LEGEND-1000 Preconceptual Design Report

Autor: LEGEND Collaboration, Abgrall, N., Abt, I., Agostini, M., Alexander, A., Andreoiu, C., Araujo, G. R., Avignone III, F. T., Bae, W., Bakalyarov, A., Balata, M., Bantel, M., Barabanov, I., Barabash, A. S., Barbeau, P. S., Barton, C. J., Barton, P. J., Baudis, L., Bauer, C., Bernieri, E., Bezrukov, L., Bhimani, K. H., Biancacci, V., Blalock, E., Bolozdynya, A., Borden, S., Bos, B., Bossio, E., Boston, A., Bothe, V., Bouabid, R., Boyd, S., Brugnera, R., Burlac, N., Busch, M., Caldwell, A., Caldwell, T. S., Carney, R., Cattadori, C., Chan, Y. -D., Chernogorov, A., Christofferson, C. D., Chu, P. -H., Clark, M., Cohen, T., Combs, D., Comellato, T., Cooper, R. J., Costa, I. A., D'Andrea, V., Detwiler, J. A., Di Giacinto, A., Di Marco, N., Dobson, J., Drobizhev, A., Durand, M. R., Edzards, F., Efremenko, Yu., Elliott, S. R., Engelhardt, A., Fajt, L., Faud, N., Febbraro, M. T., Ferella, F., Fields, D. E., Fischer, F., Fomina, M., Fox, H., Franchi, J., Gala, R., Galindo-Uribarri, A., Gangapshev, A., Garfagnini, A., Geraci, A., Gilbert, C., Gold, M., Gooch, C., Gradwohl, K. P., Green, M. P., Grinyer, G. F., Grobov, A., Gruszko, J., Guinn, I., Guiseppe, V. E., Gurentsov, V., Gurov, Y., Gusev, K., Hacket, B., Hagemann, F., Hakenmüeller, J., Haranczyk, M., Hauertmann, L., Haufe, C. R., Hayward, C., Heffron, B., Henkes, F., Henning, R., Aguilar, D. Hervas, Hinton, J., Hodak, R., Hoffmann, H., Hofmann, W., Hostiuc, A., Huang, J., Hult, M., Mirza, M. Ibrahim, Jochum, J., Jones, R., Judson, D., Junker, M., Kaizer, J., Kazalov, V., Kermaïdic, Y., Khushbakht, H., Kidd, M., Kihm, T., Kilgus, K., Kim, I., Klimenko, A., Knöpfle, K. T., Kochetov, O., Konovalov, S. I., Kontul, I., Kool, K., Kormos, L. L., Kornoukhov, V. N., Korosec, M., Krause, P., Kuzminov, V. V., López-Castaño, J. M., Lang, K., Laubenstein, M., León, E., Lehnert, B., Leonhardt, A., Li, A., Lindner, M., Lippi, I., Liu, X., Liu, J., Loomba, D., Lubashevskiy, A., Lubsandorzhiev, B., Lusardi, N., Müller, Y., Macko, M., Macolino, C., Majorovits, B., Mamedov, F., Maneschg, W., Manzanillas, L., Marshall, G., Martin, R. D., Martin, E. L., Massarczyk, R., Mei, D., Meijer, S. J., Mertens, S., Misiaszek, M., Mondragon, E., Morella, M., Morgan, B., Mroz, T., Muenstermann, D., Nave, C. J., Nemchenok, I., Neuberger, M., Oli, T. K., Gann, G. Orebi, Othman, G., Palušova, V., Panth, R., Papp, L., Paudel, L. S., Pelczar, K., Perez, J. Perez, Pertoldi, L., Pettus, W., Piseri, P., Poon, A. W. P., Povinec, P., Pullia, A., Radford, D. C., Ramachers, Y. A., Ransom, C., Rauscher, L., Redchuk, M., Reine, A. L., Riboldi, S., Rielage, K., Rozov, S., Rukhadze, E., Rumyantseva, N., Runge, J., Ruof, N. W., Saakyan, R., Sailer, S., Salamanna, G., Salamida, F., Salvat, D. J., Sandukovsky, V., Schönert, S., Schültz, A., Schütt, M., Schaper, D. C., Schreiner, J., Schulz, O., Schuster, M., Schwarz, M., Schwingenheuer, B., Selivanenko, O., Shafiee, M., Shevchik, E., Shirchenko, M., Shitov, Y., Simgen, H., Simkovic, F., Skorokhvatov, M., Slavickova, M., Smolek, K., Smolnikov, A., Solomon, J. A., Song, G., Starosta, K., Stekl, I., Stommel, M., Stukov, D., Sumathi, R. R., Sweigart, D. A., Szczepaniec, K., Taffarello, L., Tagnani, D., Tayloe, R., Tedeschi, D., Turqueti, M., Varner, R. L., Vasilyev, S., Veresnikova, A., Vetter, K., Vignoli, C., Vogl, C., von Sturm, K., Waters, D., Waters, J. C., Wei, W., Wiesinger, C., Wilkerson, J. F., Willers, M., Wiseman, C., Wojcik, M., Wu, V. H. -S., Xu, W., Yakushev, E., Ye, T., Yu, C. -H., Yumatov, V., Zaretski, N., Zeman, J., Zhitnikov, I., Zinatulina, D., Zschocke, A. -K., Zsigmond, A. J., Zuber, K., Zuzel, G.

We propose the construction of LEGEND-1000, the ton-scale Large Enriched Germanium Experiment for Neutrinoless $\beta \beta$ Decay. This international experiment is designed to answer one of the highest priority questions in fundamental physics. It c

Externí odkaz: http://arxiv.org/abs/2107.11462

Synthesizing Deep Neural Network Architectures using Biological Synaptic Strength Distributions

Autor: Karimi, A. H., Shafiee, M. J., Ghodsi, A., Wong, A.

In this work, we perform an exploratory study on synthesizing deep neural networks using biological synaptic strength distributions, and the potential influence of different distributions on modelling performance particularly for the scenario associa

Externí odkaz: http://arxiv.org/abs/1707.00081

Random Feature Maps via a Layered Random Projection (LaRP) Framework for Object Classification

Autor: Chung, A. G., Shafiee, M. J., Wong, A.

The approximation of nonlinear kernels via linear feature maps has recently gained interest due to their applications in reducing the training and testing time of kernel-based learning algorithms. Current random projection methods avoid the curse of

Externí odkaz: http://arxiv.org/abs/1602.01818

The study of mechanical and statistical properties of nitrogen ion-implanted Tantalum bulk

Autor: Ramezani, A.H. ^*, Hoseinzadeh, S. ^*, Ebrahiminejad, Zh., Hantehzadeh, M.R., Shafiee, M.

Publikováno v: In Optik January 2021 225

Invariant subspaces, exact solutions and stability analysis of nonlinear water wave equations

Autor: Hosseini, K., Inc, M., Shafiee, M., Ilie, M., Shafaroody, A., Yusuf, A., Bayram, M.

Publikováno v: In Journal of Ocean Engineering and Science March 2020 5(1):35-40