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Status and Prospects of the HEP Statistical Inference Ecosystem

  • Massimiliano Galli
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Abstract

Statistical inference is a crucial part of HEP analyses. Historically based on RooFit and RooStats, the statistical tools used by the experiments are now facing unprecedented challenges, such as the rapidly growing complexity of statistical models (involving hundreds of parameters of interest and thousands of nuisance parameters), the need for scalable performance in large likelihood minimizations, and the demand for interoperability across an increasingly diverse ecosystem of tools, computational hardware, and frameworks and libraries, especially the ones developed and used within the machine learning world. This contribution summarizes status and future plans for the statistical tools used by some of the main LHC experiments (CMS, ATLAS), with a focus on improvements coming from the ROOT world (RooFit automatic differentiation), interoperability with modern libraries (JAX) and communication across frameworks (HS3).

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Cite

BibTeXRIS
BibTeX
@misc{galli2026status,
  title = {{Status and Prospects of the HEP Statistical Inference Ecosystem}},
  author = {Massimiliano Galli},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.19761},
  eprint = {2609.19761},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.19761},
}
RIS
TY  - UNPB
TI  - Status and Prospects of the HEP Statistical Inference Ecosystem
AU  - Massimiliano Galli
PY  - 2026
DA  - 2026/09/17
PB  - arXiv
DO  - 10.48550/arxiv.2609.19761
UR  - https://arxiv.org/abs/2609.19761
AB  - Statistical inference is a crucial part of HEP analyses. Historically based on RooFit and RooStats, the statistical tools used by the experiments are now facing unprecedented challenges, such as the rapidly growing complexity of statistical models (involving hundreds of parameters of interest and thousands of nuisance parameters), the need for scalable performance in large likelihood minimizations, and the demand for interoperability across an increasingly diverse ecosystem of tools, computational hardware, and frameworks and libraries, especially the ones developed and used within the machine learning world. This contribution summarizes status and future plans for the statistical tools used by some of the main LHC experiments (CMS, ATLAS), with a focus on improvements coming from the ROOT world (RooFit automatic differentiation), interoperability with modern libraries (JAX) and communication across frameworks (HS3).
ER  -