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Book ; Thesis: Measurement of beauty quark cross sections in photoproduction with the ZEUS experiment at the electron-proton collider HERA

Gutsche, Oliver

(DESY-THESIS ; 2005,10)

2005

Author's details	by O. Gutsche
Series title	DESY-THESIS ; 2005,10
Language	English
Size	IV, 187 Sp, Ill., graph. Darst
Publisher	DESY
Publishing place	Hamburg
Document type	Book ; Thesis
Thesis / German Habilitation thesis	Univ., FB Physik, Diss.--Hamburg, 2005
Note	Zsfassung in dt. Sprache
Database	Former special subject collection: coastal and deep sea fishing

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Book ; Thesis: Measurement of beauty quark cross sections in photoproduction with the ZEUS experiment at the electron-proton collider HERA

Gutsche, Oliver

(DESY-THESIS ; 2005,10)

2005

Author's details	by O. Gutsche
Series title	DESY-THESIS ; 2005,10
Language	English
Size	IV, 187 Sp, Ill., graph. Darst
Publisher	DESY
Publishing place	Hamburg
Document type	Book ; Thesis
Thesis / German Habilitation thesis	Univ., FB Physik, Diss.--Hamburg, 2005
Note	Zsfassung in dt. Sprache
Database	Library catalogue of the German National Library of Science and Technology (TIB), Hannover

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Book ; Online: Evaluating Portable Parallelization Strategies for Heterogeneous Architectures in High Energy Physics

2023

Abstract: High-energy physics (HEP) experiments have developed millions of lines of code over decades that are optimized to run on traditional x86 CPU systems. However, we are seeing a rapidly increasing fraction of floating point computing power in leadership- ... ...

Abstract	High-energy physics (HEP) experiments have developed millions of lines of code over decades that are optimized to run on traditional x86 CPU systems. However, we are seeing a rapidly increasing fraction of floating point computing power in leadership-class computing facilities and traditional data centers coming from new accelerator architectures, such as GPUs. HEP experiments are now faced with the untenable prospect of rewriting millions of lines of x86 CPU code, for the increasingly dominant architectures found in these computational accelerators. This task is made more challenging by the architecture-specific languages and APIs promoted by manufacturers such as NVIDIA, Intel and AMD. Producing multiple, architecture-specific implementations is not a viable scenario, given the available person power and code maintenance issues. The Portable Parallelization Strategies team of the HEP Center for Computational Excellence is investigating the use of Kokkos, SYCL, OpenMP, std::execution::parallel and alpaka as potential portability solutions that promise to execute on multiple architectures from the same source code, using representative use cases from major HEP experiments, including the DUNE experiment of the Long Baseline Neutrino Facility, and the ATLAS and CMS experiments of the Large Hadron Collider. This cross-cutting evaluation of portability solutions using real applications will help inform and guide the HEP community when choosing their software and hardware suites for the next generation of experimental frameworks. We present the outcomes of our studies, including performance metrics, porting challenges, API evaluations, and build system integration. Comment: 18 pages, 9 Figures, 2 Tables
Keywords	High Energy Physics - Experiment ; Computer Science - Distributed ; Parallel ; and Cluster Computing
Subject code	004
Publishing date	2023-06-27
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Thesis: Development of the Trigger Algorithm for the MONOLITH Experiment

Gutsche, Oliver

(DESY-THESIS ; 2001,20)

2001

Author's details	by O. Gutsche
Series title	DESY-THESIS ; 2001,20
Language	English
Size	90 Sp, graph. Darst
Document type	Book ; Thesis
Thesis / German Habilitation thesis	Univ., FB Physik, Dipl.-Arb.--Hamburg, 2001
Database	Former special subject collection: coastal and deep sea fishing

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Book ; Thesis: Development of the Trigger Algorithm for the MONOLITH Experiment

Gutsche, Oliver

(DESY-THESIS ; 2001,20)

2001

Author's details	by O. Gutsche
Series title	DESY-THESIS ; 2001,20
Language	English
Size	90 Sp, graph. Darst
Document type	Book ; Thesis
Thesis / German Habilitation thesis	Univ., FB Physik, Dipl.-Arb.--Hamburg, 2001
Database	Library catalogue of the German National Library of Science and Technology (TIB), Hannover

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Book ; Online: Coffea -- Columnar Object Framework For Effective Analysis

Smith, Nicholas / Gray, Lindsey / Cremonesi, Matteo / Jayatilaka, Bo / Gutsche, Oliver / Hall, Allison / Pedro, Kevin / Acosta, Maria / Melo, Andrew / Belforte, Stefano / Pivarski, Jim

2020

Abstract: The coffea framework provides a new approach to High-Energy Physics analysis, via columnar operations, that improves time-to-insight, scalability, portability, and reproducibility of analysis. It is implemented with the Python programming language, the ... ...

Abstract	The coffea framework provides a new approach to High-Energy Physics analysis, via columnar operations, that improves time-to-insight, scalability, portability, and reproducibility of analysis. It is implemented with the Python programming language, the scientific python package ecosystem, and commodity big data technologies. To achieve this suite of improvements across many use cases, coffea takes a factorized approach, separating the analysis implementation and data delivery scheme. All analysis operations are implemented using the NumPy or awkward-array packages which are wrapped to yield user code whose purpose is quickly intuited. Various data delivery schemes are wrapped into a common front-end which accepts user inputs and code, and returns user defined outputs. We will discuss our experience in implementing analysis of CMS data using the coffea framework along with a discussion of the user experience and future directions. Comment: As presented at CHEP 2019
Keywords	Computer Science - Distributed ; Parallel ; and Cluster Computing ; High Energy Physics - Experiment
Subject code	005
Publishing date	2020-08-28
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Response to NITRD, NCO, NSF Request for Information on "Update to the 2016 National Artificial Intelligence Research and Development Strategic Plan"

Amundson, J. / Annis, J. / Avestruz, C. / Bowring, D. / Caldeira, J. / Cerati, G. / Chang, C. / Dodelson, S. / Elvira, D. / Farahi, A. / Genser, K. / Gray, L. / Gutsche, O. / Harris, P. / Kinney, J. / Kowalkowski, J. B. / Kutschke, R. / Mrenna, S. / Nord, B. /

Para, A. / Pedro, K. / Perdue, G. N. / Scheinker, A. / Spentzouris, P. / John, J. St. / Tran, N. / Trivedi, S. / Trouille, L. / Wu, W. L. K. / Bom, C. R.

2019

Abstract: We present a response to the 2018 Request for Information (RFI) from the NITRD, NCO, NSF regarding the "Update to the 2016 National Artificial Intelligence Research and Development Strategic Plan." Through this document, we provide a response to the ... ...

Abstract	We present a response to the 2018 Request for Information (RFI) from the NITRD, NCO, NSF regarding the "Update to the 2016 National Artificial Intelligence Research and Development Strategic Plan." Through this document, we provide a response to the question of whether and how the National Artificial Intelligence Research and Development Strategic Plan (NAIRDSP) should be updated from the perspective of Fermilab, America's premier national laboratory for High Energy Physics (HEP). We believe the NAIRDSP should be extended in light of the rapid pace of development and innovation in the field of Artificial Intelligence (AI) since 2016, and present our recommendations below. AI has profoundly impacted many areas of human life, promising to dramatically reshape society --- e.g., economy, education, science --- in the coming years. We are still early in this process. It is critical to invest now in this technology to ensure it is safe and deployed ethically. Science and society both have a strong need for accuracy, efficiency, transparency, and accountability in algorithms, making investments in scientific AI particularly valuable. Thus far the US has been a leader in AI technologies, and we believe as a national Laboratory it is crucial to help maintain and extend this leadership. Moreover, investments in AI will be important for maintaining US leadership in the physical sciences.
Keywords	Astrophysics - Instrumentation and Methods for Astrophysics ; Computer Science - Artificial Intelligence ; Physics - Physics and Society
Subject code	028
Publishing date	2019-11-04
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Using Big Data Technologies for HEP Analysis

2019

Abstract: The HEP community is approaching an era were the excellent performances of the particle accelerators in delivering collision at high rate will force the experiments to record a large amount of information. The growing size of the datasets could ... ...

Abstract	The HEP community is approaching an era were the excellent performances of the particle accelerators in delivering collision at high rate will force the experiments to record a large amount of information. The growing size of the datasets could potentially become a limiting factor in the capability to produce scientific results timely and efficiently. Recently, new technologies and new approaches have been developed in industry to answer to the necessity to retrieve information as quickly as possible to analyze PB and EB datasets. Providing the scientists with these modern computing tools will lead to rethinking the principles of data analysis in HEP, making the overall scientific process faster and smoother. In this paper, we are presenting the latest developments and the most recent results on the usage of Apache Spark for HEP analysis. The study aims at evaluating the efficiency of the application of the new tools both quantitatively, by measuring the performances, and qualitatively, focusing on the user experience. The first goal is achieved by developing a data reduction facility: working together with CERN Openlab and Intel, CMS replicates a real physics search using Spark-based technologies, with the ambition of reducing 1 PB of public data in 5 hours, collected by the CMS experiment, to 1 TB of data in a format suitable for physics analysis. The second goal is achieved by implementing multiple physics use-cases in Apache Spark using as input preprocessed datasets derived from official CMS data and simulation. By performing different end-analyses up to the publication plots on different hardware, feasibility, usability and portability are compared to the ones of a traditional ROOT-based workflow.
Keywords	Computer Science - Distributed ; Parallel ; and Cluster Computing
Subject code	020
Publishing date	2019-01-21
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Search for Scalar Leptoquarks Produced via τ-Lepton-Quark Scattering in pp Collisions at sqrt[s]=13 TeV.

Hayrapetyan, A / Tumasyan, A / Adam, W / Andrejkovic, J W / Bergauer, T / Chatterjee, S / Damanakis, K / Dragicevic, M / Escalante Del Valle, A / Hussain, P S / Jeitler, M / Krammer, N / Liko, D / Mikulec, I / Schieck, J / Schöfbeck, R / Schwarz, D / Sonawane, M / Templ, S /

Waltenberger, W / Wulz, C-E / Darwish, M R / Janssen, T / Van Mechelen, P / Bols, E S / D'Hondt, J / Dansana, S / De Moor, A / Delcourt, M / El Faham, H / Lowette, S / Makarenko, I / Müller, D / Sahasransu, A R / Tavernier, S / Tytgat, M / Van Putte, S / Vannerom, D / Clerbaux, B / De Lentdecker, G / Favart, L / Hohov, D / Jaramillo, J / Khalilzadeh, A / Lee, K / Mahdavikhorrami, M / Malara, A / Paredes, S / Pétré, L / Postiau, N / Thomas, L / Vanden Bemden, M / Vander Velde, C / Vanlaer, P / De Coen, M / Dobur, D / Hong, Y / Knolle, J / Lambrecht, L / Mestdach, G / Rendón, C / Samalan, A / Skovpen, K / Van Den Bossche, N / Wezenbeek, L / Benecke, A / Bruno, G / Caputo, C / Delaere, C / Donertas, I S / Giammanco, A / Jaffel, K / Jain, Sa / Lemaitre, V / Lidrych, J / Mastrapasqua, P / Mondal, K / Tran, T T / Wertz, S / Alves, G A / Coelho, E / Hensel, C / Menezes De Oliveira, T / Moraes, A / Rebello Teles, P / Soeiro, M / Aldá Júnior, W L / Alves Gallo Pereira, M / Barroso Ferreira Filho, M / Brandao Malbouisson, H / Carvalho, W / Chinellato, J / Da Costa, E M / Da Silveira, G G / De Jesus Damiao, D / Fonseca De Souza, S / Martins, J / Mora Herrera, C / Mota Amarilo, K / Mundim, L / Nogima, H / Santoro, A / Silva Do Amaral, S M / Sznajder, A / Thiel, M / Vilela Pereira, A / Bernardes, C A / Calligaris, L / Tomei, T R Fernandez Perez / Gregores, E M / Mercadante, P G / Novaes, S F / Orzari, B / Padula, Sandra S / Aleksandrov, A / Antchev, G / Hadjiiska, R / Iaydjiev, P / Misheva, M / Shopova, M / Sultanov, G / Dimitrov, A / Ivanov, T / Litov, L / Pavlov, B / Petkov, P / Petrov, A / Shumka, E / Keshri, S / Thakur, S / Cheng, T / Guo, Q / Javaid, T / Mittal, M / Yuan, L / Bauer, G / Hu, Z / Liu, J / Yi, K / Chen, G M / Chen, H S / Chen, M / Iemmi, F / Jiang, C H / Kapoor, A / Liao, H / Liu, Z-A / Monti, F / Shahzad, M A / Sharma, R / Song, J N / Tao, J / Wang, C / Wang, J / Wang, Z / Zhang, H / Agapitos, A / Ban, Y / Levin, A / Li, C / Li, Q / Mao, Y / Qian, S J / Sun, X / Wang, D / Yang, H / Zhang, L / Zhou, C / You, Z / Lu, N / Gao, X / Leggat, D / Okawa, H / Zhang, Y / Lin, Z / Lu, C / Xiao, M / Avila, C / Barbosa Trujillo, D A / Cabrera, A / Florez, C / Fraga, J / Reyes Vega, J A / Mejia Guisao, J / Ramirez, F / Rodriguez, M / Ruiz Alvarez, J D / Giljanovic, D / Godinovic, N / Lelas, D / Sculac, A / Kovac, M / Sculac, T / Bargassa, P / Brigljevic, V / Chitroda, B K / Ferencek, D / Mishra, S / Starodumov, A / Susa, T / Attikis, A / Christoforou, K / Konstantinou, S / Mousa, J / Nicolaou, C / Ptochos, F / Razis, P A / Rykaczewski, H / Saka, H / Stepennov, A / Finger, M / Kveton, A / Ayala, E / Carrera Jarrin, E / Assran, Y / Elgammal, S / Abdullah Al-Mashad, M / Mahmoud, M A / Dewanjee, R K / Ehataht, K / Kadastik, M / Lange, T / Nandan, S / Nielsen, C / Pata, J / Raidal, M / Tani, L / Veelken, C / Kirschenmann, H / Osterberg, K / Voutilainen, M / Bharthuar, S / Brücken, E / Garcia, F / Havukainen, J / Kallonen, K T S / Kim, M S / Kinnunen, R / Lampén, T / Lassila-Perini, K / Lehti, S / Lindén, T / Lotti, M / Martikainen, L / Myllymäki, M / Rantanen, M M / Siikonen, H / Tuominen, E / Tuominiemi, J / Luukka, P / Petrow, H / Tuuva, T / Besancon, M / Couderc, F / Dejardin, M / Denegri, D / Faure, J L / Ferri, F / Ganjour, S / Gras, P / Hamel de Monchenault, G / Lohezic, V / Malcles, J / Rander, J / Rosowsky, A / Sahin, M Ö / Savoy-Navarro, A / Simkina, P / Titov, M / Baldenegro Barrera, C / Beaudette, F / Buchot Perraguin, A / Busson, P / Cappati, A / Charlot, C / Damas, F / Davignon, O / De Wit, A / Falmagne, G / Fontana Santos Alves, B A / Ghosh, S / Gilbert, A / Granier de Cassagnac, R / Hakimi, A / Harikrishnan, B / Kalipoliti, L / Liu, G / Motta, J / Nguyen, M / Ochando, C / Portales, L / Salerno, R / Sarkar, U / Sauvan, J B / Sirois, Y / Tarabini, A / Vernazza, E / Zabi, A / Zghiche, A / Agram, J-L / Andrea, J / Apparu, D / Bloch, D / Brom, J-M / Chabert, E C / Collard, C / Falke, S / Goerlach, U / Grimault, C / Haeberle, R / Le Bihan, A-C / Sessini, M A / Van Hove, P / Beauceron, S / Blancon, B / Boudoul, G / Chanon, N / Choi, J / Contardo, D / Depasse, P / Dozen, C / El Mamouni, H / Fay, J / Gascon, S / Gouzevitch, M / Greenberg, C / Grenier, G / Ille, B / Laktineh, I B / Lethuillier, M / Mirabito, L / Perries, S / Purohit, A / Vander Donckt, M / Verdier, P / Xiao, J / Khvedelidze, A / Lomidze, I / Tsamalaidze, Z / Botta, V / Feld, L / Klein, K / Lipinski, M / Meuser, D / Pauls, A / Röwert, N / Teroerde, M / Diekmann, S / Dodonova, A / Eich, N / Eliseev, D / Engelke, F / Erdmann, M / Fackeldey, P / Fischer, B / Hebbeker, T / Hoepfner, K / Ivone, F / Jung, A / Lee, M Y / Mastrolorenzo, L / Merschmeyer, M / Meyer, A / Mukherjee, S / Noll, D / Novak, A / Nowotny, F / Pozdnyakov, A / Rath, Y / Redjeb, W / Rehm, F / Reithler, H / Sarkisovi, V / Schmidt, A / Schuler, S C / Sharma, A / Stein, A / Torres Da Silva De Araujo, F / Vigilante, L / Wiedenbeck, S / Zaleski, S / Dziwok, C / Flügge, G / Haj Ahmad, W / Kress, T / Nowack, A / Pooth, O / Stahl, A / Ziemons, T / Zotz, A / Aarup Petersen, H / Aldaya Martin, M / Alimena, J / Amoroso, S / An, Y / Baxter, S / Bayatmakou, M / Becerril Gonzalez, H / Behnke, O / Belvedere, A / Bhattacharya, S / Blekman, F / Borras, K / Brunner, D / Campbell, A / Cardini, A / Cheng, C / Colombina, F / Consuegra Rodríguez, S / Correia Silva, G / De Silva, M / Eckerlin, G / Eckstein, D / Estevez Banos, L I / Filatov, O / Gallo, E / Geiser, A / Giraldi, A / Greau, G / Guglielmi, V / Guthoff, M / Hinzmann, A / Jafari, A / Jeppe, L / Jomhari, N Z / Kaech, B / Kasemann, M / Kaveh, H / Kleinwort, C / Kogler, R / Komm, M / Krücker, D / Lange, W / Leyva Pernia, D / Lipka, K / Lohmann, W / Mankel, R / Melzer-Pellmann, I-A / Mendizabal Morentin, M / Metwally, J / Meyer, A B / Milella, G / Mussgiller, A / Nürnberg, A / Otarid, Y / Pérez Adán, D / Ranken, E / Raspereza, A / Ribeiro Lopes, B / Rübenach, J / Saggio, A / Scham, M / Schnake, S / Schütze, P / Schwanenberger, C / Selivanova, D / Shchedrolosiev, M / Sosa Ricardo, R E / Sreelatha Pramod, L P / Stafford, D / Vazzoler, F / Ventura Barroso, A / Walsh, R / Wang, Q / Wen, Y / Wichmann, K / Wiens, L / Wissing, C / Wuchterl, S / Yang, Y / Zimermmane Castro Santos, A / Albrecht, A / Albrecht, S / Antonello, M / Bein, S / Benato, L / Bonanomi, M / Connor, P / Eich, M / El Morabit, K / Fischer, Y / Fröhlich, A / Garbers, C / Garutti, E / Grohsjean, A / Hajheidari, M / Haller, J / Jabusch, H R / Kasieczka, G / Keicher, P / Klanner, R / Korcari, W / Kramer, T / Kutzner, V / Labe, F / Lange, J / Lobanov, A / Matthies, C / Mehta, A / Moureaux, L / Mrowietz, M / Nigamova, A / Nissan, Y / Paasch, A / Pena Rodriguez, K J / Quadfasel, T / Raciti, B / Rieger, M / Savoiu, D / Schindler, J / Schleper, P / Schröder, M / Schwandt, J / Sommerhalder, M / Stadie, H / Steinbrück, G / Tews, A / Wolf, M / Brommer, S / Burkart, M / Butz, E / Chwalek, T / Dierlamm, A / Droll, A / Faltermann, N / Giffels, M / Gottmann, A / Hartmann, F / Hofsaess, R / Horzela, M / Husemann, U / Klute, M / Koppenhöfer, R / Link, M / Lintuluoto, A / Maier, S / Mitra, S / Mormile, M / Müller, Th / Neukum, M / Oh, M / Quast, G / Rabbertz, K / Regnery, B / Shadskiy, N / Shvetsov, I / Simonis, H J / Trevisani, N / Ulrich, R / van der Linden, J / Von Cube, R F / Wassmer, M / Wieland, S / Wittig, F / Wolf, R / Wunsch, S / Zuo, X / Anagnostou, G / Assiouras, P / Daskalakis, G / Kyriakis, A / Papadopoulos, A / Stakia, A / Kontaxakis, P / Melachroinos, G / Panagiotou, A / Papavergou, I / Paraskevas, I / Saoulidou, N / Theofilatos, K / Tziaferi, E / Vellidis, K / Zisopoulos, I / Bakas, G / Chatzistavrou, T / Karapostoli, G / Kousouris, K / Papakrivopoulos, I / Siamarkou, E / Tsipolitis, G / Zacharopoulou, A / Adamidis, K / Bestintzanos, I / Evangelou, I / Foudas, C / Gianneios, P / Kamtsikis, C / Katsoulis, P / Kokkas, P / Kosmoglou Kioseoglou, P G / Manthos, N / Papadopoulos, I / Strologas, J / Csanád, M / Farkas, K / Gadallah, M M A / Kadlecsik, Á / Major, P / Mandal, K / Pásztor, G / Rádl, A J / Veres, G I / Bartók, M / Hajdu, C / Horvath, D / Sikler, F / Veszpremi, V / Raics, P / Ujvari, B / Zilizi, G / Bencze, G / Czellar, S / Karancsi, J / Molnar, J / Szillasi, Z / Csorgo, T / Nemes, F / Novak, T / Babbar, J / Bansal, S / Beri, S B / Bhatnagar, V / Chaudhary, G / Chauhan, S / Dhingra, N / Gupta, R / Kaur, A / Kaur, H / Kaur, M / Kumar, S / Meena, M / Sandeep, K / Sheokand, T / Singh, J B / Singla, A / Ahmed, A / Bhardwaj, A / Chhetri, A / Choudhary, B C / Kumar, A / Naimuddin, M / Ranjan, K / Saumya, S / Acharya, S / Baradia, S / Barman, S / Bhowmik, D / Dutta, S / Gomber, B / Palit, P / Saha, G / Sahu, B / Sarkar, S / Ameen, M M / Behera, P K / Behera, S C / Jana, P / Kalbhor, P / Komaragiri, J R / Kumar, D / Panwar, L / Pradhan, R / Pujahari, P R / Saha, N R / Sikdar, A K / Verma, S / Aziz, T / Das, I / Dugad, S / Kumar, M / Mohanty, G B / Suryadevara, P / Bala, A / Banerjee, S / Chatterjee, R M / Guchait, M / Jain, Sh / Karmakar, S / Majumder, G / Mazumdar, K / Parolia, S / Thachayath, A / Bahinipati, S / Das, A K / Kar, C / Maity, D / Mal, P / Mishra, T / Muraleedharan Nair Bindhu, V K / Naskar, K / Nayak, A / Sadangi, P / Saha, P / Swain, S K / Varghese, S / Vats, D / Alpana, A / Dube, S / Kansal, B / Laha, A / Rastogi, A / Sharma, S / Bakhshiansohi, H / Khazaie, E / Zeinali, M / Chenarani, S / Etesami, S M / Khakzad, M / Mohammadi Najafabadi, M / Grunewald, M / Abbrescia, M / Aly, R / Colaleo, A / Creanza, D / D' Anzi, B / De Filippis, N / De Palma, M / Di Florio, A / Elmetenawee, W / Fiore, L / Iaselli, G / Maggi, G / Maggi, M / Margjeka, I / Mastrapasqua, V / My, S / Nuzzo, S / Pellecchia, A / Pompili, A / Pugliese, G / Radogna, R / Ramirez-Sanchez, G / Ramos, D / Ranieri, A / Silvestris, L / Simone, F M / Sözbilir, Ü / Stamerra, A / Venditti, R / Verwilligen, P / Zaza, A / Abbiendi, G / Battilana, C / Borgonovi, L / Campanini, R / Capiluppi, P / Castro, A / Cavallo, F R / Cuffiani, M / Dallavalle, G M / Diotalevi, T / Fabbri, F / Fanfani, A / Fasanella, D / Giacomelli, P / Giommi, L / Grandi, C / Guiducci, L / Lo Meo, S / Lunerti, L / Marcellini, S / Masetti, G / Navarria, F L / Perrotta, A / Primavera, F / Rossi, A M / Rovelli, T / Siroli, G P / Costa, S / Di Mattia, A / Potenza, R / Tricomi, A / Tuve, C / Barbagli, G / Bardelli, G / Camaiani, B / Cassese, A / Ceccarelli, R / Ciulli, V / Civinini, C / D'Alessandro, R / Focardi, E / Kello, T / Latino, G / Lenzi, P / Lizzo, M / Meschini, M / Paoletti, S / Papanastassiou, A / Sguazzoni, G / Viliani, L / Benussi, L / Bianco, S / Meola, S / Piccolo, D / Chatagnon, P / Ferro, F / Robutti, E / Tosi, S / Benaglia, A / Boldrini, G / Brivio, F / Cetorelli, F / De Guio, F / Dinardo, M E / Dini, P / Gennai, S / Gerosa, R / Ghezzi, A / Govoni, P / Guzzi, L / Lucchini, M T / Malberti, M / Malvezzi, S / Massironi, A / Menasce, D / Moroni, L / Paganoni, M / Pedrini, D / Pinolini, B S / Ragazzi, S / 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Physical review letters

2024 Volume 132, Issue 6, Page(s) 61801

Abstract: The first search for scalar leptoquarks produced in τ-lepton-quark collisions is presented. It is based on a set of proton-proton collision data recorded with the CMS detector at the LHC at a center-of-mass energy of 13 TeV corresponding to an integrated ...

Abstract	The first search for scalar leptoquarks produced in τ-lepton-quark collisions is presented. It is based on a set of proton-proton collision data recorded with the CMS detector at the LHC at a center-of-mass energy of 13 TeV corresponding to an integrated luminosity of 138 fb^{-1}. The reconstructed final state consists of a jet, significant missing transverse momentum, and a τ lepton reconstructed through its hadronic or leptonic decays. Limits are set on the product of the leptoquark production cross section and branching fraction and interpreted as exclusions in the plane of the leptoquark mass and the leptoquark-τ-quark coupling strength.
Language	English
Publishing date	2024-02-23
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.132.061801
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Search for Inelastic Dark Matter in Events with Two Displaced Muons and Missing Transverse Momentum in Proton-Proton Collisions at sqrt[s]=13 TeV.

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Physical review letters

2024 Volume 132, Issue 4, Page(s) 41802

Abstract: A search for dark matter in events with a displaced nonresonant muon pair and missing transverse momentum is presented. The analysis is performed using an integrated luminosity of 138 fb^{-1} of proton-proton (pp) collision data at a center-of-mass ... ...

Abstract	A search for dark matter in events with a displaced nonresonant muon pair and missing transverse momentum is presented. The analysis is performed using an integrated luminosity of 138 fb^{-1} of proton-proton (pp) collision data at a center-of-mass energy of 13 TeV produced by the LHC in 2016-2018. No significant excess over the predicted backgrounds is observed. Upper limits are set on the product of the inelastic dark matter production cross section σ(pp→A^{'}→χ_{1}χ_{2}) and the decay branching fraction B(χ_{2}→χ_{1}μ^{+}μ^{-}), where A^{'} is a dark photon and χ_{1} and χ_{2} are states in the dark sector with near mass degeneracy. This is the first dedicated collider search for inelastic dark matter.
Language	English
Publishing date	2024-02-09
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.132.041802
Database	MEDical Literature Analysis and Retrieval System OnLINE

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