The LHCb collaboration is one of the four major experiments at the Large Hadron Collider at CERN. Many petabytes of data are produced by the detectors and Monte-Carlo simulations. The LHCb Grid ...interware LHCbDIRAC 1 is used to make data available to all collaboration members around the world. The data is replicated to the Grid sites in different locations. However the Grid disk storage is limited and does not allow keeping replicas of each file at all sites. Thus it is essential to optimize number of replicas to achieve a better Grid performance. In this study, we present a new approach of data replication and distribution strategy based on data popularity prediction. The popularity is performed based on the data access history and metadata, and uses machine learning techniques and time series analysis methods.
The LHCb Grid Simulation: Proof of Concept Hushchyn, M; Ustyuzhanin, A; Arzymatov, K ...
Journal of physics. Conference series,
10/2017, Letnik:
898, Številka:
5
Journal Article
Recenzirano
Odprti dostop
The Worldwide LHC Computing Grid provides access to data and computational resources to analyze it for researchers with different geographical locations. The grid has a hierarchical topology with ...multiple sites distributed over the world with varying number of CPUs, amount of disk storage and connection bandwidth. Job scheduling and data distribution strategy are key elements of grid performance. Optimization of algorithms for those tasks requires their testing on real grid which is hard to achieve. Having a grid simulator might simplify this task and therefore lead to more optimal scheduling and data placement algorithms. In this paper we demonstrate a grid simulator for the LHCb distributed computing software.
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.
•Advanced machine learning techniques allow to increase particle identification performance both for charged and neutral particles.•Combining information from the LHCb subdetectors allows to achieve high background rejection for charged particle identification.•Using machine learning approaches to analyse the raw energy deposits in the calorimeter provides good prospects for high quality neutral particle identification.
A
bstract
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 kilometers 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.
We propose a novel approach for a machine-learning-based detection of the type Ia supernovae using photometric information. Unlike other approaches, only real observation data is used during ...training. Despite being trained on a relatively small sample, the method shows good results on real data from the Open Supernovae Catalog. We also investigate model transfer from the PLAsTiCC simulations train dataset to real data application, and the reverse, and find the performance significantly decreases in both cases, highlighting the existing differences between simulated and real data.
The Search for Hidden Particles (SHiP) Collaboration has proposed a general-purpose experimental facility operating in beam-dump mode at the CERN SPS accelerator to search for light, feebly ...interacting particles. In the baseline configuration, the SHiP experiment incorporates two complementary detectors. The upstream detector is designed for recoil signatures of light dark matter (LDM) scattering and for neutrino physics, in particular with tau neutrinos. It consists of a spectrometer magnet housing a layered detector system with high-density LDM/neutrino target plates, emulsion-film technology and electronic high-precision tracking. The total detector target mass amounts to about eight tonnes. The downstream detector system aims at measuring visible decays of feebly interacting particles to both fully reconstructed final states and to partially reconstructed final states with neutrinos, in a nearly background-free environment. The detector consists of a 50
m
long decay volume under vacuum followed by a spectrometer and particle identification system with a rectangular acceptance of 5 m in width and 10 m in height. Using the high-intensity beam of 400
GeV
protons, the experiment aims at profiting from the
4
×
10
19
protons per year that are currently unexploited at the SPS, over a period of 5–10 years. This allows probing dark photons, dark scalars and pseudo-scalars, and heavy neutral leptons with GeV-scale masses in the direct searches at sensitivities that largely exceed those of existing and projected experiments. The sensitivity to light dark matter through scattering reaches well below the dark matter relic density limits in the range from a few
MeV
/
c
2
up to 100 MeV-scale masses, and it will be possible to study tau neutrino interactions with unprecedented statistics. This paper describes the SHiP experiment baseline setup and the detector systems, together with performance results from prototypes in test beams, as it was prepared for the 2020 Update of the European Strategy for Particle Physics. The expected detector performance from simulation is summarised at the end.
A
bstract
Dark matter is a well-established theoretical addition to the Standard Model supported by many observations in modern astrophysics and cosmology. In this context, the existence of weakly ...interacting massive particles represents an appealing solution to the observed thermal relic in the Universe. Indeed, a large experimental campaign is ongoing for the detection of such particles in the sub-GeV mass range. Adopting the benchmark scenario for light dark matter particles produced in the decay of a dark photon, with
α
D
= 0
.
1 and
m
A
′
= 3
m
χ
, we study the potential of the SHiP experiment to detect such elusive particles through its Scattering and Neutrino detector (SND). In its 5-years run, corresponding to 2
·
10
20
protons on target from the CERN SPS, we find that SHiP will improve the current limits in the mass range for the dark matter from about 1 MeV to 300 MeV. In particular, we show that SHiP will probe the thermal target for Majorana candidates in most of this mass window and even reach the Pseudo-Dirac thermal relic.
Search for the decay B 0 → ϕμ + μ Aaij, R.; Eklund, Lars; Zunica, G.
The journal of high energy physics,
05/2022, Letnik:
2022, Številka:
5
Journal Article
Recenzirano
Odprti dostop
A search for the decay B-0 -> phi mu(+) mu(-) is performed using proton-proton collisions at centre-of-mass energies of 7, 8, and 13 TeV collected by the LHCb experiment and corresponding to an ...integrated luminosity of 9 fb(-1). No evidence for the B-0 -> phi mu(+) mu(-) decay is found and an upper limit on the branching fraction, excluding the 0 and charmonium regions in the dimuon spectrum, of 4.4 x 10(-3) at a 90% credibility level, relative to that of the B-s(0) -> phi mu(+) mu(-) decay, is established. Using the measured B-s(0) -> phi mu(+) mu(-) branching fraction and assuming a phase-space model, the absolute branching fraction of the decay B-0 -> phi mu(+) mu(-) in the full q(2) range is determined to be less than 3.2 x 10(-9) at a 90% credibility level.
PDF
