Srijit Paul

TL;DR: Deep learning autoencoders for the unsupervised recognition of phase transitions in physical systems formulated on a lattice are investigated and it is demonstrated that T_c(L) extrapolates to the known theoretical value as L \to \infty suggesting that the autoencoder can also be used to extract the critical temperature of the phase transition to an adequate precision.

TL;DR: In this paper, the authors use deep learning autoencoders for the unsupervised recognition of phase transitions in physical systems formulated on a lattice. But they focus on the 2-dimensional ferromagnetic Ising model and then test the application of the autoencoder on the anti-ferromagnetic ising model, demonstrating that the proposed network can detect the phase transition successfully.

TL;DR: In this article, a lattice QCD and L\"uscher method was used to study elastic pion-nucleon scattering in the isospin 3/2/2 channel, which couples to the Δ(1232) resonance.

TL;DR: In this article, the form factors relevant for the decays of dynamical clover fermions were computed using Luscher's method, and the resonance mass was determined for two different ensembles with pion masses of $317(2)$ and $178(2)-meV.

TL;DR: In this article, the form factors relevant for the decays of dynamical clover fermions were computed using L\"uscher's method and the resonance mass and the coupling coefficient were derived for two different ensembles with pion masses of $317(2)$ and $178(2)-meV.