Pileup Interactions Research Articles

Deep learning techniques for energy clustering in the CMS ECAL

The reconstruction of electrons and photons in CMS depends on topological clustering of the energy deposited by an incident particle in different crystals of the electromagnetic calorimeter (ECAL). These clusters are formed by aggregating neighbouring crystals according to the expected topology of an electromagnetic shower in the ECAL. The presence of upstream material (beampipe, tracker and support structures) causes electrons and photons to start showering before reaching the calorimeter. This effect, combined with the 3.8T CMS magnetic field, leads to energy being spread in several clusters around the primary one. It is essential to recover the energy contained in these satellite clusters in order to achieve the best possible energy resolution for physics analyses. Historically satellite clusters have been associated to the primary cluster using a purely topological algorithm which does not attempt to remove spurious energy deposits from additional pileup interactions (PU). The performance of this algorithm is expected to degrade during LHC Run 3 (2022+) because of the larger average PU levels and the increasing levels of noise due to the ageing of the ECAL detector. New methods are being investigated that exploit state-of-the-art deep learning architectures like Graph Neural Networks (GNN) and self-attention algorithms. These more sophisticated models improve the energy collection and are more resilient to PU and noise, helping to preserve the electron and photon energy resolution achieved during LHC Runs 1 and 2. This work will cover the challenges of training the models as well the opportunity that this new approach offers to unify the ECAL energy measurement with the particle identification steps used in the global CMS photon and electron reconstruction.

Evaluation of slip transfer through WC grain boundaries in cemented carbides

Geometrical calculations are performed to evaluate the resistance of WC grain boundaries to transfer plastic deformation in WC-Co cemented carbides. The interaction of dislocation pile-ups with the most frequent CSL and random grain boundaries are studied. Based on the minimum grain boundary residue criterion, ∑ = 2 and ∑ = 4 grain boundaries are more resistant to dislocation propagation than ∑ = 13a, ∑ = 97 and a large part of investigated random grain boundaries. Controlling the amount of ∑ = 2 and ∑ = 4 grain boundaries, which mostly arise from the powder, could help in tuning the material properties according the desired application.

Digital switch boards for IV and CV measurements of large-array Low Gain Avalanche Detectors

The High-Granularity Timing Detector (HGTD), with a time resolution of approximately 30 ps, is proposed for the ATLAS Phase II upgrade to address the challenge of greatly increased pile-up interactions. The HGTD is based on the technology of Low Gain Avalanche Detectors (LGADs). The current–voltage (IV) and capacitance–voltage (CV) measurements are important methods to study the main characteristics that can be exploited for quality controlling LGADs during production. A single probe can be controlled via computer to switch channels when using an automatic probe station, but this leaves the remaining, adjacent channels floating and results in inaccurate measurements due to the punch-through effect. We designed the digital switch boards for the IV and CV measurements of 5 × 5 and 15 × 15 LGADs arrays. With the digital switch board and probe cards, channels on a large-array LGADs can be scanned automatically during the measurement, which greatly improves the measurement efficiency. The results show that the leakage currents and noise of all channels induced by the two digital switch boards are lower than 10 pA. We show the IV and CV curves of all channels on two LGADs from Hamamatsu Photonics K. K. (HPK) with our digital switch boards. Compared with the results measured by HPK with an automatic probe station, the IV and CV curves that we measured have better uniformity and accuracy, demonstrating that the quality control of the large-array LGADs can be conducted well with our digital switch board.

Performance studies of Time-of-Flight detectors at LHC

We present results of a toy model study of performance of the Time-of-Flight detectors integrated into forward proton detectors. The goal of the ToF device is to suppress effects of additional soft processes (so called pile-up) accompanying the hard-scale central diffractive event, characterized by two tagged leading protons, one on each side from the interaction point. The method of mitigation of the pile-up effects exemplified in this study is based on measuring a difference between arrival times of these leading protons at the forward proton detectors and hence estimate the z-coordinate of the production vertex. We evaluate effects of the pile-up background by studying in detail its components, and estimate the performance of the ToF method as a function of the time and spatial resolution of the ToF device and of the number of pile-up interactions per bunch crossing. We also propose a new observable with a potential to efficiently separate central diffractive signal from the harsh pile-up environment.

Background estimator for jet studies in p+p and A+A collisions

Experimentally, jet physics studies face an unavoidable task: distinguishing, at the detector level, the particles produced in the hard partonic scattering from the ones created in unrelated soft processes such as pileup interactions in high-luminosity proton-proton scattering or the underlying event in heavy-ion collisions. The fluctuating nature of the background constitutes the main source of uncertainty for any subtraction algorithm. Aiming at mitigating the effect of such fluctuations, we present a new method to estimate the background contribution to the transverse momentum on a jet-by-jet basis. Our approach is based on estimating the median background momentum density stored above a $p_{\rm T}$-cut applied at the constituent level and an experimentally accessible correction term related to the signal contribution below the cut. This allows to trade part of the uncertainty due to background contamination for that of the signal below the cut, similarly to SoftKiller method. We propose to reduce the fluctuations of the latter by exploiting intrinsic correlations among the soft and hard sectors of QCD jets. Our data-driven approach is tested against PYTHIA8 and JEWEL di-jet events embedded in a thermal background and compared to the area-median and SoftKiller methods. The main result of this study is a $\sim 5\!-\!40\%$ improvement on the resolution of the reconstructed jet $p_T$ compared to previous methods in a high-luminosity proton-proton scenario. Its applicability in a heavy-ion context is also discussed.

Predictions for energy correlators probing substructure of groomed heavy quark jets

We develop an effective field theory (EFT) framework to perform an analytic calculation for energy correlator observables computed on groomed heavy-quark jets. A soft-drop grooming algorithm is applied to a jet initiated by a massive quark to minimize soft contamination effects such as pile-up and multi-parton interactions. We specifically consider the two-particle energy correlator as an initial application of this EFT framework to compute heavy quark jet substructure. We find that there are different regimes for the event shapes, depending on the size of the measured correlator observable, that require the use of different EFT formulations, in which the quark mass and grooming parameters may be relevant or not. We use the EFT to resum large logarithms in the energy correlator observable in terms of the momentum of a reconstructed heavy hadron to NLL′ accuracy and subsequently match it to a full QCD mathcal{O} (αs) cross section, which we also compute. We compare our predictions to simulations in Pythia for e+e− collisions. We find a good agreement with partonic simulations, as well as hadronic ones with an appropriate shape function used to describe nonperturbative effects and the heavy quark hadron decay turned off. We also predict the scaling behavior for the leading nonperturbative power correction due to hadronization. Consequently, we can give a prediction for the energy correlator distribution at the level of the reconstructed heavy hadron. This work provides a general framework for the analysis of heavy quark jet substructure observables.

New techniques for pile-up simulation in ATLAS

The high-luminosity data produced by the LHC leads to many proton-proton interactions per beam crossing in ATLAS, known as pile-up. In order to understand the ATLAS data and extract physics results it is important to model these effects accurately in the simulation. As the pile-up rate continues to grow towards an eventual rate of 200 for the HL-LHC, this puts increasing demands on the computing resources required for the simulation and the current approach of simulating the pile-up interactions along with the hard-scatter for each Monte Carlo production is no longer feasible. The new ATLAS “overlay” approach to pile-up simulation is presented. Here a pre-combined set of minimum bias interactions, either from simulation or from real data, is created once and a single event drawn from this set is overlaid with the hard-scatter event being simulated. This leads to significant improvements in CPU time. This contribution will discuss the technical aspects of the implementation in the ATLAS simulation and production infrastructure and compare the performance, both in terms of computing and physics, to the previous approach.

Search strategy using LHC pileup interactions as a zero bias sample

Due to a limited bandwidth and a large proton-proton interaction cross section relative to the rate of interesting physics processes, most events produced at the Large Hadron Collider (LHC) are discarded in real time. A sophisticated trigger system must quickly decide which events should be kept and is very efficient for a broad range of processes. However, there are many processes that cannot be accommodated by this trigger system. Furthermore, there may be models of physics beyond the standard model (BSM) constructed after data taking that could have been triggered, but no trigger was implemented at run time. Both of these cases can be covered by exploiting pileup interactions as an effective zero bias sample. At the end of high-luminosity LHC operations, this zero bias dataset will have accumulated about $1\text{ }\text{ }{\mathrm{fb}}^{\ensuremath{-}1}$ of data from which a bottom line cross section limit of $\mathcal{O}(1)\text{ }\text{ }\mathrm{fb}$ can be set for BSM models already in the literature and those yet to come.

Precision Timing with Silicon Sensors for Use in Calorimetry

The high luminosity upgrade of the Large Hadron Collider (HL-LHC) at CERN is expected to provide instantaneous luminosities of 5 × 1034cm−2s−1. The high luminosities expected at the HL-LHC will be accompanied by a factor of 5 to 10 more pileup compared with LHC conditions in 2015, causing general confusion for particle identification and event reconstruction. Precision timing allows to extend calorimetric measurements into such a high density environment by subtracting the energy deposits from pileup interactions. Calorimeters employing silicon as the active component have recently become a popular choice for the HL- LHC and future collider experiments which face very high radiation environments. We present studies of basic calorimetric and precision timing measurements using a prototype composed of tungsten absorber and silicon sensor as the active medium. We show that for the bulk of electromagnetic showers induced by electrons in the range of 20 GeV to 30 GeV, we can achieve time resolutions better than 25 ps per single pad sensor.

Grain boundary mediated plasticity: The role of grain boundary atomic structure and thermal activation

The interaction of dislocation pile-ups with several tilt grain boundaries (GB) is studied in copper by using a hybrid continuum-atomistic approach. The effects of temperature, pile-up intensity and GB structure on absorption and transmission of slip as a function of local stress state are explored. By considering several high-angle GBs with different misorientation angles, we demonstrate that GB atomic structure primarily defines its ability to accommodate incoming pile-up dislocations, thus limiting the direct transmission of pile-ups through the interface.

Particle-flow reconstruction and global event description with the CMS detector

The CMS apparatus was identified, a few years before the start of the LHC operation at CERN, to feature properties well suited to particle-flow (PF) reconstruction: a highly-segmented tracker, a fine-grained electromagnetic calorimeter, a hermetic hadron calorimeter, a strong magnetic field, and an excellent muon spectrometer. A fully-fledged PF reconstruction algorithm tuned to the CMS detector was therefore developed and has been consistently used in physics analyses for the first time at a hadron collider. For each collision, the comprehensive list of final-state particles identified and reconstructed by the algorithm provides a global event description that leads to unprecedented CMS performance for jet and hadronic τ decay reconstruction, missing transverse momentum determination, and electron and muon identification. This approach also allows particles from pileup interactions to be identified and enables efficient pileup mitigation methods. The data collected by CMS at a centre-of-mass energy of 8\\TeV show excellent agreement with the simulation and confirm the superior PF performance at least up to an average of 20 pileup interactions.

Identification and rejection of pile-up jets at high pseudorapidity with the ATLAS detector

The rejection of forward jets originating from additional proton–proton interactions (pile-up) is crucial for a variety of physics analyses at the LHC, including Standard Model measurements and searches for physics beyond the Standard Model. The identification of such jets is challenging due to the lack of track and vertex information in the pseudorapidity range |eta |>2.5. This paper presents a novel strategy for forward pile-up jet tagging that exploits jet shapes and topological jet correlations in pile-up interactions. Measurements of the per-jet tagging efficiency are presented using a data set of 3.2 fb^{-1} of proton–proton collisions at a centre-of-mass energy of 13 ,text {TeV} collected with the ATLAS detector. The fraction of pile-up jets rejected in the range 2.5<|eta |<4.5 is estimated in simulated events with an average of 22 interactions per bunch-crossing. It increases with jet transverse momentum and, for jets with transverse momentum between 20 and 50 GeV, it ranges between 49% and 67% with an efficiency of 85% for selecting hard-scatter jets. A case study is performed in Higgs boson production via the vector-boson fusion process, showing that these techniques mitigate the background growth due to additional proton–proton interactions, thus enhancing the reach for such signatures.

Computing challenges of the CMS experiment

The success of the LHC experiments is due to the magnificent performance of the detector systems and the excellent operating computing systems. The CMS offline software and computing system is successfully fulfilling the LHC Run 2 requirements. For the increased data rate of future LHC operation, together with high pileup interactions, improvements of the usage of the current computing facilities and new technologies became necessary. Especially for the challenge of the future HL-LHC a more flexible and sophisticated computing model is needed. In this presentation, I will discuss the current computing system used in the LHC Run 2 and future computing facilities for the HL-LHC runs using flexible computing technologies like commercial and academic computing clouds. The cloud resources are highly virtualized and can be deployed for a variety of computing tasks providing the capacities for the increasing needs of large scale scientific computing.

Performance of algorithms that reconstruct missing transverse momentum in sqrt{s}= 8 TeV proton\u2013proton collisions in the ATLAS detector

The reconstruction and calibration algorithms used to calculate missing transverse momentum (E_{text {T}}^{text {miss}} ) with the ATLAS detector exploit energy deposits in the calorimeter and tracks reconstructed in the inner detector as well as the muon spectrometer. Various strategies are used to suppress effects arising from additional proton–proton interactions, called pileup, concurrent with the hard-scatter processes. Tracking information is used to distinguish contributions from the pileup interactions using their vertex separation along the beam axis. The performance of the E_{text {T}}^{text {miss}} reconstruction algorithms, especially with respect to the amount of pileup, is evaluated using data collected in proton–proton collisions at a centre-of-mass energy of 8 text {TeV} during 2012, and results are shown for a data sample corresponding to an integrated luminosity of 20.3, mathrm{fb}^{-1}. The simulation and modelling of E_{text {T}}^{text {miss}} in events containing a Z boson decaying to two charged leptons (electrons or muons) or a W boson decaying to a charged lepton and a neutrino are compared to data. The acceptance for different event topologies, with and without high transverse momentum neutrinos, is shown for a range of threshold criteria for E_{text {T}}^{text {miss}} , and estimates of the systematic uncertainties in the E_{text {T}}^{text {miss}} measurements are presented.

Jet substructure classification in high-energy physics with deep neural networks

At the extreme energies of the Large Hadron Collider, massive particles can be produced at such high velocities that their hadronic decays are collimated and the resulting jets overlap. Deducing whether the substructure of an observed jet is due to a low-mass single particle or due to multiple decay objects of a massive particle is an important problem in the analysis of collider data. Traditional approaches have relied on expert features designed to detect energy deposition patterns in the calorimeter, but the complexity of the data make this task an excellent candidate for the application of machine learning tools. The data collected by the detector can be treated as a two-dimensional image, lending itself to the natural application of image classification techniques. In this work, we apply deep neural networks with a mixture of locally-connected and fully-connected nodes. Our experiments demonstrate that without the aid of expert features, such networks match or modestly outperform the current state-of-the-art approach for discriminating between jets from single hadronic particles and overlapping jets from pairs of collimated hadronic particles, and that such performance gains persist in the presence of pileup interactions.

Test beam studies of silicon timing for use in calorimetry

The high luminosity upgrade of the Large Hadron Collider (HL-LHC) at CERN is expected to provide instantaneous luminosities of 5×1034cm−2s−1. The high luminosities expected at the HL-LHC will be accompanied by a factor of 5–10 more pileup compared with LHC conditions in 2015, further increasing the challenge for particle identification and event reconstruction. Precision timing allows us to extend calorimetric measurements into such a high density environment by subtracting the energy deposits from pileup interactions. Calorimeters employing silicon as the active component have recently become a viable choice for the HL-LHC and future collider experiments which face very high radiation environments. In this paper, we present studies of basic calorimetric and precision timing measurements using a prototype composed of tungsten absorber and silicon sensor as the active medium. We show that for the bulk of electromagnetic showers induced by electrons in the range of 20–30GeV, we can achieve time resolutions better than 25ps per single pad sensor.

Performance of MET reconstruction and pileup mitigation techniques in CMS

The performance of missing transverse momentum reconstruction algorithms is presented using 8 TeV pp collision data collected with the CMS detector, corresponding to an integrated luminosity of 12.2 ± 0.5 fb-1. The scale and resolution of missing transverse momentum and the effects of multiple proton-proton interactions (pileup), are measured using events with an identified Z boson or isolated photon. They are in general well described by the simulation. Advanced missing transverse momentum reconstruction algorithms are also developed specifically to mitigate the effects of large numbers of pileup interactions on missing transverse momentum resolution. Using these advanced algorithms, the dependence of the missing transverse momentum resolution on pileup interactions is reduced significantly.

Data-driven estimation of neutral pileup particle multiplicity in high-luminosity hadron collider environments

The upcoming operation regimes of the Large Hadron Collider are going to place stronger requirements on the rejection of particles originating from pileup, i.e. from interactions between other protons. For this reason, particle weighting techniques have recently been proposed in order to subtract pileup at the level of individual particles. We describe a choice of weights that, unlike others that rely on particle proximity, exploits the particle-level kinematic signatures of the high-energy scattering and of the pileup interactions. We illustrate the use of the weights to estimate the number density' of neutral pileup particles inside individual events, and we elaborate on the complementarity' between ours and other methods. We conclude by suggesting the idea of combining different sets of weights with a view to exploiting different features of the underlying processes for improved pileup subtraction at higher luminosity'.

Performance of jets at CMS

The calibration and reconstruction of jets critically relies on the performance of the calorimeters. Extending out to large pseudorapidities, the measurements depend on the interplay between forward calorimeters, central calorimeters, and the tracking system. The high number of additional pile-up interactions poses further complications. In CMS, these difficulties are overcome using the 'particle-flow' approach, which aims at reconstructing individually each particle in the event prior to the jet clustering. Measurements of the jet energy scale and the procedure for jet energy calibration in CMS are reviewed, which are performed with dijet, photon + jet, and Z+jet data collected in proton-proton collisions at a centre-of-mass energy of 8 TeV, corresponding to an integrated luminosity of 19.6 fb−1. The effect of pile-up interactions and the state of the art mitigation techniques used in CMS as well as the main sources of uncertainty of the jet energy calibration are also presented.

Performance of MET reconstruction in CMS

The performance of missing transverse momentum reconstruction algorithms is presented using 8 TeV pp collision data collected with the CMS detector, corresponding to an integrated luminosity up to 19.7 fb−1. The resolution of missing transverse momentum, including the effects of pileup interactions and advanced reconstruction algorithms, are measured using events with an identified Z boson or isolated photon.