Related papers: Testing a Neural Network for Anomaly Detection in …

Events containing a pair of high energy hadronic jet can provide clear signatures in the search for new physics at high energy hadron colliders. The ATLAS and CMS experiments collected the data from LHC collisions at $\sqrt{s}$= 13 TeV…

ALICE (A Large Ion Collider Experiment) experiment at CERN LHC is designed to study the properties of the Quark-Gluon Plasma (QGP) in heavy-ion collisions. In 2019-2020 the upgrade of CERN LHC will increase the luminosity and the collision…

An FPGA-based online trigger system has been developed for the COMET Phase-I experiment. This experiment searches for muon-to-electron conversion, which has never been observed yet. A drift chamber and trigger counters detect a…

The ATLAS Inner Detector is responsible for particle tracking in ATLAS experiment at CERN Large Hadron Collider (LHC) and comprises silicon and gas based detectors. The combination of both silicon and gas based detectors provides high…

The determination of charged particle trajectories (tracking) in collisions at the CERN Large Hadron Collider (LHC) is one of the most important aspects for event reconstruction at hadron colliders. This is especially true in the high…

Nuclei classification is a critical step in computer-aided diagnosis with histopathology images. In the past, various methods have employed graph neural networks (GNN) to analyze cell graphs that model inter-cell relationships by…

Petabytes of data are generated at the Atlas experiment at the Large Hadron Collider however not all of it is necessarily interesting, so what do we do with all of this data and how do we find these interesting needles in an uninteresting…

The High-Luminosity LHC (HL-LHC) will provide the unique opportunity to explore the nature of physics beyond the Standard Model of strong and electroweak interactions. Highly selective first-level triggers are essential for the physics…

The ALICE experiment at CERN is preparing for a major upgrade for the third phase of data taking run (Run 3), when the high luminosity phase of the Large Hadron Collider (LHC) starts. The increase in the beam luminosity will result in high…

The ALICE apparatus at CERN has undergone significant upgrade during the LHC's Long Shutdown 2 to increase the experiment's rate capability and measurement precision for Run 3 and Run 4. One of the key detectors newly introduced to the…

In the future ALICE heavy ion experiment at CERN's Large Hadron Collider input data rates of up to 25 GB/s have to be handled by the High Level Trigger (HLT) system, which has to scale them down to at most 1.25 GB/s before being written to…

Experimental studies of ultra-relativistic heavy ion collisions at the Large Hadron Collider (LHC) depend crucially on Zero Degree Calorimeters (ZDCs) that measure neutrons produced at near-beam rapidity in nucleus-nucleus collisions. In…

The LHCb experiment at CERN is undergoing an upgrade in preparation for the Run 3 data taking period of the LHC. As part of this upgrade the trigger is moving to a fully software implementation operating at the LHC bunch crossing rate. We…

We describe the outcome of a data challenge conducted as part of the Dark Machines Initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenged aims at detecting signals of new physics at the LHC using…

During the upcoming Run 3 and Run 4 at the LHC the upgraded ALICE (A Large Ion Collider Experiment) will operate at a significantly higher luminosity and will collect two orders of magnitude more events than in Run 1 and Run 2. A part of…

A web-based tool called ADFilter was developed to process collision events using autoencoders based on a deep unsupervised neural network. The autoencoders are trained on a small fraction of either collision data or Standard Model Monte…

We devise an autoencoder based strategy to facilitate anomaly detection for boosted jets, employing Graph Neural Networks (GNNs) to do so. To overcome known limitations of GNN autoencoders, we design a symmetric decoder capable of…

Gradient tracking (GT) is an algorithm designed for solving decentralized optimization problems over a network (such as training a machine learning model). A key feature of GT is a tracking mechanism that allows to overcome data…

The CMS detector at the CERN LHC features a silicon pixel detector as its innermost subdetector. The original CMS pixel detector has been replaced with an upgraded pixel system (CMS Phase-1 pixel detector) in the extended year-end technical…

A search for heavy long-lived charged particles at the LHC is presented. Particles interacting with the CMS muon detector across several bunch crossings are searched for using a data sample of proton-proton collisions at $\sqrt{s}$ = 13.6…