Zobrazeno 1 - 3
of 3
pro vyhledávání: '"Jwa, Y. J."'
Autor:
MicroBooNE Collaboration, Abratenko, P., An, R., Anthony, J., Arellano, L., Asaadi, J., Ashkenazi, A., Balasubramanian, S., Baller, B., Barnes, C., Barr, G., Basque, V., Bathe-Peters, L., Rodrigues, O. Benevides, Berkman, S., Bhanderi, A., Bhat, A., Bishai, M., Blake, A., Bolton, T., Book, J. Y., Camilleri, L., Caratelli, D., Terrazas, I. Caro, Fernandez, R. Castillo, Cavanna, F., Cerati, G., Chen, Y., Cianci, D., Conrad, J. M., Convery, M., Cooper-Troendle, L., Crespo-Anadon, J. I., Del Tutto, M., Dennis, S. R., Detje, P., Devitt, A., Diurba, R., Dorrill, R., Duffy, K., Dytman, S., Eberly, B., Ereditato, A., Evans, J. J., Fine, R., Aguirre, G. A. Fiorentini, Fitzpatrick, R. S., Fleming, B. T., Foppiani, N., Franco, D., Furmanski, A. P., Garcia-Gamez, D., Gardiner, S., Ge, G., Gollapinni, S., Goodwin, O., Gramellini, E., Green, P., Greenlee, H., Gu, W., Guenette, R., Guzowski, P., Hagaman, L., Hen, O., Hilgenberg, C., Horton-Smith, G. A., Hourlier, A., Itay, R., James, C., Ji, X., Jiang, L., Jo, J. H., Johnson, R. A., Jwa, Y. J., Kalra, D., Kamp, N., Kaneshige, N., Karagiorgi, G., Ketchum, W., Kirby, M., Kobilarcik, T., Kreslo, I., LaZur, R., Lepetic, I., Li, K., Li, Y., Lin, K., Littlejohn, B. R., Louis, W. C., Luo, X., Manivannan, K., Mariani, C., Marsden, D., Marshall, J., Caicedo, D. A. Martinez, Mason, K., Mastbaum, A., McConkey, N., Meddage, V., Mettler, T., Miller, K., Mills, J., Mistry, K., Mohayai, T., Mogan, A., Moon, J., Mooney, M., Moor, A. F., Moore, C. D., Lepin, L. Mora, Mousseau, J., Murphy, M., Naples, D., Navrer-Agasson, A., Nebot-Guinot, M., Neely, R. K., Newmark, D. A., Nowak, J., Nunes, M., Palamara, O., Paolone, V., Papadopoulou, A., Papavassiliou, V., Pate, S. F., Patel, N., Paudel, A., Pavlovic, Z., Piasetzky, E., Ponce-Pinto, I., Prince, S., Qian, X., Raaf, J. L., Radeka, V., Rafique, A., Reggiani-Guzzo, M., Ren, L., Rice, L. C. J., Rochester, L., Rondon, J. Rodriguez, Rosenberg, M., Ross-Lonergan, M., Scanavini, G., Schmitz, D. W., Schukraft, A., Seligman, W., Shaevitz, M. H., Sharankova, R., Shi, J., Sinclair, J., Smith, A., Snider, E. L., Soderberg, M., Soldner-Rembold, S., Spentzouris, P., Spitz, J., Stancari, M., John, J. St., Strauss, T., Sutton, K., Sword-Fehlberg, S., Szelc, A. M., Tang, W., Terao, K., Thorpe, C., Totani, D., Toups, M., Tsai, Y. -T., Uchida, M. A., Usher, T., Van De Pontseele, W., Viren, B., Weber, M., Wei, H., Williams, Z., Wolbers, S., Wongjirad, T., Wospakrik, M., Wresilo, K., Wright, N., Wu, W., Yandel, E., Yang, T., Yarbrough, G., Yates, L. E., Yu, H. W., Zeller, G. P., Zennamo, J., Zhang, C.
Publikováno v:
Digibug. Repositorio Institucional de la Universidad de Granada
instname
MicroBooNE collaboration 2022, ' First Measurement of Energy-Dependent Inclusive Muon Neutrino Charged-Current Cross Sections on Argon with the MicroBooNE Detector ', Phys.Rev.Lett., vol. 128, no. 15, 151801 . https://doi.org/10.1103/PhysRevLett.128.151801
Physical Review Letters
Weber, Michele; Kreslo, Igor (2022). First Measurement of Energy-Dependent Inclusive Muon Neutrino Charged-Current Cross Sections on Argon with the MicroBooNE Detector. Physical review letters, 128(15), p. 151801. American Physical Society 10.1103/PhysRevLett.128.151801
instname
MicroBooNE collaboration 2022, ' First Measurement of Energy-Dependent Inclusive Muon Neutrino Charged-Current Cross Sections on Argon with the MicroBooNE Detector ', Phys.Rev.Lett., vol. 128, no. 15, 151801 . https://doi.org/10.1103/PhysRevLett.128.151801
Physical Review Letters
Weber, Michele; Kreslo, Igor (2022). First Measurement of Energy-Dependent Inclusive Muon Neutrino Charged-Current Cross Sections on Argon with the MicroBooNE Detector. Physical review letters, 128(15), p. 151801. American Physical Society 10.1103/PhysRevLett.128.151801
This document was prepared by the MicroBooNE Collaboration using the resources of the Fermi National Accelerator Laboratory (Fermilab), a U.S. Department of Energy, Office of Science, HEP User Facility. Fermilab is managed by Fermi Research Alliance,
Autor:
MicroBooNE Collaboration, Abratenko, P., Alrashed, M., An, R., Anthony, J., Asaadi, J., Ashkenazi, A., Balasubramanian, S., Baller, B., Barnes, C., Barr, G., Basque, V., Bathe-Peters, L., Rodrigues, O. Benevides, Berkman, S., Bhanderi, A., Bhat, A., Bishai, M., Blake, A., Bolton, T., Camilleri, L., Caratelli, D., Terrazas, I. Caro, Fernandez, R. Castillo, Cavanna, F., Cerati, G., Chen, Y., Church, E., Cianci, D., Conrad, J. M., Convery, M., Cooper-Troendle, L., Crespo-Anadon, J. I., Del Tutto, M., Dennis, S. R., Devitt, D., Diurba, R., Dorrill, R., Duffy, K., Dytman, S., Eberly, B., Ereditato, A., Evans, J. J., Aguirre, G. A. Fiorentini, Fitzpatrick, R. S., Fleming, B. T., Foppiani, N., Franco, D., Furmanski, A. P., Garcia-Gamez, D., Gardiner, S., Ge, G., Gollapinni, S., Goodwin, O., Gramellini, E., Green, P., Greenlee, H., Gu, W., Guenette, R., Guzowski, P., Hagaman, L., Hall, E., Hamilton, P., Hen, O., Horton-Smith, G. A., Hourlier, A., Itay, R., James, C., de Vries, J. Jan, Ji, X., Jiang, L., Jo, J. H., Johnson, R. A., Jwa, Y. J., Kamp, N., Kaneshige, N., Karagiorgi, G., Ketchum, W., Kirby, B., Kirby, M., Kobilarcik, T., Kreslo, I., LaZur, R., Lepetic, I., Li, K., Li, Y., Littlejohn, B. R., Louis, W. C., Luo, X., Marchionni, A., Mariani, C., Marsden, D., Marshall, J., Martin-Albo, J., Caicedo, D. A. Martinez, Mason, K., Mastbaum, A., McConkey, N., Meddage, V., Mettler, T., Miller, K., Mills, J., Mistry, K., Mohayai, T., Mogan, A., Moon, J., Mooney, M., Moor, A. F., Moore, C. D., Lepin, L. Mora, Mousseau, J., Murphy, M., Naples, D., Navrer-Agasson, A., Neely, R. K., Nienaber, P., Nowak, J., Palamara, O., Paolone, V., Papadopoulou, A., Papavassiliou, V., Pate, S. F., Paudel, A., Pavlovic, Z., Piasetzky, E., Ponce-Pinto, I., Prince, S., Qian, X., Raaf, J. L., Radeka, V., Rafique, A., Reggiani-Guzzo, M., Ren, L., Rochester, L., Rondon, J. Rodriguez, Rogers, H. E., Rosenberg, M., Ross-Lonergan, M., Russell, B., Scanavini, G., Schmitz, D. W., Schukraft, A., Seligman, W., Shaevitz, M. H., Sharankova, R., Sinclair, J., Smith, A., Snider, E. L., Soderberg, M., Soldner-Rembold, S., Soleti, S. R., Spentzouris, P., Spitz, J., Stancari, M., John, J. St., Strauss, T., Sutton, K., Sword-Fehlberg, S., Szelc, A. M., Tagg, N., Tang, W., Terao, K., Thorpe, C., Toups, M., Tsai, Y. -T., Uchida, M. A., Usher, T., Van De Pontseele, W., Viren, B., Weber, M., Wei, H., Williams, Z., Wolbers, S., Wongjirad, T., Wospakrik, M., Wu, W., Yandel, E., Yang, T., Yarbrough, G., Yates, L. E., Zeller, G. P., Zennamo, J., Zhang, C.
Publikováno v:
Physical Review D. 103
We present the performance of a semantic segmentation network, SparseSSNet, that provides pixel-level classification of MicroBooNE data. The MicroBooNE experiment employs a liquid argon time projection chamber for the study of neutrino properties and
Autor:
Adams, C, Alrashed, M, An, R, Anthony, J, Asaadi, J, Ashkenazi, A, Auger, M, Balasubramanian, S, Baller, B, Barnes, C, Barr, G, Bass, M, Bay, F, Bhat, A, Bhattacharya, K, Bishai, M, Blake, A, Bolton, T, Camilleri, L, Caratelli, D, Terrazas, I Caro, Carr, R, Fernandez, R Castillo, Cavanna, F, Cerati, G, Chen, Y, Church, E, Cianci, D, Cohen, EO, Collin, GH, Conrad, JM, Convery, M, Cooper-Troendle, L, Crespo-Anadón, JI, Del Tutto, M, Devitt, A, Diaz, A, Duffy, K, Dytman, S, Eberly, B, Ereditato, A, Sanchez, L Escudero, Esquivel, J, Evans, JJ, Fadeeva, AA, Fitzpatrick, RS, Fleming, BT, Franco, D, Furmanski, AP, Garcia-Gamez, D, Genty, V, Goeldi, D, Gollapinni, S, Goodwin, O, Gramellini, E, Greenlee, H, Grosso, R, Guenette, R, Guzowski, P, Hackenburg, A, Hamilton, P, Hen, O, Hewes, J, Hill, C, Horton-Smith, GA, Hourlier, A, Huang, E-C, James, C, de Vries, J Jan, Ji, X, Jiang, L, Johnson, RA, Joshi, J, Jostlein, H, Jwa, Y-J, Karagiorgi, G, Ketchum, W, Kirby, B, Kirby, M, Kobilarcik, T, Kreslo, I, Lepetic, I, Li, Y, Lister, A, Littlejohn, BR, Lockwitz, S, Lorca, D, Louis, WC, Luethi, M, Lundberg, B, Luo, X, Marchionni, A, Marcocci, S, Mariani, C, Marshall, J, Martin-Albo, J, Caicedo, DA Martinez, Mastbaum, A, Meddage, V, Mettler, T
Publikováno v:
Physical Review D, vol 99, iss 9
We have developed a convolutional neural network that can make a pixel-level prediction of objects in image data recorded by a liquid argon time projection chamber (LArTPC) for the first time. We describe the network design, training techniques, and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c8e64c9c73d2be7d5f74423fe9d32cdb
https://www.repository.cam.ac.uk/handle/1810/310420
https://www.repository.cam.ac.uk/handle/1810/310420