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- European Organization for Nuclear ResearchCH283 papers
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13 papers · 1 filter
Generative Adversarial Networks for the fast simulation of the Time Projection Chamber responses at the MPD detector
A. Maevskiy, F. Ratnikov, A. Zinchenko +3
The detailed detector simulation models are vital for the successful operation of modern high-energy physics experiments. In most cases, such detailed models require a significant…
Track reconstruction and matching between emulsion and silicon pixel detectors for the SHiP-charm experiment
SHiP Collaboration
In July 2018 an optimization run for the proposed charm cross section measurement for SHiP was performed at the CERN SPS. A heavy, moving target instrumented with nuclear emulsion…
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…
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…
Machine Learning approach to boosting neutral particles identification in the LHCb calorimeter
Alexey Boldyrev, Viktoria Chekalina, Fedor Ratnikov
We present a new approach to identification of boosted neutral particles using Electromagnetic Calorimeter (ECAL) of the LHCb detector. The identification of photons and neutral pi…