The High Luminosity Large Hadron Collider (HL-LHC) at CERN will involve a significant increase in complexity and sheer size of data with respect to the current LHC experimental complex. Hence, the task of reconstructing the particle trajectories will become more involved due to the number of simultaneous collisions and the resulting increased detector occupancy. Aiming to identify the particle paths, machine learning techniques such as graph neural networks are being explored in the HEP.TrkX project and its successor, the Exa.TrkX project. Both show promising results and reduce the combinatorial nature of the problem. Previous results of our team have demonstrated the successful attempt of applying quantum graph neural networks to reconstruct the particle track based on the hits of the detector. A higher overall accuracy is gained by representing the training data in a meaningful way within an embedded space. That has been included in the Exa.TrkX project by applying a classical MLP. Consequently, pairs of hits belonging to different trajectories are pushed apart while those belonging to the same ones stay close together. We explore the applicability of variational quantum circuits that include a relatively low number of qubits applicable to NISQ devices within the task of embedding and show preliminary results.

The expected increase in simultaneous collisions creates a challenge for accurate particle track reconstruction in High Luminosity LHC experiments. Similar challenges can be seen in non-HEP trajectory reconstruction use-cases, where tracking and track evaluation algorithms are used. High occupancy, track density, complexity and fast growth therefore exponentially increase the demand of algorithms in terms of time, memory and computing resources. While traditionally Kalman filter (or even simpler algorithms) are used, they are expected to scale worse than quadratic and thus strongly increasing the total processing time. Graph Neural Networks (GNN) are currently explored for HEP, but also non HEP trajectory reconstruction applications. Quantum Computers with their feature of evaluating a very large number of states simultaneously are therefore good candidates for such complex searches in large parameter and graph spaces. In this paper we present our work on implementing a quantum-based graph tracking machine learning algorithm to evaluate Traffic collision avoidance system (TCAS) probabilities of commercial flights.

The Large Hadron Collider (LHC) at the European Organisation for Nuclear Research (CERN) will be upgraded to further increase the instantaneous rate of particle collisions (luminosity) and become the High Luminosity LHC. This increase in luminosity, will yield many more detector hits (occupancy), and thus measurements will pose a challenge to track reconstruction algorithms being responsible to determine particle trajectories from those hits. This work explores the possibility of converting a novel Graph Neural Network model, that proven itself for the track reconstruction task, to a Hybrid Graph Neural Network in order to benefit the exponentially growing Hilbert Space. Several Parametrized Quantum Circuits (PQC) are tested and their performance against the classical approach is compared. We show that the hybrid model can perform similar to the classical approach. We also present a future road map to further increase the performance of the current hybrid model.

Accurate determination of particle track reconstruction parameters will be a major challenge for the High Luminosity Large Hadron Collider (HL-LHC) experiments. The expected increase in the number of simultaneous collisions at the HL-LHC and the resulting high detector occupancy will make track reconstruction algorithms extremely demanding in terms of time and computing resources. The increase in number of hits will increase the complexity of track reconstruction algorithms. In addition, the ambiguity in assigning hits to particle tracks will be increased due to the finite resolution of the detector and the physical "closeness" of the hits. Thus, the reconstruction of charged particle tracks will be a major challenge to the correct interpretation of the HL-LHC data. Most methods currently in use are based on Kalman filters which are shown to be robust and to provide good physics performance. However, they are expected to scale worse than quadratically. Designing an algorithm capable of reducing the combinatorial background at the hit level, would provide a much "cleaner" initial seed to the Kalman filter, strongly reducing the total processing time. One of the salient features of Quantum Computers is the ability to evaluate a very large number of states simultaneously, making them an ideal instrument for searches in a large parameter space. In fact, different R&D initiatives are exploring how Quantum Tracking Algorithms could leverage such capabilities. In this paper, we present our work on the implementation of a quantum-based track finding algorithm aimed at reducing combinatorial background during the initial seeding stage. We use the publicly available dataset designed for the kaggle TrackML challenge.

When humanitarian and social challenges from the United Nations, Red Cross and NonGovernmental Organisations meet HEP expertise impactful innovation becomes reality. THE Port association at CERN combines physicists and engineers working on HEP topics in their day job with researchers, refugees, entrepreneurs, artists, designers, humanitarian workers and other creative minds. During 60-hour-long curated hackathons they co-create new technology opportunities, identify new methods, materials and processes, that can be used in the humanitarian context and sometimes even feedback into HEP. Examples from the last 5 humanitarian hackathons at CERN, whose outcomes are now utilised by UN, ICRC and others as well as future initiatives for HEP society impact, are presented.

A search is presented for the direct pair production of the stop, the supersymmetric partner of the top quark, that decays through an R-parity-violating coupling to a final state with two leptons and two jets, at least one of which is identified as a b-jet. The data set corresponds to an integrated luminosity of 36.1 fb(-1) of proton-proton collisions at a center-of-mass energy of root s = 13 TeV, collected in 2015 and 2016 by the ATLAS detector at the LHC. No significant excess is observed over the Standard Model background, and exclusion limits are set on stop pair production at a 95% confidence level. Lower limits on the stop mass are set between 600 GeV and 1.5 TeV for branching ratios above 10% for decays to an electron or muon and a b-quark.

A measurement of the fragmentation functions of jets into charged particles in p Pb collisions and pp collisions is presented. The analysis utilizes 28 nb(-1) of p Pb data and 26 pb(-1) of pp data, both at root(TN)-T-s= 5.02 TeV, collected in 2013 and 2015, respectively, with the ATLAS detector at the LHC. The measurement is reported in the centre-of-mass frame of the nucleon-nucleon system for jets in the rapidity range vertical bar y*vertical bar <1.6 and with transverse momentum 45 < p(T) < 260 GeV. Results are presented both as a function of the charged-particle transverse momentum and as a function of the longitudinal momentum fraction of the particle with respect to the jet. The pp fragmentation functions are compared with results from Monte Carlo event generators and two theoretical models. The ratios of the p +Pb to pp fragmentation functions are found to be consistent with unity. (C) 2018 CERN for the benefit of the ATLAS Collaboration. Published by Elsevier B.V.

The production of exclusive gamma gamma -> mu(+)mu(-) events in proton-proton collisions at a centre-of-mass energy of 13 TeV is measured with the ATLAS detector at the LHC, using data corresponding to an integrated luminosity of 3.2 fb(-1). The measurement is performed for a dimuon invariant mass of 12 GeV < m(mu+mu-) < 70 GeV. The integrated cross-section is determined within a fiducial acceptance region of the ATLAS detector and differential cross-sections are measured as a function of the dimuon invariant mass. The results are compared to theoretical predictions both with and without corrections for absorptive effects. (c) 2017 The Author(s). Published by Elsevier B.V.

Bose-Einstein correlations between identified charged pions are measured for $p$+Pb collisions at $\sqrt{s_{\mathrm{NN}}}=5.02$ TeV using data recorded by the ATLAS detector at the LHC corresponding to a total integrated luminosity of $28$ $\mathrm{nb}^{-1}$. Pions are identified using ionization energy loss measured in the pixel detector. Two-particle correlation functions and the extracted source radii are presented as a function of collision centrality as well as the average transverse momentum ($k_{\mathrm{T}}$) and rapidity ($y^{\star}_{\pi\pi}$) of the pair. Pairs are selected with a rapidity $-2<y^{\star}_{\pi\pi}<1$ and with an average transverse momentum $0.1<k_{\mathrm{T}}<0.8$ GeV. The effect of jet fragmentation on the two-particle correlation function is studied, and a method using opposite-charge pair data to constrain its contributions to the measured correlations is described. The measured source sizes are substantially larger in more central collisions and are observed to decrease with increasing pair $k_{\mathrm{T}}$. A correlation of the radii with the local charged-particle density is demonstrated. The scaling of the extracted radii with the mean number of participating nucleons is also used to compare a selection of initial-geometry models. The cross-term $R_\mathrm{ol}$ is measured as a function of rapidity, and a nonzero value is observed with $5.1\sigma$ combined significance for $-1<y^{\star}_{\pi\pi}<1$ in the most central events.

THE Port association organises interdisciplinary co-creational humanitarian hackathons at CERN. Combining physicists and engineers working on HEP related topics in their day job with entrepreneurs, artists, researchers, designers, humanitarian workers and other creative minds helps identifying similar material and engineering solutions for humanitarian challenges. It allow cross collaboration between many different disciplines. Concentrating on humanitarian and social benefitting topics the technology opportunities identify new methods, materials and processes, that can be feed back into HEP. The methodology of humanitarian hackathons is described and some examples of challenge outcomes are showcased.

Measurements of the temperature dependence of the charge carrier mobility in single-crystal chemical vapour deposition diamond using the transient current technique are presented in a temperature range from 2 K to room temperature. An α-source is used to create free charge carriers in the diamond bulk. The evolution of the current signal induced by their drift under the influence of an externally applied field is studied as a function of the temperature and the electric field strength. The electric field strength is varied by a factor of 30. The measurements are used to extract the transit time, the drift velocity, the saturation velocity, and the low-field mobility in terms of which the results are interpreted. Three samples have been studied which show the same behaviour. For holes, the mobility increases with decreasing temperature due to the acoustic phonon scattering, but it saturates for ultra-cold temperatures. The low-field mobility for holes at room temperature is measured as μ0h(295K)=(2534±20) cm2/Vs saturating against μ0h(→2K)=(11130±120) cm2/Vs. For electrons, only a lower limit on the low-field mobility can be given. It is measured as μ¯0e(295K)=(1802±14) cm2/Vs saturating against μ¯0e(→2K)=(3058±27) cm2/Vs. The electron transit time at low fields shows a different behaviour than the hole transit time and is not following the expected behaviour. This is likely to be caused by a high temperature valley re-population effect.

Single crystal chemical-vapour deposition (scCVD) diamonds are interesting for a wide range of applications. For many of them a good understanding of the temperature dependence of charge carrier transport mechanisms and properties is crucial. Measurements on the temperature dependence of charge carrier movement in scCVD diamond semiconductor detectors are presented. The evolution of the pulse shape of the detector response from impinging α particles is measured as a function of temperature employing the α-induced transient current technique (α-TCT) within a temperature range from 67 K to 295 K. The measurements are used to extract the drift mobility, drift velocity, and saturation velocity, in terms of which the results are interpreted.

The Pixel detector of the LHC experiment ATLAS is an 80 million channels silicon tracking system designed to detect charged tracks and secondary vertices with very high precision. To verify that the integrated assembly will perform as expected subsequent to installation into the experimental area, a fraction (~8%) of the detector, the DAQ readout chain and the requisite ancillary services has been assembled and operated in a large-scale system test setting. An overview of the system and results from these tests are presented. Cosmic muon data has been taken with the setup to measure the trigger performance, check the alignment and test the reconstruction chain. All aspects of the system test, including the detector control and safety system, the monitoring system and the DAQ system, the techniques for calibrating the detector and the analysis of noise tests and cosmics data are illustrated.