ORCID Profile
0000-0002-0041-0566
Current Organisation
Sorbonne-Universite
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Publisher: Springer Science and Business Media LLC
Date: 04-2012
Publisher: Springer Science and Business Media LLC
Date: 23-01-2118
Publisher: American Physical Society (APS)
Date: 31-08-2020
Publisher: Springer Science and Business Media LLC
Date: 09-2008
Publisher: Springer Science and Business Media LLC
Date: 07-2021
Abstract: We investigate the phenomenology of a scalar top-philic dark matter candidate when adding a dimension-five contact interaction term, as motivated by possible underlying extensions of the Standard Model such as composite Higgs models. We show that the presence of contact interactions can have a major impact on the dark matter relic density as well as on its direct and indirect detection prospects, while the collider phenomenology of the model is unaffected. This underlines the complementarity of collider and cosmological constraints on dark matter models.
Publisher: CERN
Date: 2017
Publisher: Springer Science and Business Media LLC
Date: 05-2016
Publisher: Elsevier BV
Date: 2020
Publisher: Stichting SciPost
Date: 21-08-2020
DOI: 10.21468/SCIPOSTPHYS.9.2.022
Abstract: We report on the status of efforts to improve the reinterpretation of searches and measurements at the LHC in terms of models for new physics, in the context of the LHC Reinterpretation Forum. We detail current experimental offerings in direct searches for new particles, measurements, technical implementations and Open Data, and provide a set of recommendations for further improving the presentation of LHC results in order to better enable reinterpretation in the future. We also provide a brief description of existing software reinterpretation frameworks and recent global analyses of new physics that make use of the current data.
Publisher: Springer Science and Business Media LLC
Date: 04-04-2022
Abstract: Machine learning algorithms are growing increasingly popular in particle physics analyses, where they are used for their ability to solve difficult classification and regression problems. While the tools are very powerful, they may often be under- or mis-utilised. In the following, we investigate the use of gradient boosting techniques as applicable to a generic particle physics problem. We use as an ex le a Beyond the Standard Model smuon collider analysis which applies to both current and future hadron colliders, and we compare our results to a traditional cut-and-count approach. In particular, we interrogate the use of metrics in imbalanced datasets which are characteristic of high energy physics problems, offering an alternative to the widely used area under the curve ( auc ) metric through a novel use of the F-score metric. We present an in-depth comparison of feature selection and investigation using a principal component analysis, Shapley values, and feature permutation methods in a way which we hope will be widely applicable to future particle physics analyses. Moreover, we show that a machine learning model can extend the 95% confidence level exclusions obtained in a traditional cut-and-count analysis, while potentially bypassing the need for complicated feature selections. Finally, we discuss the possibility of constructing a general machine learning model which is applicable to probe a two-dimensional mass plane.
Publisher: American Physical Society (APS)
Date: 07-04-2023
Publisher: CERN
Date: 2019
Location: Belgium
No related grants have been discovered for Benjamin Fuks.