35 research outputs found
Exploring Composite Dark Matter with an SU(4) gauge theory with 1 fermion flavor
Several SU(N) gauge theories have been explored as candidates for producing
stable dark matter particles that can explain their relative abundance, while
also evading current constraints from direct, indirect and collider searches.
In this talk, I will present the confinement and spectral properties of a new
model we name "Hyper Stealth Dark Matter", which involves an SU(4) gauge theory
with 1 quark flavor. The lightest baryon in this theory can be a potential dark
matter candidate as it is protected from decay and hence can evade detection
with a mass of just a few GeV. Existence of a first order confinement
transition would open the possibility of potential detection of gravitational
waves from such a transition at future observatories.Comment: Proceedings: The 40th International Symposium on Lattice Field Theory
(Lattice 2023) July 31st - August 4th, 2023 Fermi National Accelerator
Laboratory. 8 page
Benchmark results in the 2D lattice Thirring model with a chemical potential
We study the two-dimensional lattice Thirring model in the presence of a fermion chemical potential. Our model is asymptotically free and contains massive fermions that mimic a baryon and light bosons that mimic pions. Hence, it is a useful toy model for QCD, especially since it, too, suffers from a sign problem in the auxiliary field formulation in the presence of a fermion chemical potential. In this work, we formulate the model in both the world line and fermion-bag representations and show that the sign problem can be completely eliminated with open boundary conditions when the fermions are massless. Hence, we are able accurately compute a variety of interesting quantities in the model, and these results could provide benchmarks for other methods that are being developed to solve the sign problem in QCD
The use of Convolutional Neural Networks for signal-background classification in Particle Physics experiments
The success of Convolutional Neural Networks (CNNs) in image classification
has prompted efforts to study their use for classifying image data obtained in
Particle Physics experiments. Here, we discuss our efforts to apply CNNs to 2D
and 3D image data from particle physics experiments to classify signal from
background.
In this work we present an extensive convolutional neural architecture
search, achieving high accuracy for signal/background discrimination for a HEP
classification use-case based on simulated data from the Ice Cube neutrino
observatory and an ATLAS-like detector. We demonstrate among other things that
we can achieve the same accuracy as complex ResNet architectures with CNNs with
less parameters, and present comparisons of computational requirements,
training and inference times.Comment: Contribution to Proceedings of CHEP 2019, Nov 4-8, Adelaide,
Australi