Neutrinoless Double Beta Decay Sensitivity of the XLZD Rare Event Observatory

Autor: XLZD Collaboration, Aalbers, J., Abe, K., Adrover, M., Maouloud, S. Ahmed, Akerib, D. S., Musalhi, A. K. Al, Alder, F., Althueser, L., Amaral, D. W. P., Amarasinghe, C. S., Ames, A., Andrieu, B., Angelides, N., Angelino, E., Antunovic, B., Aprile, E., Araújo, H. M., Armstrong, J. E., Arthurs, M., Babicz, M., Bajpai, D., Baker, A., Balzer, M., Bang, J., Barberio, E., Bargemann, J. W., Barillier, E., Basharina-Freshville, A., Baudis, L., Bauer, D., Bazyk, M., Beattie, K., Beaupere, N., Bell, N. F., Bellagamba, L., Benson, T., Bhatti, A., Biesiadzinski, T. P., Biondi, R., Biondi, Y., Birch, H. J., Bishop, E., Bismark, A., Boehm, C., Boese, K., Bolotnikov, A., Brás, P., Braun, R., Breskin, A., Brew, C. A. J., Brommer, S., Brown, A., Bruni, G., Budnik, R., Burdin, S., Cai, C., Capelli, C., Carini, G., Carmona-Benitez, M. C., Carter, M., Chauvin, A., Chawla, A., Chen, H., Cherwinka, J. J., Chin, Y. T., Chott, N. I., Chavez, A. P. Cimental, Clark, K., Colijn, A. P., Colling, D. J., Conrad, J., Converse, M. V., Coronel, R., Costanzo, D., Cottle, A., Cox, G., Cuenca-García, J. J., Curran, D., Cussans, D., D'Andrea, V., Garcia, L. C. Daniel, Darlington, I., Dave, S., David, A., Davies, G. J., Decowski, M. P., Deisting, A., Delgaudio, J., Dey, S., Di Donato, C., Di Felice, L., Di Gangi, P., Diglio, S., Ding, C., Dobson, J. E. Y., Doerenkamp, M., Drexlin, G., Druszkiewicz, E., Dunbar, C. L., Eitel, K., Elykov, A., Engel, R., Eriksen, S. R., Fayer, S., Fearon, N. M., Ferella, A. D., Ferrari, C., Fieldhouse, N., Fischer, H., Flaecher, H., Flehmke, T., Flierman, M., Fraser, E. D., Fruth, T. M. A., Fujikawa, K., Fulgione, W., Fuselli, C., Gaemers, P., Gaior, R., Gaitskell, R. J., Gallice, N., Galloway, M., Gao, F., Garroum, N., Geffre, A., Genovesi, J., Ghag, C., Ghosh, S., Giacomobono, R., Gibbons, R., Girard, F., Glade-Beucke, R., Glück, F., Gokhale, S., Grandi, L., Green, J., Grigat, J., van der Grinten, M. G. D., Größle, R., Guan, H., Guida, M., Gyorgy, P., Haiston, J. J., Hall, C. R., Hall, T., Hammann, R., Hannen, V., Hansmann-Menzemer, S., Hargittai, N., Hartigan-O'Connor, E., Haselschwardt, S. J., Hernandez, M., Hertel, S. A., Higuera, A., Hils, C., Hiraoka, K., Hoetzsch, L., Hoferichter, M., Homenides, G. J., Hood, N. F., Horn, M., Huang, D. Q., Hughes, S., Hunt, D., Iacovacci, M., Itow, Y., Jacquet, E., Jakob, J., James, R. S., Joerg, F., Jones, S., Kaboth, A. C., Kahlert, F., Kamaha, A. C., Kaminaga, Y., Kara, M., Kavrigin, P., Kazama, S., Keller, M., Kemp-Russell, P., Khaitan, D., Kharbanda, P., Kilminster, B., Kim, J., Kirk, R., Kleifges, M., Klute, M., Kobayashi, M., Kodroff, D., Koke, D., Kopec, A., Korolkova, E. V., Kraus, H., Kravitz, S., Kreczko, L., von Krosigk, B., Kudryavtsev, V. A., Kuger, F., Kurita, N., Landsman, H., Lang, R. F., Lawes, C., Lee, J., Lehnert, B., Leonard, D. S., Lesko, K. T., Levinson, L., Li, A., Li, I., Li, S., Liang, S., Liang, Z., Lin, J., Lin, Y. -T., Lindemann, S., Linden, S., Lindner, M., Lindote, A., Lippincott, W. H., Liu, K., Loizeau, J., Lombardi, F., Lopes, J. A. M., Lopes, M. I., Lorenzon, W., Loutit, M., Lu, C., Lucchetti, G. M., Luce, T., Luitz, S., Ma, Y., Macolino, C., Mahlstedt, J., Maier, B., Majewski, P. A., Manalaysay, A., Mancuso, A., Manenti, L., Mannino, R. L., Marignetti, F., Marley, T., Undagoitia, T. Marrodán, Martens, K., Masbou, J., Masson, E., Mastroianni, S., Maupin, C., McCabe, C., McCarthy, M. E., McKinsey, D. N., McLaughlin, J. B., Melchiorre, A., Menéndez, J., Messina, M., Miller, E. H., Milosovic, B., Milutinovic, S., Miuchi, K., Miyata, R., Mizrachi, E., Molinario, A., Monteiro, C. M. B., Monzani, M. E., Morå, K., Moriyama, S., Morrison, E., Morteau, E., Mosbacher, Y., Mount, B. J., Müller, J., Murdy, M., Murphy, A. St. J., Murra, M., Naylor, A., Nelson, H. N., Neves, F., Newstead, J. L., Nguyen, A., Ni, K., O'Hare, C., Oberlack, U., Obradovic, M., Olcina, I., Oliver-Mallory, K. C., Gann, G. D. Orebi, Orpwood, J., Ostrowskiy, I., Ouahada, S., Oyulmaz, K., Paetsch, B., Palladino, K. J., Palmer, J., Pan, Y., Pandurovic, M., Pannifer, N. J., Paramesvaran, S., Patton, S. J., Pellegrini, Q., Penning, B., Pereira, G., Peres, R., Perry, E., Pershing, T., Piastra, F., Pienaar, J., Piepke, A., Pierre, M., Plante, G., Pollmann, T. R., Principe, L., Qi, J., Qiao, K., Qie, Y., Qin, J., Radeka, S., Radeka, V., Rajado, M., García, D. Ramírez, Ravindran, A., Razeto, A., Reichenbacher, J., Rhyne, C. A., Richards, A., Rischbieter, G. R. C., Riyat, H. S., Rosero, R., Roy, A., Rushton, T., Rynders, D., Saakyan, R., Sanchez, L., Sanchez-Lucas, P., Santone, D., Santos, J. M. F. dos, Sartorelli, G., Sazzad, A. B. M. R., Scaffidi, A., Schnee, R. W., Schreiner, J., Schulte, P., Schulze, H., Eißing, Schumann, M., Schwenck, A., Schwenk, A., Lavina, L. Scotto, Selvi, M., Semeria, F., Shagin, P., Sharma, S., Shaw, S., Shen, W., Sherman, L., Shi, S., Shi, S. Y., Shimada, T., Shutt, T., Silk, J. J., Silva, C., Simgen, H., Sinev, G., Singh, R., Siniscalco, J., Solmaz, M., Solovov, V. N., Song, Z., Sorensen, P., Soria, J., Stanley, O., Steidl, M., Stenhouse, T., Stevens, A., Stifter, K., Sumner, T. J., Takeda, A., Tan, P. -L., Taylor, D. J., Taylor, W. C., Thers, D., Thümmler, T., Tiedt, D. R., Tönnies, F., Tong, Z., Toschi, F., Tovey, D. R., Tranter, J., Trask, M., Trinchero, G., Tripathi, M., Tronstad, D. R., Trotta, R., Tunnell, C. D., Urquijo, P., Usón, A., Utoyama, M., Vaitkus, A. C., Valentino, O., Valerius, K., Vecchi, S., Velan, V., Vetter, S., de Viveiros, L., Volta, G., Vorkapic, D., Wang, A., Wang, J. J., Wang, W., Wang, Y., Waters, D., Weerman, K. M., Weinheimer, C., Weiss, M., Wenz, D., Whitis, T. J., Wild, K., Williams, M., Wilson, M., Wilson, S. T., Wittweg, C., Wolf, J., Wolfs, F. L. H., Woodford, S., Woodward, D., Worcester, M., Wright, C. J., Wu, V. H. S., üstling, S. W, Wurm, M., Xia, Q., Xing, Y., Xu, D., Xu, J., Xu, Y., Xu, Z., Yamashita, M., Yang, L., Ye, J., Yeh, M., Yu, B., Zavattini, G., Zha, W., Zhong, M., Zuber, K.

The XLZD collaboration is developing a two-phase xenon time projection chamber with an active mass of 60 to 80 t capable of probing the remaining WIMP-nucleon interaction parameter space down to the so-called neutrino fog. In this work we show that,

Externí odkaz: http://arxiv.org/abs/2410.19016

Constant flux layers with gravitational settling: links to aerosols, fog and deposition velocities

Autor: P. A. Taylor

Publikováno v: Atmospheric Chemistry and Physics, Vol 21, Pp 18263-18269 (2021)

Turbulent boundary layer concepts of constant flux layers and surface roughness lengths are extended to include aerosols and the effects of gravitational settling. Interactions between aerosols and the Earth's surface are represented via a roughness

Externí odkaz: https://doaj.org/article/15c74566572d49fda3cc4ed348de119d

Adapting While Learning: Grounding LLMs for Scientific Problems with Intelligent Tool Usage Adaptation

Autor: Lyu, Bohan, Cao, Yadi, Watson-Parris, Duncan, Bergen, Leon, Berg-Kirkpatrick, Taylor, Yu, Rose

Large Language Models (LLMs) demonstrate promising capabilities in solving simple scientific problems but often produce hallucinations for complex ones. While integrating LLMs with tools can increase reliability, this approach typically results in ov

Externí odkaz: http://arxiv.org/abs/2411.00412

LLM-Inference-Bench: Inference Benchmarking of Large Language Models on AI Accelerators

Autor: Chitty-Venkata, Krishna Teja, Raskar, Siddhisanket, Kale, Bharat, Ferdaus, Farah, Tanikanti, Aditya, Raffenetti, Ken, Taylor, Valerie, Emani, Murali, Vishwanath, Venkatram

Large Language Models (LLMs) have propelled groundbreaking advancements across several domains and are commonly used for text generation applications. However, the computational demands of these complex models pose significant challenges, requiring e

Externí odkaz: http://arxiv.org/abs/2411.00136

FISC: Federated Domain Generalization via Interpolative Style Transfer and Contrastive Learning

Autor: Nguyen, Dung Thuy, Johnson, Taylor T., Leach, Kevin

Federated Learning (FL) shows promise in preserving privacy and enabling collaborative learning. However, most current solutions focus on private data collected from a single domain. A significant challenge arises when client data comes from diverse

Externí odkaz: http://arxiv.org/abs/2410.22622