The MINOS Detectors (Technical Design Report)

Ambats, I.; Antipov, Yu.M.; Sadovski, A.; Lang, K.; Allison, W.W.M.; Wall, D.; Napier, A.; Lai, Y.; Heinz, R.; Wright, D.H.; Soldatov, A. P.; Vakili, M.; Baller, B.; Read, Alex; Shen, B. W.; Царев, В. А.; Xia, X.M.; West, N.; Kirichenko, T.; Gornushkin, Y.; Drake, G.; Courant, H.; Longley, N.; Ryabov, V. A.; Trostin, I.S.; Anderson, D.F.; Irwin, G. M.; Thron, J.L.; Boehnlein, D.; Musser, J.; Mufson, S.; Halsall, R.; Ladran, A.; Drew, M.M.; Mann, W. A.; Merzon, G. I.; Eroshin, O. V.; Choudhary, B. C.; Bogert, D.; Макеев, В. В.; Trevor, J.; Madani, S.; Melnikov, E.; Meier, J. R.; Alner, G. J.; Schneps, J.; Border, P.; Talaga, R. L.; Gutnikov, Yu. E.; Feldman, G. J.; Fahrutdinov, R.; Nelson, J.; Ignatenko, M.; Oliver, W. P.; Chernichenko, S.; Onuchìn, V.; Anderson, B.; Rusack, R.; Soesbe, Todd C.; Guarino, V.; Hylen, J.; Harris, Philip; Nozdrin, A.; Peck, C. W.; Fields, T.; Grossman, N.; O’Day, S.; Krumstein, Z.; Маршак, М. Л.; Kundu, N.; Smotriaev, V.; Schwienhorst, R.; Hanson, J.; Proga, M.; Kim, H.; Hartouni, E. P.; Wehmann, A.; Ruddick, K.; Smirnitsky, V. A.; Semenov, V.G.; Sanchez, M. C.; Gebhard, M.; Price, L.E.; Wojcicki, S. G.; Dervan, P.; Miller, William H.; Dawson, J. W.; Joffe–Minor, T.; Webb, R.; Newman, H. B.; Kochetkov, V.; Hatcher, R.; Petyt, D.; Fujino, D.; Ayres, D.S.; Tyurin, N.; Barrett, W. L.; May, E. N.; Schoessow, P.; Diaczenko, N.; Edgecock, R.; Shivane, R.; Huang, Y.; Bocean, V.; Hu, Tang; Michael, D.G.; Durum, A.; Cossairt, J.D.; Soltz, R. A.; James, C.; Wai, L.; Mao, H.S.; Barnes, P. D.; Childress, S.; Denisov, A. V.; Plunkett, R.; Rameika, R.; Chase, T.; Starkov, N.; Petruhin, A.; Mishra, Shailendra; Hill, N.; Smart, W.; Turner, L.R.; Ermilova, V. K.; Barish, B.; Yan, W.G.; Milburn, R. H.; Olshevski, A. G.; Tovee, D.N.; Gallagher, H.; Adamson, P.; Yun, J. C.; Litchfield, P.J.; Arroyo, C.; Sisakian, A. N.; Morfín, J.; Perry, C.; Koizumi, G.; Heller, K.; Goodman, M. C.; Graham, M.; Pearce, G. F.; Bernstein, R. H.; Mualem, L.; Alexandrov, Konstantin V.; Laughton, C.; Pushka, D.; Para, A. Foglio; Peterson, E. A.; Bower, C.; Maxam, D.; Byrne, J.; Cobb, J.H.; White, R.F.; Kafka, T.; Kilmer, J.; Miller, Laura Stephanie; Blair, R. E.; Shield, P.D.; Gorin, Yu.P.; Cushman, Pauline K.; Lucas, P.; Gilitsky, Yu.; Thomas, Jennifer A.

doi:10.2172/1861363

Cited by 43 publications

(6 citation statements)

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“…Challenges: Accelerator neutrino experiments employ a variety of detector technologies. These range from scintillator detectors such as NOvA (Ayres et al, 2007 ) (liquid), MINOS (Ambats et al, 1998 ) (solid), and MINERvA (MIN, 2006 ) (solid), to water Cherenkov detectors such as T2K (Abe et al, 2011 ), and finally liquid argon time projection chambers such as MicroBooNE (Fleming, 2012 ), ICARUS (Amerio et al, 2004 ), and DUNE (Abi et al, 2020a ). Pion decay-at-rest experiments (COHERENT Akimov et al, 2015 , JSNS 2 Ajimura et al, 2017 ) use yet different technologies (liquid and solid scintillators, as well as solid-state detectors).…”

Section: Exemplars Of Domain Applicationsmentioning

confidence: 99%

Applications and Techniques for Fast Machine Learning in Science

Deiana¹,

Tran²,

Agar³

et al. 2022

Front. Big Data

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In this community review report, we discuss applications and techniques for fast machine learning (ML) in science—the concept of integrating powerful ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. We also present overlapping challenges across the multiple scientific domains where common solutions can be found. This community report is intended to give plenty of examples and inspiration for scientific discovery through integrated and accelerated ML solutions. This is followed by a high-level overview and organization of technical advances, including an abundance of pointers to source material, which can enable these breakthroughs.

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Section: Exemplars Of Domain Applicationsmentioning

confidence: 99%

Applications and Techniques for Fast Machine Learning in Science

Deiana¹,

Tran²,

Agar³

et al. 2022

Front. Big Data

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“…Although scintillator detectors have sensitivity to fast neutrons, it is difficult to distinguish a neutron event from ordinary ionizing radiation. In addition, conventional plastic scintillator has limited 5I 0 comes from the product of the signal integrated for 10 s shown in table 1 (305) times the ratio (12) of the area of the counter used in this example to that of the 1 2 Di-counter times the ratio of the 3.7 × 10 8 Bq used here to that of our test source, 2.2 × 10 4 Bq, 1.68 × 10 4 . capability to distinguish a neutron interaction from ionizing radiation.…”

Section: Neutron Sensitivity Enhancementmentioning

confidence: 98%

“…Plastic scintillator detectors have been used in high-energy physics experiments for decades, and with the development of extruded plastic scintillator at Fermilab [1][2][3][4][5][6][7][8], their use has expanded considerably. A recent example of an extruded scintillator detector is the one that has been developed for the Mu2e experiment at Fermilab [9].…”

Section: Overviewmentioning

confidence: 99%

Advanced radiation detector design for applications in food safety and national security

Bross,

Dukes,

Hansen

et al. 2024

J. Inst.

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We describe a new concept for a radiation detector based on extruded scintillator technology and commercially available solid-state photo-detectors. The detector is simple in construction, robust, very efficient, cost-effective and easily scalable in size from tens of cm2 to tens of m2. We describe two possible applications: flagging radioactive food contamination and detection of illicit radioactive materials, such as those potentially used in a dirty bomb.

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“…The procedure presented here relies first upon a Monte Carlo neutrino event generator package, GENIE [18], accounting for interactions of the neutrino beam with the rock material in the surrounding walls of the FNAL underground MINOS experimental hall [19]. This first step is followed by a GEANT4 [20] simulation, which accounts for the propagation of the end-state particles generated in the GENIE calculation and which potentially can enter the detector fiducial volume.…”

Section: Introductionmentioning

confidence: 99%