Generative Models for Fast Calorimeter Simulation.LHCb case

Simulation is one of the key components in high energy physics. Historically it relies on the Monte Carlo methods which require a tremendous amount of computation resources. These methods may have difficulties with the expected High Luminosity Large Hadron Collider (HL LHC) need, so the experiment is in urgent need of new fast simulation techniques. We introduce a new Deep Learning framework based on Generative Adversarial Networks which can be faster than traditional simulation methods by 5 order of magnitude with reasonable simulation accuracy. This approach will allow physicists to produce a big enough amount of simulated data needed by the next HL LHC experiments using limited computing resources.Comment: Proceedings of the presentation at CHEP 2018 Conferenc

Machine Learning approach to γ/π0\gamma/\pi^0 separation in the LHCb calorimeter

Reconstruction and identification of particles in calorimeters of modern High Energy Physics experiments is a complicated task. Solutions are usually driven by a priori knowledge about expected properties of reconstructed objects. Such an approach is also used to distinguish single photons in the electromagnetic calorimeter of the LHCb detector at the LHC from overlapping photons produced from decays of high momentum π 0. We studied an alternative solution based on first principles. This approach applies neural networks and classifier based on gradient boosting method to primary calorimeter information, that is energies collected in individual cells of the energy cluster. Mutial application of this methods allows to improve separation performance based on Monte Carlo data analysis. Receiver operating characteristic score of classifier increases from 0.81 to 0.95, that means reducing primary photons fake rate by factor of two or more

Machine Learning approach to boosting neutral particles identification in the LHCb calorimeter

We present a new approach to identifcation of boosted neutral particles using Electromagnetic Calorimeter (ECAL) of the LHCb detector. The identifcation of photons and neutral pions is currently based on the geometric parameters which characterise the expected shape of energy deposition in the calorimeter. This allows to distinguish single photons in the electromagnetic calorimeter from overlapping photons produced from high momentum π0 decays. The novel approach proposed here is based on applying machine learning techniques to primary calorimeter information, that are energies collected in individual cells around the energy cluster. This method allows to improve separation performance of photons and neutral pions and has no signifcant energy dependence.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 pions is currently based on the geometric parameters which characterise the expected shape of energy deposition in the calorimeter. This allows to distinguish single photons in the electromagnetic calorimeter from overlapping photons produced from high momentum $\pi^0$ decays. The novel approach proposed here is based on applying machine learning techniques to primary calorimeter information, that are energies collected in individual cells around the energy cluster. This method allows to improve separation performance of photons and neutral pions and has no significant energy dependence

Generative Models for Fast Calorimeter Simulation: the LHCb case>

Simulation is one of the key components in high energy physics. Historically it relies on the Monte Carlo methods which require a tremendous amount of computation resources. These methods may have difficulties with the expected High Luminosity Large Hadron Collider (HL-LHC) needs, so the experiments are in urgent need of new fast simulation techniques. We introduce a new Deep Learning framework based on Generative Adversarial Networks which can be faster than traditional simulation methods by 5 orders of magnitude with reasonable simulation accuracy. This approach will allow physicists to produce a sufficient amount of simulated data needed by the next HL-LHC experiments using limited computing resources