Related papers: Using machine learning to separate hadronic and el…
ALP-mediated decays and other as-yet unobserved $B$ decays to di-photon final states are a challenge to select in hadron collider environments due to the large backgrounds that come directly from the $pp$ collision. We present the strategy…
The FCAL collaboration is preparing large scale prototypes of special calorimeters to be used in the very forward region at a future linear electron positron collider for a precise and fast luminosity measurement and beam-tuning. These…
Electromagnetic (EM) probes, including photons and dileptons, do not interact strongly after their production in heavy-ion collisions, allowing them to carry undistorted information from their points of origin. This makes them powerful…
Commissioning studies of the CMS hadron calorimeter have identified sporadic uncharacteristic noise and a small number of malfunctioning calorimeter channels. Algorithms have been developed to identify and address these problems in the…
Direct photons provide a insightful tool to study the different stages of a heavy ion collision, especially the formation of a quark-gluon plasma, without being influenced by the strong interaction and hadronization processes. The yield of…
Machine learning offers an unprecedented perspective for the problem of classifying phases in condensed matter physics. We employ neural-network machine learning techniques to distinguish finite-temperature phases of the strongly correlated…
Metallic microcalorimeters (MMCs) are cryogenic single-particle detectors that rely on a calorimetric detection principle. Due to their excellent energy resolution, close-to-ideal linear detector response, fast signal rise time and the…
The low-x gluon density in the proton and, in particular, in nuclei is only very poorly constrained, while a better understanding of the low-x structure is crucial for measurements at the LHC and also for the planning of experiments at…
The expected increase of total integrated luminosity by a factor of ten at the HL-LHC compared to the design goals for LHC essentially eliminates the safety factor for radiation hardness realized at the current cold amplifiers of the ATLAS…
This paper presents a novel method for the reconstruction of interaction vertices in particle collision data. The algorithm is an agglomerative clustering technique designed for high-luminosity environments in current and future…
Multiplicity data up to 200 GeV in e+e- annihilation are described well by the two-stage model based on pQCD and suggested the phenomenological scheme of hadronization. This model confirms the fragmentation mechanism of hadronization (in…
The ATLAS inner detector is used to reconstruct secondary vertices due to hadronic interactions of primary collision products, so probing the location and amount of material in the inner region of ATLAS. Data collected in 7 TeV pp…
We investigate MeV-scale electron neutrino charged current interactions in a liquid argon time projection chamber equipped with an enhanced photon detection system. Using simulations of deposited energy in charge and light calorimetry, we…
The ALICE experiment at LHC studies the strong interaction sector of the Standard Model with pp, pA and AA collisions. Within the scope of the physics program, measurements of photons, neutral mesons and jets in ALICE are performed by two…
A digital hadronic calorimeter using MICROMEGAS as active elements is a very promising choice for particle physics experiments at future lepton colliders. These experiments will be optimized for application of the particle flow algorithm…
We consider machine learning techniques associated with the application of a Boosted Decision Tree (BDT) to searches at the Large Hadron Collider (LHC) for pair-produced lepton partners which decay to leptons and invisible particles. This…
This paper presents the electron and photon energy calibration achieved with the ATLAS detector using about 25 fb$^{-1}$ of LHC proton--proton collision data taken at centre-of-mass energies of $\sqrt{s}$ = 7 and 8 TeV. The reconstruction…
Machine learning technologies have found fertile ground in optics due to its promising features based on speed and parallelism. Feed-forward neural networks are one of the most widely used machine learning algorithms due to their simplicity…
We develop machine learning techniques for estimating physical properties of laser-cooled potassium-39 atoms in a magneto-optical trap using only the scattered light -- i.e., fluorescence -- that is intrinsic to the cooling process. In-situ…
This short paper presents the potential of using machine learning to predict materials behaviour in the context of hydrogen interaction with steel. Effort has been made to understand the quality, and amount of data needed to get improved…