SearcharxivSearch

arXiv subjects

Shannon Egan

Publications and source records attributed to Shannon Egan.

4 recordsLinked to original sources

Non-Abelian topological superconductivity in maximally twisted double-layer spin-triplet valley-singlet superconductors

Recent theoretical and experimental studies point to a novel spin-triplet valley-singlet (STVS) superconducting phase in certain two-valley electron liquids, including rhombohedral trilayer graphene, Bernal bilayer graphene and ZrNCl. This fully gapped phase is exotic in that it combines into Cooper pairs same-spin electrons from valleys centered around the opposing corners of a hexagonal Brillouin zone, but is, nevertheless, topologically trivial. Here, we predict that upon stacking two layers of an STVS material with an angular twist, a novel chiral topological phase -- an $f \pm if'$-wave superconductor -- emerges in the vicinity of the `maximal' twist angle of 30$^{\circ}$ where the system becomes an extrinsic quasi-crystal with 12-fold tiling. The resulting composite is a non-Abelian topological superconductor (TSC) with an odd number of chiral Majorana modes at its edges and a single Majorana zero mode (MZM) localized in the vortex core. Through symmetry analysis and detailed microscopic modelling based on a novel quasi-crystal band structure technique, we demonstrate that the non-Abelian TSC forms when the isolated Fermi pockets coalesce into a single connected Fermi surface around the center of the moiré Brillouin zone and is stable over a wide range of electron density. We further discuss how the energetics leading to the $f \pm if'$-wave phase results in anomalous $π$-periodic inter-layer Josephson effect, which can serve as a distinctive signature of the chiral phase. Distinct from the valley-preserving moiré physics in small-angle twisted graphene, our results establish the large-angle moiré physics arising near maximal twist as a new avenue toward intrinsic TSC with non-Abelian excitations.

cond-mat.supr-con

Moiré flat Chern bands and correlated quantum anomalous Hall states generated by spin-orbit couplings in twisted homobilayer MoS$_2$

We predict that in a twisted homobilayer of transition-metal dichalcogenide MoS$_2$, spin-orbit coupling in the conduction band states from $\pm K$ valleys can give rise to moiré flat bands with nonzero Chern numbers in each valley. The nontrivial band topology originates from a unique combination of angular twist and local mirror symmetry breaking in each individual layer, which results in unusual skyrmionic spin textures in momentum space with skyrmion number $\mathcal{S} = \pm 2$. Our Hartree-Fock analysis further suggests that density-density interactions generically drive the system at $1/2$-filling into a valley-polarized state, which realizes a correlated quantum anomalous Hall state with Chern number $\mathcal{C} = \pm 2$. Effects of displacement fields are discussed with comparison to nontrivial topology from layer-pseudospin magnetic fields.

cond-mat.mes-hall

The State of AI Ethics Report (Volume 4)

The 4th edition of the Montreal AI Ethics Institute's The State of AI Ethics captures the most relevant developments in the field of AI Ethics since January 2021. This report aims to help anyone, from machine learning experts to human rights activists and policymakers, quickly digest and understand the ever-changing developments in the field. Through research and article summaries, as well as expert commentary, this report distills the research and reporting surrounding various domains related to the ethics of AI, with a particular focus on four key themes: Ethical AI, Fairness & Justice, Humans & Tech, and Privacy. In addition, The State of AI Ethics includes exclusive content written by world-class AI Ethics experts from universities, research institutes, consulting firms, and governments. Opening the report is a long-form piece by Edward Higgs (Professor of History, University of Essex) titled "AI and the Face: A Historian's View." In it, Higgs examines the unscientific history of facial analysis and how AI might be repeating some of those mistakes at scale. The report also features chapter introductions by Alexa Hagerty (Anthropologist, University of Cambridge), Marianna Ganapini (Faculty Director, Montreal AI Ethics Institute), Deborah G. Johnson (Emeritus Professor, Engineering and Society, University of Virginia), and Soraj Hongladarom (Professor of Philosophy and Director, Center for Science, Technology and Society, Chulalongkorn University in Bangkok). This report should be used not only as a point of reference and insight on the latest thinking in the field of AI Ethics, but should also be used as a tool for introspection as we aim to foster a more nuanced conversation regarding the impacts of AI on the world.

cs.CY

Long Short-Term Memory (LSTM) networks with jet constituents for boosted top tagging at the LHC

Multivariate techniques based on engineered features have found wide adoption in the identification of jets resulting from hadronic top decays at the Large Hadron Collider (LHC). Recent Deep Learning developments in this area include the treatment of the calorimeter activation as an image or supplying a list of jet constituent momenta to a fully connected network. This latter approach lends itself well to the use of Recurrent Neural Networks. In this work the applicability of architectures incorporating Long Short-Term Memory (LSTM) networks is explored. Several network architectures, methods of ordering of jet constituents, and input pre-processing are studied. The best performing LSTM network achieves a background rejection of 100 for 50% signal efficiency. This represents more than a factor of two improvement over a fully connected Deep Neural Network (DNN) trained on similar types of inputs.

hep-ex