The MoEDAL experiment at the LHC

Pinfold, James

doi:10.1051/epjconf/201714512002

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…MoEDAL is proposing to deploy the MoEDAL apparatus for detecting penetrating particles (MAPP) in a tunnel shielded by some 30-50 m of rock and concrete from the IP8 [430,431]. A prototype of the MAPP detector was installed in 2017.…”

Section: Displaced Mesonsmentioning

confidence: 99%

Searching for long-lived particles beyond the Standard Model at the Large Hadron Collider

Alimena

Beacham

Borsato

et al. 2020

J. Phys. G: Nucl. Part. Phys.

282

280

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Particles beyond the Standard Model (SM) can generically have lifetimes that are long compared to SM particles at the weak scale. When produced at experiments such as the Large Hadron Collider (LHC) at CERN, these long-lived particles (LLPs) can decay far from the interaction vertex of the primary proton–proton collision. Such LLP signatures are distinct from those of promptly decaying particles that are targeted by the majority of searches for new physics at the LHC, often requiring customized techniques to identify, for example, significantly displaced decay vertices, tracks with atypical properties, and short track segments. Given their non-standard nature, a comprehensive overview of LLP signatures at the LHC is beneficial to ensure that possible avenues of the discovery of new physics are not overlooked. Here we report on the joint work of a community of theorists and experimentalists with the ATLAS, CMS, and LHCb experiments—as well as those working on dedicated experiments such as MoEDAL, milliQan, MATHUSLA, CODEX-b, and FASER—to survey the current state of LLP searches at the LHC, and to chart a path for the development of LLP searches into the future, both in the upcoming Run 3 and at the high-luminosity LHC. The work is organized around the current and future potential capabilities of LHC experiments to generally discover new LLPs, and takes a signature-based approach to surveying classes of models that give rise to LLPs rather than emphasizing any particular theory motivation. We develop a set of simplified models; assess the coverage of current searches; document known, often unexpected backgrounds; explore the capabilities of proposed detector upgrades; provide recommendations for the presentation of search results; and look towards the newest frontiers, namely high-multiplicity ‘dark showers’, highlighting opportunities for expanding the LHC reach for these signals.

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Section: Displaced Mesonsmentioning

confidence: 99%

Searching for long-lived particles beyond the Standard Model at the Large Hadron Collider

Alimena

Beacham

Borsato

et al. 2020

J. Phys. G: Nucl. Part. Phys.

282

280

View full text Add to dashboard Cite

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Hipster

2019

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HIPSTER (Heavily Ionising Particle Standard Toolkit for Event Recognition) is an open source Python package designed to facilitate the use of TensorFlow in a high energy physics analysis context. The core functionality of the software is presented, with images from the MoEDAL experiment Nuclear Track Detectors (NTDs) serving as an example dataset. Convolutional neural networks are selected as the classification algorithm for this dataset and the process of training a variety of models with different hyper-parameters is detailed. Next the results are shown for the MoEDAL problem demonstrating the rich information output by HIPSTER that enables the user to probe the performance of their model in

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The MoEDAL experiment at the LHC

Cited by 2 publications

References 14 publications

Searching for long-lived particles beyond the Standard Model at the Large Hadron Collider

Searching for long-lived particles beyond the Standard Model at the Large Hadron Collider

Hipster

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