Zobrazeno 1 - 10
of 20 741
pro vyhledávání: '"Poole P"'
Autor:
Ottomano, Federico, Goulermas, John Y., Gusev, Vladimir, Savani, Rahul, Gaultois, Michael W., Manning, Troy D., Lin, Hai, Manzanera, Teresa P., Poole, Emmeline G., Dyer, Matthew S., Claridge, John B., Alaria, Jon, Daniels, Luke M., Varma, Su, Rimmer, David, Sanderson, Kevin, Rosseinsky, Matthew J.
Machine Learning (ML) has offered innovative perspectives for accelerating the discovery of new functional materials, leveraging the increasing availability of material databases. Despite the promising advances, data-driven methods face constraints i
Externí odkaz:
http://arxiv.org/abs/2411.14034
Autor:
McCullough, J., Amon, A., Legnani, E., Gruen, D., Roodman, A., Friedrich, O., MacCrann, N., Becker, M. R., Myles, J., Dodelson, S., Samuroff, S., Blazek, J., Prat, J., Honscheid, K., Pieres, A., Ferté, A., Alarcon, A., Drlica-Wagner, A., Choi, A., Navarro-Alsina, A., Campos, A., Malagón, A. A. Plazas, Porredon, A., Farahi, A., Ross, A. J., Rosell, A. Carnero, Yin, B., Flaugher, B., Yanny, B., Sánchez, C., Chang, C., Davis, C., To, C., Doux, C., Brooks, D., James, D. J., Cid, D. Sanchez, Hollowood, D. L., Huterer, D., Rykoff, E. S., Gaztanaga, E., Huff, E. M., Suchyta, E., Sheldon, E., Sanchez, E., Tarsitano, F., Andrade-Oliveira, F., Castander, F. J., Bernstein, G. M., Gutierrez, G., Giannini, G., Tarle, G., Diehl, H. T., Huang, H., Harrison, I., Sevilla-Noarbe, I., Tutusaus, I., Ferrero, I., Elvin-Poole, J., Marshall, J. L., Muir, J., Weller, J., Zuntz, J., Carretero, J., DeRose, J., Frieman, J., Cordero, J., De Vicente, J., García-Bellido, J., Mena-Fernández, J., Eckert, K., Romer, A. K., Bechtol, K., Herner, K., Kuehn, K., Secco, L. F., da Costa, L. N., Paterno, M., Soares-Santos, 21 M., Gatti, M., Raveri, M., Yamamoto, M., Smith, M., Kind, M. Carrasco, Troxel, M. A., Aguena, M., Jarvis, M., Swanson, M. E. C., Weaverdyck, N., Lahav, O., Doel, P., Wiseman, P., Miquel, R., Gruendl, R. A., Cawthon, R., Allam, S., Hinton, S. R., Bridle, S. L., Bocquet, S., Desai, S., Pandey, S., Everett, S., Lee, S., Shin, T., Palmese, A., Conselice, C., Burke, D. L., Buckley-Geer, E., Lima, M., Vincenzi, M., Pereira, M. E. S., Crocce, M., Schubnell, M., Jeffrey, N., Alves, O., Vikram, V., Zhang, Y., Collaboration, DES
Modeling the intrinsic alignment (IA) of galaxies poses a challenge to weak lensing analyses. The Dark Energy Survey is expected to be less impacted by IA when limited to blue, star-forming galaxies. The cosmological parameter constraints from this b
Externí odkaz:
http://arxiv.org/abs/2410.22272
Autor:
Gao, Jun, Krishna, Govind, Yeung, Edith, Yu, Lingxi, Gangopadhyay, Sayan, Chan, Kai-Sum, Huang, Chiao-Tzu, Descamps, Thomas, Reimer, Michael E., Poole, Philip J., Dalacu, Dan, Zwiller, Val, Elshaari, Ali W.
Coherent control of single photon sources is a key requirement for the advancement of photonic quantum technologies. Among them, nanowire-based quantum dot sources are popular due to their potential for on-chip hybrid integration. Here we demonstrate
Externí odkaz:
http://arxiv.org/abs/2409.14964
Publikováno v:
Geophysics, 2022, vol. 87, no. 3, pp. V215-V226
To streamline fast-track processing of large data volumes, we have developed a deep learning approach to deblend seismic data in the shot domain based on a practical strategy for generating high-quality training data along with a list of data conditi
Externí odkaz:
http://arxiv.org/abs/2409.08602
Neural Algorithmic Reasoning (NAR) aims to optimize classical algorithms. However, canonical implementations of NAR train neural networks to return only a single solution, even when there are multiple correct solutions to a problem, such as single-so
Externí odkaz:
http://arxiv.org/abs/2409.06953
Autor:
Fulay, Suyash, Brannon, William, Mohanty, Shrestha, Overney, Cassandra, Poole-Dayan, Elinor, Roy, Deb, Kabbara, Jad
Language model alignment research often attempts to ensure that models are not only helpful and harmless, but also truthful and unbiased. However, optimizing these objectives simultaneously can obscure how improving one aspect might impact the others
Externí odkaz:
http://arxiv.org/abs/2409.05283
Thermomechanical controlled processing (TMCP) is widely used to optimize the final properties of high strength low alloy (HSLA) steels, via microstructure engineering. The room temperature microstructures are influenced by the high temperature austen
Externí odkaz:
http://arxiv.org/abs/2408.16788
Recent cutting-edge experiments have provided $in\,situ$ structure characterization and measurements of the pressure ($P$), density ($\bar{\rho}$) and temperature ($T$) of shock compressed silicon in the 100 GPa range of pressures and up to $\sim$10,
Externí odkaz:
http://arxiv.org/abs/2408.04173
Autor:
Campos, A., Yin, B., Dodelson, S., Amon, A., Alarcon, A., Sánchez, C., Bernstein, G. M., Giannini, G., Myles, J., Samuroff, S., Alves, O., Andrade-Oliveira, F., Bechtol, K., Becker, M. R., Blazek, J., Camacho, H., Rosell, A. Carnero, Kind, M. Carrasco, Cawthon, R., Chang, C., Chen, R., Choi, A., Cordero, J., Davis, C., DeRose, J., Diehl, H. T., Doux, C., Drlica-Wagner, A., Eckert, K., Eifler, T. F., Elvin-Poole, J., Everett, S., Fang, X., Ferté, A., Friedrich, O., Gatti, M., Gruen, D., Gruendl, R. A., Harrison, I., Hartley, W. G., Herner, K., Huang, H., Huff, E. M., Jarvis, M., Krause, E., Kuropatkin, N., Leget, P. -F., MacCrann, N., McCullough, J., Navarro-Alsina, A., Pandey, S., Prat, J., Raveri, M., Rollins, R. P., Roodman, A., Rosenfeld, R., Ross, A. J., Rykoff, E. S., Sanchez, J., Secco, L. F., Sevilla-Noarbe, I., Sheldon, E., Shin, T., Troxel, M. A., Tutusaus, I., Varga, T. N., Wechsler, R. H., Yanny, B., Zhang, Y., Zuntz, J., Aguena, M., Annis, J., Bacon, D., Bocquet, S., Brooks, D., Burke, D. L., Carretero, J., Castander, F. J., Costanzi, M., da Costa, L. N., De Vicente, J., Doel, P., Ferrero, I., Flaugher, B., Frieman, J., García-Bellido, J., Gaztanaga, E., Gutierrez, G., Hinton, S. R., Hollowood, D. L., Honscheid, K., James, D. J., Kuehn, K., Lima, M., Lin, H., Marshall, J. L., Mena-Fernández, J., Menanteau, F., Miquel, R., Ogando, R. L. C., Paterno, M., Pereira, M. E. S., Pieres, A., Malagón, A. A. Plazas, Porredon, A., Sanchez, E., Cid, D. Sanchez, Smith, M., Suchyta, E., Swanson, M. E. C., Tarle, G., To, C., Vikram, V., Weaverdyck, N.
Characterization of the redshift distribution of ensembles of galaxies is pivotal for large scale structure cosmological studies. In this work, we focus on improving the Self-Organizing Map (SOM) methodology for photometric redshift estimation (SOMPZ
Externí odkaz:
http://arxiv.org/abs/2408.00922
Autor:
Chicoine, N., Prat, J., Zacharegkas, G., Chang, C., Tanoglidis, D., Drlica-Wagner, A., Anbajagane, D., Adhikari, S., Amon, A., Wechsler, R. H., Alarcon, A., Bechtol, K., Becker, M. R., Bernstein, G. M., Campos, A., Rosell, A. Carnero, Kind, M. Carrasco, Cawthon, R., Chen, R., Choi, A., Cordero, J., Davis, C., DeRose, J., Dodelson, S., Doux, C., Eckert, K., Elvin-Poole, J., Everett, S., Ferté, A., Gatti, M., Giannini, G., Gruen, D., Gruendl, R. A., Harrison, I., Herner, K., Jarvis, M., Leget, P. -F., MacCrann, N., McCullough, J., Myles, J., Navarro-Alsina, A., Pandey, S., Raveri, M., Rollins, R. P., Roodman, A., Ross, A. J., Rykoff, E. S., Sánchez, C., Secco, L. F., Sevilla-Noarbe, I., Sheldon, E., Shin, T., Troxel, M. A., Tutusaus, I., Varga, T. N., Yanny, B., Yin, B., Zuntz, J., Aguena, M., Alves, O., Bacon, D., Brooks, D., Carretero, J., Castander, F. J., Conselice, C., Desai, S., De Vicente, J., Doel, P., Ferrero, I., Flaugher, B., Frieman, J., García-Bellido, J., Gaztanaga, E., Gutierrez, G., Hinton, S. R., Hollowood, D. L., Honscheid, K., James, D. J., Kuehn, K., Lee, S., Lidman, C., Lima, M., Marshall, J. L., Mena-Fernández, J., Miquel, R., Muir, J., Ogando, R. L. C., Palmese, A., Pereira, M. E. S., Pieres, A., Malagón, A. A. Plazas, Porredon, A., Walker, A. R., Samuroff, S., Sanchez, E., Cid, D. Sanchez, Smith, M., Suchyta, E., Swanson, M. E. C., Tarle, G., To, C., Tucker, D. L., Vikram, V., Weaverdyck, N., Wiseman, P.
We present galaxy-galaxy lensing measurements using a sample of low surface brightness galaxies (LSBGs) drawn from the Dark Energy Survey Year 3 (Y3) data as lenses. LSBGs are diffuse galaxies with a surface brightness dimmer than the ambient night s
Externí odkaz:
http://arxiv.org/abs/2407.19081