manrique; Marchionni, A.; Marciano, W.; Marfatia, Danny; Mariani, C.; Maricic, J.; Marinho, F.; Marino, A. D.; Маршак, М. Л.; Marshall, C.; Marshall, J.; Marteau, J.; Martín-Albo, J.; Martinez, N. Lorenzo; Caicedo, D. A. Martínez; Martynenko, S.; Mason, K.; Mastbaum, A.; Masud, Mehedi; Matsuno, S.; Matthews, J. A. J.; Mauger, C.; Mauri, N.; Mavrokoridis, K.; Mazza, R.; Mazzacane, A.; Mazzucato, E.; McCluskey, E.; McConkey, N.; McFarland, K. S.; McGrew, C.; McNab, A.; Mefodiev, A.; Mehta, Poonam; Melas, P.; Mellinato, M.; Mena, Olga; Menary, S.; Santos, L. Mendes; Méndez, H.; Menegolli, A.; Meng, G.; Messier, M. D.; Metcalf, W.; Mewes, Matthew; Meyer, H.; Miao, T.; Michna, Gregory J.; Miedema, T.; Migenda, J.; Milinčić, R.; Miller, W.; Mills, J.; Milne, Christopher J.; Mineev, O.; Miranda, O. G.; Miryala, Sandeep; Mishra, C. S.; Mishra, S. R.; Mislivec, A.; Mladenov, D.; Mocioiu, I.; Moffat, K.; Moggi, N.; Mohanta, Rukmani; Mohayai, T.; Mokhov, N.; Molina, Jorge; Bueno, L. Molina; Montanari, A.; Montanari, C.; Montanari, D.; Zetina, L. Montaño; Moon, J.; Mooney, M.; Moor, A. F.; Moreno, D.; Morgan, B.; Morris, C. L.; Mossey, C.; Motuk, E.; Moura, C. A.; Mousseau, J.; Mu, Wei; Mualem, L.; Mueller, J.; Muether, M.; Mufson, S.; Muheim, F.; Muir, A.; Mulhearn, M.; Muramatsu, H.; Murphy, S.; Musser, J.; Nachtman, J.; Nagu, S.; Nalbandyan, M.; Nandakumar, R.; Naples, D.; Narita, S.; Navas-Nicolás, D.; Nayak, N.; Nebot-Guinot, M.; Necib, Lina; Negishi, K.; Nelson, J. K.; Nesbit, J.; Nessi, M.; Newbold, D. M.; Newcomer, M.; Newhart, D.; Nichol, R. J.; Niner, E.; Nishimura, K.; Norman, A.; Northrop, R.; Novella, P.; Nowak, J.; Oberling, M.; Campo, A. 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doi:10.48550/arxiv.2002.03008

Cited by 12 publications

(18 citation statements)

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“…With the use of increasingly larger neutrino interaction target volumes and finer readout segmentation, data challenges for neutrino experiments begin to approach those of current collider experiments. For example, DUNE's multiple far detectors [228] will each generate raw data rates of several terabytes per second, and plan to be operated for at least a decade, requiring also 100% live-time in order to be sensitive to neutrinos from nearby supernova bursts or other stochastic BSM signals. With ML becoming increasingly common in neutrino experiments, the community is further steering its attention toward hardware acceleration of ML-based inference.…”

Section: B Neutrino Experimentsmentioning

confidence: 99%

Machine Learning in the Search for New Fundamental Physics

Karagiorgi¹,

Kasieczka²,

Kravitz³

et al. 2021

Preprint

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Machine learning plays a crucial role in enhancing and accelerating the search for new fundamental physics. We review the state of machine learning methods and applications for new physics searches in the context of terrestrial high energy physics experiments, including the Large Hadron Collider, rare event searches, and neutrino experiments. While machine learning has a long history in these fields, the deep learning revolution (early 2010s) has yielded a qualitative shift in terms of the scope and ambition of research. These modern machine learning developments are the focus of the present review.

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Section: B Neutrino Experimentsmentioning

confidence: 99%

Machine Learning in the Search for New Fundamental Physics

Karagiorgi¹,

Kasieczka²,

Kravitz³

et al. 2021

Preprint

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“…The light mediators of this model also can be probed at various beam-dump based neutrino experiments, COHERENT [50][51][52][53][54][55][56][57][58][59], CCM [60,61], MicroBooNE [62][63][64][65][66][67], ICARUS [68], SBND [69,70], DUNE [71][72][73][74][75][76] etc. In these facilities, the quark couplings will allow the mediators to be produced from the charged and neutral pion and kaon decays at the detector the mediators can be detected from the their visible, invisible decays products and inverse -Primakoff type interactions.…”

Section: Introductionmentioning

confidence: 99%

$U(1)_{T3R}$ Extension of Standard Model: A Sub-GeV Dark Matter Model

Dutta¹,

Ghosh²,

Kumar³

2022

Preprint

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We present a model based on a U (1)T 3R extension of the Standard Model. The model addresses the mass hierarchy between the third generation and the first two generation fermions. U (1)T 3R is spontaneously broken at ∼ 1 − 10 GeV. The model contains a sub-GeV dark matter candidate and two sub-GeV light scalar and vector mediators. The model explains the thermal dark matter abundance, measurements of the muon g − 2 and R K ( * ) anomalies. The model can be probed at the LHC, FASER, dark matter experiments and various beam-dump based neutrino facilities, e.g., COHERENT, CCM, MicroBooNE, SBND, ICARUS, DUNE etc.

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“…The proposed Deep Underground Neutrino Experiment ( DUNE ) will be a long-baseline neutrino oscillation experiment [1][2][3][4]. The primary scientific goal of this experiment is to make precise measurements of the parameters governing neutrino oscillation and constraining the CP violation phase in the leptonic sector [5].…”

Section: Introductionmentioning

confidence: 99%

Uncertainties in QE and RES events at LBNF due to hadronic production in FSI

Devi,

Singh,

Potukuchi

2020

Preprint

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Recent neutrino oscillation experiments used high atomic number nuclear targets to attain sufficient interaction rates. The use of these complex targets introduced systematic uncertainties due to the nuclear effects in the experimental observable and need to be measured properly to pin down the discovery. Through this simulation work, we are trying to quantify the nuclear effects in the Ar target with the H target which are proposed to be used at DUNE far detector and near detector respectively. The DUNE flux is peaking around 2.5 GeV and CCRES is the dominant process at this energy. So, this work will be focused only on the CCQE and CCRES interactions and the simulations will be done using two different neutrino event generators. To quantify the systematic uncertainties in the observables, we presented the ratio of the oscillation probability (P(Ar)/P(H)) as a function of reconstructed neutrino energy for CCRES channels.

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

Cited by 12 publications

References 0 publications

Machine Learning in the Search for New Fundamental Physics

Machine Learning in the Search for New Fundamental Physics

$U(1)_{T3R}$ Extension of Standard Model: A Sub-GeV Dark Matter Model

Uncertainties in QE and RES events at LBNF due to hadronic production in FSI

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