Publications

In this work, we propose an approach for electromagnetic shower generation on a track level. Currently, Monte Carlo simulation occupies 50-70\% of total computing resources that are used by physicists experiments worldwide. Thus, speedup of the simulation step allows to reduce simulation cost and accelerate synthetic experiments. In this paper, we suggest dividing the problem of shower generation into two separate issues: graph generation and tracks features generation. Both these problems can be efficiently solved with a cascade of deep autoregressive generative network and graph convolution network. The novelty of the proposed approach lies in the Neural networks application to the generation of the complex recursive physical process.

Formal language theory has a deep connection with such areas as static code analysis, graph database querying, formal verifica- tion, and compressed data processing. Many application problems can be formulated in terms of languages intersection. The Bar-Hillel theo- rem states that context-free languages are closed under intersection with a regular set. This theorem has a constructive proof and thus provides a formal justification of correctness of the algorithms for applications mentioned above. Mechanization of the Bar-Hillel theorem, therefore, is both a fundamental result of formal language theory and a basis for the certified implementation of the algorithms for applications. In this work, we present the mechanized proof of the Bar-Hillel theorem in Coq.

In HEP experiments CPU resources required by MC simulations are constantly growing and become a very large fraction of the total computing power (greater than 75\%). At the same time the pace of performance improvements from technology is slowing down, so the only solution is a more efficient use of resources. Efforts are ongoing in the LHC experiments to provide multiple options for simulating events in a faster way when higher statistics is needed. A key of the success for this strategy is the possibility of enabling fast simulation options in a common framework with minimal action by the final user. In this talk we will describe the solution adopted in Gauss, the LHCb simulation software framework, to selectively exclude particles from being simulated by the Geant4 toolkit and to insert the corresponding hits generated in a faster way. The approach, integrated within the Geant4 toolkit, has been applied to the LHCb calorimeter but it could also be used for other subdetectors. The hits generation can be carried out by any external tool, e.g. by a static library of showers or more complex machine-learning techniques. In LHCb generative models, which are nowadays widely used for computer vision and image processing are being investigated in order to accelerate the generation of showers in the calorimeter. These models are based on maximizing the likelihood between reference samples and those produced by a generator. The two main approaches are Generative Adversarial Networks (GAN), that takes into account an explicit description of the reference, and Variational Autoencoders (VAE), that uses latent variables to describe them. We will present how both approaches can be applied to the LHCb calorimeter simulation, their advantages as well as their drawbacks.

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.

A search for the rare leptonic decays was performed, using the proton-proton collision data collected with the LHCb experiment at center-ofmass energies of 8TeV and 13TeV. The following analysis are reviewed in this work:

• Measurements of the B0s → µ+µ− branching fraction and effective lifetime and search for B0 → µ+µ− decays

• Search for the decays B0s → τ+τ− and B0 → τ+τ−

• Search for the lepton-flavour violating decays B0(s) → e±µ∓

• Search for the lepton-flavour-violating decays B0s → τ±µ∓ and B0 → τ±µ∓

• Search for the rare decay B+ → µ+µ−µ+νµ

All results are consistent with the Standard Model. Nearly all results presented are either unique or the most accurate for the time.

Data analysis in high energy physics often deals with data samples consisting of a mixture of signal and background events. The sPlot technique is a common method to subtract the contribution of the background by assigning weights to events. Part of the weights are by design negative. Negative weights lead to the divergence of some machine learning algorithms training due to absence of the lower bound in the loss function. In this paper we propose a mathematically rigorous way to train machine learning algorithms on data samples with background described by sPlot to obtain signal probabilities conditioned on observables, without encountering negative event weight at all. This allows usage of any out-of-the-box machine learning methods on such data.

Measurements of CP observables in B0 → DK∗0 decays are presented, where D represents a superposition of D0 and 𝐷⎯⎯⎯⎯⎯0D¯0 states. The D meson is reconstructed in the two-body final states K+π−, π+K−, K+K− and π+π−, and, for the first time, in the fourbody final states K+π−π+π−, π+K−π+π− and π+π−π+π−. The analysis uses a sample of neutral B mesons produced in proton-proton collisions, corresponding to an integrated luminosity of 1.0, 2.0 and 1.8 fb−1 collected with the LHCb detector at centre-of-mass energies of 𝑠√=7,8s=7,8 and 13 TeV, respectively. First observations of the decays B0 → D(π+K−)K∗0 and B0 → D(π+π−π+π−)K∗0 are obtained. The measured observables are interpreted in terms of the CP -violating weak phase γ.

A measurement of the time-dependent CP-violating asymmetry in B0 s → φφ decays is presented. Using a sample of proton-proton collision data corresponding to an integrated luminosity of 5.0 fb−1 collected by the LHCb experiment at centre-of-mass energies √ s = 7 TeV in 2011, 8 TeV in 2012 and 13 TeV in 2015 and 2016, a signal yield of around 9000 B0 s → φφ decays is obtained. The CP-violating phase φ sss¯ s is measured to be −0.073 ± 0.115(stat) ± 0.027(syst) rad, under the assumption it is independent of the helicity of the φφ decay. In addition, the CP-violating phases of the transverse polarisations under the assumption of CP conservation of the longitudinal phase are measured. The helicity-independent direct CP-violation parameter is also measured, and is found to be |λ| = 0.99 ± 0.05(stat) ± 0.01(syst). In addition, T-odd triple-product asymmetries are measured. The results obtained are consistent with the hypothesis of CP conservation in ¯b → ss¯ ss¯ transitions. Finally, a limit on the branching fraction of the B0 → φφ decay is determined to be B(B0 → φφ) < 2.7 × 10−8 at 90 % confidence level.

The production fraction of the B−c meson with respect to the sum of B− and ¯B0 mesons is measured in both 7 and 13 TeV center-of-mass (c.m.) energy pp collisions produced by the Large Hadron Collider (LHC), using the LHCb detector. The rate, approximately 3.7 per mille, does not change with energy, but shows a transverse momentum dependence. The B−c−B+c production asymmetry is also measured and is consistent with zero within the determined statistical and systematic uncertainties of a few percent.

A measurement of the charm-mixing parameter yCP using D0 → KþK−, D0 → πþπ−, and D0 → K−πþ decays is reported. The D0 mesons are required to originate from semimuonic decays of B− and B0 mesons. These decays are partially reconstructed in a data set of proton-proton collisions at center-of-mass energies of 7 and 8 TeV collected with the LHCb experiment and corresponding to an integrated luminosity of 3 fb−1. The yCP parameter is measured to be ð0.57 0.13ðstatÞ 0.09ðsystÞÞ%, in agreement with, and as precise as, the current world-average value.

A narrow pentaquark state, Pc(4312)+, decaying to J/ψp, is discovered with a statistical significance of 7.3σ in a data sample of Λ0b→J/ψpK− decays, which is an order of magnitude larger than that previously analyzed by the LHCb Collaboration. The Pc(4450)+ pentaquark structure formerly reported by LHCb is confirmed and observed to consist of two narrow overlapping peaks, Pc(4440)+ and Pc(4457)+, where the statistical significance of this two-peak interpretation is 5.4σ. The proximity of the Σ+c¯D0 and Σ+c¯D*0 thresholds to the observed narrow peaks suggests that they play an important role in the dynamics of these states.

We report the observation of a new structure in the Λ0bπ+π− spectrum using the full LHCb data set of pp collisions, corresponding to an integrated luminosity of 9  fb−1, collected at √s=7, 8, and 13 TeV. A study of the structure suggests its interpretation as a superposition of two almost degenerate narrow states. The masses and widths of these states are measured to bemΛb(6146)0=6146.17±0.33±0.22±0.16  MeV,mΛb(6152)0=6152.51±0.26±0.22±0.16  MeV,ΓΛb(6146)0=2.9±1.3±0.3  MeV,ΓΛb(6152)0=2.1±0.8±0.3  MeV,with a mass splitting of Δm=6.34±0.32±0.02  MeV, where the first uncertainty is statistical, the second systematic. The third uncertainty for the mass measurements derives from the knowledge of the mass of the Λ0b baryon. The measured masses and widths of these new excited states suggest their possible interpretation as a doublet of Λb(1D)0 states.

Using proton-proton collision data, collected with the LHCb detector and corresponding to 1.0, 2.0 and 1.9 fb−1 of integrated luminosity at the centre-of-mass energies of 7, 8, and 13 TeV, respectively, the decay Λ0b→χc1Λb0→χc1(3872)pK− with χc1(3872) → J/ψ π+π− is observed for the first time. The significance of the observed signal is in excess of seven standard deviations. It is found that (58 ± 15)% of the decays proceed via the two-body intermediate state χc1(3872)Λ(1520). The branching fraction with respect to that of the Λ0bΛb0 → ψ(2S)pK− decay mode, where the ψ(2S) meson is reconstructed in the J/ψ π+π− final state, is measured to be:

𝛽(Λ0b→χc1(3872)pK−)𝛽(Λ0b→ψ(2S)pK−)×𝛽(χc1(3872)→J/ψπ+π−)𝛽(ψ(2S)→J/ψπ+π−)=(5.4±1.1±0.2)×10−2,β(Λb0→χc1(3872)pK−)β(Λb0→ψ(2S)pK−)×β(χc1(3872)→J/ψπ+π−)β(ψ(2S)→J/ψπ+π−)=(5.4±1.1±0.2)×10−2,

where the first uncertainty is statistical and the second is systematic.

The problem of maximization of the horizontal coordinate of a mass-point moving in the vertical plane driven by gravity, non-linear viscous drag, and thrust is considered. The slope angle and the thrust are considered as a control variables. The problem is related to the modified brachistochrone problem. Principle maximum procedure allows to reduce the optimal control problem to the boundary value problem for a set of systems of two non-linear differential equations. The qualitative analysis of the trajectories of these systems is performed, and the characteristic features of the optimal solutions are determined. Thrust control depending on the velocity and slope angle is designed. Results obtained allow to construct quasi-optimal solutions for the more complex systems, where phase plane method is not applicable.

One of the most challenging data analysis tasks of modern High Energy Physics experiments is the identification of particles. In this proceedings we review the new approaches used for particle identification at the LHCb experiment. Machine-Learning based techniques are used to identify the species of charged and neutral particles using several observables obtained by the LHCb sub-detectors. We show the performances of various solutions based on Neural Network and Boosted Decision Tree models.

One of the most challenging data analysis tasks of modern High Energy Physics experiments is the identification of particles. In this proceedings we review the new approaches used for particle identification at the LHCb experiment. Machine-Learning based techniques are used to identify the species of charged and neutral particles using several observables obtained by the LHCb sub-detectors. We show the performances of various solutions based on Neural Network and Boosted Decision Tree models.

A search for CP violation in the Cabibbo-suppressed D0 → K+K−π+π− decay mode is performed using an amplitude analysis. The measurement uses a sample of pp collisions recorded by the LHCb experiment during 2011 and 2012, corresponding to an integrated luminosity of 3.0 fb−1. The D0 mesons are reconstructed from semileptonic b-hadron decays into D0μ−X final states. The selected sample contains more than 160 000 signal decays, allowing the most precise amplitude modelling of this D0 decay to date. The obtained amplitude model is used to perform the search for CP violation. The result is compatible with CP symmetry, with a sensitivity ranging from 1% to 15% depending on the amplitude considered.

A search for the lepton-flavor violating decays B+→K+μ±e∓ is performed using a sample of proton-proton collision data, collected with the LHCb experiment at center-of-mass energies of 7 and 8 TeV and corresponding to an integrated luminosity of 3  fb−1. No significant signal is observed, and upper limits on the branching fractions are set as B(B+→K+μ−e+)<7.0(9.5)×10−9 and B(B+→K+μ+e−)<6.4(8.8)×10−9 at 90% (95)% confidence level. The results improve the current best limits on these decays by more than one order of magnitude.