Unitarity Triangle Fitter Results for CKM Angles

We present the status of the Unitarity Triangle analysis focused on the analyses connected to the CKM angles extraction. The angle values are found to be $\alpha = (90.6\pm 6.6)^{\circ}$, $\sin(2\beta) = 0.68\pm 0.023$, and $\gamma=(72.2\pm 9.2)^{\circ}$.Comment: Proceedings of CKM 2012, the 7th International Workshop on the CKM Unitarity Triangle, University of Cincinnati, USA, 28 September - 2 October 201

Inclusive Flavour Tagging Algorithm

Identifying the flavour of neutral $B$ mesons production is one of the most important components needed in the study of time-dependent $CP$ violation. The harsh environment of the Large Hadron Collider makes it particularly hard to succeed in this task. We present an inclusive flavour-tagging algorithm as an upgrade of the algorithms currently used by the LHCb experiment. Specifically, a probabilistic model which efficiently combines information from reconstructed vertices and tracks using machine learning is proposed. The algorithm does not use information about underlying physics process. It reduces the dependence on the performance of lower level identification capacities and thus increases the overall performance. The proposed inclusive flavour-tagging algorithm is applicable to tag the flavour of $B$ mesons in any proton-proton experiment.Comment: 5 pages, 5 figures, 17th International workshop on Advanced Computing and Analysis Techniques in physics research (ACAT-2016

Numerical optimization for Artificial Retina Algorithm

High-energy physics experiments rely on reconstruction of the trajectories of particles produced at the interaction point. This is a challenging task, especially in the high track multiplicity environment generated by p-p collisions at the LHC energies. A typical event includes hundreds of signal examples (interesting decays) and a significant amount of noise (uninteresting examples). This work describes a modification of the Artificial Retina algorithm for fast track finding: numerical optimization methods were adopted for fast local track search. This approach allows for considerable reduction of the total computational time per event. Test results on simplified simulated model of LHCb VELO (VErtex LOcator) detector are presented. Also this approach is well-suited for implementation of paralleled computations as GPGPU which look very attractive in the context of upcoming detector upgrades

Cherenkov Detectors Fast Simulation Using Neural Networks

We propose a way to simulate Cherenkov detector response using a generative adversarial neural network to bypass low-level details. This network is trained to reproduce high level features of the simulated detector events based on input observables of incident particles. This allows the dramatic increase of simulation speed. We demonstrate that this approach provides simulation precision which is consistent with the baseline and discuss possible implications of these results.Comment: In proceedings of 10th International Workshop on Ring Imaging Cherenkov Detector

Multi-pomeron exchange model for pppp and ppˉp\bar{p} collisions at ultra-high energy

A new variant of the effective pomeron exchange model is proposed for the description of the correlation, observed in $pp$ and $p\bar{p}$ collisions at center-of-mass energy from SPS to LHC, between mean transverse momentum and charged particles multiplicity. The model is based on the Regge-Gribov approach. Smooth logarithmic growth with the collision energy was established for the parameter k, the mean rapidity density of charged particles produced by a single string. It was obtained in the model by the fitting of the available experimental data on charged particles rapidity density in $pp$ and $p\bar{p}$ collisions. The main effect of the model, a gradual onset of string collectivity with the growth of collision energy, is accounted by a free parameter {\beta} that is responsible in an effective way for the string fusion phenomenon. Another free parameter, t, is used to define string tension. We extract parameters {\beta} and t from the available experimental results on -multiplicity correlation at nucleon collision energy $\sqrt{s}$ from 17 GeV to 7 TeV. Smooth dependence of both {\beta} and t on energy allows to make predictions for the correlation behavior at the collision energy of 14 TeV. The indications to the string interaction effects in high multiplicity events in $pp$ collisions at the LHC energies are also discussed.Comment: 7 pages, 7 figures, to appear in proc. QFTHEP'201

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

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. Such large scale productions are extremely demanding in terms of computing resources. Thus new approaches to event generation and simulation of detector responses are needed. In LHCb, the accurate simulation of Cherenkov detectors takes a sizeable fraction of CPU time. An alternative approach is described here, when one generates high-level reconstructed observables using a generative neural network to bypass low level details. This network is trained to reproduce the particle species likelihood function values based on the track kinematic parameters and detector occupancy. The fast simulation is trained using real data samples collected by LHCb during run 2. We demonstrate that this approach provides high-fidelity results.Comment: Proceedings for 19th International Workshop on Advanced Computing and Analysis Techniques in Physics Research. (Fixed typos and added one missing reference in the revised version.