Monte Carlo method for constructing confidence intervals with unconstrained and constrained nuisance parameters in the NOvA experiment

作者：M. A. Acero, B. S. Acharya, P. Adamson, L. Aliaga, Н. Анфимов, A. Antoshkin, E. Arrieta-Diaz, L. Asquith, A. Aurisano, A. Back, C. Backhouse, M. Baird, N. Balashov, P. Baldi, Bindu A. Bambah, S. Bashar, A. Baty, K. Bays, R. Bernstein, V. Bhatnagar, D. Bhattarai, B. Bhuyan, J. G. Bian, Andrew Booth, R. Bowles, B. Brahma, C. Bromberg, N. Buchanan, A. Butkevich, S. Calvez, Timothy J. Carroll, E. Catano-Mur, A. Chatla, R. Chirco, B. C. Choudhary, S. Choudhary, A. Christensen, T. E. Coan, M. Colo, L. Cremonesi, G. S. Davies, P. F. Derwent, P. Ding, Z. Djurčić, M. Dolce, D. Doyle, D. Dueñas Tonguino, E. C. Dukes, A. Dye, R. Ehrlich, M. Elkins, E. Ewart, G. J. Feldman, P. Filip, J. Franc, M. J. Frank, H. Gallagher, R. Gandrajula, F. Gao, A. Giri, R. A. Gomes, M. C. Goodman, V. Grichine, M. Groh, R. Group, B. Guo, A. Habig, F. Hakl, A. Hall, J. Hartnell, Robert D. Hatcher, H. Hausner, M. He, K. Heller, V. Hewes, Alexander Himmel, B. Jargowsky, J. Jarosz, F. Jediny, Calvin W. Johnson, M. Judah, I. Kakorin, Daniel M. Kaplan, A. Kalitkina, J. Kleykamp, O. Klimov, L. W. Koerner, L. Kolupaeva, S. Kotelnikov, R. Kralik, Ch. Kullenberg, M. Kubu, A. Kumar, C. Kuruppu, V. Kus, T. Lackey, K. Lang, P. Lasorak, J. Lesmeister, S. Lin, A. Lister, Jinzhi Liu, M. Lokajicek, J. M. C. Lopez, R. Mahji, S. Magill, M. Manrique Plata, W. A. Mann, M. T. Manoharan, Marvin L. Marshak, M. Martinez-Casales, V. Matveev, B. Mayes, B. Mehta, M. D. Messier, H. Meyer, T. Miao, V. Mikola, William H. Miller, S. Mishra, S. Mishra, A. Mislivec, R. Mohanta, A. Moren, A. Morozova, W. Mu, L. Mualem, M. Muether, K. Mulder, D. Naples, A. Nath, N. Nayak, S. Nelleri, J. K. Nelson, R. J. Nichol, E. Niner, A. Norman, A. Norrick, T. Nosek, H. Oh, A. Olshevskiy, T. Olson, J. Ott, A. Pal, J. Paley, L. Panda, R. B. Patterson, G. Pawloski, D. Pershey, O. Petrova, R. Petti, D. D. Phan, R. Plunkett, A. Pobedimov, Joshua Porter, A. Rafique, L. R. Prais, V. Raj, M. Rajaoalisoa, B. Ramson, B. Rebel, P. Rojas, P. Roy, V. A. Ryabov, O. Samoylov, M. C. Sánchez, S. Sánchez Falero, P. Shanahan, P. Sharma, S. Shukla, A. Sheshukov, I. Singh, P. Singh, Jaydip Singh, E. Smith, J. Smolík, P. Snopok, N. Solomey, A. Sousa, K. Soustruznik, M. Strait, L. Suter, A. Sutton, S. K. Swain, C. Sweeney, A. A. Sztuc, B. Tapia Oregui, P. Tas, B. N. Temizel, T. Thakore, R. B. Thayyullathil, J. Thomas, E. Tiras, J. Tripathi, J. Trokan-Tenorio, Y. Torun, J. Urheim, P. Vahle, Z. Vallari, J. Vasel, T. Vrba, M. Wallbank, T. K. Warburton, M. Wetstein, D. Whittington, D. A. Wickremasinghe, T. Wieber, J. Wolcott, M. Wrobel, W. Wu, Y. Xiao, B. Yaeggy, A. Yallappa Dombara, A. Yankelevich, K. Yonehara, S. Yu, Y. Yu, S. Zadorozhnyy, J. Zalesak, Y. Zhang, R. Zwaska · 发表于：Journal of Instrumentation · 年份：2025 · DOI：10.1088/1748-0221/20/02/t02001 · 被引用次数：8 · 研究领域：Neutrino Physics Research、Radiation Detection and Scintillator Technologies、Particle Detector Development and Performance

Abstract Measuring observables to constrain models using maximum-likelihood estimation is fundamental to many physics experiments. Wilks' theorem provides a simple way to construct confidence intervals on model parameters, but it only applies under certain conditions. These conditions, such as nested hypotheses and unbounded parameters, are often violated in neutrino oscillation measurements and other experimental scenarios. Monte Carlo methods can address these issues, albeit at increased computational cost. In the presence of nuisance parameters, however, the best way to implement a Monte Carlo method is ambiguous. This paper documents the method selected by the NOvA experiment, the profile construction. It presents the toy studies that informed the choice of method, details of its implementation, and tests performed to validate it. It also includes some practical considerations which may be of use to others choosing to use the profile construction.