309 citations · 788 across the 31 of their papers we have counts for
5 papers · 1 filter
Glueballs and Strings in Yang-Mills theories
Ed Bennett, Jack Holligan, Deog Ki Hong +5
Motivated in part by the pseudo-Nambu Goldstone Boson mechanism of electroweak symmetry breaking in Composite Higgs Models, in part by dark matter scenarios with strongly coupled o…
Adding machine learning within Hamiltonians: Renormalization group transformations, symmetry breaking and restoration
Dimitrios Bachtis, Gert Aarts, Biagio Lucini
We present a physical interpretation of machine learning functions, opening up the possibility to control properties of statistical systems via the inclusion of these functions in…
Mapping distinct phase transitions to a neural network
Dimitrios Bachtis, Gert Aarts, Biagio Lucini
We demonstrate, by means of a convolutional neural network, that the features learned in the two-dimensional Ising model are sufficiently universal to predict the structure of symm…
Extending machine learning classification capabilities with histogram reweighting
Dimitrios Bachtis, Gert Aarts, Biagio Lucini
We propose the use of Monte Carlo histogram reweighting to extrapolate predictions of machine learning methods. In our approach, we treat the output from a convolutional neural net…
Color dependence of tensor and scalar glueball masses in Yang-Mills theories
Ed Bennett, Jack Holligan, Deog Ki Hong +5
We report the masses of the lightest spin-0 and spin-2 glueballs obtained in an extensive lattice study of the continuum and infinite volume limits of gauge theories for…