Publications

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.

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.

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 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.

Heavy Neutral Leptons (HNLs) are hypothetical particles predicted by many extensions of the Standard Model. These particles can, among other things, explain the origin of neutrino masses, generate the observed matter-antimatter asymmetry in the Universe and provide a dark matter candidate.

The SHiP experiment will be able to search for HNLs produced in decays of heavy mesons and travelling distances ranging between O(50 m) and tens of kilometres before decaying. We present the sensitivity of the SHiP experiment to a number of HNL’s benchmark models and provide a way to calculate the SHiP’s sensitivity to HNLs for arbitrary patterns of flavour mixings. The corresponding tools and data files are also made publicly available.

The Search for Hidden Particles (SHiP) Collaboration has shown that the CERN SPS accelerator with its 400 GeV/c proton beam offers a unique opportunity to explore the Hidden Sector [1–3]. The proposed experiment is an intensity frontier experiment which is capable of searching for hidden particles through both visible decays and through scattering signatures from recoil of electrons or nuclei. The high-intensity experimental facility developed by the SHiP Collaboration is based on a number of key features and developments which provide the possibility of probing a large part of the parameter space for a wide range of models with light long-lived super-weakly interacting particles with masses up to (10) GeV/c2 in an environment of extremely clean background conditions. This paper describes the proposal for the experimental facility together with the most important feasibility studies. The paper focuses on the challenging new ideas behind the beam extraction and beam delivery, the proton beam dump, and the suppression of beam-induced background.

A prototype of a transition radiation detector based on straw proportional chambers has been tested at the CERN SPS accelerator beams. A detailed Monte Carlo simulation program has been developed to describe the results obtained during these measurements. Some data obtained in beam tests and their comparison with simulation results are presented.

Daily operation of a large-scale experiment is a resource consuming task, particularly from perspectives of routine data quality monitoring. Typically, data comes from different sub-detectors and the global quality of data depends on the combinatorial performance of each of them. In this paper, the problem of identifying channels in which anomalies occurred is considered. We introduce a generic deep learning model and prove that, under reasonable assumptions, the model learns to identify ’channels’ which are affected by an anomaly. Such model could be used for data quality manager cross-check and assistance and identifying good channels in anomalous data samples. The main novelty of the method is that the model does not require ground truth labels for each channel, only global flag is used. This effectively distinguishes the model from classical classification methods. Being applied to CMS data collected in the year 2010, this approach proves its ability to decompose anomaly by separate channels.

In the research, a new approach for finding rare events in high-energy physics was tested. As an example of physics channel the decay of \tau -> 3 \mu is taken that has been published on Kaggle within LHCb-supported challenge. The training sample consists of simulated signal and real background, so the challenge is to train classifier in such way that it picks up signal/background differences and doesn’t overfits to simulation-specific features. The approach suggested is based on cross-domain adaptation using neural networks with gradient reversal. The network architecture is a dense multi-branch structure. One branch is responsible for signal/background discrimination, the second branch helps to avoid overfitting on Monte-Carlo training dataset. The tests showed that this architecture is a robust a mechanism for choosing tradeoff between discrimination power and overfitting, moreover, it also improves the quality of the baseline prediction. Thus, this approach allowed us to train deep learning models without reducing the quality, which allow us to distinguish physical parameters, but do not allow us to distinguish simulated events from real ones. The third network branch helps to eliminate the correlation between classifier predictions and reconstructed mass of the decay, thereby making such approach highly viable for great variety of physics searches.

A search for the associated production of the Higgs boson with a top quark pair (t¯tH) is reported. The search is performed in multilepton final states using a data set corresponding to an integrated luminosity of 36.1  fb−1 of proton-proton collision data recorded by the ATLAS experiment at a center-of-mass energy √s=13  TeV at the Large Hadron Collider. Higgs boson decays to WW∗, ττ, and ZZ∗ are targeted. Seven final states, categorized by the number and flavor of charged-lepton candidates, are examined for the presence of the Standard Model Higgs boson with a mass of 125 GeV and a pair of top quarks. An excess of events over the expected background from Standard Model processes is found with an observed significance of 4.1 standard deviations, compared to an expectation of 2.8 standard deviations. The best fit for the t¯tH production cross section is σ(t¯tH)=790+230−210  fb, in agreement with the Standard Model prediction of 507+35−50  fb. The combination of this result with other t¯tH searches from the ATLAS experiment using the Higgs boson decay modes to b¯b, γγ and ZZ∗→4ℓ, has an observed significance of 4.2 standard deviations, compared to an expectation of 3.8 standard deviations. This provides evidence for the t¯tH production mode.

The OPERA experiment was designed to study νμ→ντ oscillations in the appearance mode in the CERN to Gran Sasso Neutrino beam (CNGS). In this Letter, we report the final analysis of the full data sample collected between 2008 and 2012, corresponding to 17.97×1019 protons on target. Selection criteria looser than in previous analyses have produced ten ντ candidate events, thus reducing the statistical uncertainty in the measurement of the oscillation parameters and of ντ properties. A multivariate approach for event identification has been applied to the candidate events and the discovery of ντ appearance is confirmed with an improved significance level of 6.1σ. |Δm232| has been measured, in appearance mode, with an accuracy of 20%. The measurement of the ντ charged-current cross section, for the first time with a negligible contamination from ¯ντ, and the first direct evidence for the ντ lepton number are also reported.

A flavour-tagged decay-time-dependent amplitude analysis of B s 0  → (K+π−)(K−π+) decays is presented in the K±π∓ mass range from 750 to 1600MeV/c2. The analysis uses pp collision data collected with the LHCb detector at centre-of-mass energies of 7 and 8 TeV, corresponding to an integrated luminosity of 3.0 fb−1. Several quasi-two-body decay modes are considered, corresponding to K±π∓ combinations with spin 0, 1 and 2, which are dominated by the K 0 *(800)0 and K 0 * (1430)0, the K*(892)0 and the K 2 * (1430)0 resonances, respectively. The longitudinal polarisation fraction for the B0s→K∗(892)∘K∗(892)0Bs0→K∗(892)∘K¯∗(892)0 decay is measured as fL = 0.208 ± 0.032 ± 0.046, where the first uncertainty is statistical and the second is systematic. The first measurement of the mixing-induced CP-violating phase, ϕdd⎯⎯⎯⎯sϕsdd¯, in b→dd⎯⎯⎯sb→dd¯s transitions is performed, yielding a value of ϕdd⎯⎯⎯⎯s=−0.10±0.13(stat)±0.14ϕsdd¯=−0.10±0.13(stat)±0.14 (syst) rad.

Fits to the final combined HERA deep-inelastic scattering cross-section data within the conventional DGLAP framework of QCD have shown some tension at low x and low Q2. A resolution of this tension incorporating ln(1/x)-resummation terms into the HERAPDF fits is investigated using the xFitter program. The kinematic region where this resummation is important is delineated. Such high-energy resummation not only gives a better description of the data, particularly of the longitudinal structure function FL, it also results in a gluon PDF which is steeply rising at low x for low scales, Q2≃2.5 GeV2, contrary to the fixed-order NLO and NNLO gluon PDF.

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.

One of the most important aspects of data analysis at the LHC experiments is the particle identification (PID). In LHCb, several different sub-detectors provide PID information: two Ring Imaging Cherenkov (RICH) detectors, the hadronic and electromagnetic calorimeters, and the muon chambers. To improve charged particle identification, we have developed models based on deep learning and gradient boosting. The new approaches, tested on simulated samples, provide higher identification performances than the current solution for all charged particle types. It is also desirable to achieve a flat dependency of efficiencies from spectator variables such as particle momentum, in order to reduce systematic uncertainties in the physics results. For this purpose, models that improve the flatness property for efficiencies have also been developed. This paper presents this new approach and its performance.

Traces of electro-magnetic showers in the neutrino experiments may be considered as signals of dark-matter particles. For example, SHiP experiment is going to use emulsion film detectors similar to the ones designed for OPERA experiment from dark matter search. The goal of this research is to develop an algorithm that can identify traces of electro-magnetic showers in particle detectors, so it would be possible to analyse and compare various dark matter hypothesis. Both real data and signal simulation samples for this research come from OPERA experiment. Also we've used OPERA algorithm for electromagnetic showers identification as a baseline. Although in this research we've used no hints about shower origin.

The cross section for prompt antiproton production in collisions of protons with an energy of 6.5 TeV incident on helium nuclei at rest is measured with the LHCb experiment from a data set corresponding to an integrated luminosity of 0.5  nb-1. The target is provided by injecting helium gas into the LHC beam line at the LHCb interaction point. The reported results, covering antiproton momenta between 12 and 110  GeV/c, represent the first direct determination of the antiproton production cross section in p-He collisions, and impact the interpretation of recent results on antiproton cosmic rays from space-borne experiments.

Measurements of CP observables in B±→D(⁎)K± and B±→D(⁎)π± decays are presented, where D(⁎) indicates a neutral D or D⁎ meson that is an admixture of D(⁎)0 and D¯(⁎)0states. Decays of the D⁎ meson to the Dπ0 and Dγ final states are partially reconstructed without inclusion of the neutral pion or photon, resulting in distinctive shapes in the Bcandidate invariant mass distribution. Decays of the D meson are fully reconstructed in the K±π∓, K+K− and π+π− final states. The analysis uses a sample of charged B mesons produced in pp collisions collected by the LHCb experiment, corresponding to an integrated luminosity of 2.0, 1.0 and 2.0 fb−1 taken at centre-of-mass energies of s=7, 8 and 13 TeV, respectively. The study of B±→D⁎K± and B±→D⁎π± decays using a partial reconstruction method is the first of its kind, while the measurement of B±→DK± and B±→Dπ± decays is an update of previous LHCb measurements. The B±→DK± results are the most precise to date.