root-project/root: v6.18/02

Brun, R.; Rademakers, Fons; Canal, Philippe; Naumann, N. A.; Couet, Olivier; Moneta, L.; Vassilev, Vassil; Linev, Sergey; Piparo, D.; Ganis, G.; Bellenot, Bertrand; Guiraud, Enrico; Amádio, G.; wverkerke,; Mató, P.; TimurP,; Tadel, M.; wlav,; Tejedor, Enric; Blomer, Jakob; Gheaţă, A.; Hageboeck, Stephan; Roiser, Stefan; marsupial,; Wünsch, Stefan; Shadura, O.; Bose, Anirudha; CristinaCristescu,; Valls, Xavier; Isemann, Raphael

doi:10.5281/zenodo.3895860

Cited by 21 publications

(10 citation statements)

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“…Particle physics uses a variety of data formats (and analysis ecosystems), including the ROOT library 44 . ROOT is a framework for data processing, created at CERN that is widely used by the high-energy physics community.…”

Section: Methodsmentioning

confidence: 99%

A FAIR and AI-ready Higgs boson decay dataset

et al. 2022

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To enable the reusability of massive scientific datasets by humans and machines, researchers aim to adhere to the principles of findability, accessibility, interoperability, and reusability (FAIR) for data and artificial intelligence (AI) models. This article provides a domain-agnostic, step-by-step assessment guide to evaluate whether or not a given dataset meets these principles. We demonstrate how to use this guide to evaluate the FAIRness of an open simulated dataset produced by the CMS Collaboration at the CERN Large Hadron Collider. This dataset consists of Higgs boson decays and quark and gluon background, and is available through the CERN Open Data Portal. We use additional available tools to assess the FAIRness of this dataset, and incorporate feedback from members of the FAIR community to validate our results. This article is accompanied by a Jupyter notebook to visualize and explore this dataset. This study marks the first in a planned series of articles that will guide scientists in the creation of FAIR AI models and datasets in high energy particle physics.

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Section: Methodsmentioning

confidence: 99%

A FAIR and AI-ready Higgs boson decay dataset

et al. 2022

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“…A sophisticated analysis framework has been developed for all FCC analyses using the common EDM4hep data format. It is based on RDataFrames [49], where C++ code is conveniently compiled in a ROOT [50] dictionary as "analysers" which are subsequently called in Python [51]. Several external packages such as ACTS [52], FastJet [53], and awkward [54] are included.…”

Section: Analysis Frameworkmentioning

confidence: 99%

Prospects for $$ {B}_c^{+} $$→ τ +ντ at FCC-ee

Amhis

Hartmann

Helsens

et al. 2021

J. High Energ. Phys.

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This paper presents the prospects for a precise measurement of the branching fraction of the leptonic $$ {B}_c^{+} $$ B c + → τ+ντ decay at the Future Circular Collider (FCC-ee) running at the Z -pole. A detailed description of the simulation and analysis framework is provided. To select signal candidates, two Boosted Decision Tree algorithms are employed and optimised. The first stage suppresses inclusive $$ b\overline{b} $$ b b ¯ , $$ c\overline{c} $$ c c ¯ , and $$ q\overline{q} $$ q q ¯ backgrounds using event-based topological information. A second stage utilises the properties of the hadronic τ+→ π+π+π−$$ \overline{\nu} $$ ν ¯ τ decay to further suppress these backgrounds, and is also found to achieve high rejection for the B+→ τ+ντ background. The number of $$ {B}_c^{+} $$ B c + → τ+ντ candidates is estimated for various Tera-Z scenarios, and the potential precision of signal yield and branching fraction measurements evaluated. The phenomenological impact of such measurements on various New Physics scenarios is also explored.

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“…Particle physics uses a data format (and analysis ecosystem) based on the ROOT library 65 . ROOT is a framework for data processing, created at CERN that is widely used by the high-energy physics community.…”

Section: Dataset Structurementioning

confidence: 99%

A FAIR and AI-ready Higgs boson decay dataset

Chen,

Huerta,

Duarte

et al. 2021

Preprint

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To enable the reusability of massive scientific datasets by humans and machines, researchers aim to create scientific datasets that adhere to the principles of findability, accessibility, interoperability, and reusability (FAIR) for data and artificial intelligence (AI) models. This article provides a domain-agnostic, step-by-step assessment guide to evaluate whether or not a given dataset meets each FAIR principle. We then demonstrate how to use this guide to evaluate the FAIRness of an open simulated dataset produced by the CMS Collaboration at the CERN Large Hadron Collider. This dataset consists of Higgs boson decays and quark and gluon background, and is available through the CERN Open Data Portal. We also use other available tools to assess the FAIRness of this dataset, and incorporate feedback from members of the FAIR community to validate our results. This article is accompanied by a Jupyter notebook to facilitate an understanding and exploration of the dataset, including visualization of its elements. This study marks the first in a planned series of articles that will guide scientists in the creation and quantification of FAIRness in high energy particle physics datasets and AI models.

show abstract

root-project/root: v6.18/02

Cited by 21 publications

References 0 publications

A FAIR and AI-ready Higgs boson decay dataset

A FAIR and AI-ready Higgs boson decay dataset

Prospects for $$ {B}_c^{+} $$→ τ +ντ at FCC-ee

A FAIR and AI-ready Higgs boson decay dataset

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