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Aguilar-Arevalo AA, Alves DSM, Biedron S, Boissevain J, Borrego M, Chavez-Estrada M, Chavez A, Conrad JM, Cooper RL, Diaz A, Distel JR, D'Olivo JC, Dunton E, Dutta B, Elliott A, Evans D, Fields D, Greenwood J, Gold M, Gordon J, Guarincerri E, Huang EC, Kamp N, Kelsey C, Knickerbocker K, Lake R, Louis WC, Mahapatra R, Maludze S, Mirabal J, Moreno R, Neog H, deNiverville P, Pandey V, Plata-Salas J, Poulson D, Ray H, Renner E, Schaub TJ, Shaevitz MH, Smith D, Sondheim W, Szelc AM, Taylor C, Thompson WH, Thornton RT, Tripathi M, Van Berg R, Van de Water RG, Verma S, Walker K. First Leptophobic Dark Matter Search from the Coherent-CAPTAIN-Mills Liquid Argon Detector. Phys Rev Lett 2022; 129:021801. [PMID: 35867467 DOI: 10.1103/physrevlett.129.021801] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 05/17/2022] [Indexed: 06/15/2023]
Abstract
We report the first results of a search for leptophobic dark matter (DM) from the Coherent-CAPTAIN-Mills (CCM) liquid argon (LAr) detector. An engineering run with 120 photomultiplier tubes (PMTs) and 17.9×10^{20} protons on target (POT) was performed in fall 2019 to study the characteristics of the CCM detector. The operation of this 10-ton detector was strictly light based with a threshold of 50 keV and used coherent elastic scattering off argon nuclei to detect DM. Despite only 1.5 months of accumulated luminosity, contaminated LAr, and nonoptimized shielding, CCM's first engineering run has already achieved sensitivity to previously unexplored parameter space of light dark matter models with a baryonic vector portal. With an expected background of 115 005 events, we observe 115 005+16.5 events which is compatible with background expectations. For a benchmark mediator-to-DM mass ratio of m_{V_{B}}/m_{χ}=2.1, DM masses within the range 9 MeV≲m_{χ}≲50 MeV are excluded at 90% C. L. in the leptophobic model after applying the Feldman-Cousins test statistic. CCM's upgraded run with 200 PMTs, filtered LAr, improved shielding, and 10 times more POT will be able to exclude the remaining thermal relic density parameter space of this model, as well as probe new parameter space of other leptophobic DM models.
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Affiliation(s)
| | - D S M Alves
- Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - S Biedron
- University of New Mexico, Albuquerque, New Mexico 87131, USA
| | - J Boissevain
- Bartoszek Engineering, Aurora, Illinois 60506, USA
| | - M Borrego
- Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | | | - A Chavez
- Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - J M Conrad
- Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - R L Cooper
- Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
- New Mexico State University, Las Cruces, New Mexico 88003, USA
| | - A Diaz
- Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - J R Distel
- Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - J C D'Olivo
- Universidad Nacional Autónoma de México, CDMX 04510, México
| | - E Dunton
- Columbia University, New York, New York 10027, USA
| | - B Dutta
- Texas A&M University, College Station, Texas 77843, USA
| | - A Elliott
- Embry-Riddle Aeronautical University, Prescott, Arizona 86301, USA
| | - D Evans
- Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - D Fields
- University of New Mexico, Albuquerque, New Mexico 87131, USA
| | - J Greenwood
- Embry-Riddle Aeronautical University, Prescott, Arizona 86301, USA
| | - M Gold
- University of New Mexico, Albuquerque, New Mexico 87131, USA
| | - J Gordon
- Embry-Riddle Aeronautical University, Prescott, Arizona 86301, USA
| | - E Guarincerri
- Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - E C Huang
- Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - N Kamp
- Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - C Kelsey
- Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - K Knickerbocker
- Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - R Lake
- Embry-Riddle Aeronautical University, Prescott, Arizona 86301, USA
| | - W C Louis
- Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - R Mahapatra
- Texas A&M University, College Station, Texas 77843, USA
| | - S Maludze
- Texas A&M University, College Station, Texas 77843, USA
| | - J Mirabal
- Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - R Moreno
- Embry-Riddle Aeronautical University, Prescott, Arizona 86301, USA
| | - H Neog
- Texas A&M University, College Station, Texas 77843, USA
| | - P deNiverville
- Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - V Pandey
- University of Florida, Gainesville, Florida 32611, USA
| | - J Plata-Salas
- Universidad Nacional Autónoma de México, CDMX 04510, México
| | - D Poulson
- Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - H Ray
- University of Florida, Gainesville, Florida 32611, USA
| | - E Renner
- Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - T J Schaub
- University of New Mexico, Albuquerque, New Mexico 87131, USA
| | - M H Shaevitz
- Columbia University, New York, New York 10027, USA
| | - D Smith
- Embry-Riddle Aeronautical University, Prescott, Arizona 86301, USA
| | - W Sondheim
- Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - A M Szelc
- University of Edinburgh, Edinburgh EH9 3FD, United Kingdom
| | - C Taylor
- Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - W H Thompson
- Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - R T Thornton
- Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - M Tripathi
- University of Florida, Gainesville, Florida 32611, USA
| | - R Van Berg
- Bartoszek Engineering, Aurora, Illinois 60506, USA
| | - R G Van de Water
- Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - S Verma
- Texas A&M University, College Station, Texas 77843, USA
| | - K Walker
- Embry-Riddle Aeronautical University, Prescott, Arizona 86301, USA
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Wolfe BT, Iaroshenko O, Chu P, Kenyon GT, Schaub TJ, Thresher A, Watkins Y, Zhao L, Wang Z. Unsupervised learning about 4D features of microparticle motion. Rev Sci Instrum 2018; 89:10K109. [PMID: 30399843 DOI: 10.1063/1.5037462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2018] [Accepted: 07/26/2018] [Indexed: 06/08/2023]
Abstract
Material clusters of different sizes are known to exist in high-temperature plasmas due to plasma-wall interactions. The facts that these clusters, ranging from sub-microns to above mm in size, can move from one location to another quickly and that there are a lot of them make high-speed imaging and tracking one of the best, effective, and sometimes only diagnostic. An unsupervised machine learning technique based on deconvolutional neural networks is developed to analyze two-camera videos of high-temperature microparticles generated from exploding wires. The neural network utilizes a locally competitive algorithm to infer representations and optimize a dictionary composed of kernels, or basis vectors, for image analysis. Our primary goal is to use this method for feature recognition and prediction of the time-dependent three-dimensional (or "4D") microparticle motion. Features equivalent to local velocity vectors have been identified as the dictionary kernels or "building blocks" of the scene. The dictionary elements from the left and right camera views are found to be strongly correlated and satisfy the projection geometrical constraints. The results show that unsupervised machine learning techniques are promising approaches to process large sets of images for high-temperature plasmas and other scientific experiments. Machine learning techniques can be useful to handle the large amount of data and therefore aid the understanding of plasma-wall interaction.
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Affiliation(s)
- Bradley T Wolfe
- Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - O Iaroshenko
- Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - Pinghan Chu
- Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - G T Kenyon
- Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - T J Schaub
- Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - A Thresher
- Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - Y Watkins
- Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - L Zhao
- Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - Zhehui Wang
- Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
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