Zobrazeno 1 - 10
of 3 768
pro vyhledávání: '"O'Dea, P."'
Autor:
Ubertosi, F., Gong, Y., Nulsen, P., Leahy, J. P., Gitti, M., McNamara, B. R., Gaspari, M., Singha, M., O'Dea, C., Baum, S.
We present a detailed analysis of jet activity in the radio galaxy 3C348 at the center of the galaxy cluster Hercules A. We use archival Chandra data to investigate the jet-driven shock front, the radio-faint X-ray cavities, the eastern jet, and the
Externí odkaz:
http://arxiv.org/abs/2411.12804
We generalize the proof of stability of topological order, due to Bravyi, Hastings and Michalakis, to stabilizer Hamiltonians corresponding to low-density parity check (LDPC) codes without the restriction of geometric locality in Euclidean space. We
Externí odkaz:
http://arxiv.org/abs/2411.02384
Autor:
O'Dea, Nicholas, Sriram, Adithya
Quantum scars are nonthermal eigenstates that prevent thermalization of initial states with weight on the scars. When the scar states are equally spaced in energy, superpositions of scars show oscillating local observables that can be detected in exp
Externí odkaz:
http://arxiv.org/abs/2410.11822
The performance of quantum error correcting (QEC) codes are often studied under the assumption of spatio-temporally uniform error rates. On the other hand, experimental implementations almost always produce heterogeneous error rates, in either space
Externí odkaz:
http://arxiv.org/abs/2409.03325
We introduce a family of classical stochastic processes describing diffusive particles undergoing branching and long-range annihilation in the presence of a parity constraint. The probability for a pair-annihilation event decays as a power-law in the
Externí odkaz:
http://arxiv.org/abs/2409.03280
Autor:
Kiehlmann, Sebastian, De La Parra, Philipe Vergara, Sullivan, Andrew, Synani, A., Liodakis, Ioannis, Readhead, Anthony, Graham, Matthew, Begelman, Mitchell, Blandford, Roger, Chatziioannou, Katerina, Ding, Yuanze, Harrison, Fiona, Homan, D., Hovatta, Talvikki, Kulkarni, Shrinivas, Lister, Matthew, Maiolino, Roberto, Max-Moerbeck, Walter, Molina, B., Mroz, Przemyslaw, O'Dea, Christopher, Pavlidou, Vasiliki, Pearson, Timothy J., Aller, Margo, Lawrence, C., Lazio, T. Joseph, O'Neill, S., Prince, Thomas, Ravi, Vikram, Reeves, Rodrigo, Tassis, Konstantinos, Vallisneri, Michele, Zensus, J.
Haystack and Owens Valley Radio Observatory (OVRO) observations recently revealed strong sinusoidal total flux density variations that maintained coherence between 1975 and 2021 in the blazar PKS 2131-021 ($z=1.283)$. This was interpreted as possible
Externí odkaz:
http://arxiv.org/abs/2407.09647
Autor:
Stanghellini, C., Orienti, M., Spingola, C., Zanichelli, A., Dallacasa, D., Cassaro, P., O'Dea, C. P., Baum, S. A., Pérez-Torres, M.
The long-standing question concerning Jetted Sub-Galactic Size (JSS) radio sources is whether they will evolve into large radio galaxies, die before escaping the host galaxy, or remain indefinitely confined to their compact size. Our main goal is to
Externí odkaz:
http://arxiv.org/abs/2407.02029
Topologically-ordered phases are stable to local perturbations, and topological quantum error-correcting codes enjoy thresholds to local errors. We connect the two notions of stability by constructing classical statistical mechanics models for decodi
Externí odkaz:
http://arxiv.org/abs/2406.15757
Autor:
O'Dea, Nicholas
Level statistics are a useful probe for detecting symmetries and distinguishing integrable and non-integrable systems. I show by way of several examples that level statistics detect the presence of generalized symmetries that go beyond conventional l
Externí odkaz:
http://arxiv.org/abs/2406.03983
Autor:
Vantyghem, A. N., Galvin, T. J., Sebastian, B., O'Dea, C. P., Gordon, Y. A., Boyce, M., Rudnick, L., Polsterer, K., Andernach, Heinz, Dionyssiou, M., Venkataraman, P., Norris, R., Baum, S. A., Wang, X. R., Huynh, M.
Modern wide field radio surveys typically detect millions of objects. Techniques based on machine learning are proving to be useful for classifying large numbers of objects. The self-organizing map (SOM) is an unsupervised machine learning algorithm
Externí odkaz:
http://arxiv.org/abs/2404.10109