Results 91 to 100 of about 350,654 (301)
HGPflow: extending hypergraph particle flow to collider event reconstruction
In high energy physics, the ability to reconstruct particles based on their detector signatures is essential for downstream data analyses. A particle reconstruction algorithm based on learning hypergraphs (HGPflow) has previously been explored in the ...
Nilotpal Kakati +6 more
doaj +1 more source
Two-particle angular correlations in pp collisions recorded with the ALICE detector at the LHC
We report on the studies of two-particle angular correlations measured in proton-proton collisions at a center-of-mass energy of √s = 7 TeV recorded by ALICE at the LHC.
Janik Małgorzata
doaj +1 more source
FPGA-RICH: A low-latency, high-throughput online partial particle identification system for the NA62 experiment [PDF]
FPGA-RICH is an FPGA-based online partial particle identification system for the NA62 experiment utilizing Artificial Intelligence (AI) techniques. Integrated between the readout of the Ring Imaging Cherenkov detector (RICH) and the low-level trigger ...
Perticaroli Pierpaolo +16 more
doaj +1 more source
Solar neutrinos as background in dark matter searches involving electron detection
In the present work we estimate the potential background of solar neutrinos on electron detectors. These detectors are considered relevant for detecting light dark matter particles in the MeV region, currently sought by experiments.
Thomas, A., Vergados, J. D.
core +1 more source
X‐Ray Tomography Analysis of Damage Mechanisms in Metal Matrix Syntactic Foams During Compression
In situ synchrotron X‐ray tomography is used to investigate the internal damage mechanisms of AlSi12 metal matrix syntactic foam with ceramic hollow spheres during compressive loading. It is concluded that a homogeneous distribution of the second‐phase filler material results in a sequential collapse in a localized region; this leads to controlled and ...
Indrajeet Tambe +8 more
wiley +1 more source
Machine learning uncertainties with adversarial neural networks
Machine Learning is a powerful tool to reveal and exploit correlations in a multi-dimensional parameter space. Making predictions from such correlations is a highly non-trivial task, in particular when the details of the underlying dynamics of a ...
Christoph Englert +3 more
doaj +1 more source
High dimensional parameter tuning for event generators
Monte Carlo Event Generators are important tools for the understanding of physics at particle colliders like the LHC. In order to best predict a wide variety of observables, the optimization of parameters in the Event Generators based on precision data ...
Johannes Bellm, Leif Gellersen
doaj +1 more source
This study reports the fabrication of trabecular bioactive glass scaffolds (composition “1d”: 46.1SiO2‐28.7CaO‐8.8MgO‐6.2P2O5‐5.7CaF2‐4.5Na2O wt%) through vat photopolymerization and the relevant results from mechanical testing and in vivo implantation procedures in rabbit femora, showing great promise for bone tissue engineering applications.
Dilshat Tulyaganov +8 more
wiley +1 more source
The puncture prevention and energy absorption performance of composite metal foam (CMF) are studied experimentally and numerically for hazardous materials transportation protection. The numerical model implementing air within the CMF (nonhomogeneous model using fluid cavity technique) predicts puncture more accurately compared to the homogeneous CMF ...
Aman Kaushik, Afsaneh Rabiei
wiley +1 more source
TrackHHL: A Quantum Computing Algorithm for Track Reconstruction at the LHCb [PDF]
In the future high-luminosity LHC era, high-energy physics experiments face unprecedented computational challenges for event reconstruction. Employing the LHCb vertex locator as a case study we investigate a novel approach for charged particle track ...
Chiotopoulos Xenofon +6 more
doaj +1 more source

