Accès ouvert déclaré
2022
preprint
Monte Carlo method for constructing confidence intervals with unconstrained and constrained nuisance parameters in the NOvA experiment
M. A. Acero, Bijaya 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, B. A. Bambah, S. Bashar, A. Baty, K. Bays, R. Bernstein, V. Bhatnagar, D. Bhattarai, Bhaskar Jyoti Bhuyan, J. Bian, A. C. Booth, R. Bowles, B. Brahma, C. Bromberg, N. Buchanan, A. Butkevich, S. Calvez, T. 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. Djurcic, 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, B. Guo, A. Habig, F. Hakl, A. Hall, J. Hartnell, R. Hatcher, H. Hausner, M. He, Krzysztof Heller, V Hewes, A. Himmel, B. Jargowsky, J. Jarosz, F. Jediny, C. Johnson, M. Judah, I. Kakorin, D. M. Kaplan, A. Kalitkina, J. Kleykamp, O. Klimov, L. W. Koerner, L. Kolupaeva, S. Kotelnikov, R. Kralik, Ch. Kullenberg, M. Kubu, Anuj Kumar, C. D. Kuruppu, V. Kus, T. Lackey, K. Lang, P. Lasorak, J. Lesmeister, S. Lin, A. Lister, J. Liu, M. Lokajicek, J. M. C. Lopez, R. Mahji, S. Magill, M. Manrique Plata, W. A. Mann, M. T. Manoharan, M. L. Marshak, M. Martinez-Casales, V. Matveev, B. Mayes, B. Mehta, M. D. Messier, H. Meyer, Tianyi Miao, V. Mikola, W. H. Miller, S. Mishra, S. R. 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. 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, Luiz Prais, V. Raj, M. Rajaoalisoa, B. Ramson, B. Rebel, P. Rojas, P. Roy, V. Ryabov, O. Samoylov, M. C. Sánchez, S. Sánchez Falero, P. Shanahan, P. Sharma, S. Shukla, A. Sheshukov, I. Singh, P. Singh, V. Singh, E. Smith, J. Smolik, P. Snopok, N. Solomey, A. Sousa, K. Soustruznik, M. Strait, L. Suter, A. Sutton, S. 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
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Résumé fourni par la source
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.
Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.
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Sujets associés
Neutrino Physics Research