© Published under licence by IOP Publishing Ltd. Data quality monitoring, DQM, is crucial in a high-energy physics experiment to ensure the correct functioning of the experimental apparatus during the data taking. DQM at LHCb is carried out in two phases. The first one is performed on-site, in real time, using unprocessed data directly from the LHCb detector, while the second, also performed on-site, requires the reconstruction of the data selected by the LHCb trigger system and occurs later. For the LHC Run II data taking the LHCb collaboration has re-engineered the DQM protocols and the DQM graphical interface, moving the latter to a web-based monitoring system, called Monet, thus allowing researchers to perform the second phase off-site. In order to support the operator's task, Monet is also equipped with an automated, fully configurable alarm system, thus allowing its use not only for DQM purposes, but also to track and assess the quality of LHCb software and simulation over time.
The main b-physics trigger algorithm used by the LHCb experiment is the so-called topological trigger. The topological trigger selects vertices which are a) detached from the primary proton-proton collision and b) compatible with coming from the decay of a b-hadron. In the LHC Run 1, this trigger, which utilized a custom boosted decision tree algorithm, selected a nearly 100% pure sample of b-hadrons with a typical efficiency of 60-70%; its output was used in about 60% of LHCb papers. This talk presents studies carried out to optimize the topological trigger for LHC Run 2. In particular, we have carried out a detailed comparison of various machine learning classifier algorithms, e.g., AdaBoost, MatrixNet and neural networks. The topological trigger algorithm is designed to select all "interesting" decays of b-hadrons, but cannot be trained on every such decay. Studies have therefore been performed to determine how to optimize the performance of the classification algorithm on decays not used in the training. Methods studied include cascading, ensembling and blending techniques. Furthermore, novel boosting techniques have been implemented that will help reduce systematic uncertainties in Run 2 measurements. We demonstrate that the reoptimized topological trigger is expected to significantly improve on the Run 1 performance for a wide range of b-hadron decays.
The LHCb experiment stores around 1011 collision events per year. A typical physics analysis deals with a final sample of up to 107 events. Event preselection algorithms (lines) are used for data reduction. Since the data are stored in a format that requires sequential access, the lines are grouped into several output file streams, in order to increase the efficiency of user analysis jobs that read these data. The scheme efficiency heavily depends on the stream composition. By putting similar lines together and balancing the stream sizes it is possible to reduce the overhead. We present a method for finding an optimal stream composition. The method is applied to a part of the LHCb data (Turbo stream) on the stage where it is prepared for user physics analysis. This results in an expected improvement of 15% in the speed of user analysis jobs, and will be applied on data to be recorded in 2017.
Spin-gap magnet (C7H10N)2Cu(1-x)ZnxBr4 (DIMPY) is an example of a strongleg spin ladder. We report here the results of the ESR study of pure and diamagnetically diluted DIMPY. ESR study of the pure system (x=0) revealed that the spin dynamics of DIMPY is a ected by uniform Dzyaloshinskii-Moriya (DM) interaction. We observe narrowing of the ESR absorption line in diamagnetically diluted DIMPY pointing to suppression of DM channel of spin relaxation by doping.
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.
Reconstruction and identification 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 on LHC from overlapping photons produced from high momentum pi0 decays. We studied an alternative solution based on applying machine learning techniques to primary calorimeter information, that are energies collected in individual cells around the energy cluster. Constructing such a discriminator from “first principles” allowed improve separation performance from 80% to 93%, that means reducing primary photons fake rate by factor of two. In presentation we discuss different approaches to the problem, architecture of the classifier, its optimization, and compare performance of the ML approach with classical one.
We report on comparative study of magnetic phase diagram and critical current of the hole- and electron-doped BaFe2As2 single crystals with close values of superconducting critical temperature, Tc, (slightly underdoped Ba0.64K0.36Fe2As2 with Tc=25K and optimally doped BaFe1.9Ni0.1As2 with Tc=20K) obtained from measurements of the temperature dependence of ac-susceptibility and isothermal irreversible magnetization loops, M(H), in magnetic fields parallel to the c-axis of the crystal. From ac-susceptibility measurements we get estimation of a slope of the upper critical eld, Hc2, in dependence on temperature, dHc2/dT =- 4.2T/K for BaFe1:9Ni0:1As2 single crystal and dHc2/dT =-1.75T/K for Ba0.64K0.36Fe2As2 sample that in accordance with Werthamer, Helfand, and Hohenberg (WHH) model gives Hc2(0) = 0.69Tc((dHc2)/dT) = 56T for BaFe1.9Ni0.1As2 sample and lower value of Hc2(0) = 31T for Ba0.64K0.36Fe2As2 crystal. However, obtained from M(H) measurements temperature dependence of the irreversibility field, Hirr(T), for BaFe1.9Ni0.1As2 crystal located below the one for Ba0.64K0.36Fe2As2 crystal. Furthermore, at T=4.2K and higher temperatures our results for critical current density, Jc, calculated from M(H) curves clearly show slower reduction of Jc with increasing field for even underdoped Ba0.64K0.36Fe2As2 sample compared to optimally doped BaFe1.9Ni0.1As2 crystal demonstrating higher capacity of K-doped 122 compounds for production of superconducting cables and wires with high critical current in strong magnetic fields.
The work presents a study of manganese-doped copper metaborate (Cu, Mn) B2O4 using optical spectroscopy. The temperature of the antiferromagnetic phase transition T-N = 19 K has been defined according to the absorption spectra. Polarization studies (Cu, Mn) B2O4 in isotropic ab-plane show the presence of linear antiferromagnetic dichroism in the magnetically ordered state previously observed in pure copper metaborate CuB2O4. This measurement allows to find the magnetic phase transition into an elliptical structure at the temperature T* = 7.0 K.
We report preparation of nanoribbons (crossection ~ 250*25 nm2) by focused ion beam etching of single-crystalline Bi2Se3 and detailed measurements of their magnetoresistance at temperatures down to 4.2 K, magnetic field up to 9 T. In a magnetic field parallel to the axis of nanowire the magnetoresistance shows up oscillations. Surprisingly, the Fourier analysis shows the presence not only of oscillations with a period corresponding to the flux quantum (Φ0 = hc/e), but also oscillations with a period of 2Φ0 and 4Φ0. Possible mechanisms of the observed effect are discussed.
In this article, the dynamics of the qubits states based on solution of the time-dependent Schrödinger equation is investigated. Using the Magnus method we obtain an explicit interpolation representation for the propagator, which allows to find wave function at an arbitrary time. To illustrate the effectiveness of the approach, the population of the levels a single and two coupled qubits have been calculated by applying the Magnus propagator and the result has been compared with the numerical solution of the Schrödinger equation. As a measure of the approximation of the wave function, we calculate fidelity, which indicates proximity when the exact and approximate evolution operator acts on the initial state. We discuss the possibility of extending the developed methods to a multi-qubits system when highspeed calculation methods of the operators of evolution are particularly relevant
Matrix multiplication is one of the core operations in many areas of scientific computing. We present the results of the experiments with the matrix multiplication of the big size comparable with the big size of the onboard memory, which is 1.5 terabyte in our case. We run experiments on the computing board with two sockets and with two Intel Xeon Platinum 8164 processors, each with 26 cores and with multi-threading. The most interesting result of our study is the observation of the perfect scalability law of the matrix multiplication, and of the universality of this law.
A measurement of CP-violating weak phase s and meson decay width difference with decays in the ATLAS experiment is presented. It is based on integrated luminosity of 14.3 fb−1 collected by the ATLAS detector from 8 TeV pp collisions at the LHC. The measured values are statistically combined with those from 4.9 fb−1 of 7 TeV collisions data, yielding an overall Run-1 ATLAS result.
We investigate synchronisation aspects of an optimistic algorithm for parallel discrete event simulations (PDES). We present a model for the time evolution in optimistic PDES. This model evaluates the local virtual time profile of the processing elements. We argue that the evolution of the time profile is reminiscent of the surface profile in the directed percolation problem and in unrestricted surface growth. We present results of the simulation of the model and emphasise predictive features of our approach.
Pressure of plasma is calculated by using classical molecular dynamics method. The formula based on virial theorem was used. Spectrum pressure's fluctuations of singly ionized non-ideal plasma are studied. 1/f-like spectrum behavior is observed. In other words, flicker noise is observed in fluctuations of pressure equilibrium non-ideal plasma. Relations between the obtained result and pressure fluctuations within the Gibbs and Einstein approaches are discussed. Special attention is paid to features of calculating the pressure in strongly coupled systems.
A new idea is developed that the fluid–fluid phase transition in warm dense hydrogen is related to the partial ionization of molecular hydrogen H2 with formation of molecular ions H2+ and H3+ . Conventional ab initio quantum modeling is applied. Proton pair correlation functions (PCF) obtained are used for the nonconventional diagnostics of the phase transition and elucidation of its nature for temperatures 700-1500 K. Short- and longrange changes of PCF’s are studied. H2 molecules ionization and molecular ions H2+ and H3+ appearance is revealed. The validity of the soft sphere model is tested for the long-range order.
The article shows that large artificial neural networks can be used for mutual ordering of a set of multi-dimensional patterns of the same nature (handwritten text, voice, smells, taste). Each neural network must be pre-trained to recognize one of the patterns. As a measure of ordering one can use the entropy of patterns "Strangers" that are input to a neural network trained to recognize only examples of the pattern "familiar". The neural network after training reduces the entropy of the examples of the pattern "Familiar" and increases the entropy of examples of pattern "Stranger." It is shown that the entropy measure of the ordering always has two global minima. The first minimum corresponds to the pattern "Familiar", the second to the inversion of the pattern "Familiar". It is also shown that the Hamming distance between the patterns belonging to two different groups (groups of the two global minima) is always as large as possible.
In this work we discuss complex dynamics arising in a model describing behavior of an encapsulated bubble contrast agent oscillating close to an elastic wall. We demonstrate presence of three coexisting attractors in the system. We propose an efficient numerical procedure based on the continuation method that can be used to locate the area of coexistence of these attractors in the parameters space. We provide area of coexistence of three attractors obtained by means of the proposed procedure.
When combined with error-correction codes reception techniques based on nonparametric hypothesis testing and order statistics provide strong immunity to different types of interference including multiuser interference. That makes communication systems using such reception techniques most appealing candidates for various applications such as Machine-to-Machine (M2M) communications and Internet Of Things (IOT). Unfortunately analytical treatment of communication systems with nonparametric reception remains a cumbersome task. Therefore simulation remains the main tool for the development of such systems. For multiuser systems supporting hundreds of active users and operating in fading channels (e.g. IOT) time spending grows drastically hampering the design process. Thus the development of simplified multiuser channel models is of great interest. In this paper two simplified mathematical models of multiuser interference for the case of a single user nonparametric reception are proposed. The effectiveness of the proposed models is compared by means of modulation. Special attention is paid to the problem of software implementation of the models proposed.