论文标题

蒙特卡洛方法用于在Nova实验中使用不受约束和约束的滋扰参数构建置信区间

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

论文作者

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

论文摘要

测量可观察到使用最大样本估计来限制模型的物体是许多物理实验的基础。威尔克斯定理提供了一种简单的方法来构建模型参数的置信区间,但仅在某些条件下适用。这些条件,例如嵌套的假设和无界参数,通常在中微子振荡测量和其他实验场景中受到侵犯。蒙特卡洛方法可以解决这些问题,尽管计算成本增加。但是,在存在滋扰参数的情况下,实施蒙特卡洛方法的最佳方法是模棱两可的。本文记录了Nova实验(概要构建)选择的方法。它提出了玩具研究,该研究介绍了方法的选择,其实施的详细信息以及对其进行验证的测试。它还包括一些实际考虑的考虑因素,这些考虑可能对其他选择使用配置文件构建。

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.

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