Final Results of the MAJORANA DEMONSTRATOR's Search for Double-Beta Decay of $^{76}$Ge to Excited States of $^{76}$Se

Autor: Arnquist, I. J., Avignone III, F. T., Barabash, A. S., Blalock, E., Bos, B., Busch, M., Chan, Y. -D., Chapman, J. R., Christofferson, C. D., Chu, P. -H., Cuesta, C., Detwiler, J. A., Efremenko, Yu., Ejiri, H., Elliott, S. R., Fuad, N., Giovanetti, G. K., Green, M. P., Gruszko, J., Guinn, I. S., Guiseppe, V. E., Haufe, C. R., Henning, R., Aguilar, D. Hervas, Hoppe, E. W., Kim, I., Kouzes, R. T., Lannen V, T. E., Li, A., Massarczyk, R., Meijer, S. J., Oli, T. K., Paudel, L. S., Pettus, W., Poon, A. W. P., Radford, D. C., Reine, A. L., Rielage, K., Schaper, D. C., Schleich, S. J., Tedeschi, D., Varner, R. L., Vasilyev, S., Watkins, S. L., Wilkerson, J. F., Wiseman, C., Yu, C. -H., Zhu, B. X.

$^{76}$Ge can $\beta\beta$ decay into three possible excited states of $^{76}$Se, with the emission of two or, if the neutrino is Majorana, zero neutrinos. None of these six transitions have yet been observed. The MAJORANA DEMONSTRATOR was designed t

Externí odkaz: http://arxiv.org/abs/2410.03995

dlordinal: a Python package for deep ordinal classification

Autor: Bérchez-Moreno, Francisco, Vargas, Víctor M., Ayllón-Gavilán, Rafael, Guijo-Rubio, David, Hervás-Martínez, César, Fernández, Juan C., Gutiérrez, Pedro A.

dlordinal is a new Python library that unifies many recent deep ordinal classification methodologies available in the literature. Developed using PyTorch as underlying framework, it implements the top performing state-of-the-art deep learning techniq

Externí odkaz: http://arxiv.org/abs/2407.17163

Euclid preparation. Sensitivity to non-standard particle dark matter model

Autor: Euclid Collaboration, Lesgourgues, J., Schwagereit, J., Bucko, J., Parimbelli, G., Giri, S. K., Hervas-Peters, F., Schneider, A., Archidiacono, M., Pace, F., Sakr, Z., Amara, A., Amendola, L., Andreon, S., Auricchio, N., Aussel, H., Baccigalupi, C., Baldi, M., Bardelli, S., Bender, R., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Cardone, V. F., Carretero, J., Casas, S., Castellano, M., Castignani, G., Cavuoti, S., Cimatti, A., Colodro-Conde, C., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Courbin, F., Courtois, H. M., Da Silva, A., Degaudenzi, H., De Lucia, G., Di Giorgio, A. M., Douspis, M., Dubath, F., Dupac, X., Dusini, S., Farina, M., Farrens, S., Ferriol, S., Fosalba, P., Frailis, M., Franceschi, E., Fumana, M., Galeotta, S., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Guzzo, L., Haugan, S. V. H., Hoekstra, H., Holmes, W., Hook, I., Hormuth, F., Hornstrup, A., Ilić, S., Jahnke, K., Joachimi, B., Keihänen, E., Kermiche, S., Kiessling, A., Kubik, B., Kunz, M., Kurki-Suonio, H., Laureijs, R., Ligori, S., Lilje, P. B., Lindholm, V., Lloro, I., Mainetti, G., Maino, D., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Martinelli, M., Martinet, N., Marulli, F., Massey, R., Medinaceli, E., Mei, S., Mellier, Y., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Nakajima, R., Neissner, C., Niemi, S. -M., Nightingale, J. W., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W. J., Pettorino, V., Polenta, G., Poncet, M., Popa, L. A., Raison, F., Rebolo, R., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Saglia, R., Sánchez, A. G., Sapone, D., Sartoris, B., Scaramella, R., Schewtschenko, J. A., Schneider, P., Schrabback, T., Secroun, A., Sefusatti, E., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Steinwagner, J., Tallada-Crespí, P., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valenziano, L., Vassallo, T., Veropalumbo, A., Wang, Y., Weller, J., Zamorani, G., Zucca, E., Biviano, A., Boucaud, A., Bozzo, E., Burigana, C., Calabrese, M., Di Ferdinando, D., Vigo, J. A. Escartin, Fabbian, G., Farinelli, R., Gracia-Carpio, J., Mauri, N., Nucita, A. A., Scottez, V., Tenti, M., Viel, M., Wiesmann, M., Akrami, Y., Anselmi, S., Ballardini, M., Bertacca, D., Blot, L., Böhringer, H., Borgani, S., Bruton, S., Cabanac, R., Calabro, A., Cappi, A., Carvalho, C. S., Castro, T., Chambers, K. C., Contarini, S., Cooray, A. R., Davini, S., De Caro, B., de la Torre, S., Desprez, G., Díaz-Sánchez, A., Di Domizio, S., Dole, H., Escoffier, S., Ferrari, A. G., Ferreira, P. G., Ferrero, I., Finelli, F., Fornari, F., Gabarra, L., Ganga, K., García-Bellido, J., Gaztanaga, E., Giacomini, F., Gozaliasl, G., Hildebrandt, H., Hjorth, J., Munñoz, A. Jimenez, Joudaki, S., Kajava, J. J. E., Kansal, V., Karagiannis, D., Kirkpatrick, C. C., Legrand, L., Libet, G., Loureiro, A., Macias-Perez, J., Maggio, G., Magliocchetti, M., Mannucci, F., Maoli, R., Martins, C. J. A. P., Matthew, S., Maurin, L., Metcalf, R. B., Migliaccio, M., Monaco, P., Moretti, C., Morgante, G., Nadathur, S., Walton, Nicholas A., Patrizii, L., Pezzotta, A., Pöntinen, M., Popa, V., Porciani, C., Potter, D., Reimberg, P., Risso, I., Rocci, P. -F., Sahlén, M., Sereno, M., Simon, P., Mancini, A. Spurio, Tao, C., Tessore, N., Testera, G., Teyssier, R., Toft, S., Tosi, S., Troja, A., Tucci, M., Valieri, C., Valiviita, J., Vergani, D., Verza, G.

The Euclid mission of the European Space Agency will provide weak gravitational lensing and galaxy clustering surveys that can be used to constrain the standard cosmological model and its extensions, with an opportunity to test the properties of dark

Externí odkaz: http://arxiv.org/abs/2406.18274

Point-Spread Function errors for weak lensing - density cross-correlations. Application to UNIONS

Autor: Zhang, Ziwen, Kilbinger, Martin, Peters, Fabian Hervas, Li, Qinxun, Luo, Wentao, Baumont, Lucie, Cuillandre, Jean-Charles, Fabbro, Sebastien, Gwyn, Stephen, McConnachie, Alan, Wittje, Anna

Publikováno v: A&A 691, A75 (2024)

Aims:Calibrating the point spread function (PSF) is a fundamental part of weak gravitational lensing analyses. Even with corrected galaxy images, imperfect calibrations can introduce biases. We propose an analytical framework for quantifying PSF-indu

Externí odkaz: http://arxiv.org/abs/2405.03434

A two-stage algorithm in evolutionary product unit neural networks for classification

Autor: Tallón-Ballesteros, Antonio J., Hervás-Martínez, César

Publikováno v: Tall\'on-Ballesteros, A. J., & Herv\'as-Mart\'inez, C. (2011). A two-stage algorithm in evolutionary product unit neural networks for classification. Expert Systems with Applications, 38(1), 743-754

This paper presents a procedure to add broader diversity at the beginning of the evolutionary process. It consists of creating two initial populations with different parameter settings, evolving them for a small number of generations, selecting the b

Externí odkaz: http://arxiv.org/abs/2402.06622