Azzia0000-0002-3129-828X, N. Bacchettaa,540000-0002-2205-5737, P.
Bortignona0000-0002-5360-1454, A. Bragagnoloa,b0000-0003-3474-2099, R.
Carlina,b0000-0001-7915-1650, P. Checchiaa0000-0002-8312-1531, T.
Dorigoa0000-0002-1659-8727, F. Gasparinia,b0000-0002-1315-563X, U.
Gasparinia,b0000-0002-7253-2669, G. Grossoa, L. Layera,55, E.
Lusiania0000-0001-8791-7978, M. Margonia,b0000-0003-1797-4330, G.
Marona,560000-0003-3970-6986, A.T. Meneguzzoa,b0000-0002-5861-8140, M.
Migliorinia,b0000-0002-5441-7755, J. Pazzinia,b0000-0002-1118-6205, P.
Ronchesea,b0000-0001-7002-2051, R. Rossina,b0000-0003-3466-7500, F.
Simonettoa,b0000-0002-8279-2464, G. Stronga0000-0002-4640-6108, M.
Tosia,b0000-0003-4050-1769, A. Triossia,b0000-0001-5140-9154, S.
Venturaa0000-0002-8938-2193, H. Yarara,b, M. Zanettia,b0000-0003-4281-4582, P.
Zottoa,b0000-0003-3953-5996, A. Zucchettaa,b0000-0003-0380-1172, G.
Zumerlea,b0000-0003-3075-2679

INFN Sezione di Paviaa, Università di Paviab, Pavia, Italy S. Abu
Zeida,200000-0002-0820-0483, C. Aimèa,b0000-0003-0449-4717, A.
Braghieria0000-0002-9606-5604, S. Calzaferria,b0000-0002-1162-2505, D.
Fiorinaa,b0000-0002-7104-257X, P. Montagnaa,b0000-0001-9647-9420, V.
Rea0000-0003-0697-3420, C. Riccardia,b0000-0003-0165-3962, P.
Salvinia0000-0001-9207-7256, I. Vaia,b0000-0003-0037-5032, P.
Vituloa,b0000-0001-9247-7778

INFN Sezione di Perugiaa, Università di Perugiab, Perugia, Italy S.
Ajmala,b0000-0002-2726-2858, P. Asenova,570000-0003-2379-9903, G.M.
Bileia0000-0002-4159-9123, D. Ciangottinia,b0000-0002-0843-4108, L.
Fanòa,b0000-0002-9007-629X, M. Magherinia,b0000-0003-4108-3925, G.
Mantovania,b, V. Mariania,b0000-0001-7108-8116, M.
Menichellia0000-0002-9004-735X, F. Moscatellia,570000-0002-7676-3106, A.
Piccinellia,b0000-0003-0386-0527, M. Presillaa,b0000-0003-2808-7315, A.
Rossia,b0000-0002-2031-2955, A. Santocchiaa,b0000-0002-9770-2249, D.
Spigaa0000-0002-2991-6384, T. Tedeschia,b0000-0002-7125-2905

INFN Sezione di Pisaa, Università di Pisab, Scuola Normale Superiore di Pisac,
Pisa, Italy; Università di Sienad, Siena, Italy P.
Azzurria0000-0002-1717-5654, G. Bagliesia0000-0003-4298-1620, R.
Bhattacharyaa0000-0002-7575-8639, L. Bianchinia,b0000-0002-6598-6865, T.
Boccalia0000-0002-9930-9299, E. Bossinia0000-0002-2303-2588, D.
Bruschinia,c0000-0001-7248-2967, R. Castaldia0000-0003-0146-845X, M.A.
Cioccia,b0000-0003-0002-5462, M. Cipriania,b0000-0002-0151-4439, V.
D’Amantea,d0000-0002-7342-2592, R. Dell’Orsoa0000-0003-1414-9343, S.
Donatoa0000-0001-7646-4977, A. Giassia0000-0001-9428-2296, F.
Ligabuea,c0000-0002-1549-7107, D. Matos Figueiredoa0000-0003-2514-6930, A.
Messineoa,b0000-0001-7551-5613, M. Musicha,b0000-0001-7938-5684, F.
Pallaa0000-0002-6361-438X, S. Paroliaa0000-0002-9566-2490, A.
Rizzia,b0000-0002-4543-2718, G. Rolandia,c0000-0002-0635-274X, S. Roy
Chowdhurya0000-0001-5742-5593, T. Sarkara0000-0003-0582-4167, A.
Scribanoa0000-0002-4338-6332, P. Spagnoloa0000-0001-7962-5203, R.
Tenchinia0000-0003-2574-4383, G. Tonellia,b0000-0003-2606-9156, N.
Turinia,d0000-0002-9395-5230, A. Venturia0000-0002-0249-4142, P.G.
Verdinia0000-0002-0042-9507

INFN Sezione di Romaa, Sapienza Università di Romab, Roma, Italy P.
Barriaa0000-0002-3924-7380, M. Campanaa,b0000-0001-5425-723X, F.
Cavallaria0000-0002-1061-3877, L. Cunqueiro Mendeza,b0000-0001-6764-5370, D.
Del Rea,b0000-0003-0870-5796, E. Di Marcoa0000-0002-5920-2438, M.
Diemoza0000-0002-3810-8530, F. Erricoa,b0000-0001-8199-370X, E.
Longoa,b0000-0001-6238-6787, P. Meridiania0000-0002-8480-2259, J.
Mijuskovica,b0009-0009-1589-9980, G. Organtinia,b0000-0002-3229-0781, F.
Pandolfia0000-0001-8713-3874, R. Paramattia,b0000-0002-0080-9550, C.
Quarantaa,b0000-0002-0042-6891, S. Rahatloua,b0000-0001-9794-3360, C.
Rovellia0000-0003-2173-7530, F. Santanastasioa,b0000-0003-2505-8359, L.
Soffia0000-0003-2532-9876, R. Tramontanoa,b0000-0001-5979-5299

INFN Sezione di Torinoa, Università di Torinob, Torino, Italy; Università del
Piemonte Orientalec, Novara, Italy N. Amapanea,b0000-0001-9449-2509, R.
Arcidiaconoa,c0000-0001-5904-142X, S. Argiroa,b0000-0003-2150-3750, M.
Arneodoa,c0000-0002-7790-7132, N. Bartosika0000-0002-7196-2237, R.
Bellana,b0000-0002-2539-2376, A. Belloraa,b0000-0002-2753-5473, C.
Biinoa0000-0002-1397-7246, N. Cartigliaa0000-0002-0548-9189, M.
Costaa,b0000-0003-0156-0790, R. Covarellia,b0000-0003-1216-5235, N.
Demariaa0000-0003-0743-9465, L. Fincoa0000-0002-2630-5465, M.
Grippoa,b0000-0003-0770-269X, B. Kiania,b0000-0002-1202-7652, F.
Leggera0000-0003-1400-0709, F. Luongoa,b0000-0003-2743-4119, C.
Mariottia0000-0002-6864-3294, S. Masellia0000-0001-9871-7859, A.
Meccaa,b0000-0003-2209-2527, E. Migliorea,b0000-0002-2271-5192, M.
Montenoa0000-0002-3521-6333, R. Mulargiaa0000-0003-2437-013X, M.M.
Obertinoa,b0000-0002-8781-8192, G. Ortonaa0000-0001-8411-2971, L.
Pachera,b0000-0003-1288-4838, N. Pastronea0000-0001-7291-1979, M.
Pelliccionia0000-0003-4728-6678, M. Ruspaa,c0000-0002-7655-3475, F.
Sivieroa,b0000-0002-4427-4076, V. Solaa,b0000-0001-6288-951X, A.
Solanoa,b0000-0002-2971-8214, D. Soldia,b0000-0001-9059-4831, A.
Staianoa0000-0003-1803-624X, C. Tarriconea,b0000-0001-6233-0513, M.
Tornagoa,b0000-0001-6768-1056, D. Trocinoa0000-0002-2830-5872, G.
Umoreta,b0000-0002-6674-7874, E. Vlasova,b0000-0002-8628-2090

INFN Sezione di Triestea, Università di Triesteb, Trieste, Italy S.
Belfortea0000-0001-8443-4460, V. Candelisea,b0000-0002-3641-5983, M.
Casarsaa0000-0002-1353-8964, F. Cossuttia0000-0001-5672-214X, K. De
Leoa,b0000-0002-8908-409X, G. Della Riccaa,b0000-0003-2831-6982

Kyungpook National University, Daegu, Korea S. Dogra0000-0002-0812-0758, J.
Hong0000-0002-9463-4922, C. Huh0000-0002-8513-2824, B. Kim0000-0002-9539-6815,
D.H. Kim0000-0002-9023-6847, J. Kim, H. Lee, S.W. Lee0000-0002-1028-3468, C.S.
Moon0000-0001-8229-7829, Y.D. Oh0000-0002-7219-9931, M.S.
Ryu0000-0002-1855-180X, S. Sekmen0000-0003-1726-5681, Y.C.
Yang0000-0003-1009-4621

Chonnam National University, Institute for Universe and Elementary Particles,
Kwangju, Korea G. Bak0000-0002-0095-8185, P. Gwak0009-0009-7347-1480, H.
Kim0000-0001-8019-9387, D.H. Moon0000-0002-5628-9187

Hanyang University, Seoul, Korea E. Asilar0000-0001-5680-599X, D.
Kim0000-0002-8336-9182, T.J. Kim0000-0001-8336-2434, J.A. Merlin, J.
Park0000-0002-4683-6669

Korea University, Seoul, Korea S. Choi0000-0001-6225-9876, S. Han, B.
Hong0000-0002-2259-9929, K. Lee, K.S. Lee0000-0002-3680-7039, S.
Lee0000-0001-9257-9643, J. Park, S.K. Park, J. Yoo0000-0003-0463-3043

Kyung Hee University, Department of Physics, Seoul, Korea J.
Goh0000-0002-1129-2083

Sejong University, Seoul, Korea H. S. Kim0000-0002-6543-9191, Y. Kim, S. Lee

Seoul National University, Seoul, Korea J. Almond, J.H. Bhyun, J.
Choi0000-0002-2483-5104, W. Jun0009-0001-5122-4552, J. Kim0000-0001-9876-6642,
J.S. Kim, S. Ko0000-0003-4377-9969, H. Kwon0009-0002-5165-5018, H.
Lee0000-0002-1138-3700, J. Lee0000-0001-6753-3731, J. Lee0000-0002-5351-7201,
B.H. Oh0000-0002-9539-7789, S.B. Oh0000-0003-0710-4956, H.
Seo0000-0002-3932-0605, U.K. Yang, I. Yoon0000-0002-3491-8026

University of Seoul, Seoul, Korea W. Jang0000-0002-1571-9072, D.Y. Kang, Y.
Kang0000-0001-6079-3434, S. Kim0000-0002-8015-7379, B. Ko, J.S.H.
Lee0000-0002-2153-1519, Y. Lee0000-0001-5572-5947, I.C.
Park0000-0003-4510-6776, Y. Roh, I.J. Watson0000-0003-2141-3413, S.
Yang0000-0001-6905-6553

Yonsei University, Department of Physics, Seoul, Korea S.
Ha0000-0003-2538-1551, H.D. Yoo0000-0002-3892-3500

Sungkyunkwan University, Suwon, Korea M. Choi0000-0002-4811-626X, M.R.
Kim0000-0002-2289-2527, H. Lee, Y. Lee0000-0001-6954-9964, I.
Yu0000-0003-1567-5548

College of Engineering and Technology, American University of the Middle East
(AUM), Dasman, Kuwait T. Beyrouthy, Y. Maghrbi0000-0002-4960-7458

Riga Technical University, Riga, Latvia K. Dreimanis0000-0003-0972-5641, A.
Gaile0000-0003-1350-3523, G. Pikurs, A. Potrebko0000-0002-3776-8270, M.
Seidel0000-0003-3550-6151, V. Veckalns580000-0003-3676-9711

University of Latvia (LU), Riga, Latvia N.R. Strautnieks0000-0003-4540-9048

Vilnius University, Vilnius, Lithuania M. Ambrozas0000-0003-2449-0158, A.
Juodagalvis0000-0002-1501-3328, A. Rinkevicius0000-0002-7510-255X, G.
Tamulaitis0000-0002-2913-9634

National Centre for Particle Physics, Universiti Malaya, Kuala Lumpur,
Malaysia N. Bin Norjoharuddeen0000-0002-8818-7476, I.
Yusuff590000-0003-2786-0732, Z. Zolkapli

Universidad de Sonora (UNISON), Hermosillo, Mexico J.F.
Benitez0000-0002-2633-6712, A. Castaneda Hernandez0000-0003-4766-1546, H.A.
Encinas Acosta, L.G. Gallegos Maríñez, M. León Coello0000-0002-3761-911X, J.A.
Murillo Quijada0000-0003-4933-2092, A. Sehrawat0000-0002-6816-7814, L.
Valencia Palomo0000-0002-8736-440X

Centro de Investigacion y de Estudios Avanzados del IPN, Mexico City, Mexico
G. Ayala0000-0002-8294-8692, H. Castilla-Valdez0009-0005-9590-9958, E. De La
Cruz-Burelo0000-0002-7469-6974, I. Heredia-De La Cruz600000-0002-8133-6467, R.
Lopez-Fernandez0000-0002-2389-4831, C.A. Mondragon Herrera, A. Sánchez
Hernández0000-0001-9548-0358

Universidad Iberoamericana, Mexico City, Mexico C. Oropeza
Barrera0000-0001-9724-0016, M. Ramírez García0000-0002-4564-3822

Benemerita Universidad Autonoma de Puebla, Puebla, Mexico I.
Bautista0000-0001-5873-3088, I. Pedraza0000-0002-2669-4659, H.A. Salazar
Ibarguen0000-0003-4556-7302, C. Uribe Estrada0000-0002-2425-7340

University of Montenegro, Podgorica, Montenegro I. Bubanja0009-0005-4364-277X,
N. Raicevic0000-0002-2386-2290

University of Canterbury, Christchurch, New Zealand P.H.
Butler0000-0001-9878-2140

National Centre for Physics, Quaid-I-Azam University, Islamabad, Pakistan A.
Ahmad0000-0002-4770-1897, M.I. Asghar, A. Awais0000-0003-3563-257X, M.I.M.
Awan, H.R. Hoorani0000-0002-0088-5043, W.A. Khan0000-0003-0488-0941

AGH University of Krakow, Faculty of Computer Science, Electronics and
Telecommunications, Krakow, Poland V. Avati, L. Grzanka0000-0002-3599-854X, M.
Malawski0000-0001-6005-0243

National Centre for Nuclear Research, Swierk, Poland H.
Bialkowska0000-0002-5956-6258, M. Bluj0000-0003-1229-1442, B.
Boimska0000-0002-4200-1541, M. Górski0000-0003-2146-187X, M.
Kazana0000-0002-7821-3036, M. Szleper0000-0002-1697-004X, P.
Zalewski0000-0003-4429-2888

Institute of Experimental Physics, Faculty of Physics, University of Warsaw,
Warsaw, Poland K. Bunkowski0000-0001-6371-9336, K. Doroba0000-0002-7818-2364,
A. Kalinowski0000-0002-1280-5493, M. Konecki0000-0001-9482-4841, J.
Krolikowski0000-0002-3055-0236, A. Muhammad0000-0002-7535-7149

Warsaw University of Technology, Warsaw, Poland K. Pozniak0000-0001-5426-1423,
W. Zabolotny0000-0002-6833-4846

Laboratório de Instrumentação e Física Experimental de Partículas, Lisboa,
Portugal M. Araujo0000-0002-8152-3756, D. Bastos0000-0002-7032-2481, C. Beirão
Da Cruz E Silva0000-0002-1231-3819, A. Boletti0000-0003-3288-7737, M.
Bozzo0000-0002-1715-0457, P. Faccioli0000-0003-1849-6692, M.
Gallinaro0000-0003-1261-2277, J. Hollar0000-0002-8664-0134, N.
Leonardo0000-0002-9746-4594, T. Niknejad0000-0003-3276-9482, A.
Petrilli0000-0003-0887-1882, M. Pisano0000-0002-0264-7217, J.
Seixas0000-0002-7531-0842, J. Varela0000-0003-2613-3146, J.W. Wulff

Faculty of Physics, University of Belgrade, Belgrade, Serbia P.
Adzic0000-0002-5862-7397, P. Milenovic0000-0001-7132-3550

VINCA Institute of Nuclear Sciences, University of Belgrade, Belgrade, Serbia
M. Dordevic0000-0002-8407-3236, J. Milosevic0000-0001-8486-4604, V. Rekovic

Centro de Investigaciones Energéticas Medioambientales y Tecnológicas
(CIEMAT), Madrid, Spain M. Aguilar-Benitez, J. Alcaraz
Maestre0000-0003-0914-7474, Cristina F. Bedoya0000-0001-8057-9152, M.
Cepeda0000-0002-6076-4083, M. Cerrada0000-0003-0112-1691, N.
Colino0000-0002-3656-0259, B. De La Cruz0000-0001-9057-5614, A. Delgado
Peris0000-0002-8511-7958, D. Fernández Del Val0000-0003-2346-1590, J.P.
Fernández Ramos0000-0002-0122-313X, J. Flix0000-0003-2688-8047, M.C.
Fouz0000-0003-2950-976X, O. Gonzalez Lopez0000-0002-4532-6464, S. Goy
Lopez0000-0001-6508-5090, J.M. Hernandez0000-0001-6436-7547, M.I.
Josa0000-0002-4985-6964, J. León Holgado0000-0002-4156-6460, D.
Moran0000-0002-1941-9333, C. M. Morcillo Perez0000-0001-9634-848X, Á. Navarro
Tobar0000-0003-3606-1780, C. Perez Dengra0000-0003-2821-4249, A. Pérez-Calero
Yzquierdo0000-0003-3036-7965, J. Puerta Pelayo0000-0001-7390-1457, I.
Redondo0000-0003-3737-4121, D.D. Redondo Ferrero0000-0002-3463-0559, L.
Romero, S. Sánchez Navas0000-0001-6129-9059, L. Urda Gómez0000-0002-7865-5010,
J. Vazquez Escobar0000-0002-7533-2283, C. Willmott

Universidad Autónoma de Madrid, Madrid, Spain J.F. de
Trocóniz0000-0002-0798-9806

Universidad de Oviedo, Instituto Universitario de Ciencias y Tecnologías
Espaciales de Asturias (ICTEA), Oviedo, Spain B. Alvarez
Gonzalez0000-0001-7767-4810, J. Cuevas0000-0001-5080-0821, J. Fernandez
Menendez0000-0002-5213-3708, S. Folgueras0000-0001-7191-1125, I. Gonzalez
Caballero0000-0002-8087-3199, J.R. González Fernández0000-0002-4825-8188, E.
Palencia Cortezon0000-0001-8264-0287, C. Ramón Álvarez0000-0003-1175-0002, V.
Rodríguez Bouza0000-0002-7225-7310, A. Soto Rodríguez0000-0002-2993-8663, A.
Trapote0000-0002-4030-2551, C. Vico Villalba0000-0002-1905-1874, P.
Vischia0000-0002-7088-8557

Instituto de Física de Cantabria (IFCA), CSIC-Universidad de Cantabria,
Santander, Spain S. Bhowmik0000-0003-1260-973X, S. Blanco
Fernández0000-0001-7301-0670, J.A. Brochero Cifuentes0000-0003-2093-7856, I.J.
Cabrillo0000-0002-0367-4022, A. Calderon0000-0002-7205-2040, J. Duarte
Campderros0000-0003-0687-5214, M. Fernandez0000-0002-4824-1087, C. Fernandez
Madrazo0000-0001-9748-4336, G. Gomez0000-0002-1077-6553, C. Lasaosa
García0000-0003-2726-7111, C. Martinez Rivero0000-0002-3224-956X, P. Martinez
Ruiz del Arbol0000-0002-7737-5121, F. Matorras0000-0003-4295-5668, P. Matorras
Cuevas0000-0001-7481-7273, E. Navarrete Ramos0000-0002-5180-4020, J. Piedra
Gomez0000-0002-9157-1700, L. Scodellaro0000-0002-4974-8330, I.
Vila0000-0002-6797-7209, J.M. Vizan Garcia0000-0002-6823-8854

University of Colombo, Colombo, Sri Lanka M.K. Jayananda0000-0002-7577-310X,
B. Kailasapathy610000-0003-2424-1303, D.U.J. Sonnadara0000-0001-7862-2537,
D.D.C. Wickramarathna0000-0002-6941-8478

University of Ruhuna, Department of Physics, Matara, Sri Lanka W.G.D.
Dharmaratna620000-0002-6366-837X, K. Liyanage0000-0002-3792-7665, N.
Perera0000-0002-4747-9106, N. Wickramage0000-0001-7760-3537

CERN, European Organization for Nuclear Research, Geneva, Switzerland D.
Abbaneo0000-0001-9416-1742, C. Amendola0000-0002-4359-836X, E.
Auffray0000-0001-8540-1097, G. Auzinger0000-0001-7077-8262, J. Baechler, D.
Barney0000-0002-4927-4921, A. Bermúdez Martínez0000-0001-8822-4727, M.
Bianco0000-0002-8336-3282, B. Bilin0000-0003-1439-7128, A.A. Bin
Anuar0000-0002-2988-9830, A. Bocci0000-0002-6515-5666, E.
Brondolin0000-0001-5420-586X, C. Caillol0000-0002-5642-3040, T.
Camporesi0000-0001-5066-1876, G. Cerminara0000-0002-2897-5753, N.
Chernyavskaya0000-0002-2264-2229, D. d’Enterria0000-0002-5754-4303, A.
Dabrowski0000-0003-2570-9676, A. David0000-0001-5854-7699, A. De
Roeck0000-0002-9228-5271, M.M. Defranchis0000-0001-9573-3714, M.
Deile0000-0001-5085-7270, M. Dobson0009-0007-5021-3230, F. Fallavollita63, L.
Forthomme0000-0002-3302-336X, G. Franzoni0000-0001-9179-4253, W.
Funk0000-0003-0422-6739, S. Giani, D. Gigi, K. Gill0009-0001-9331-5145, F.
Glege0000-0002-4526-2149, L. Gouskos0000-0002-9547-7471, M.
Haranko0000-0002-9376-9235, J. Hegeman0000-0002-2938-2263, V.
Innocente0000-0003-3209-2088, T. James0000-0002-3727-0202, P.
Janot0000-0001-7339-4272, J. Kieseler0000-0003-1644-7678, S.
Laurila0000-0001-7507-8636, P. Lecoq0000-0002-3198-0115, E.
Leutgeb0000-0003-4838-3306, C. Lourenço0000-0003-0885-6711, B.
Maier0000-0001-5270-7540, L. Malgeri0000-0002-0113-7389, M.
Mannelli0000-0003-3748-8946, A.C. Marini0000-0003-2351-0487, F.
Meijers0000-0002-6530-3657, S. Mersi0000-0003-2155-6692, E.
Meschi0000-0003-4502-6151, V. Milosevic0000-0002-1173-0696, F.
Moortgat0000-0001-7199-0046, M. Mulders0000-0001-7432-6634, S. Orfanelli, F.
Pantaleo0000-0003-3266-4357, M. Peruzzi0000-0002-0416-696X, G.
Petrucciani0000-0003-0889-4726, A. Pfeiffer0000-0001-5328-448X, M.
Pierini0000-0003-1939-4268, D. Piparo0009-0006-6958-3111, H.
Qu0000-0002-0250-8655, D. Rabady0000-0001-9239-0605, G. Reales Gutiérrez, M.
Rovere0000-0001-8048-1622, H. Sakulin0000-0003-2181-7258, S.
Scarfi0009-0006-8689-3576, M. Selvaggi0000-0002-5144-9655, A.
Sharma0000-0002-9860-1650, K. Shchelina0000-0003-3742-0693, P.
Silva0000-0002-5725-041X, P. Sphicas640000-0002-5456-5977, A.G. Stahl
Leiton0000-0002-5397-252X, A. Steen0009-0006-4366-3463, S.
Summers0000-0003-4244-2061, D. Treille0009-0005-5952-9843, P.
Tropea0000-0003-1899-2266, A. Tsirou, D. Walter0000-0001-8584-9705, J.
Wanczyk650000-0002-8562-1863, K.A. Wozniak660000-0002-4395-1581, P.
Zehetner0009-0002-0555-4697, P. Zejdl0000-0001-9554-7815, W.D. Zeuner

Paul Scherrer Institut, Villigen, Switzerland T.
Bevilacqua670000-0001-9791-2353, L. Caminada670000-0001-5677-6033, A.
Ebrahimi0000-0003-4472-867X, W. Erdmann0000-0001-9964-249X, R.
Horisberger0000-0002-5594-1321, Q. Ingram0000-0002-9576-055X, H.C.
Kaestli0000-0003-1979-7331, D. Kotlinski0000-0001-5333-4918, C.
Lange0000-0002-3632-3157, M. Missiroli670000-0002-1780-1344, L.
Noehte670000-0001-6125-7203, T. Rohe0009-0005-6188-7754

ETH Zurich - Institute for Particle Physics and Astrophysics (IPA), Zurich,
Switzerland T.K. Aarrestad0000-0002-7671-243X, K.
Androsov650000-0003-2694-6542, M. Backhaus0000-0002-5888-2304, A.
Calandri0000-0001-7774-0099, C. Cazzaniga0000-0003-0001-7657, K.
Datta0000-0002-6674-0015, A. De Cosa0000-0003-2533-2856, G.
Dissertori0000-0002-4549-2569, M. Dittmar, M. Donegà0000-0001-9830-0412, F.
Eble0009-0002-0638-3447, M. Galli0000-0002-9408-4756, K.
Gedia0009-0006-0914-7684, F. Glessgen0000-0001-5309-1960, C.
Grab0000-0002-6182-3380, D. Hits0000-0002-3135-6427, W.
Lustermann0000-0003-4970-2217, A.-M. Lyon0009-0004-1393-6577, R.A.
Manzoni0000-0002-7584-5038, M. Marchegiani0000-0002-0389-8640, L.
Marchese0000-0001-6627-8716, C. Martin Perez0000-0003-1581-6152, A.
Mascellani650000-0001-6362-5356, F. Nessi-Tedaldi0000-0002-4721-7966, F.
Pauss0000-0002-3752-4639, V. Perovic0009-0002-8559-0531, S.
Pigazzini0000-0002-8046-4344, M.G. Ratti0000-0003-1777-7855, M.
Reichmann0000-0002-6220-5496, C. Reissel0000-0001-7080-1119, T.
Reitenspiess0000-0002-2249-0835, B. Ristic0000-0002-8610-1130, F.
Riti0000-0002-1466-9077, D. Ruini, D.A. Sanz Becerra0000-0002-6610-4019, R.
Seidita0000-0002-3533-6191, J. Steggemann650000-0003-4420-5510, D.
Valsecchi0000-0001-8587-8266, R. Wallny0000-0001-8038-1613

Universität Zürich, Zurich, Switzerland C. Amsler680000-0002-7695-501X, P.
Bärtschi0000-0002-8842-6027, C. Botta0000-0002-8072-795X, D. Brzhechko, M.F.
Canelli0000-0001-6361-2117, K. Cormier0000-0001-7873-3579, R. Del Burgo, J.K.
Heikkilä0000-0002-0538-1469, M. Huwiler0000-0002-9806-5907, W.
Jin0009-0009-8976-7702, A. Jofrehei0000-0002-8992-5426, B.
Kilminster0000-0002-6657-0407, S. Leontsinis0000-0002-7561-6091, S.P.
Liechti0000-0002-1192-1628, A. Macchiolo0000-0003-0199-6957, P.
Meiring0009-0001-9480-4039, V.M. Mikuni0000-0002-1579-2421, U.
Molinatti0000-0002-9235-3406, I. Neutelings0009-0002-6473-1403, A.
Reimers0000-0002-9438-2059, P. Robmann, S. Sanchez Cruz0000-0002-9991-195X, K.
Schweiger0000-0002-5846-3919, M. Senger0000-0002-1992-5711, Y.
Takahashi0000-0001-5184-2265

National Central University, Chung-Li, Taiwan C. Adloff69, C.M. Kuo, W. Lin,
P.K. Rout0000-0001-8149-6180, P.C. Tiwari430000-0002-3667-3843, S.S.
Yu0000-0002-6011-8516

National Taiwan University (NTU), Taipei, Taiwan L. Ceard, Y.
Chao0000-0002-5976-318X, K.F. Chen0000-0003-1304-3782, P.s. Chen, Z.g. Chen,
W.-S. Hou0000-0002-4260-5118, T.h. Hsu, Y.w. Kao, R. Khurana, G.
Kole0000-0002-3285-1497, Y.y. Li0000-0003-3598-556X, R.-S.
Lu0000-0001-6828-1695, E. Paganis0000-0002-1950-8993, A. Psallidas, X.f.
Su0009-0009-0207-4904, J. Thomas-Wilsker0000-0003-1293-4153, H.y. Wu, E.
Yazgan0000-0001-5732-7950

High Energy Physics Research Unit, Department of Physics, Faculty of Science,
Chulalongkorn University, Bangkok, Thailand C.
Asawatangtrakuldee0000-0003-2234-7219, N. Srimanobhas0000-0003-3563-2959, V.
Wachirapusitanand0000-0001-8251-5160

Çukurova University, Physics Department, Science and Art Faculty, Adana,
Turkey D. Agyel0000-0002-1797-8844, F. Boran0000-0002-3611-390X, Z.S.
Demiroglu0000-0001-7977-7127, F. Dolek0000-0001-7092-5517, I.
Dumanoglu700000-0002-0039-5503, E. Eskut0000-0001-8328-3314, Y.
Guler710000-0001-7598-5252, E. Gurpinar Guler710000-0002-6172-0285, C.
Isik0000-0002-7977-0811, O. Kara, A. Kayis Topaksu0000-0002-3169-4573, U.
Kiminsu0000-0001-6940-7800, G. Onengut0000-0002-6274-4254, K.
Ozdemir720000-0002-0103-1488, A. Polatoz0000-0001-9516-0821, B.
Tali730000-0002-7447-5602, U.G. Tok0000-0002-3039-021X, S.
Turkcapar0000-0003-2608-0494, E. Uslan0000-0002-2472-0526, I.S.
Zorbakir0000-0002-5962-2221

Middle East Technical University, Physics Department, Ankara, Turkey M.
Yalvac740000-0003-4915-9162

Bogazici University, Istanbul, Turkey B. Akgun0000-0001-8888-3562, I.O.
Atakisi0000-0002-9231-7464, E. Gülmez0000-0002-6353-518X, M.
Kaya750000-0003-2890-4493, O. Kaya760000-0002-8485-3822, S.
Tekten770000-0002-9624-5525

Istanbul Technical University, Istanbul, Turkey A. Cakir0000-0002-8627-7689,
K. Cankocak700000-0002-3829-3481, Y. Komurcu0000-0002-7084-030X, S.
Sen780000-0001-7325-1087

Istanbul University, Istanbul, Turkey O. Aydilek0000-0002-2567-6766, S.
Cerci730000-0002-8702-6152, V. Epshteyn0000-0002-8863-6374, B.
Hacisahinoglu0000-0002-2646-1230, I. Hos790000-0002-7678-1101, B.
Isildak800000-0002-0283-5234, B. Kaynak0000-0003-3857-2496, S.
Ozkorucuklu0000-0001-5153-9266, O. Potok0009-0005-1141-6401, H.
Sert0000-0003-0716-6727, C. Simsek0000-0002-7359-8635, D. Sunar
Cerci730000-0002-5412-4688, C. Zorbilmez0000-0002-5199-061X

Institute for Scintillation Materials of National Academy of Science of
Ukraine, Kharkiv, Ukraine A. Boyaryntsev0000-0001-9252-0430, B.
Grynyov0000-0003-1700-0173

National Science Centre, Kharkiv Institute of Physics and Technology, Kharkiv,
Ukraine L. Levchuk0000-0001-5889-7410

University of Bristol, Bristol, United Kingdom D. Anthony0000-0002-5016-8886,
J.J. Brooke0000-0003-2529-0684, A. Bundock0000-0002-2916-6456, F.
Bury0000-0002-3077-2090, E. Clement0000-0003-3412-4004, D.
Cussans0000-0001-8192-0826, H. Flacher0000-0002-5371-941X, M. Glowacki, J.
Goldstein0000-0003-1591-6014, H.F. Heath0000-0001-6576-9740, L.
Kreczko0000-0003-2341-8330, B. Krikler0000-0001-9712-0030, S.
Paramesvaran0000-0003-4748-8296, S. Seif El Nasr-Storey, V.J.
Smith0000-0003-4543-2547, N. Stylianou810000-0002-0113-6829, K. Walkingshaw
Pass, R. White0000-0001-5793-526X

Rutherford Appleton Laboratory, Didcot, United Kingdom A.H. Ball, K.W.
Bell0000-0002-2294-5860, A. Belyaev820000-0002-1733-4408, C.
Brew0000-0001-6595-8365, R.M. Brown0000-0002-6728-0153, D.J.A.
Cockerill0000-0003-2427-5765, C. Cooke0000-0003-3730-4895, K.V. Ellis, K.
Harder0000-0002-2965-6973, S. Harper0000-0001-5637-2653, M.-L.
Holmberg830000-0002-9473-5985, Sh. Jain0000-0003-1770-5309, J.
Linacre0000-0001-7555-652X, K. Manolopoulos, D.M. Newbold0000-0002-9015-9634,
E. Olaiya, D. Petyt0000-0002-2369-4469, T. Reis0000-0003-3703-6624, G.
Salvi0000-0002-2787-1063, T. Schuh, C.H. Shepherd-
Themistocleous0000-0003-0551-6949, I.R. Tomalin0000-0003-2419-4439, T.
Williams0000-0002-8724-4678

Imperial College, London, United Kingdom R. Bainbridge0000-0001-9157-4832, P.
Bloch0000-0001-6716-979X, C.E. Brown0000-0002-7766-6615, O. Buchmuller, V.
Cacchio, C.A. Carrillo Montoya0000-0002-6245-6535, G.S.
Chahal840000-0003-0320-4407, D. Colling0000-0001-9959-4977, J.S. Dancu, P.
Dauncey0000-0001-6839-9466, G. Davies0000-0001-8668-5001, J. Davies, M. Della
Negra0000-0001-6497-8081, S. Fayer, G. Fedi0000-0001-9101-2573, G.
Hall0000-0002-6299-8385, M.H. Hassanshahi0000-0001-6634-4517, A. Howard, G.
Iles0000-0002-1219-5859, M. Knight0009-0008-1167-4816, J.
Langford0000-0002-3931-4379, L. Lyons0000-0001-7945-9188, A.-M.
Magnan0000-0002-4266-1646, S. Malik, A. Martelli0000-0003-3530-2255, M.
Mieskolainen0000-0001-8893-7401, J. Nash850000-0003-0607-6519, M.
Pesaresi0000-0002-9759-1083, B.C. Radburn-Smith0000-0003-1488-9675, A.
Richards, A. Rose0000-0002-9773-550X, C. Seez0000-0002-1637-5494, R.
Shukla0000-0001-5670-5497, A. Tapper0000-0003-4543-864X, K.
Uchida0000-0003-0742-2276, G.P. Uttley0009-0002-6248-6467, L.H. Vage, T.
Virdee330000-0001-7429-2198, M. Vojinovic0000-0001-8665-2808, N.
Wardle0000-0003-1344-3356, D. Winterbottom0000-0003-4582-150X

Brunel University, Uxbridge, United Kingdom K. Coldham, J.E.
Cole0000-0001-5638-7599, A. Khan, P. Kyberd0000-0002-7353-7090, I.D.
Reid0000-0002-9235-779X

Baylor University, Waco, Texas, USA S. Abdullin0000-0003-4885-6935, A.
Brinkerhoff0000-0002-4819-7995, B. Caraway0000-0002-6088-2020, J.
Dittmann0000-0002-1911-3158, K. Hatakeyama0000-0002-6012-2451, J.
Hiltbrand0000-0003-1691-5937, A.R. Kanuganti0000-0002-0789-1200, B.
McMaster0000-0002-4494-0446, M. Saunders0000-0003-1572-9075, S.
Sawant0000-0002-1981-7753, C. Sutantawibul0000-0003-0600-0151, M.
Toms860000-0002-7703-3973, J. Wilson0000-0002-5672-7394

Catholic University of America, Washington, DC, USA R.
Bartek0000-0002-1686-2882, A. Dominguez0000-0002-7420-5493, C. Huerta
Escamilla, A.E. Simsek0000-0002-9074-2256, R. Uniyal0000-0001-7345-6293, A.M.
Vargas Hernandez0000-0002-8911-7197

The University of Alabama, Tuscaloosa, Alabama, USA R.
Chudasama0009-0007-8848-6146, S.I. Cooper0000-0002-4618-0313, S.V.
Gleyzer0000-0002-6222-8102, C.U. Perez0000-0002-6861-2674, P.
Rumerio870000-0002-1702-5541, E. Usai0000-0001-9323-2107, C.
West0000-0003-4460-2241, R. Yi0000-0001-5818-1682

Boston University, Boston, Massachusetts, USA A. Akpinar0000-0001-7510-6617,
A. Albert0000-0003-2369-9507, D. Arcaro0000-0001-9457-8302, C.
Cosby0000-0003-0352-6561, Z. Demiragli0000-0001-8521-737X, C.
Erice0000-0002-6469-3200, E. Fontanesi0000-0002-0662-5904, D.
Gastler0009-0000-7307-6311, S. Jeon0000-0003-1208-6940, J.
Rohlf0000-0001-6423-9799, K. Salyer0000-0002-6957-1077, D.
Sperka0000-0002-4624-2019, D. Spitzbart0000-0003-2025-2742, I.
Suarez0000-0002-5374-6995, A. Tsatsos0000-0001-8310-8911, S.
Yuan0000-0002-2029-024X

Brown University, Providence, Rhode Island, USA G. Benelli0000-0003-4461-8905,
X. Coubez28, D. Cutts0000-0003-1041-7099, M. Hadley0000-0002-7068-4327, U.
Heintz0000-0002-7590-3058, J.M. Hogan880000-0002-8604-3452, T.
Kwon0000-0001-9594-6277, G. Landsberg0000-0002-4184-9380, K.T.
Lau0000-0003-1371-8575, D. Li0000-0003-0890-8948, J. Luo0000-0002-4108-8681,
S. Mondal0000-0003-0153-7590, M.
Narain${}^{\textrm{\textdagger}}$0000-0002-7857-7403, N.
Pervan0000-0002-8153-8464, S. Sagir890000-0002-2614-5860, F.
Simpson0000-0001-8944-9629, M. Stamenkovic0000-0003-2251-0610, W.Y. Wong, X.
Yan0000-0002-6426-0560, W. Zhang

University of California, Davis, Davis, California, USA S.
Abbott0000-0002-7791-894X, J. Bonilla0000-0002-6982-6121, C.
Brainerd0000-0002-9552-1006, R. Breedon0000-0001-5314-7581, M. Calderon De La
Barca Sanchez0000-0001-9835-4349, M. Chertok0000-0002-2729-6273, M.
Citron0000-0001-6250-8465, J. Conway0000-0003-2719-5779, P.T.
Cox0000-0003-1218-2828, R. Erbacher0000-0001-7170-8944, G.
Haza0009-0001-1326-3956, F. Jensen0000-0003-3769-9081, O.
Kukral0009-0007-3858-6659, G. Mocellin0000-0002-1531-3478, M.
Mulhearn0000-0003-1145-6436, D. Pellett0009-0000-0389-8571, W.
Wei0000-0003-4221-1802, Y. Yao0000-0002-5990-4245, F. Zhang0000-0002-6158-2468

University of California, Los Angeles, California, USA M.
Bachtis0000-0003-3110-0701, R. Cousins0000-0002-5963-0467, A.
Datta0000-0003-2695-7719, J. Hauser0000-0002-9781-4873, M.
Ignatenko0000-0001-8258-5863, M.A. Iqbal0000-0001-8664-1949, T.
Lam0000-0002-0862-7348, E. Manca0000-0001-8946-655X, W.A.
Nash0009-0004-3633-8967, D. Saltzberg0000-0003-0658-9146, B.
Stone0000-0002-9397-5231, V. Valuev0000-0002-0783-6703

University of California, Riverside, Riverside, California, USA R.
Clare0000-0003-3293-5305, M. Gordon, G. Hanson0000-0002-7273-4009, W.
Si0000-0002-5879-6326, S.
Wimpenny${}^{\textrm{\textdagger}}$0000-0003-0505-4908

University of California, San Diego, La Jolla, California, USA J.G.
Branson0009-0009-5683-4614, S. Cittolin0000-0002-0922-9587, S.
Cooperstein0000-0003-0262-3132, D. Diaz0000-0001-6834-1176, J.
Duarte0000-0002-5076-7096, R. Gerosa0000-0001-8359-3734, L.
Giannini0000-0002-5621-7706, J. Guiang0000-0002-2155-8260, R.
Kansal0000-0003-2445-1060, V. Krutelyov0000-0002-1386-0232, R.
Lee0009-0000-4634-0797, J. Letts0000-0002-0156-1251, M.
Masciovecchio0000-0002-8200-9425, F. Mokhtar0000-0003-2533-3402, M.
Pieri0000-0003-3303-6301, M. Quinnan0000-0003-2902-5597, B.V. Sathia
Narayanan0000-0003-2076-5126, V. Sharma0000-0003-1736-8795, M.
Tadel0000-0001-8800-0045, E. Vourliotis0000-0002-2270-0492, F.
Würthwein0000-0001-5912-6124, Y. Xiang0000-0003-4112-7457, A.
Yagil0000-0002-6108-4004

University of California, Santa Barbara - Department of Physics, Santa
Barbara, California, USA A. Barzdukas0000-0002-0518-3286, L.
Brennan0000-0003-0636-1846, C. Campagnari0000-0002-8978-8177, G.
Collura0000-0002-4160-1844, A. Dorsett0000-0001-5349-3011, J.
Incandela0000-0001-9850-2030, M. Kilpatrick0000-0002-2602-0566, J.
Kim0000-0002-2072-6082, A.J. Li0000-0002-3895-717X, P.
Masterson0000-0002-6890-7624, H. Mei0000-0002-9838-8327, M.
Oshiro0000-0002-2200-7516, J. Richman0000-0002-5189-146X, U.
Sarica0000-0002-1557-4424, R. Schmitz0000-0003-2328-677X, F.
Setti0000-0001-9800-7822, J. Sheplock0000-0002-8752-1946, D.
Stuart0000-0002-4965-0747, S. Wang0000-0001-7887-1728

California Institute of Technology, Pasadena, California, USA A.
Bornheim0000-0002-0128-0871, O. Cerri, A. Latorre, J.M.
Lawhorn0000-0002-8597-9259, J. Mao0009-0002-8988-9987, H.B.
Newman0000-0003-0964-1480, T. Q. Nguyen0000-0003-3954-5131, M.
Spiropulu0000-0001-8172-7081, J.R. Vlimant0000-0002-9705-101X, C.
Wang0000-0002-0117-7196, S. Xie0000-0003-2509-5731, R.Y.
Zhu0000-0003-3091-7461

Carnegie Mellon University, Pittsburgh, Pennsylvania, USA J.
Alison0000-0003-0843-1641, S. An0000-0002-9740-1622, M.B.
Andrews0000-0001-5537-4518, P. Bryant0000-0001-8145-6322, V.
Dutta0000-0001-5958-829X, T. Ferguson0000-0001-5822-3731, A.
Harilal0000-0001-9625-1987, C. Liu0000-0002-3100-7294, T.
Mudholkar0000-0002-9352-8140, S. Murthy0000-0002-1277-9168, M.
Paulini0000-0002-6714-5787, A. Roberts0000-0002-5139-0550, A.
Sanchez0000-0002-5431-6989, W. Terrill0000-0002-2078-8419

University of Colorado Boulder, Boulder, Colorado, USA J.P.
Cumalat0000-0002-6032-5857, W.T. Ford0000-0001-8703-6943, A.
Hassani0009-0008-4322-7682, G. Karathanasis0000-0001-5115-5828, E. MacDonald,
N. Manganelli0000-0002-3398-4531, F. Marini0000-0002-2374-6433, A.
Perloff0000-0001-5230-0396, C. Savard0009-0000-7507-0570, N.
Schonbeck0009-0008-3430-7269, K. Stenson0000-0003-4888-205X, K.A.
Ulmer0000-0001-6875-9177, S.R. Wagner0000-0002-9269-5772, N.
Zipper0000-0002-4805-8020

Cornell University, Ithaca, New York, USA J. Alexander0000-0002-2046-342X, S.
Bright-Thonney0000-0003-1889-7824, X. Chen0000-0002-8157-1328, D.J.
Cranshaw0000-0002-7498-2129, J. Fan0009-0003-3728-9960, X.
Fan0000-0003-2067-0127, D. Gadkari0000-0002-6625-8085, S.
Hogan0000-0003-3657-2281, J. Monroy0000-0002-7394-4710, J.R.
Patterson0000-0002-3815-3649, J. Reichert0000-0003-2110-8021, M.
Reid0000-0001-7706-1416, A. Ryd0000-0001-5849-1912, J.
Thom0000-0002-4870-8468, P. Wittich0000-0002-7401-2181, R.
Zou0000-0002-0542-1264

Fermi National Accelerator Laboratory, Batavia, Illinois, USA M.
Albrow0000-0001-7329-4925, M. Alyari0000-0001-9268-3360, O.
Amram0000-0002-3765-3123, G. Apollinari0000-0002-5212-5396, A.
Apresyan0000-0002-6186-0130, L.A.T. Bauerdick0000-0002-7170-9012, D.
Berry0000-0002-5383-8320, J. Berryhill0000-0002-8124-3033, P.C.
Bhat0000-0003-3370-9246, K. Burkett0000-0002-2284-4744, J.N.
Butler0000-0002-0745-8618, A. Canepa0000-0003-4045-3998, G.B.
Cerati0000-0003-3548-0262, H.W.K. Cheung0000-0001-6389-9357, F.
Chlebana0000-0002-8762-8559, G. Cummings0000-0002-8045-7806, J.
Dickinson0000-0001-5450-5328, I. Dutta0000-0003-0953-4503, V.D.
Elvira0000-0003-4446-4395, Y. Feng0000-0003-2812-338X, J.
Freeman0000-0002-3415-5671, A. Gandrakota0000-0003-4860-3233, Z.
Gecse0009-0009-6561-3418, L. Gray0000-0002-6408-4288, D. Green, A.
Grummer0000-0003-2752-1183, S. Grünendahl0000-0002-4857-0294, D.
Guerrero0000-0001-5552-5400, O. Gutsche0000-0002-8015-9622, R.M.
Harris0000-0003-1461-3425, R. Heller0000-0002-7368-6723, T.C.
Herwig0000-0002-4280-6382, J. Hirschauer0000-0002-8244-0805, L.
Horyn0000-0002-9512-4932, B. Jayatilaka0000-0001-7912-5612, S.
Jindariani0009-0000-7046-6533, M. Johnson0000-0001-7757-8458, U.
Joshi0000-0001-8375-0760, T. Klijnsma0000-0003-1675-6040, B.
Klima0000-0002-3691-7625, K.H.M. Kwok0000-0002-8693-6146, S.
Lammel0000-0003-0027-635X, D. Lincoln0000-0002-0599-7407, R.
Lipton0000-0002-6665-7289, T. Liu0009-0007-6522-5605, C.
Madrid0000-0003-3301-2246, K. Maeshima0009-0000-2822-897X, C.
Mantilla0000-0002-0177-5903, D. Mason0000-0002-0074-5390, P.
McBride0000-0001-6159-7750, P. Merkel0000-0003-4727-5442, S.
Mrenna0000-0001-8731-160X, S. Nahn0000-0002-8949-0178, J.
Ngadiuba0000-0002-0055-2935, D. Noonan0000-0002-3932-3769, V.
Papadimitriou0000-0002-0690-7186, N. Pastika0009-0006-0993-6245, K.
Pedro0000-0003-2260-9151, C. Pena900000-0002-4500-7930, F.
Ravera0000-0003-3632-0287, A. Reinsvold Hall910000-0003-1653-8553, L.
Ristori0000-0003-1950-2492, E. Sexton-Kennedy0000-0001-9171-1980, N.
Smith0000-0002-0324-3054, A. Soha0000-0002-5968-1192, L.
Spiegel0000-0001-9672-1328, S. Stoynev0000-0003-4563-7702, J.
Strait0000-0002-7233-8348, L. Taylor0000-0002-6584-2538, S.
Tkaczyk0000-0001-7642-5185, N.V. Tran0000-0002-8440-6854, L.
Uplegger0000-0002-9202-803X, E.W. Vaandering0000-0003-3207-6950, I.
Zoi0000-0002-5738-9446

University of Florida, Gainesville, Florida, USA C. Aruta0000-0001-9524-3264,
P. Avery0000-0003-0609-627X, D. Bourilkov0000-0003-0260-4935, L.
Cadamuro0000-0001-8789-610X, P. Chang0000-0002-2095-6320, V.
Cherepanov0000-0002-6748-4850, R.D. Field, E. Koenig0000-0002-0884-7922, M.
Kolosova0000-0002-5838-2158, J. Konigsberg0000-0001-6850-8765, A.
Korytov0000-0001-9239-3398, K.H. Lo, K. Matchev0000-0003-4182-9096, N.
Menendez0000-0002-3295-3194, G. Mitselmakher0000-0001-5745-3658, A.
Muthirakalayil Madhu0000-0003-1209-3032, N. Rawal0000-0002-7734-3170, D.
Rosenzweig0000-0002-3687-5189, S. Rosenzweig0000-0002-5613-1507, K.
Shi0000-0002-2475-0055, J. Wang0000-0003-3879-4873

Florida State University, Tallahassee, Florida, USA T.
Adams0000-0001-8049-5143, A. Al Kadhim0000-0003-3490-8407, A.
Askew0000-0002-7172-1396, N. Bower0000-0001-8775-0696, R.
Habibullah0000-0002-3161-8300, V. Hagopian0000-0002-3791-1989, R.
Hashmi0000-0002-5439-8224, R.S. Kim0000-0002-8645-186X, S.
Kim0000-0003-2381-5117, T. Kolberg0000-0002-0211-6109, G. Martinez, H.
Prosper0000-0002-4077-2713, P.R. Prova, O. Viazlo0000-0002-2957-0301, M.
Wulansatiti0000-0001-6794-3079, R. Yohay0000-0002-0124-9065, J. Zhang

Florida Institute of Technology, Melbourne, Florida, USA B. Alsufyani, M.M.
Baarmand0000-0002-9792-8619, S. Butalla0000-0003-3423-9581, T.
Elkafrawy200000-0001-9930-6445, M. Hohlmann0000-0003-4578-9319, R. Kumar
Verma0000-0002-8264-156X, M. Rahmani

University of Illinois Chicago, Chicago, USA, Chicago, USA M.R.
Adams0000-0001-8493-3737, C. Bennett, R. Cavanaugh0000-0001-7169-3420, S.
Dittmer0000-0002-5359-9614, R. Escobar Franco0000-0003-2090-5010, O.
Evdokimov0000-0002-1250-8931, C.E. Gerber0000-0002-8116-9021, D.J.
Hofman0000-0002-2449-3845, J.h. Lee0000-0002-5574-4192, D. S.
Lemos0000-0003-1982-8978, A.H. Merrit0000-0003-3922-6464, C.
Mills0000-0001-8035-4818, S. Nanda0000-0003-0550-4083, G.
Oh0000-0003-0744-1063, B. Ozek0009-0000-2570-1100, D.
Pilipovic0000-0002-4210-2780, T. Roy0000-0001-7299-7653, S.
Rudrabhatla0000-0002-7366-4225, M.B. Tonjes0000-0002-2617-9315, N.
Varelas0000-0002-9397-5514, X. Wang0000-0003-2792-8493, Z.
Ye0000-0001-6091-6772, J. Yoo0000-0002-3826-1332

The University of Iowa, Iowa City, Iowa, USA M. Alhusseini0000-0002-9239-470X,
D. Blend, K. Dilsiz920000-0003-0138-3368, L. Emediato0000-0002-3021-5032, G.
Karaman0000-0001-8739-9648, O.K. Köseyan0000-0001-9040-3468, J.-P. Merlo, A.
Mestvirishvili930000-0002-8591-5247, J. Nachtman0000-0003-3951-3420, O. Neogi,
H. Ogul940000-0002-5121-2893, Y. Onel0000-0002-8141-7769, A.
Penzo0000-0003-3436-047X, C. Snyder, E. Tiras950000-0002-5628-7464

Johns Hopkins University, Baltimore, Maryland, USA B.
Blumenfeld0000-0003-1150-1735, L. Corcodilos0000-0001-6751-3108, J.
Davis0000-0001-6488-6195, A.V. Gritsan0000-0002-3545-7970, L.
Kang0000-0002-0941-4512, S. Kyriacou0000-0002-9254-4368, P.
Maksimovic0000-0002-2358-2168, M. Roguljic0000-0001-5311-3007, J.
Roskes0000-0001-8761-0490, S. Sekhar0000-0002-8307-7518, M.
Swartz0000-0002-0286-5070, T.Á. Vámi0000-0002-0959-9211

The University of Kansas, Lawrence, Kansas, USA A. Abreu0000-0002-9000-2215,
L.F. Alcerro Alcerro0000-0001-5770-5077, J. Anguiano0000-0002-7349-350X, P.
Baringer0000-0002-3691-8388, A. Bean0000-0001-5967-8674, Z.
Flowers0000-0001-8314-2052, D. Grove0000-0002-0740-2462, J.
King0000-0001-9652-9854, G. Krintiras0000-0002-0380-7577, M.
Lazarovits0000-0002-5565-3119, C. Le Mahieu0000-0001-5924-1130, C. Lindsey, J.
Marquez0000-0003-3887-4048, N. Minafra0000-0003-4002-1888, M.
Murray0000-0001-7219-4818, M. Nickel0000-0003-0419-1329, M.
Pitt0000-0003-2461-5985, S. Popescu960000-0002-0345-2171, C.
Rogan0000-0002-4166-4503, C. Royon0000-0002-7672-9709, R.
Salvatico0000-0002-2751-0567, S. Sanders0000-0002-9491-6022, C.
Smith0000-0003-0505-0528, Q. Wang0000-0003-3804-3244, G.
Wilson0000-0003-0917-4763

Kansas State University, Manhattan, Kansas, USA B. Allmond0000-0002-5593-7736,
A. Ivanov0000-0002-9270-5643, K. Kaadze0000-0003-0571-163X, A.
Kalogeropoulos0000-0003-3444-0314, D. Kim, Y. Maravin0000-0002-9449-0666, K.
Nam, J. Natoli0000-0001-6675-3564, D. Roy0000-0002-8659-7762, G.
Sorrentino0000-0002-2253-819X

Lawrence Livermore National Laboratory, Livermore, California, USA F.
Rebassoo0000-0001-8934-9329, D. Wright0000-0002-3586-3354

University of Maryland, College Park, Maryland, USA E.
Adams0000-0003-2809-2683, A. Baden0000-0002-6159-3861, O. Baron, A.
Belloni0000-0002-1727-656X, A. Bethani0000-0002-8150-7043, Y.M.
Chen0000-0002-5795-4783, S.C. Eno0000-0003-4282-2515, N.J.
Hadley0000-0002-1209-6471, S. Jabeen0000-0002-0155-7383, R.G.
Kellogg0000-0001-9235-521X, T. Koeth0000-0002-0082-0514, B. Kronheim, Y.
Lai0000-0002-7795-8693, S. Lascio0000-0001-8579-5874, A.C.
Mignerey0000-0001-5164-6969, S. Nabili0000-0002-6893-1018, C.
Palmer0000-0002-5801-5737, C. Papageorgakis0000-0003-4548-0346, M.M. Paranjpe,
L. Wang0000-0003-3443-0626, K. Wong0000-0002-9698-1354

Massachusetts Institute of Technology, Cambridge, Massachusetts, USA J.
Bendavid0000-0002-7907-1789, W. Busza0000-0002-3831-9071, I.A.
Cali0000-0002-2822-3375, Y. Chen0000-0003-2582-6469, M.
D’Alfonso0000-0002-7409-7904, J. Eysermans0000-0001-6483-7123, C.
Freer0000-0002-7967-4635, G. Gomez-Ceballos0000-0003-1683-9460, M. Goncharov,
P. Harris, D. Hoang, D. Kovalskyi0000-0002-6923-293X, J.
Krupa0000-0003-0785-7552, L. Lavezzo0000-0002-1364-9920, Y.-J.
Lee0000-0003-2593-7767, K. Long0000-0003-0664-1653, C.
Mironov0000-0002-8599-2437, C. Paus0000-0002-6047-4211, D.
Rankin0000-0001-8411-9620, C. Roland0000-0002-7312-5854, G.
Roland0000-0001-8983-2169, S. Rothman0000-0002-1377-9119, Z.
Shi0000-0001-5498-8825, G.S.F. Stephans0000-0003-3106-4894, J. Wang, Z.
Wang0000-0002-3074-3767, B. Wyslouch0000-0003-3681-0649, T. J.
Yang0000-0003-4317-4660

University of Minnesota, Minneapolis, Minnesota, USA B.
Crossman0000-0002-2700-5085, B.M. Joshi0000-0002-4723-0968, C.
Kapsiak0009-0008-7743-5316, M. Krohn0000-0002-1711-2506, D.
Mahon0000-0002-2640-5941, J. Mans0000-0003-2840-1087, B.
Marzocchi0000-0001-6687-6214, S. Pandey0000-0003-0440-6019, M.
Revering0000-0001-5051-0293, R. Rusack0000-0002-7633-749X, R.
Saradhy0000-0001-8720-293X, N. Schroeder0000-0002-8336-6141, N.
Strobbe0000-0001-8835-8282, M.A. Wadud0000-0002-0653-0761

University of Mississippi, Oxford, Mississippi, USA L.M.
Cremaldi0000-0001-5550-7827

University of Nebraska-Lincoln, Lincoln, Nebraska, USA K.
Bloom0000-0002-4272-8900, M. Bryson, D.R. Claes0000-0003-4198-8919, C.
Fangmeier0000-0002-5998-8047, F. Golf0000-0003-3567-9351, J.
Hossain0000-0001-5144-7919, C. Joo0000-0002-5661-4330, I.
Kravchenko0000-0003-0068-0395, I. Reed0000-0002-1823-8856, J.E.
Siado0000-0002-9757-470X, G.R. Snow${}^{\textrm{\textdagger}}$, W.
Tabb0000-0002-9542-4847, A. Vagnerini0000-0001-8730-5031, A.
Wightman0000-0001-6651-5320, F. Yan0000-0002-4042-0785, D.
Yu0000-0001-5921-5231, A.G. Zecchinelli0000-0001-8986-278X

State University of New York at Buffalo, Buffalo, New York, USA G.
Agarwal0000-0002-2593-5297, H. Bandyopadhyay0000-0001-9726-4915, L.
Hay0000-0002-7086-7641, I. Iashvili0000-0003-1948-5901, A.
Kharchilava0000-0002-3913-0326, C. McLean0000-0002-7450-4805, M.
Morris0000-0002-2830-6488, D. Nguyen0000-0002-5185-8504, J.
Pekkanen0000-0002-6681-7668, S. Rappoccio0000-0002-5449-2560, H. Rejeb Sfar,
A. Williams0000-0003-4055-6532

Northeastern University, Boston, Massachusetts, USA G.
Alverson0000-0001-6651-1178, E. Barberis0000-0002-6417-5913, Y.
Haddad0000-0003-4916-7752, Y. Han0000-0002-3510-6505, A.
Krishna0000-0002-4319-818X, J. Li0000-0001-5245-2074, M.
Lu0000-0002-6999-3931, G. Madigan0000-0001-8796-5865, D.M.
Morse0000-0003-3163-2169, V. Nguyen0000-0003-1278-9208, T.
Orimoto0000-0002-8388-3341, A. Parker0000-0002-9421-3335, L.
Skinnari0000-0002-2019-6755, A. Tishelman-Charny0000-0002-7332-5098, B.
Wang0000-0003-0796-2475, D. Wood0000-0002-6477-801X

Northwestern University, Evanston, Illinois, USA S.
Bhattacharya0000-0002-0526-6161, J. Bueghly, Z. Chen0000-0003-4521-6086, K.A.
Hahn0000-0001-7892-1676, Y. Liu0000-0002-5588-1760, Y.
Miao0000-0002-2023-2082, D.G. Monk0000-0002-8377-1999, M.H.
Schmitt0000-0003-0814-3578, A. Taliercio0000-0002-5119-6280, M. Velasco

University of Notre Dame, Notre Dame, Indiana, USA R. Band0000-0003-4873-0523,
R. Bucci, S. Castells0000-0003-2618-3856, M. Cremonesi, A.
Das0000-0001-9115-9698, R. Goldouzian0000-0002-0295-249X, M.
Hildreth0000-0002-4454-3934, K.W. Ho0000-0003-2229-7223, K. Hurtado
Anampa0000-0002-9779-3566, C. Jessop0000-0002-6885-3611, K.
Lannon0000-0002-9706-0098, J. Lawrence0000-0001-6326-7210, N.
Loukas0000-0003-0049-6918, L. Lutton0000-0002-3212-4505, J. Mariano, N.
Marinelli, I. Mcalister, T. McCauley0000-0001-6589-8286, C.
Mcgrady0000-0002-8821-2045, K. Mohrman0009-0007-2940-0496, C.
Moore0000-0002-8140-4183, Y. Musienko160009-0006-3545-1938, H.
Nelson0000-0001-5592-0785, M. Osherson0000-0002-9760-9976, R.
Ruchti0000-0002-3151-1386, A. Townsend0000-0002-3696-689X, M.
Wayne0000-0001-8204-6157, H. Yockey, M. Zarucki0000-0003-1510-5772, L.
Zygala0000-0001-9665-7282

The Ohio State University, Columbus, Ohio, USA A. Basnet0000-0001-8460-0019,
B. Bylsma, M. Carrigan0000-0003-0538-5854, L.S. Durkin0000-0002-0477-1051, C.
Hill0000-0003-0059-0779, M. Joyce0000-0003-1112-5880, A.
Lesauvage0000-0003-3437-7845, M. Nunez Ornelas0000-0003-2663-7379, K. Wei,
B.L. Winer0000-0001-9980-4698, B. R. Yates0000-0001-7366-1318

Princeton University, Princeton, New Jersey, USA F.M.
Addesa0000-0003-0484-5804, H. Bouchamaoui0000-0002-9776-1935, P.
Das0000-0002-9770-1377, G. Dezoort0000-0002-5890-0445, P.
Elmer0000-0001-6830-3356, A. Frankenthal0000-0002-2583-5982, B.
Greenberg0000-0002-4922-1934, N. Haubrich0000-0002-7625-8169, S.
Higginbotham0000-0002-4436-5461, G. Kopp0000-0001-8160-0208, S.
Kwan0000-0002-5308-7707, D. Lange0000-0002-9086-5184, A.
Loeliger0000-0002-5017-1487, D. Marlow0000-0002-6395-1079, I.
Ojalvo0000-0003-1455-6272, J. Olsen0000-0002-9361-5762, D.
Stickland0000-0003-4702-8820, C. Tully0000-0001-6771-2174

University of Puerto Rico, Mayaguez, Puerto Rico, USA S.
Malik0000-0002-6356-2655

Purdue University, West Lafayette, Indiana, USA A.S.
Bakshi0000-0002-2857-6883, V.E. Barnes0000-0001-6939-3445, S.
Chandra0009-0000-7412-4071, R. Chawla0000-0003-4802-6819, S.
Das0000-0001-6701-9265, A. Gu0000-0002-6230-1138, L. Gutay, M.
Jones0000-0002-9951-4583, A.W. Jung0000-0003-3068-3212, D.
Kondratyev0000-0002-7874-2480, A.M. Koshy, M. Liu0000-0001-9012-395X, G.
Negro0000-0002-1418-2154, N. Neumeister0000-0003-2356-1700, G.
Paspalaki0000-0001-6815-1065, S. Piperov0000-0002-9266-7819, A.
Purohit0000-0003-0881-612X, V. Scheurer, J.F. Schulte0000-0003-4421-680X, M.
Stojanovic0000-0002-1542-0855, J. Thieman0000-0001-7684-6588, A. K.
Virdi0000-0002-0866-8932, F. Wang0000-0002-8313-0809, W.
Xie0000-0003-1430-9191

Purdue University Northwest, Hammond, Indiana, USA J.
Dolen0000-0003-1141-3823, N. Parashar0009-0009-1717-0413, A.
Pathak0000-0001-9861-2942

Rice University, Houston, Texas, USA D. Acosta0000-0001-5367-1738, A.
Baty0000-0001-5310-3466, T. Carnahan0000-0001-7492-3201, S.
Dildick0000-0003-0554-4755, K.M. Ecklund0000-0002-6976-4637, P.J. Fernández
Manteca0000-0003-2566-7496, S. Freed, P. Gardner, F.J.M.
Geurts0000-0003-2856-9090, A. Kumar0000-0002-5180-6595, W.
Li0000-0003-4136-3409, O. Miguel Colin0000-0001-6612-432X, B.P.
Padley0000-0002-3572-5701, R. Redjimi, J. Rotter0009-0009-4040-7407, E.
Yigitbasi0000-0002-9595-2623, Y. Zhang0000-0002-6812-761X

University of Rochester, Rochester, New York, USA A. Bodek0000-0003-0409-0341,
P. de Barbaro0000-0002-5508-1827, R. Demina0000-0002-7852-167X, J.L.
Dulemba0000-0002-9842-7015, C. Fallon, A. Garcia-Bellido0000-0002-1407-1972,
O. Hindrichs0000-0001-7640-5264, A. Khukhunaishvili0000-0002-3834-1316, P.
Parygin860000-0001-6743-3781, E. Popova860000-0001-7556-8969, R.
Taus0000-0002-5168-2932, G.P. Van Onsem0000-0002-1664-2337

The Rockefeller University, New York, New York, USA K.
Goulianos0000-0002-6230-9535

Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA B.
Chiarito, J.P. Chou0000-0001-6315-905X, Y. Gershtein0000-0002-4871-5449, E.
Halkiadakis0000-0002-3584-7856, A. Hart0000-0003-2349-6582, M.
Heindl0000-0002-2831-463X, D. Jaroslawski0000-0003-2497-1242, O.
Karacheban310000-0002-2785-3762, I. Laflotte0000-0002-7366-8090, A.
Lath0000-0003-0228-9760, R. Montalvo, K. Nash, H. Routray0000-0002-9694-4625,
S. Salur0000-0002-4995-9285, S. Schnetzer, S. Somalwar0000-0002-8856-7401, R.
Stone0000-0001-6229-695X, S.A. Thayil0000-0002-1469-0335, S. Thomas, J.
Vora0000-0001-9325-2175, H. Wang0000-0002-3027-0752

University of Tennessee, Knoxville, Tennessee, USA H. Acharya, D.
Ally0000-0001-6304-5861, A.G. Delannoy0000-0003-1252-6213, S.
Fiorendi0000-0003-3273-9419, T. Holmes0000-0002-3959-5174, N.
Karunarathna0000-0002-3412-0508, L. Lee0000-0002-5590-335X, E.
Nibigira0000-0001-5821-291X, S. Spanier0000-0002-7049-4646

Texas A&M University, College Station, Texas, USA D. Aebi0000-0001-7124-6911,
M. Ahmad0000-0001-9933-995X, O. Bouhali970000-0001-7139-7322, M.
Dalchenko0000-0002-0137-136X, R. Eusebi0000-0003-3322-6287, J.
Gilmore0000-0001-9911-0143, T. Huang0000-0002-0793-5664, T.
Kamon980000-0001-5565-7868, H. Kim0000-0003-4986-1728, S.
Luo0000-0003-3122-4245, S. Malhotra, R. Mueller0000-0002-6723-6689, D.
Overton0009-0009-0648-8151, D. Rathjens0000-0002-8420-1488, A.
Safonov0000-0001-9497-5471

Texas Tech University, Lubbock, Texas, USA N. Akchurin0000-0002-6127-4350, J.
Damgov0000-0003-3863-2567, V. Hegde0000-0003-4952-2873, A.
Hussain0000-0001-6216-9002, Y. Kazhykarim, K. Lamichhane0000-0003-0152-7683,
S.W. Lee0000-0002-3388-8339, A. Mankel0000-0002-2124-6312, T. Mengke, S.
Muthumuni0000-0003-0432-6895, T. Peltola0000-0002-4732-4008, I.
Volobouev0000-0002-2087-6128, A. Whitbeck0000-0003-4224-5164

Vanderbilt University, Nashville, Tennessee, USA E. Appelt0000-0003-3389-4584,
S. Greene, A. Gurrola0000-0002-2793-4052, W. Johns0000-0001-5291-8903, R.
Kunnawalkam Elayavalli0000-0002-9202-1516, A. Melo0000-0003-3473-8858, F.
Romeo0000-0002-1297-6065, P. Sheldon0000-0003-1550-5223, S.
Tuo0000-0001-6142-0429, J. Velkovska0000-0003-1423-5241, J.
Viinikainen0000-0003-2530-4265

University of Virginia, Charlottesville, Virginia, USA B.
Cardwell0000-0001-5553-0891, B. Cox0000-0003-3752-4759, J.
Hakala0000-0001-9586-3316, R. Hirosky0000-0003-0304-6330, A.
Ledovskoy0000-0003-4861-0943, A. Li0000-0002-4547-116X, C.
Neu0000-0003-3644-8627, C.E. Perez Lara0000-0003-0199-8864

Wayne State University, Detroit, Michigan, USA P.E. Karchin0000-0003-1284-3470

University of Wisconsin - Madison, Madison, Wisconsin, USA A. Aravind, S.
Banerjee0000-0001-7880-922X, K. Black0000-0001-7320-5080, T.
Bose0000-0001-8026-5380, S. Dasu0000-0001-5993-9045, I. De
Bruyn0000-0003-1704-4360, P. Everaerts0000-0003-3848-324X, C. Galloni, H.
He0009-0008-3906-2037, M. Herndon0000-0003-3043-1090, A.
Herve0000-0002-1959-2363, C.K. Koraka0000-0002-4548-9992, A. Lanaro, R.
Loveless0000-0002-2562-4405, J. Madhusudanan Sreekala0000-0003-2590-763X, A.
Mallampalli0000-0002-3793-8516, A. Mohammadi0000-0001-8152-927X, S. Mondal, G.
Parida0000-0001-9665-4575, D. Pinna, A. Savin, V. Shang0000-0002-1436-6092, V.
Sharma0000-0003-1287-1471, W.H. Smith0000-0003-3195-0909, D. Teague, H.F.
Tsoi0000-0002-2550-2184, W. Vetens0000-0003-1058-1163, A.
Warden0000-0001-7463-7360

Authors affiliated with an institute or an international laboratory covered by
a cooperation agreement with CERN S. Afanasiev0009-0006-8766-226X, V.
Andreev0000-0002-5492-6920, Yu. Andreev0000-0002-7397-9665, T.
Aushev0000-0002-6347-7055, M. Azarkin0000-0002-7448-1447, A.
Babaev0000-0001-8876-3886, A. Belyaev0000-0003-1692-1173, V. Blinov99, E.
Boos0000-0002-0193-5073, V. Borshch0000-0002-5479-1982, D.
Budkouski0000-0002-2029-1007, V. Bunichev0000-0003-4418-2072, V. Chekhovsky,
R. Chistov990000-0003-1439-8390, M. Danilov990000-0001-9227-5164, A.
Dermenev0000-0001-5619-376X, T. Dimova990000-0002-9560-0660, D.
Druzhkin1000000-0001-7520-3329, M. Dubinin900000-0002-7766-7175, L.
Dudko0000-0002-4462-3192, A. Ershov0000-0001-5779-142X, G.
Gavrilov0000-0001-9689-7999, V. Gavrilov0000-0002-9617-2928, S.
Gninenko0000-0001-6495-7619, V. Golovtcov0000-0002-0595-0297, N.
Golubev0000-0002-9504-7754, I. Golutvin0009-0007-6508-0215, I.
Gorbunov0000-0003-3777-6606, A. Gribushin0000-0002-5252-4645, Y.
Ivanov0000-0001-5163-7632, V. Kachanov0000-0002-3062-010X, L.
Kardapoltsev990009-0000-3501-9607, V. Karjavine0000-0002-5326-3854, A.
Karneyeu0000-0001-9983-1004, V. Kim990000-0001-7161-2133, M. Kirakosyan, D.
Kirpichnikov0000-0002-7177-077X, M. Kirsanov0000-0002-8879-6538, V.
Klyukhin0000-0002-8577-6531, O. Kodolova1010000-0003-1342-4251, D.
Konstantinov0000-0001-6673-7273, V. Korenkov0000-0002-2342-7862, A.
Kozyrev990000-0003-0684-9235, N. Krasnikov0000-0002-8717-6492, A.
Lanev0000-0001-8244-7321, P. Levchenko1020000-0003-4913-0538, N.
Lychkovskaya0000-0001-5084-9019, V. Makarenko0000-0002-8406-8605, A.
Malakhov0000-0001-8569-8409, V. Matveev990000-0002-2745-5908, V.
Murzin0000-0002-0554-4627, A. Nikitenko103,1010000-0002-1933-5383, S.
Obraztsov0009-0001-1152-2758, V. Oreshkin0000-0003-4749-4995, V.
Palichik0009-0008-0356-1061, V. Perelygin0009-0005-5039-4874, M. Perfilov, S.
Petrushanko0000-0003-0210-9061, S. Polikarpov990000-0001-6839-928X, V.
Popov0000-0001-8049-2583, O. Radchenko990000-0001-7116-9469, M.
Savina0000-0002-9020-7384, V. Savrin0009-0000-3973-2485, V.
Shalaev0000-0002-2893-6922, S. Shmatov0000-0001-5354-8350, S.
Shulha0000-0002-4265-928X, Y. Skovpen990000-0002-3316-0604, S.
Slabospitskii0000-0001-8178-2494, V. Smirnov0000-0002-9049-9196, D.
Sosnov0000-0002-7452-8380, V. Sulimov0009-0009-8645-6685, E.
Tcherniaev0000-0002-3685-0635, A. Terkulov0000-0003-4985-3226, O.
Teryaev0000-0001-7002-9093, I. Tlisova0000-0003-1552-2015, A.
Toropin0000-0002-2106-4041, L. Uvarov0000-0002-7602-2527, A.
Uzunian0000-0002-7007-9020, A. Vorobyev${}^{\textrm{\textdagger}}$, N.
Voytishin0000-0001-6590-6266, B.S. Yuldashev104, A.
Zarubin0000-0002-1964-6106, I. Zhizhin0000-0001-6171-9682, A.
Zhokin0000-0001-7178-5907

†: Deceased 1Also at Yerevan State University, Yerevan, Armenia 2Also at TU
Wien, Vienna, Austria 3Also at Institute of Basic and Applied Sciences,
Faculty of Engineering, Arab Academy for Science, Technology and Maritime
Transport, Alexandria, Egypt 4Also at Ghent University, Ghent, Belgium 5Also
at Universidade Estadual de Campinas, Campinas, Brazil 6Also at Federal
University of Rio Grande do Sul, Porto Alegre, Brazil 7Also at UFMS, Nova
Andradina, Brazil 8Also at Nanjing Normal University, Nanjing, China 9Now at
Henan Normal University, Xinxiang, China 10Now at The University of Iowa, Iowa
City, Iowa, USA 11Also at University of Chinese Academy of Sciences, Beijing,
China 12Also at China Center of Advanced Science and Technology, Beijing,
China 13Also at University of Chinese Academy of Sciences, Beijing, China
14Also at China Spallation Neutron Source, Guangdong, China 15Also at
Université Libre de Bruxelles, Bruxelles, Belgium 16Also at an institute or an
international laboratory covered by a cooperation agreement with CERN 17Also
at Helwan University, Cairo, Egypt 18Now at Zewail City of Science and
Technology, Zewail, Egypt 19Also at British University in Egypt, Cairo, Egypt
20Now at Ain Shams University, Cairo, Egypt 21Also at Birla Institute of
Technology, Mesra, Mesra, India 22Also at Purdue University, West Lafayette,
Indiana, USA 23Also at Université de Haute Alsace, Mulhouse, France 24Also at
Department of Physics, Tsinghua University, Beijing, China 25Also at The
University of the State of Amazonas, Manaus, Brazil 26Also at Erzincan Binali
Yildirim University, Erzincan, Turkey 27Also at University of Hamburg,
Hamburg, Germany 28Also at RWTH Aachen University, III. Physikalisches
Institut A, Aachen, Germany 29Also at Isfahan University of Technology,
Isfahan, Iran 30Also at Bergische University Wuppertal (BUW), Wuppertal,
Germany 31Also at Brandenburg University of Technology, Cottbus, Germany
32Also at Forschungszentrum Jülich, Juelich, Germany 33Also at CERN, European
Organization for Nuclear Research, Geneva, Switzerland 34Also at Institute of
Physics, University of Debrecen, Debrecen, Hungary 35Also at Institute of
Nuclear Research ATOMKI, Debrecen, Hungary 36Now at Universitatea Babes-Bolyai
- Facultatea de Fizica, Cluj-Napoca, Romania 37Also at Physics Department,
Faculty of Science, Assiut University, Assiut, Egypt 38Also at HUN-REN Wigner
Research Centre for Physics, Budapest, Hungary 39Also at Faculty of
Informatics, University of Debrecen, Debrecen, Hungary 40Also at Punjab
Agricultural University, Ludhiana, India 41Also at University of Hyderabad,
Hyderabad, India 42Also at University of Visva-Bharati, Santiniketan, India
43Also at Indian Institute of Science (IISc), Bangalore, India 44Also at IIT
Bhubaneswar, Bhubaneswar, India 45Also at Institute of Physics, Bhubaneswar,
India 46Also at Deutsches Elektronen-Synchrotron, Hamburg, Germany 47Also at
Department of Physics, Isfahan University of Technology, Isfahan, Iran 48Also
at Sharif University of Technology, Tehran, Iran 49Also at Department of
Physics, University of Science and Technology of Mazandaran, Behshahr, Iran
50Also at Italian National Agency for New Technologies, Energy and Sustainable
Economic Development, Bologna, Italy 51Also at Centro Siciliano di Fisica
Nucleare e di Struttura Della Materia, Catania, Italy 52Also at Università
degli Studi Guglielmo Marconi, Roma, Italy 53Also at Scuola Superiore
Meridionale, Università di Napoli ’Federico II’, Napoli, Italy 54Also at Fermi
National Accelerator Laboratory, Batavia, Illinois, USA 55Also at Università
di Napoli ’Federico II’, Napoli, Italy 56Also at Laboratori Nazionali di
Legnaro dell’INFN, Legnaro, Italy 57Also at Consiglio Nazionale delle Ricerche
- Istituto Officina dei Materiali, Perugia, Italy 58Also at Riga Technical
University, Riga, Latvia 59Also at Department of Applied Physics, Faculty of
Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Malaysia 60Also
at Consejo Nacional de Ciencia y Tecnología, Mexico City, Mexico 61Also at
Trincomalee Campus, Eastern University, Sri Lanka, Nilaveli, Sri Lanka 62Also
at Saegis Campus, Nugegoda, Sri Lanka 63Also at INFN Sezione di Pavia,
Università di Pavia, Pavia, Italy 64Also at National and Kapodistrian
University of Athens, Athens, Greece 65Also at Ecole Polytechnique Fédérale
Lausanne, Lausanne, Switzerland 66Also at University of Vienna Faculty of
Computer Science, Vienna, Austria 67Also at Universität Zürich, Zurich,
Switzerland 68Also at Stefan Meyer Institute for Subatomic Physics, Vienna,
Austria 69Also at Laboratoire d’Annecy-le-Vieux de Physique des Particules,
IN2P3-CNRS, Annecy-le-Vieux, France 70Also at Near East University, Research
Center of Experimental Health Science, Mersin, Turkey 71Also at Konya
Technical University, Konya, Turkey 72Also at Izmir Bakircay University,
Izmir, Turkey 73Also at Adiyaman University, Adiyaman, Turkey 74Also at Bozok
Universitetesi Rektörlügü, Yozgat, Turkey 75Also at Marmara University,
Istanbul, Turkey 76Also at Milli Savunma University, Istanbul, Turkey 77Also
at Kafkas University, Kars, Turkey 78Also at Hacettepe University, Ankara,
Turkey 79Also at Istanbul University - Cerrahpasa, Faculty of Engineering,
Istanbul, Turkey 80Also at Yildiz Technical University, Istanbul, Turkey
81Also at Vrije Universiteit Brussel, Brussel, Belgium 82Also at School of
Physics and Astronomy, University of Southampton, Southampton, United Kingdom
83Also at University of Bristol, Bristol, United Kingdom 84Also at IPPP Durham
University, Durham, United Kingdom 85Also at Monash University, Faculty of
Science, Clayton, Australia 86Now at an institute or an international
laboratory covered by a cooperation agreement with CERN 87Also at Università
di Torino, Torino, Italy 88Also at Bethel University, St. Paul, Minnesota, USA
89Also at Karamanoğlu Mehmetbey University, Karaman, Turkey 90Also at
California Institute of Technology, Pasadena, California, USA 91Also at United
States Naval Academy, Annapolis, Maryland, USA 92Also at Bingol University,
Bingol, Turkey 93Also at Georgian Technical University, Tbilisi, Georgia
94Also at Sinop University, Sinop, Turkey 95Also at Erciyes University,
Kayseri, Turkey 96Also at Horia Hulubei National Institute of Physics and
Nuclear Engineering (IFIN-HH), Bucharest, Romania 97Also at Texas A&M
University at Qatar, Doha, Qatar 98Also at Kyungpook National University,
Daegu, Korea 99Also at another institute or international laboratory covered
by a cooperation agreement with CERN 100Also at Universiteit Antwerpen,
Antwerpen, Belgium 101Also at Yerevan Physics Institute, Yerevan, Armenia
102Also at Northeastern University, Boston, Massachusetts, USA 103Also at
Imperial College, London, United Kingdom 104Also at Institute of Nuclear
Physics of the Uzbekistan Academy of Sciences, Tashkent, Uzbekistan


# Learning-Based UE Classification in Millimeter-Wave Cellular Systems With
Mobility

###### Abstract

Millimeter-wave cellular communication requires beamforming procedures that
enable alignment of the transmitter and receiver beams as the user equipment
(UE) moves. For efficient beam tracking it is advantageous to classify users
according to their traffic and mobility patterns. Research to date has
demonstrated efficient ways of machine learning based UE classification.
Although different machine learning approaches have shown success, most of
them are based on physical layer attributes of the received signal. This,
however, imposes additional complexity and requires access to those lower
layer signals. In this paper, we show that traditional supervised and even
unsupervised machine learning methods can successfully be applied on higher
layer channel measurement reports in order to perform UE classification,
thereby reducing the complexity of the classification process.

Index Terms—  5G, classification, beam management, machine learning,
millimeter-wave, mobility.

##  1 Introduction

In wireless communication systems, optimization of the radio access network
(RAN) has always been an important area [1, 2]. In the context of fifth-
generation (5G) systems, a key issue from a network performance viewpoint is
how the RAN can adapt to the dynamic radio environment [3]. Irrespective of
the deployment mode of 5G systems, it is understood that massive multiple-
input multiple-output (MIMO) is expected to be the workhorse of its RAN front-
haul at the cellular base stations (BSs) [3]. In the 24.250 - 52.6 GHz band,
analog or digital beamforming in both azimuth and elevation domains is
typically used to increase the received signal levels. The beamforming
procedure provides access to control and payload signals for network-level
decision making. By studying the characteristics of these signals to/from each
UE, _spatial fingerprints_ unique to each UE can be found [4]. In the related
literature, spatial fingerprinting has been used in conjunction with classical
machine learning (ML) methods for UE localization via the learned “features”
of the environment [5], or by direct matching [6] without learning the
environment features. The authors in [7] train a six-layer fully connected
network on real-time observations at sub-6 GHz bands to predict beamforming
weight coefficients and blockages, while the study of [8] demonstrates the use
of a simple, feed-forward neural network for band assignment to different UEs.
While [9] surveys an extensive list of related literature, the vast majority
of the works in the literature only consider physical layer (PHY) properties
of the transmitted/received signal and do not capture the interaction of the
PHY with the data link and media access control layers of the system. In
reality, these higher system layers greatly manipulate the PHY signals seen
to/from the phased array ports which capture the physical amplitude and phase
properties, as well as embed protocol level detail for RAN performance
optimization.
Considering a cross-layer perspective, this paper investigates whether
classical ML methods are capable of classifying UEs into different groups by
simultaneously processing layer 2 (L2) uplink channel state information-
reference signals (CSI-RSs). A key consideration in our analysis is that of UE
mobility, thus making the CSI-RSs time varying for each UE. We consider
different mobility patterns of the UEs in a typical urban setting and include
movements such as walking, cycling, and traveling with cars or public
transportation. The objective is to investigate the use of learning algorithms
towards UE classification based solely on the reported narrow beams and
corresponding Reference Signal Received Power (RSRP) values in dynamic
millimeter-wave (mmWave) scenarios. Previous studies on beam management have
mostly focused on best beam predictions [10] and have not dealt with combining
RSRP measurements across multiple wide beams and the corresponding narrow
beams [11]. In particular, we employ both supervised and unsupervised methods
such as tSNE [12] and K-means clustering [9], combined with principal
component analysis (PCA) in order to classify the UE types. Furthermore we
train a number of decision tree classifiers in order to further explore
whether it is indeed possible to characterize the modality (i.e. pedestrian,
car etc) from the measurement reports.

Our analysis here assumes line-of-sight (LOS) propagation at a center
frequency of 28 GHz across a 100 MHz bandwidth. There are several reasons for
this: First, one of our objectives is to assess whether unsupervised learning
is able to detect different UE types given their individual time evolving CSI-
RS reference signals. Second, the majority of the existing studies (see
earlier references) confine their focus to PHY parameters and do not consider
the effects of PHY-higher layer co-design since it is difficult to decouple
the PHY effects from higher layers once certain access control and protocol-
level decisions are made. Finally, the results can be useful for approximating
the achievable performance obtained by very sparse mmWave channels, since the
consensus is that there only seems to be a few _dominant_ multipath components
which are active [1, 2]. We note that since our target is to study UE
classification at standardized mmWave frequencies, bands outside the one
mentioned above is not within the direct scope of the study and hence
conclusions made here can not be extrapolated to other frequencies.

##  2 Simulated System Description and Methodology

We now describe the commercial grade simulated system which is responsible for
generating our data. Unlike previous works, we consider a radio system
simulator supporting detailed beam management procedures compliant with
standardized 5G systems at mmWave frequencies [13]. As a result, we are able
to simulate a commercial grade 28 GHz phased array antenna module (PAAM) in BS
type 1-O configuration containing 192 cross-polarized elements distributed
across 8 rows and 12 columns. Analog beamforming capability is modelled with
horizontal and vertical inter-element spacings fixed to 0.5 $\lambda$ and 0.7
$\lambda$ with each cross-polarized element having a direction-specific gain
pattern given in [14]. The PAAM is tuned for operation within a bandwidth of
100 MHz (standards compliant). The generated grid-of-beams (GoB) from the PAAM
is depicted in Fig. 1 yielding a total of 12 wide beams (WB) and 136 narrow
beams (NB).

The simulated three-dimensional area deploys a single site with one hexagon
shaped sector having a radius of 200 m. The BS is deployed in the corner of
the hexagon as depicted in Fig. 2, with height of 30 m having an overall
field-of-view of 120∘ in the azimuth and 40∘ in elevation. No mechanical
downtilt of the BS is assumed. A protection radius of 5 m is kept from the BS
periphery where no UEs can be located within 5 m from the BS.

Fig. 1: Generated GoB with fixed wide/narrow beam numbering. Conceptual
overview of beam refinement procedure: During a typical P2 procedure and with
the help of incoming UE measurement reports, NB tracking is performed within
the current WB and if RSRP of the best NB is higher than the currently serving
NB, a beam switch is initiated. Note: UEs are moving in front of the BS along
their lane in same direction as the depicted arrow.

Prior to data collection, two UE mobility patterns have been designed, namely
slow and fast moving UEs. The first group mimics pedestrian and bicycle while
the latter one mimics vehicular UEs such as, motorcycle, car and bus, 5 main
classes in total. In addition, user specific mobility patterns have been
modelled, presenting a pavement and a street. In order to reduce built-in bias
in our data sets, randomness was applied to the starting points as well as UEs
movement trajectory. The UEs are moving from left to right in front of the BS
having free LOS propagation. Velocities and spacing between UEs are modelled
in a way that corresponds to their mobility pattern, as depicted in Fig. 2.
For instance, a car carrying two persons is represented by two UEs physically
separated by one meter and moving together at the same time and velocity. The
height of each UE is set to 1.5 m. During simulations UEs emerge at predefined
starting points along their route and continue moving to the endpoint covering
the entire length of the route. In contrast to those, some UEs would emerge at
intermediate points and thereby cover just fraction of the entire route. A
predefined number of pedestrian UEs is scheduled to cross the street
simulating northward movement away from the BS. During the simulation multiple
parameter settings are triggered and evaluated for different loads of UEs, and
several seeds are used to ensure statistical confidence. Fig. 2 shows narrow
beam coverage with corresponding numbering/indexing across the simulated
geographical area (as generated from the system explained in Fig. 1). We note
the above was done in conjunction with uplink file transfer protocol traffic
patterns.

Fig. 2: (Top) Site model. The UEs are placed in LOS moving along two different
lanes. (Bottom) An example of narrow-beam coverage showing a subset of beams
present in the horizontal plane at a given time instant (as depicted in Fig.
1).

##  3 Problem Definition and Formulation

Maintaining good radio link reliability is a key challenge for mmWave
communication systems, especially when mobility is incorporated. Directional
links, however, require fine alignment of the transmitter and receiver beams,
achieved through a mechanism known as beam management [15]. In line with [16],
three downlink layer 1/2 (L1/L2) beam management procedures, commonly known as
P1, P2 and P3 are involved. In this study, our primary focus was on the P2
procedure which handles beam tracking at the BS (a.k.a. gNB). Note that
tracking refers to gNB refining beams (e.g., sweeping through all the narrow
beams over a small range) where UEs detect the best (service) beam and report
its index to gNB. As illustrated in Fig. 1, the P2 CSI-RS measurements were
performed by the moving UEs (as specified in [17]) for all NB within the same
WB (synchronization signal block (SSB) beam) and the reported RSRP
measurements were collected.
A considerable amount of literature has been proposing different ML approaches
for classification of UEs, mostly applied on data sets generated on physical
radio channel attributes such as: e.g., precoding schemes, modulation scheme
[18] or channel covariance [19]. As UEs tend to move along predefined routes
in the physical environment while establishing fingerprints at the BS, e.g. by
channel measurement, we argue that using L2 report data-sets can be
advantageously utilized in order to simplify the UE classification due its
less complex nature compared to physical channel parameters. This approach
opens up for new ways of learning from UE mobility patterns and thereby
possibly prevent UEs from ending up in unfavorable radio channel conditions.

##  4 Un/supervised Learning Framework

To perform our analysis we utilize the fact that mmWave communication is
sensitive to UE motion. We explore whether machine learning methods can learn
motion characteristics and perhaps even users intent from RSRP measurements as
reported on different narrow-beams. One difficulty is that our measurement
report, as in real life, is typically ill-balanced. Therefore, in the case of
K-means, we first perform Principal Component Analysis (PCA) in order to
uncover the clustering structure of our data in an unsupervised setting. We
use 50 components for the PCA which accounts for 97% of the cumulative
explained variation. In order to ascertain the number of clusters $K$ we
employ the elbow method [20] and subsequently explore classification of UEs
into the main five classes comprising our data: pedestrian, bicycle, car, bus
and motorcycle.

In the subsequent analysis, we explicitly avoid comparing multiple
unsupervised classification methods and questions about optimality of one
approach over another. Our primary objective is to understand whether it is
feasible to utilize knowledge of the time evolving CSI-RS signals for
classifying different UEs. Such information can then be used for predicting
and optimizing their radio resource requirements. As such, from a ML
viewpoint, we opt for the simplest combination of the K-means clustering with
PCA, rather than a more complex supervised or unsupervised learning approach
based on deep learning methods. We point out that while these are extremely
interesting directions, we cover them in detail in a follow up extension of
this paper.

###  4.1 Feature extraction and the data

Each UE measurement report contains 12 RSRP CSI-RS measurements together with
their corresponding narrow-beams and arrives on a approximate 40 ms basis. The
total number of such reports, which essentially indicates the size of our
dataset, is highly dependent on the duration of simulation, as well as the UE
velocities as described in Section 2. Identity information such as the UE
identity number, UE location and time are not exposed to the ML algorithm but
rather used as labeled references when interpreting results of classification.
When dealing with incomplete and unbalanced data sets a number of approaches
are possible. To counter the data imbalance we employ PCA as a pre-processing
step. As explained previously, we apply PCA with 50 components which accounts
for 97% of explained variation in the data. Furthermore, due to data
inhomogeneity we apply feature scaling where the range of each feature is
normalized so that it contributes proportionately. This approach is essential
since the clustering methods calculate distances between data points.
Subsequently the two features, RSRP and corresponding narrow-beams, are
stacked on top of each other representing one single UE fingerprint at the
base station at a certain report time. In tests, not presented here, we
observed that having only RSRP or only the narrow beam indices as input to our
ML model results in degradation of the classification rate.

##  5 Results and Discussion

We now present results from both unsupervised and supervised ML models towards
our goal: UE classification from their signals. As discussed in Section 2, our
data consists of 5 main classes, with sub-classes (groups), of UEs according
to their mobility pattern. We first train our algorithm to detect all 5
classes. The results are presented in Fig. 3. There is significant overlap
between the different classes which may not be so surprising given how similar
UEs must look for groups such as cars and busses or pedestrians and cyclists.
This underscores the need to explore whether we can learn to identify larger
groups such as for instance slow and fast movers. We will undertake this task
next.

Fig. 3: K-means clustering results for all 5 classes of UEs. Training on 5
groups: 165 bicycles, 180 buses, 320 cars, 130 motorcycles and 424
pedestrians. Class separation is not obvious at this scale but shows pattern
similarities may exist between classes of slow movers or classes of fast
movers.

Using the sama data from Fig. 3 a similar although perhaps more intriguing
result emerges when we set $K=2$ groups. Specifically we observe in Fig. 4
that some separation exist between fast (cars, busses, motorcycles) and slow
(bicycles and pedestrians) moving UEs. The overlap has now reduced
significantly when only grouping into 2 groups. We explore this initial result
further below.

Fig. 4: K-means clustering results for all 5 classes of UEs. Training on 2
groups: fast 630 (cars, busses, motorcycles) versus 589 slow-moving (bicycles,
pedestrians) UEs.

###  5.1 Slow-moving UEs - pedestrians vs bicycles

It is not surprising, especially based on the baseline results of Fig.4, that
velocity of a given UE can be a good indicator as to whether it should be
classified as a car or a pedestrian. Differentiating however between similarly
moving UEs should be more challenging. We undertake this task in an
unsupervised setting for now by examining a mixed group of 589 UEs consisting
of 424 pedestrians and 165 bicycles. We note that UEs in that dataset move
along identical trajectories on the pavement with either small or no variation
and ending/starting locations unrelated to their group type. We also note that
some of the pedestrians will also cross the street while the bicycles do not.
We train on this data with a PCA method followed by K-means as discussed
earlier. The results in Fig. 5 show a rather clear separation between the two
classes of users with a surprisingly small overlap even though the 2 groups
have almost similar moving velocities. Specifically, we observe a clear
diagonal linear yet separated trend in the majority of the data - we should
point out, that the clustering space is non-dimensional and has no physical
meaning. We also note an interesting cluster of pedestrians at the top left
corner of that figure which seems to defy the overall pattern. To better
understand this cluster we then train our algorithm on the single group of 424
pedestrians from Fig. 5 while requiring that $K=2$ once again. The results
presented in Fig. 6 verify that the majority of that sub-group is attributed
to the street-crossing pedestrians. The results therefore show that
identification and separation among all classes and even sub-group of the slow
UEs could be possible.

Fig. 5: K-means clustering results for slow-moving UEs. Training on 2 groups:
165 bicycles versus 424 pedestrians. Compare with Fig. 6 where pedestrians are
further distinguished into crossing/non-crossing the street.

Fig. 6: K-means clustering results after training on just the class of
pedestrian UEs in Fig. 5. Training on 2 groups: 182 pedestrians non-crossing
the street versus 242 crossing the street. Pedestrians crossing the street
seem to be mainly responsible for that single cluster.

###  5.2 Classification

We now train a number of different decision tree type (supervised) classifiers
in order to identify specific UE mobility classes based on this data.
Furthermore we task the classifiers to distinguish modality with high
probability (see Table 1) based on only a short (40 ms) sequence of RSRP and
narrow-beam numbers reported from each UE. We present a list of the most
successful of those and the respective type of UE data used for their training
in Table 1. Our main metric for success or failure here is the miss-
classification rate on unseen data. The best classifier, the Extra Trees
Regressor, achieves a miss-classification rate of $2\%$.

Classifier | Data used | Miss-Classif %.
---|---|---
Extra Trees Regres. | Ped-nc, Car, MC | 1.8
Extra Trees Regres. | Ped-cr, Car, MC | 2.2
Extra Trees Regres. | Ped, Car, MC | 5.2
Ada Boost Regres. | Ped, Car, MC | 8.1
Table 1: Decision trees and respective miss-classification rates. Data
distribution used: All pedestrians 424 (Ped), 242 Pedestrians crossing (Ped-
cr) and 182 non-crossing (Ped-nc). 320 Cars and 130 MC$=$Motorcycles.

Overall we found that training a classifier to distinguish the pedestrians
crossing the road from those not crossing (see also clustering Fig. 7) was the
most difficult task. That however was expected since we essentially requested
an impossible task from the classifier. The fraction of pedestrians who
eventually cross the street do so only for a brief part of their motion and
until that moment they would be indistinguishable from pedestrians intending
not to cross the street. Thus the classifier, based on the information it
receives, correctly identifies those pedestrians as part of the non-crossing
group. It is therefore not surprising that the classifier was not able to
detect that particular sub-group of UEs based on the short (40 ms) history of
the training sequence provided.

Fig. 7: tSNE clustering results. 242 pedestrians crossing street vs 182 non-
crossing. As expected, due to short (40ms) reporting the non-crossing are
miss-identified.

##  6 Conclusions and Future Work

We identified ways of classifying UEs in dynamic millimeter wave scenarios by
employing conventional ML techniques on CSI-RS measurements. This initial
study in clustering and classification of UEs based on their network
measurement reports alone, without considering physical positioning or other
supporting information, shows that it is possible to infer the mobility mode
of the UEs with some success. The work presented here offers insights towards
new beam prediction mechanisms in mobility-aware MIMO scenarios. These results
together with trajectory forecasting (future research) could in turn provide
useful information when preparing hand-over and expected resource demands in
order to ease and avoid operational bottlenecks.

## References

  * [1] H. Tataria, M. Shafi, A. F. Molisch, M. Dohler, H. Sjöland, and F. Tufvesson, “6G wireless systems: Vision, requirements, challenges, insights, and opportunities,” _Proceedings of the IEEE_ , vol. 109, no. 7, pp. 1166-1199, Jul. 2021.
  * [2] M. Shafi, J. Zhang, H. Tataria, A. F. Molisch, S. Sun, T. S. Rappaport, F. Tufvesson, S. Wu, and K. Kitao, “Microwave vs. millimeter-wave propagation channels: Key differences and impact on 5G cellular systems,” _IEEE Commun. Mag._ , vol. 56, no. 12, pp. 14-20, Dec. 2018.
  * [3] M. Shafi, A. F. Molisch, P. J. Smith, T. Haustein, P. Zhu, P. De Silva, F. Tufvesson, A. Benjebbour, and G. Wunder,“5G: A tutorial overview of standards, trials, challenges, deployment, and practice,” _IEEE Journal on Selected Areas in Communications_ , vol. 35, no. 6, pp. 1201-1221, Apr. 2017.
  * [4] X. Gao, O. Edfors, F. Rusek, and F. Tufvesson, “Massive MIMO performance evaluation based on measured propagation data”, _IEEE Transactions on Communications_ , vol. 14, no. 7, pp. 3899-3911. Jul. 2015.
  * [5] X. Li, K. Batstone, K. Åstrom, M. Oskarsson, C. Gustafson, and F. Tufvesson, “Robust phase-based positioning using massive MIMO with limited bandwidth”, _IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)_ , Oct. 2017.
  * [6] J. Vieira, E. Leitinger, M. Sarajlic, X. Li, and F. Tufvesson, “Deep convolutional neural networks for massive MIMO fingerprint-based positioning,” _IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)_ , Oct. 2017.
  * [7] M. Alrabeiah and A. Alkhateeb, “Deep learning for mmWave beam and blockage prediction using sub-6 GHz channels,” _IEEE Transactions on Communications_ , vol. 68, no. 9, pp. 5504-5518, Sep. 2020.
  * [8] D. Burghal, R. Wang, and A. F. Molisch, “Band assignment in dual band Systems: A learning-based approach,” _2018 IEEE Military Communications Conference (MILCOM)_ , Oct. 2018, pp. 7-13.
  * [9] D. Burghal, A. Ravi, V. Rao, A. Alghafis, and A. F. Molisch, “A comprehensive survey of machine learning based localization with wireless signals,” _arXiv:2012.11171_ , Dec. 2020.
  * [10] B. Ekman, “Machine learning for beam based mobility optimization in NR”, _Master of Science Thesis_ , Department of Electrical Engineering, Linköping University, Sweden, 2017.
  * [11] H. Patel, “Beam refinement and beam tracking using machine learning techniques in 5G NR RAN,” _Master of Science Thesis_ , Faculty of Computing, Blekinge Institute of Technology, Sweden, 2021.
  * [12] A. Courville, R. Fergus and C. Manning, “Accelerating t-SNE using tree-based algorithms,” _Journal of Machine Learning Research_ , vol. 15, no. 93, pp. 3221-3245, Oct. 2014.
  * [13] 3GPP TR 38.104, “Base Station (BS) radio transmission and reception”, _Third Generation Partnership Project (3GPP)_ , Dec. 2020.
  * [14] 3GPP TR 36.873, “Study on 3D channel model for LTE”, _Third Generation Partnership Project (3GPP)_ , Nov. 2017.
  * [15] M. Giordani, M. Polese, A. Roy, D. Castor, and M. Zorzi, “A tutorial on beam management for 3GPP NR at mmWave frequencies”, _IEEE Communications Surveys & Tutorials_, vol. 21, no. 1, pp. 173-196, Sep. 2018.
  * [16] 3GPP TR 38.802, “Study on new radio access technology - physical layer aspects”, _Third Generation Partnership Project (3GPP)_ , Nov. 2017.
  * [17] 3GPP TR 36.133, “5G NR – Requirements for support of radio resource management”, _Third Generation Partnership Project (3GPP)_ , Dec. 2020.
  * [18] T. Kuber, “Learning and prediction using radio and non-radio attributes in wireless systems,” _Ph.D. Thesis_ , Rutgers University, USA, 2021.
  * [19] S. Qiu, D. Gesbert, and T. Jiang, “Enabling covariance-based feedback in massive MIMO: A user classification approach,” _52nd Asilomar Conference on Signals, Systems, and Computers_ , Oct. 2018.
  * [20] P. Rousseeuw, “Silhouettes: A graphical aid to the interpretation and validation of cluster analysis,” _Journal of Computational and Applied Mathematics_ , vol. 20, pp. 3221-3245, Nov. 1987.