LUX-ZEPLIN

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Transforming a rare event search into a not-so-rare event search in real-time with deep learning-based object detection

ArXiv 2406.07538 (2024)

Authors:

J Schueler, HM Araújo, SN Balashov, JE Borg, C Brew, FM Brunbauer, C Cazzaniga, A Cottle, CD Frost, F Garcia, D Hunt, AC Kaboth, M Kastriotou, I Katsioulas, A Khazov, P Knights, H Kraus, VA Kudryavtsev, S Lilley, A Lindote, M Lisowska, D Loomba, MI Lopes, E Lopez Asamar, P Luna Dapica, PA Majewski, T Marley, C McCabe, L Millins, AF Mills, M Nakhostin, R Nandakumar, T Neep, F Neves, K Nikolopoulos, E Oliveri, L Ropelewski, VN Solovov, TJ Sumner, J Tarrant, E Tilly, R Turnley, R Veenhof
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Probing the Scalar WIMP-Pion Coupling with the first LUX-ZEPLIN data

(2024)

Authors:

J Aalbers, DS Akerib, AK Al Musalhi, F Alder, CS Amarasinghe, A Ames, TJ Anderson, N Angelides, HM Araújo, JE Armstrong, M Arthurs, A Baker, S Balashov, J Bang, EE Barillier, JW Bargemann, K Beattie, T Benson, A Bhatti, A Biekert, TP Biesiadzinski, HJ Birch, EJ Bishop, GM Blockinger, B Boxer, CAJ Brew, P Brás, S Burdin, M Buuck, MC Carmona-Benitez, M Carter, A Chawla, H Chen, JJ Cherwinka, YT Chin, NI Chott, MV Converse, A Cottle, G Cox, D Curran, CE Dahl, A David, J Delgaudio, S Dey, L deViveiros, L DiFelice, C Ding, JEY Dobson, E Druszkiewicz, SR Eriksen, A Fan, NM Fearon, S Fiorucci, H Flaecher, ED Fraser, TMA Fruth, RJ Gaitskell, A Geffre, J Genovesi, C Ghag, R Gibbons, S Gokhale, J Green, MGD vanderGrinten, JJ Haiston, CR Hall, S Han, E Hartigan-O'Connor, SJ Haselschwardt, MA Hernandez, SA Hertel, G Heuermann, GJ Homenides, M Horn, DQ Huang, D Hunt, E Jacquet, RS James, J Johnson, AC Kaboth, AC Kamaha, M Kannichankandy, D Khaitan, A Khazov, I Khurana, J DKim, J Kim, J Kingston, R Kirk, D Kodroff, L Korley, EV Korolkova, H Kraus, S Kravitz, L Kreczko, VA Kudryavtsev, DS Leonard, KT Lesko, C Levy, J Lin, A Lindote, R Linehan, WH Lippincott, MI Lopes, W Lorenzon, C Lu, S Luitz, PA Majewski, A Manalaysay, RL Mannino, C Maupin, ME McCarthy, G McDowell, DN McKinsey, J McLaughlin, JB McLaughlin, R McMonigle, EH Miller, E Mizrachi, A Monte, ME Monzani, JD Morales Mendoza, E Morrison, BJ Mount, M Murdy, A St J Murphy, A Naylor, HN Nelson, F Neves, A Nguyen, JA Nikoleyczik, I Olcina, KC Oliver-Mallory, J Orpwood, KJ Palladino, J Palmer, NJ Pannifer, N Parveen, SJ Patton, B Penning, G Pereira, E Perry, T Pershing, A Piepke, Y Qie, J Reichenbacher, CA Rhyne, Q Riffard, GRC Rischbieter, HS Riyat, R Rosero, T Rushton, D Rynders, D Santone, ABMR Sazzad, RW Schnee, S Shaw, T Shutt, JJ Silk, C Silva, G Sinev, J Siniscalco, R Smith, VN Solovov, P Sorensen, J Soria, I Stancu, A Stevens, K Stifter, B Suerfu, TJ Sumner, M Szydagis, WC Taylor, DR Tiedt, M Timalsina, Z Tong, DR Tovey, J Tranter, M Trask, M Tripathi, DR Tronstad, A Vacheret, AC Vaitkus, O Valentino, V Velan, A Wang, JJ Wang, Y Wang, JR Watson, RC Webb, L Weeldreyer, TJ Whitis, M Williams, WJ Wisniewski, FLH Wolfs, S Woodford, D Woodward, CJ Wright, Q Xia, X Xiang, J Xu, M Yeh, EA Zweig
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New constraints on ultraheavy dark matter from the LZ experiment

Physical Review D American Physical Society (APS) 109:11 (2024) 112010

Authors:

J Aalbers, DS Akerib, AK Al Musalhi, F Alder, CS Amarasinghe, A Ames, TJ Anderson, N Angelides, HM Araújo, JE Armstrong, M Arthurs, A Baker, S Balashov, J Bang, EE Barillier, JW Bargemann, A Baxter, K Beattie, T Benson, A Bhatti, A Biekert, TP Biesiadzinski, HJ Birch, EJ Bishop, GM Blockinger, B Boxer, CAJ Brew, P Brás, S Burdin, M Buuck, MC Carmona-Benitez, M Carter, A Chawla, H Chen, JJ Cherwinka, YT Chin, NI Chott, MV Converse, A Cottle, G Cox, D Curran, CE Dahl, A David, J Delgaudio, S Dey, L de Viveiros, L Di Felice, C Ding, JEY Dobson, E Druszkiewicz, SR Eriksen, A Fan, NM Fearon, S Fiorucci, H Flaecher, ED Fraser, TMA Fruth, RJ Gaitskell, A Geffre, J Genovesi, C Ghag, R Gibbons, S Gokhale, J Green, MGD van der Grinten, JH Haiston, CR Hall, S Han, E Hartigan-O’Connor, SJ Haselschwardt, MA Hernandez, SA Hertel, G Heuermann, GJ Homenides, M Horn, DQ Huang, D Hunt, CM Ignarra, E Jacquet, RS James, J Johnson, AC Kaboth, AC Kamaha, M Kannichankandy, D Khaitan, A Khazov, I Khurana, J Kim, J Kingston, R Kirk, D Kodroff, L Korley, EV Korolkova, H Kraus, S Kravitz, L Kreczko, B Krikler, VA Kudryavtsev, J Lee, DS Leonard, KT Lesko, C Levy, J Lin, A Lindote, R Linehan, WH Lippincott, MI Lopes, E Lopez Asamar, W Lorenzon, C Lu, S Luitz, PA Majewski, A Manalaysay, RL Mannino, C Maupin, ME McCarthy, G McDowell, DN McKinsey, J McLaughlin, JB McLaughlin, R McMonigle, EH Miller, E Mizrachi, A Monte, ME Monzani, JD Morales Mendoza, E Morrison, BJ Mount, M Murdy, A St. J. Murphy, A Naylor, C Nedlik, HN Nelson, F Neves, A Nguyen, JA Nikoleyczik, I Olcina, KC Oliver-Mallory, J Orpwood, KJ Palladino, J Palmer, NJ Pannifer, N Parveen, SJ Patton, B Penning, G Pereira, E Perry, T Pershing, A Piepke, Y Qie, J Reichenbacher, CA Rhyne, Q Riffard, GRC Rischbieter, HS Riyat, R Rosero, T Rushton, D Rynders, D Santone, ABMR Sazzad, RW Schnee, S Shaw, T Shutt, JJ Silk, C Silva, G Sinev, J Siniscalco, R Smith, VN Solovov, P Sorensen, J Soria, I Stancu, A Stevens, K Stifter, B Suerfu, TJ Sumner, M Szydagis, WC Taylor, DR Tiedt, M Timalsina, Z Tong, DR Tovey, J Tranter, M Trask, M Tripathi, DR Tronstad, W Turner, A Vacheret, AC Vaitkus, O Valentino, V Velan, A Wang, JJ Wang, Y Wang, JR Watson, RC Webb, L Weeldreyer, TJ Whitis, M Williams, WJ Wisniewski, FLH Wolfs, S Woodford, D Woodward, CJ Wright, Q Xia, X Xiang, J Xu, M Yeh, EA Zweig
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The Data Acquisition System of the LZ Dark Matter Detector: FADR

ArXiv 2405.14732 (2024)

Authors:

J Aalbers, DS Akerib, AK Al Musalhi, F Alder, CS Amarasinghe, A Ames, TJ Anderson, N Angelides, HM Araújo, JE Armstrong, M Arthurs, A Baker, S Balashov, J Bang, EE Barillier, JW Bargemann, K Beattie, T Benson, A Bhatti, A Biekert, TP Biesiadzinski, HJ Birch, E Bishop, GM Blockinger, B Boxer, CAJ Brew, P Brás, JH Buckley, S Burdin, M Buuck, MC Carmona-Benitez, M Carter, A Chawla, H Chen, JJ Cherwinka, YT Chin, NI Chott, MV Converse, A Cottle, G Cox, D Curran, CE Dahl, A David, J Delgaudio, S Dey, L de Viveiros, L Di Felice, T Dimino, C Ding, JEY Dobson, E Druszkiewicz, SR Eriksen, A Fan, NM Fearon, N Fieldhouse, S Fiorucci, H Flaecher, ED Fraser, TMA Fruth, RJ Gaitskell, A Geffre, R Gelfand, J Genovesi, C Ghag, R Gibbons, S Gokhale, J Green, MGD van der Grinten, JJ Haiston, CR Hall, S Han, E Hartigan-O'Connor, SJ Haselschwardt, MA Hernandez, SA Hertel, G Heuermann, GJ Homenides, M Horn, DQ Huang, D Hunt, E Jacquet, RS James, J Johnson, AC Kaboth, AC Kamaha, M Kannichankandy, D Khaitan, A Khazov, I Khurana, J Kim, YD Kim, J Kingston, R Kirk, D Kodroff, L Korley, EV Korolkova, M Koyuncu, H Kraus, S Kravitz, L Kreczko, VA Kudryavtsev, DS Leonard, KT Lesko, C Levy, J Lin, A Lindote, R Linehan, WH Lippincott, C Loniewski, MI Lopes, W Lorenzon, C Lu, S Luitz, PA Majewski, A Manalaysay, RL Mannino, C Maupin, ME McCarthy, G McDowell, DN McKinsey, J McLaughlin, JB Mclaughlin, R McMonigle, EH Miller, E Mizrachi, A Monte, ME Monzani, M Moongweluwan, JD Morales Mendoza, E Morrison, BJ Mount, M Murdy, A St J Murphy, A Naylor, HN Nelson, F Neves, A Nguyen, JA Nikoleyczik, H Oh, I Olcina, MA Olevitch, KC Oliver-Mallory, J Orpwood, KJ Palladino, J Palmer, NJ Pannifer, N Parveen, SJ Patton, B Penning, G Pereira, E Perry, T Pershing, A Piepke, Y Qie, J Reichenbacher, CA Rhyne, Q Riffard, GRC Rischbieter, HS Riyat, R Rosero, T Rushton, D Rynders, D Santone, R Sarkis, ABMR Sazzad, RW Schnee, S Shaw, T Shutt, JJ Silk, C Silva, G Sinev, J Siniscalco, W Skulski, R Smith, VN Solovov, P Sorensen, J Soria, I Stancu, A Stevens, K Stifter, B Suerfu, TJ Sumner, M Szydagis, WC Taylor, DR Tiedt, M Timalsina, Z Tong, DR Tovey, J Tranter, M Trask, M Tripathi, DR Tronstad, A Vacheret, AC Vaitkus, J Vaitkus, O Valentino, V Velan, A Wang, JJ Wang, Y Wang, JR Watson, RC Webb, L Weeldreyer, TJ Whitis, M Williams, WJ Wisniewski, FLH Wolfs, JD Wolfs, S Woodford, D Woodward, CJ Wright, Q Xia, X Xiang, J Xu, M Yeh, J Yin
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Optimal Operation of Cryogenic Calorimeters Through Deep Reinforcement Learning

Computing and Software for Big Science Springer 8:1 (2024) 10

Authors:

G Angloher, S Banik, G Benato, A Bento, A Bertolini, R Breier, C Bucci, J Burkhart, L Canonica, A D’Addabbo, S Di Lorenzo, L Einfalt, A Erb, F v. Feilitzsch, S Fichtinger, D Fuchs, A Garai, VM Ghete, P Gorla, PV Guillaumon, S Gupta, D Hauff, M Ješkovský, J Jochum, H Kraus

Abstract:

Cryogenic phonon detectors with transition-edge sensors achieve the best sensitivity to sub-GeV/c2 dark matter interactions with nuclei in current direct detection experiments. In such devices, the temperature of the thermometer and the bias current in its readout circuit need careful optimization to achieve optimal detector performance. This task is not trivial and is typically done manually by an expert. In our work, we automated the procedure with reinforcement learning in two settings. First, we trained on a simulation of the response of three Cryogenic Rare Event Search with Superconducting Thermometers (CRESST) detectors used as a virtual reinforcement learning environment. Second, we trained live on the same detectors operated in the CRESST underground setup. In both cases, we were able to optimize a standard detector as fast and with comparable results as human experts. Our method enables the tuning of large-scale cryogenic detector setups with minimal manual interventions.
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