output
20022024
most citedEntanglement negativity in quantum field theory

492 citations

Showing 2024Show all

8 papers · 1 filter

math.PR2024

The Yaglom limit for branching Brownian motion with absorption and slightly subcritical drift

Julien Berestycki, Jiaqi Liu, Bastien Mallein +1

Consider branching Brownian motion with absorption in which particles move independently as one-dimensional Brownian motions with drift , each particle splits into two particle…

astro-ph.CO2024

Cosmology from HSC Y1 Weak Lensing with Combined Higher-Order Statistics and Simulation-based Inference

Camila P. Novaes, Leander Thiele, Joaquin Armijo +7

We present cosmological constraints from weak lensing with the Subaru Hyper Suprime-Cam (HSC) first-year (Y1) data, using a simulation-based inference (SBI) method. % We explore th…

hep-ex2024

Improving neutrino energy estimation of charged-current interaction events with recurrent neural networks in MicroBooNE

MicroBooNE collaboration, P. Abratenko, O. Alterkait +186

We present a deep learning-based method for estimating the neutrino energy of charged-current neutrino-argon interactions. We employ a recurrent neural network (RNN) architecture f…

math.PR2024

Phase transition of the consistent maximal displacement of branching Brownian motion

Julien Berestycki, Jiaqi Liu, Bastien Mallein +1

Consider branching Brownian motion in which we begin with one particle at the origin, particles independently move according to Brownian motion, and particles split into two at rat…

hep-ex20241 cited

Measurement of double-differential cross sections for mesonless charged-current muon neutrino interactions on argon with final-state protons using the MicroBooNE detector

MicroBooNE collaboration, P. Abratenko, O. Alterkait +185

Charged-current neutrino interactions with final states containing zero mesons and at least one proton are of high interest for current and future accelerator-based neutrino oscill…

stat.ME2024

Enhancing the Power of Gaussian Graphical Model Inference by Modeling the Graph Structure

Valentin Kilian, Tabea Rebafka, Fanny Villers

For the problem of inferring a Gaussian graphical model (GGM), this work explores the application of a recent approach from the multiple testing literature for graph inference. The…