Zobrazeno 1 - 10
of 29
pro vyhledávání: '"Udrescu, Silviu-Marian"'
Autor:
Karthein, Jonas, Udrescu, Silviu-Marian, Moroch, Scott B., Belosevic, Ivana, Blaum, Klaus, Borschevsky, Anastasia, Chamorro, Yuly, DeMille, David, Dilling, Jens, Ruiz, Ronald F. Garcia, Hutzler, Nick R., Pašteka, Lukáš F., Ringle, Ryan
Publikováno v:
Phys. Rev. Lett. 133, 033003 (2024)
We present a novel technique to probe electroweak nuclear properties by measuring parity violation (PV) in single molecular ions in a Penning trap. The trap's strong magnetic field Zeeman shifts opposite-parity rotational and hyperfine molecular stat
Externí odkaz:
http://arxiv.org/abs/2310.11192
Publikováno v:
Phys. Rev. Research 6, 013128, 31 January 2024
We propose an experimental scheme for performing sensitive, high-precision laser spectroscopy studies on fast exotic isotopes. By inducing a step-wise resonant ionization of the atoms travelling inside an electric field and subsequently detecting the
Externí odkaz:
http://arxiv.org/abs/2304.13120
Autor:
Arrowsmith-Kron, Gordon, Athanasakis-Kaklamanakis, Michail, Au, Mia, Ballof, Jochen, Berger, Robert, Borschevsky, Anastasia, Breier, Alexander A., Buchinger, Fritz, Budker, Dmitry, Caldwell, Luke, Charles, Christopher, Dattani, Nike, de Groote, Ruben P., DeMille, David, Dickel, Timo, Dobaczewski, Jacek, Düllmann, Christoph E., Eliav, Ephraim, Engel, Jon, Fan, Mingyu, Flambaum, Victor, Flanagan, Kieran T., Gaiser, Alyssa, Ruiz, Ronald Garcia, Gaul, Konstantin, Giesen, Thomas F., Ginges, Jacinda, Gottberg, Alexander, Gwinner, Gerald, Heinke, Reinhard, Hoekstra, Steven, Holt, Jason D., Hutzler, Nicholas R., Jayich, Andrew, Karthein, Jonas, Leach, Kyle G., Madison, Kirk, Malbrunot-Ettenauer, Stephan, Miyagi, Takayuki, Moore, Iain D., Moroch, Scott, Navrátil, Petr, Nazarewicz, Witold, Neyens, Gerda, Norrgard, Eric, Nusgart, Nicholas, Pašteka, Lukáš F., Petrov, Alexander N., Plass, Wolfgang, Ready, Roy A., Reiter, Moritz Pascal, Reponen, Mikael, Rothe, Sebastian, Safronova, Marianna, Scheidenberger, Christoph, Shindler, Andrea, Singh, Jaideep T., Skripnikov, Leonid V., Titov, Anatoly V., Udrescu, Silviu-Marian, Wilkins, Shane G., Yang, Xiaofei
Publikováno v:
Rep. Prog. Phys. 87 084301 (2024)
Molecules containing short-lived, radioactive nuclei are uniquely positioned to enable a wide range of scientific discoveries in the areas of fundamental symmetries, astrophysics, nuclear structure, and chemistry. Recent advances in the ability to cr
Externí odkaz:
http://arxiv.org/abs/2302.02165
Autor:
Kasieczka, Gregor, Nachman, Benjamin, Shih, David, Amram, Oz, Andreassen, Anders, Benkendorfer, Kees, Bortolato, Blaz, Brooijmans, Gustaaf, Canelli, Florencia, Collins, Jack H., Dai, Biwei, De Freitas, Felipe F., Dillon, Barry M., Dinu, Ioan-Mihail, Dong, Zhongtian, Donini, Julien, Duarte, Javier, Faroughy, D. A., Gonski, Julia, Harris, Philip, Kahn, Alan, Kamenik, Jernej F., Khosa, Charanjit K., Komiske, Patrick, Pottier, Luc Le, Martín-Ramiro, Pablo, Matevc, Andrej, Metodiev, Eric, Mikuni, Vinicius, Ochoa, Inês, Park, Sang Eon, Pierini, Maurizio, Rankin, Dylan, Sanz, Veronica, Sarda, Nilai, Seljak, Urous, Smolkovic, Aleks, Stein, George, Suarez, Cristina Mantilla, Szewc, Manuel, Thaler, Jesse, Tsan, Steven, Udrescu, Silviu-Marian, Vaslin, Louis, Vlimant, Jean-Roch, Williams, Daniel, Yunus, Mikaeel
A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. In order to develop and benchmark new anomaly detection methods within
Externí odkaz:
http://arxiv.org/abs/2101.08320
Publikováno v:
J. High Energ. Phys. 2021, 30 (2021)
Discoveries of new phenomena often involve a dedicated search for a hypothetical physics signature. Recently, novel deep learning techniques have emerged for anomaly detection in the absence of a signal prior. However, by ignoring signal priors, the
Externí odkaz:
http://arxiv.org/abs/2011.03550
Publikováno v:
34th Conference on Neural Information Processing Systems (Neurips 2020), Vancouver, Canada
We present an improved method for symbolic regression that seeks to fit data to formulas that are Pareto-optimal, in the sense of having the best accuracy for a given complexity. It improves on the previous state-of-the-art by typically being orders
Externí odkaz:
http://arxiv.org/abs/2006.10782
Autor:
Udrescu, Silviu-Marian, Tegmark, Max
Publikováno v:
Phys. Rev. E 103, 043307 (2021)
We present a method for unsupervised learning of equations of motion for objects in raw and optionally distorted unlabeled video. We first train an autoencoder that maps each video frame into a low-dimensional latent space where the laws of motion ar
Externí odkaz:
http://arxiv.org/abs/2005.11212
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Udrescu, Silviu-Marian, Tegmark, Max
Publikováno v:
Science Advances, 6:eaay2631, April 15, 2020
A core challenge for both physics and artificial intellicence (AI) is symbolic regression: finding a symbolic expression that matches data from an unknown function. Although this problem is likely to be NP-hard in principle, functions of practical in
Externí odkaz:
http://arxiv.org/abs/1905.11481
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.