activity
20152020
most citedA facility to Search for Hidden Particles (SHiP) at the CERN SPS

272 citations · 339 across the 9 of their papers we have counts for

collaborators

18 papers

astro-ph.IM20205 cited

Deep learning for Directional Dark Matter search

Artem Golovatiuk, Giovanni De Lellis, Andrey Ustyuzhanin

We provide an algorithm for detection of possible dark matter particle interactions recorded within NEWSdm detector. The NEWSdm (Nuclear Emulsions for WIMP Search directional measu…

physics.ins-det202018 cited

SND@LHC

SHiP Collaboration, C. Ahdida, A. Akmete +341

We propose to build and operate a detector that, for the first time, will measure the process at the LHC and search for feebly interacting particles (FIPs) in an unexplor…

cs.LG2020

Black-Box Optimization with Local Generative Surrogates

Sergey Shirobokov, Vladislav Belavin, Michael Kagan +2

We propose a novel method for gradient-based optimization of black-box simulators using differentiable local surrogate models. In fields such as physics and engineering, many proce…

cs.LG2019

Adaptive Divergence for Rapid Adversarial Optimization

Maxim Borisyak, Tatiana Gaintseva, Andrey Ustyuzhanin

Adversarial Optimization (AO) provides a reliable, practical way to match two implicitly defined distributions, one of which is usually represented by a sample of real data, and th…

stat.ML2019

-class Classification: an Anomaly Detection Method for Highly Imbalanced or Incomplete Data Sets

Maxim Borisyak, Artem Ryzhikov, Andrey Ustyuzhanin +3

Anomaly detection is not an easy problem since distribution of anomalous samples is unknown a priori. We explore a novel method that gives a trade-off possibility between one-class…

physics.ins-det2019

Fast Data-Driven Simulation of Cherenkov Detectors Using Generative Adversarial Networks

Artem Maevskiy, Denis Derkach, Nikita Kazeev +3

The increasing luminosities of future Large Hadron Collider runs and next generation of collider experiments will require an unprecedented amount of simulated events to be produced…