699 citations · 2.7k across the 97 of their papers we have counts for
10 papers · 1 filter
Simulating the Time Projection Chamber responses at the MPD detector using Generative Adversarial Networks
A. Maevskiy, F. Ratnikov, A. Zinchenko +1
High energy physics experiments rely heavily on the detailed detector simulation models in many tasks. Running these detailed models typically requires a notable amount of the comp…
Sensitivity of the SHiP experiment to dark photons decaying to a pair of charged particles
SHiP Collaboration, C. Ahdida, A. Akmete +331
Dark photons are hypothetical massive vector particles that could mix with ordinary photons. The simplest theoretical model is fully characterised by only two parameters: the mass…
First branching fraction measurement of the suppressed decay
LHCb collaboration, R. Aaij, C. Abellán Beteta +963
The baryon is unstable and usually decays into charmless final states by the transition. It can, however, also disintegrate into a meson and a…
ML-assisted versatile approach to Calorimeter R&D
Alexey Boldyrev, Denis Derkach, Fedor Ratnikov +1
Advanced detector R&D for both new and ongoing experiments in HEP requires performing computationally intensive and detailed simulations as part of the detector-design optimisation…
Study of the and states in decays
LHCb collaboration, R. Aaij, C. Abellán Beteta +956
The decays are studied using a data set corresponding to an integrated luminosity of 9fb collected with the LHCb detector in proton-proton co…
Using Machine Learning to Speed Up and Improve Calorimeter R&D
Fedor Ratnikov
Design of new experiments, as well as upgrade of ongoing ones, is a continuous process in the experimental high energy physics. Since the best solution is a trade-off between diffe…
Generative Adversarial Networks for LHCb Fast Simulation
Fedor Ratnikov
LHCb is one of the major experiments operating at the Large Hadron Collider at CERN. The richness of the physics program and the increasing precision of the measurements in LHCb le…
Using machine learning to speed up new and upgrade detector studies: a calorimeter case
F. Ratnikov, D. Derkach, A. Boldyrev +3
In this paper, we discuss the way advanced machine learning techniques allow physicists to perform in-depth studies of the realistic operating modes of the detectors during the sta…
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…
Measurement of the muon flux for the SHiP experiment
SHiP Collaboration
The SHiP experiment will search for very weakly interacting particles beyond the Standard Model which are produced in a 400 \GeV/ proton beam dump at the CERN SPS. About $10^{11…