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.
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.
A search for the decay K0S→μ+μ− is performed, based on a data sample of proton-proton collisions corresponding to an integrated luminosity of 3 fb−1, collected by the LHCb experiment at centre-of-mass energies of 7 and 8 TeV. The observed yield is consistent with the background-only hypothesis, yielding a limit on the branching fraction of B(K0S→μ+μ−)<0.8 (1.0)×10−9 at 90% (95%) confidence level. This result improves the previous upper limit on the branching fraction by an order of magnitude.
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.
A search for CP violation in D± → η′ π± and Ds± → η′ π± decays is performed using proton-proton collision data, corresponding to an inte- grated luminosity of 3fb−1, recorded by the LHCb experiment at centre-of- mass energies of 7 and 8 TeV. The measured CP -violating charge asym- metries are ACP(D± → η′π±) = (−0.61 ± 0.72 ± 0.53 ± 0.12)% and ACP (Ds± → η′π±) = (−0.82 ± 0.36 ± 0.22 ± 0.27)%, where the first uncertain- ties are statistical, the second systematic, and the third are the uncertainties on the ACP (D± → KS0π±) and ACP (Ds± → φπ±) measurements used for calibration. The results represent the most precise measurements of these asymmetries to date.
A measurement of the time-integrated CP asymmetry in the Cabibbo-suppressed decay D0→K−K+ is performed using pp collision data, corresponding to an integrated luminosity of 3fb−1, collected with the LHCb detector at centre-of-mass energies of 7 and 8 TeV. The flavour of the charm meson at production is determined from the charge of the pion in ⁎D⁎+→D0π+ and ⁎D⁎−→D‾0π− decays. The time-integrated CP asymmetry ACP(K−K+) is obtained assuming negligible CP violation in charm mixing and in Cabibbo-favoured D0→K−π+, D+→K−π+π+ and D+→K‾0π+ decays used as calibration channels. It is found to beACP(K−K+)=(0.14±0.15(stat)±0.10(syst))%.
A combination of this result with previous LHCb measurements yieldsACP(K−K+)=(0.04±0.12(stat)±0.10(syst))%,ACP(π−π+)=(0.07±0.14(stat)±0.11(syst))%.
These are the most precise measurements from a single experiment. The result for ACP(K−K+) is the most precise determination of a time-integrated CP asymmetry in the charm sector to date, and neither measurement shows evidence of CP asymmetry.
Differences in the behaviour of matter and antimatter have been observed in K and B meson decays, but not yet in any baryon decay. Such differences are associated with the non-invariance of fundamental interactions under the combined charge-conjugation and parity transformations, known as CP violation. Here, using data from the LHCb experiment at the Large Hadron Collider, we search for CP-violating asymmetries in the decay angle distributions of Λb0 baryons decaying to pπ−π+π− and pπ−K+K− final states. These four-body hadronic decays are a promising place to search for sources of CP violation both within and beyond the standard model of particle physics. We find evidence for CPviolation in Λb0 to pπ−π+π− decays with a statistical significance corresponding to 3.3 standard deviations including systematic uncertainties. This represents the first evidence for CP violation in the baryon sector.
A search for the rare decays Bs0→μ+μ- and B0→μ+μ- is performed at the LHCb experiment using data collected in pp collisions corresponding to a total integrated luminosity of 4.4 fb-1. An excess of Bs0→μ+μ- decays is observed with a significance of 7.8 standard deviations, representing the first observation of this decay in a single experiment. The branching fraction is measured to be B(Bs0→μ+μ-)=(3.0±0.6-0.2+0.3)×10-9, where the first uncertainty is statistical and the second systematic. The first measurement of the Bs0→μ+μ- effective lifetime, τ(Bs0→μ+μ-)=2.04±0.44±0.05 ps, is reported. No significant excess of B0→μ+μ- decays is found, and a 95% confidence level upper limit, B(B0→μ+μ-)<3.4×10-10, is determined. All results are in agreement with the standard model expectations.
The CRAYFIS experiment proposes to use privately owned mobile phones as a ground detector array for Ultra High Energy Cosmic Rays. Upon interacting with Earth’s atmosphere, these events produce extensive particle showers which can be detected by cameras on mobile phones. A typical shower contains minimally-ionizing particles such as muons. As these particles interact with CMOS image sensors, they may leave tracks of faintly-activated pixels that are sometimes hard to distinguish from random detector noise. Triggers that rely on the presence of very bright pixels within an image frame are not efficient in this case. We present a trigger algorithm based on Convolutional Neural Networks which selects images containing such tracks and are evaluated in a lazy manner: the response of each successive layer is computed only if activation of the current layer satisfies a continuation criterion. Usage of neural networks increases the sensitivity considerably comparable with image thresholding, while the lazy evaluation allows for execution of the trigger under the limited computational power of mobile phones.
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.
Using proton-proton collision data corresponding to an integrated luminosity of 3.0fb−1, recorded by the LHCb detector at centre-of-mass energies of 7 and 8TeV, the Bc+ → D0K+ decay is observed with a statistical significance of 5.1 standard deviations. By normalising to B+ → D0π+ decays, a measurement of the branching fraction multiplied by the production rates for Bc+ relative to B+ mesons in the LHCb acceptance is obtained,
R 0 =fc ×B(B+→D0K+)=(9.3+2.8±0.6)×10−7, DKfu c −2.5
where the first uncertainty is statistical and the second is systematic. This decay is expected to proceed predominantly through weak annihilation and penguin amplitudes, and is the first Bc+ decay of this nature to be observed.
A highly significant structure is observed in the Λ+cK−π+π+ mass spectrum, where the Λ+c baryon is reconstructed in the decay mode pK−π+. The structure is consistent with originating from a weakly decaying particle, identified as the doubly charmed baryon Ξ++cc. The difference between the masses of the Ξ++cc and Λ+c states is measured to be 1334.94±0.72(stat)±0.27(syst MeV/c2, and the Ξ++cc mass is then determined to be 3621.40±0.72(stat)±0.27(syst±0.14(Λ+c) MeV/c2, where the last uncertainty is due to the limited knowledge of the Λ+c mass. The state is observed in a sample of proton-proton collision data collected by the LHCb experiment at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 1.7 fb−1, and confirmed in an additional sample of data collected at 8 TeV.
The SHiP experiment is designed to search for very weakly interacting particles beyond the Standard Model which are produced in a 400 GeV/c proton beam dump at the CERN SPS. The critical challenge for this experiment is to keep the Standard Model background level negligible. In the beam dump, around 10^11 muons will be produced per second. The muon rate in the spectrometer has to be reduced by at least four orders of magnitude to avoid muoninduced backgrounds. It is demonstrated that new improved active muon shield may be used to magnetically deflect the muons out of the acceptance of the spectrometer.
In this paper we suggest an algorithm for ringing suppression based on a sparse representation method. As one of its steps, the suggested method includes image deblurring based on the Wiener-Hunt deconvolution algorithm. The ringing suppression algorithm uses the signals' mutual coherence and sparsities analysis when dealing with the ringing effect based on the sparse representation method. We also analyze the mutual coherence and sparsities for blurred images and images with white Gaussian noise.
A search for baryon-number violating Ξb0 oscillations is performed with a sample of pp collision data recorded by the LHCb experiment, corresponding to an integrated luminosity of 3 fb−1. The baryon number at the moment of production is identified by requiring that the Ξb0 come from the decay of a resonance Ξb*−→Ξb0π− or Ξb′−→Ξb0π−, and the baryon number at the moment of decay is identified from the final state using the decays Ξb0→Ξc+π−,Ξc+→pK−π+. No evidence of baryon-number violation is found, and an upper limit at the 95% confidence level is set on the oscillation rate of ω<0.08 ps−1, where ω is the associated angular frequency.
A search for the rare decays Bs0→τ+τ- and B0→τ+τ- is performed using proton–proton collision data collected with the LHCb detector. The data sample corresponds to an integrated luminosity of 3 fb-1 collected in 2011 and 2012. The τ leptons are reconstructed through the decay τ-→π-π+π-ντ. Assuming no contribution from B0→τ+τ- decays, an upper limit is set on the branching fraction B(Bs0→τ+τ-)<6.8×10-3 at the 95% confidence level. If instead no contribution from Bs0→τ+τ- decays is assumed, the limit is B(B0→τ+τ-)<2.1×10-3 at the 95% confidence level. These results correspond to the first direct limit on B(Bs0→τ+τ-) and the world’s best limit on B(B0→τ+τ-).
In this work we discuss methods for image ringing detection and suppression that are based on the sparse representations approach and suggest a new ringing suppression method. The ringing detection algorithm is based on construction of the synthetic dictionary that is used to represent ringing effect as a sum of blurred edge and pure ringing component. This decomposition enables us to estimate image ringing level. We analyze two ringing suppression methods. First method is based on learning joint dictionaries and shows good performance for the whole image on average. However for high ringing levels the performance of this method decreases due to the influence of the ringing artefact on the sparse representation parameters. The second method is based on separate learning of natural images dictionary and pure ringing dictionary and it does not suffer from this problem. In this article we present a new ringing suppression method that is based on the method using separate dictionaries. The method works best in the areas of edges and for higher levels of ringing effect.
Using decays to ϕ-meson pairs, the inclusive production of charmonium states in b-hadron decays is studied with pp collision data corresponding to an integrated luminosity of 3.0fb−1, collected by the LHCb experiment at centre-of-mass energies of 7 and 8 TeV. Denoting by BC≡B(b→CX)×B(C→ϕϕ) the inclusive branching fraction of a b hadron to a charmonium state C that decays into a pair of ϕ mesons, ratios RC1C2≡BC1/BC2 are determined as Rχc0ηc(1S)=0.147±0.023±0.011, Rχc1ηc(1S)=0.073±0.016±0.006, Rχc2ηc(1S)=0.081±0.013±0.005, Rχc1χc0=0.50±0.11±0.01, Rχc2χc0=0.56±0.10±0.01 and Rηc(2S)ηc(1S)=0.040±0.011±0.004. Here and below the first uncertainties are statistical and the second systematic. Upper limits at 90% confidence level for the inclusive production of X(3872), X(3915) and χc2(2P) states are obtained as RX(3872)χc1<0.34, RX(3915)χc0<0.12 and Rχc2(2P)χc2<0.16. Differential cross-sections as a function of transverse momentum are measured for the ηc(1S) and χc states. The branching fraction of the decay B0s→ϕϕϕ is measured for the first time, B(B0s→ϕϕϕ)=(2.15±0.54±0.28±0.21B)×10−6. Here the third uncertainty is due to the branching fraction of the decay B0s→ϕϕ, which is used for normalization. No evidence for intermediate resonances is seen. A preferentially transverse ϕ polarization is observed. The measurements allow the determination of the ratio of the branching fractions for the ηc(1S) decays to ϕϕ and pp¯ as B(ηc(1S)→ϕϕ)/B(ηc(1S)→pp¯)=1.79±0.14±0.32.
Daily operation of a large-scale experiment is a challenging task, particularly from perspectives of routine monitoring of quality for data being taken. We describe an approach that uses Machine Learning for the automated system to monitor data quality, which is based on partial use of data qualified manually by detector experts. The system automatically classifies marginal cases: both of good an bad data, and use human expert decision to classify remaining “grey area” cases. This study uses collision data collected by the CMS experiment at LHC in 2010. We demonstrate that proposed workflow is able to automatically process at least 20% of samples without noticeable degradation of the result.
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.