Zobrazeno 1 - 10
of 74
pro vyhledávání: '"Osborn, James C."'
Autor:
Osborn, James C.
Publikováno v:
PoS(LATTICE2023)023
We investigate the effectiveness of tuning HMC parameters using information from the gradients of the HMC acceptance probability with respect to the parameters. In particular, the optimization of the trajectory length and parameters for higher order
Externí odkaz:
http://arxiv.org/abs/2402.04976
We present a trainable framework for efficiently generating gauge configurations, and discuss ongoing work in this direction. In particular, we consider the problem of sampling configurations from a 4D $SU(3)$ lattice gauge theory, and consider a gen
Externí odkaz:
http://arxiv.org/abs/2312.08936
Autor:
Brower, Richard C., Culver, Christopher, Cushman, Kimmy K., Fleming, George T., Hasenfratz, Anna, Howarth, Dean, Ingoldby, James, Jin, Xiao Yong, Kribs, Graham D., Meyer, Aaron S., Neil, Ethan T., Osborn, James C., Owen, Evan, Park, Sungwoo, Rebbi, Claudio, Rinaldi, Enrico, Schaich, David, Vranas, Pavlos, Weinberg, Evan, Witzel, Oliver
We present non-perturbative lattice calculations of the low-lying meson and baryon spectrum of the SU(4) gauge theory with fundamental fermion constituents. This theory is one instance of stealth dark matter, a class of strongly coupled theories, whe
Externí odkaz:
http://arxiv.org/abs/2312.07836
Autor:
Kronfeld, Andreas S., Bhattacharya, Tanmoy, Blum, Thomas, Christ, Norman H., DeTar, Carleton, Detmold, William, Edwards, Robert, Hasenfratz, Anna, Lin, Huey-Wen, Mukherjee, Swagato, Orginos, Konstantinos, Brower, Richard, Cirigliano, Vincenzo, Davoudi, Zohreh, Jóo, Bálint, Jung, Chulwoo, Lehner, Christoph, Meinel, Stefan, Neil, Ethan T., Petreczky, Peter, Richards, David G., Bazavov, Alexei, Catterall, Simon, Dudek, Jozef J., El-Khadra, Aida X., Engelhardt, Michael, Fleming, George T., Giedt, Joel, Gupta, Rajan, Hansen, Maxwell T., Izubuchi, Taku, Karsch, Frithjof, Laiho, Jack, Liu, Keh-Fei, Meyer, Aaron S., Rinaldi, Enrico, Savage, Martin, Schaich, David, Shanahan, Phiala E., Sharpe, Stephen R., Sufian, Raza, Syritsyn, Sergey, Van de Water, Ruth S., Wagman, Michael L., Weinberg, Evan, Witzel, Oliver, Aubin, Christopher, Boyle, Peter, Chandrasekharan, Shailesh, Clöet, Ian C., Constantinou, Martha, Cushman, Kimmy, DeGrand, Thomas, Fodor, Zoltan, Foreman, Sam, Gottlieb, Steven, Hoying, Daniel, Jang, Yong-Chull, Jay, William I., Jin, Xiao-Yong, Kelly, Christopher, Kuti, Julius, Lamm, Henry, Lin, Meifeng, Lin, Yin, Lytle, Andrew T., Mackenzie, Paul, Mandula, Jeffrey, Meurice, Yannick, Monahan, Christopher, Morningstar, Colin, Osborn, James C., Park, Sungwoo, Simone, James N., Strelchenko, Alexei, Tomii, Masaaki, Vaquero, Alejandro, Vranas, Pavlos, Wang, Bigeng, Wilcox, Walter, Yoon, Boram, Zhao, Yong
Contribution from the USQCD Collaboration to the Proceedings of the US Community Study on the Future of Particle Physics (Snowmass 2021).
Comment: 27 pp. main text, 4 pp. appendices, 29 pp. references, 1 p. index
Comment: 27 pp. main text, 4 pp. appendices, 29 pp. references, 1 p. index
Externí odkaz:
http://arxiv.org/abs/2207.07641
We study the effects of discretization on the U(1) symmetric XY model in two dimensions using the Higher Order Tensor Renormalization Group (HOTRG) approach. Regarding the $Z_N$ symmetric clock models as specific discretizations of the XY model, we c
Externí odkaz:
http://arxiv.org/abs/2205.03548
Autor:
Meurice, Yannick, Osborn, James C., Sakai, Ryo, Unmuth-Yockey, Judah, Catterall, Simon, Somma, Rolando D.
Tensor network methods are becoming increasingly important for high-energy physics, condensed matter physics and quantum information science (QIS). We discuss the impact of tensor network methods on lattice field theory, quantum gravity and QIS in th
Externí odkaz:
http://arxiv.org/abs/2203.04902
We propose using Normalizing Flows as a trainable kernel within the molecular dynamics update of Hamiltonian Monte Carlo (HMC). By learning (invertible) transformations that simplify our dynamics, we can outperform traditional methods at generating i
Externí odkaz:
http://arxiv.org/abs/2112.01586
We introduce LeapfrogLayers, an invertible neural network architecture that can be trained to efficiently sample the topology of a 2D $U(1)$ lattice gauge theory. We show an improvement in the integrated autocorrelation time of the topological charge
Externí odkaz:
http://arxiv.org/abs/2112.01582
We generalize the Hamiltonian Monte Carlo algorithm with a stack of neural network layers and evaluate its ability to sample from different topologies in a two dimensional lattice gauge theory. We demonstrate that our model is able to successfully mi
Externí odkaz:
http://arxiv.org/abs/2105.03418
Autor:
Appelquist, Thomas, Brower, Richard C., Cushman, Kimmy K., Fleming, George T., Gasbarro, Andrew D., Hasenfratz, Anna, Jin, Xiao-Yong, Neil, Ethan T., Osborn, James C., Rebbi, Claudio, Rinaldi, Enrico, Schaich, David, Vranas, Pavlos, Witzel, Oliver
Publikováno v:
Phys. Rev. D 103, 014504 (2021)
Composite Higgs models must exhibit very different dynamics from quantum chromodynamics (QCD) regardless whether they describe the Higgs boson as a dilatonlike state or a pseudo-Nambu-Goldstone boson. Large separation of scales and large anomalous di
Externí odkaz:
http://arxiv.org/abs/2007.01810