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Energy - CERN (European Council for Nuclear Research)

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Last Updated: 03 September 2022

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Particle production as a function of underlying-event activity and very forward energy with ALICE

In the 2̆21a(s)_NN=5. 02 TeV's pp and p-Pb collisions, charges of particle manufacturing in pp and p-Pb collisions are discussed. These three regions are characterized by a case relative to the one with the most transverse momentum. In the three specified transverse regions, KNO-like scaling properties are investigated. Finally, the correlation between p_T^trig and the acceleration found in a region close to beam rapidity is reported.

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A Low Energy H$^{-}$ Beamline for the ALPHA Antihydrogen Experiment

ALPHA has embarked on an ambitious upgrade program aimed at directly comparing hydrogen and antihydrogen within their existing atom trap, with high-resolution measurements of fundamental symmetries. One aspect of this upgrade will be the construction of a low-energy hydrogen ion source that is compatible with ALPHA's existing magnetic particle beam lines. We explore the possibility of a proposed electrostatic beamline layout to transport H ^- ions from a PELLIS-type ion source to ALPHA-u2019s various particle traps.

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Review on Higgs Hidden–Dark Sector Physics at High-Energy Colliders

Many extensions of SM use obscure or dark sector states to name a specific candidate for dark matter in the universe or to summarize astrophysical findings. The primary point is on a few examples of searches at the LHC that are relevant to Higgs Hiddenu2013Dark Sector Physics.

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Optimization of the missing transverse energy + photon triggers for the search of dark matter with Atlas Detector.

At colliders such as the Large Hadron Collider, considerable efforts have been put into looking for dark matter particles. The Higgs portal may have links to industry's inscrutable and apparent sector interactions. The DM photon is a hypothetical particle that can be made in the Higgs boson decay Hu2192 defuse Hu03b3d. We consider the Higgs boson's production by the fusion of gluons, which has the highest production rate, while the triggering of events from the ggF H3192/12b3b3u03b3d is quite challenging, considering the relatively small missing transverse energy and the single photon.

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Compute, Storage and Throughput Trade-offs for High-Energy Physics Data Acquisition

Real-time DAQs are installed at the European Organization for Nuclear Research, where High Energy Physics experiments collide particles, are used to filter the vast amount of data. When working with large amounts of data, data compression can result in increasing capacity and transfer rates. SMT scales are increasing in number, but they are less robust in terms of results. Arms outperforms IBM by a factor of 2. 8 and Intel by a factor of 1. 3 when it comes to computing. Only with NUMA binding and either polling or interrupting for interface with the device, the achievement of resource-unsaturated host workloads has increased by a factor of 1. 5 — 2. 1 for the host, and by a factor of 1. 8— 2. 3 for the host. It rises by a factor of 1. 8 -- 1. 9 for the device, but it falls by 0. 1 -- 0. 4 for the host. Huffman's coding on the original data compresses to mbox40--260 percent faster than any tried general-purpose algorithms. Huffman coded on delta encoded data interprets poorly for HEP results, according to Huffman. Autoencoders are a common machine learning tool. To enhance neural networks with large layers' performance, they need more technological advancements to improve neural network's success. Huffman's coding on absolute values results in a higher compression ratio than any general-purpose device, but it is still too slow. Although the purpose of this study is real-time DAQs in the HEP community with specific needs and limitations, we believe the results of this investigation are general enough to be applicable to the majority of environments and data characteristics.

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Quantum Machine Learning in High Energy Physics

Machine learning has been used in high energy physics for a long time, mainly at the measurement level with supervised classification. It's unclear if there are ways to apply quantum machine learning to High Energy Physics, which is an interesting question. This paper reviews the first generation of quantum machine learning-based research in high energy physics and give an insight into future applications.

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Study of strangeness production in pp collisions as a function of multiplicity and effective energy with ALICE

The investigation of strange hadron production in proton-proton collisions provides a powerful tool to analyze sim-ilarities and differences between small and large collision systems. The introduction of a new cost-effective energy estimator based on the energy stored in ALICE Zero Degree Calorimeters is the centerpiece of this report.

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Low-Energy Physics in Neutrino LArTPCs

We present some of the scientific opportunities and challenges involved with the finding and reconstruction of low-energy signatures in liquid argon time-projection chamber detectors in this white paper. L&TPCs have exceptional sensitivity to a variety of physical and astrophysics signatures by the detection of event features above and below the few tens of MeV range. Two Low-energy signatures are an integral part of the GeV-scale accelerator neutrino interaction final states, and their reconstruction can greatly improve the oscillation physics sensitivities of LArTPC experiments. Different signatures from accelerator and natural sources also appear in the low-energy range, and reconstruction of these signatures can expand the breadth of BSM scenarios available in LArTPC-based searches. In argon relevant to sub-MeV LarTPC signatures, four Neutrino interaction cross sections and other nuclear physics reactions are poorly understood. 6 Novel suggestions for future LArTPC technology that increase low-power capabilities should be explored.

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* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions