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docs: Add talks and abstracts to documentation (#129)
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‎docs/_static/css/custom.css‎

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‎docs/abstracts.rst‎

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Abstracts
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=========
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Here you can find all abstracts and the events they were used for.
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.. toctree::
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:maxdepth: 2
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abstracts/20251028.rst
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abstracts/20251011.rst
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abstracts/20250716.rst

‎docs/abstracts/20250716.rst‎

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Graph Me If You Can: Modern Python Meets HEP Statistical Models
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===============================================================
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**Used for:** :cite:p:`Stark_20250716`
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**Abstract:**
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Statistical tooling in the scientific python ecosystem continues to advance, while at the same time ``ROOT`` has recently adopted the HEP Statistics Serialization Standard (HS3) as the way of serializing RooWorkspaces for any probability model that has been built. There is a gap between packages such as jax and ``scipy.stats`` and what ``HS3`` provides. This is where ``pyhs3`` comes in—a modern Python implementation of ``HS3`` designed with modern scientific python development practices. Prioritizing a developer-friendly interface and cross-platform compatibility, pyhs3 provides a python-callable function built from the computational graph encoded in serialized ``HS3`` probability models.
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The goal of this effort is to facilitate existing efforts in statistical inference (pyhf, zfit, cabinetry) and auto-differentiability (neos, MadJax, evermore, relaxed) by providing a common core for bidirectional translation of HS3-compatible workspaces.
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We'll discuss the design of the library, how the pieces are defined, how to extend or contribute to it, and proof-of-concept with a real-world workspace from the ATLAS :math:`HH\to bb\gamma\gamma` analysis. The talk presents the ``pyhs3`` package as a step towards a common 'inference API' and providing implementations of many mathematical probability distributions common in HEP.
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.. code-block:: latex
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Statistical tooling in the scientific python ecosystem continues to advance, while at the same time \texttt{ROOT} has recently adopted the HEP Statistics Serialization Standard (HS3) as the way of serializing RooWorkspaces for any probability model that has been built. There is a gap between packages such as jax and \texttt{scipy.stats} and what \texttt{HS3} provides. This is where \texttt{pyhs3} comes in—a modern Python implementation of \texttt{HS3} designed with modern scientific python development practices. Prioritizing a developer-friendly interface and cross-platform compatibility, pyhs3 provides a python-callable function built from the computational graph encoded in serialized \texttt{HS3} probability models.
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The goal of this effort is to facilitate existing efforts in statistical inference (pyhf, zfit, cabinetry) and auto-differentiability (neos, MadJax, evermore, relaxed) by providing a common core for bidirectional translation of HS3-compatible workspaces.
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We'll discuss the design of the library, how the pieces are defined, how to extend or contribute to it, and proof-of-concept with a real-world workspace from the ATLAS $HH\to bb\gamma\gamma$ analysis. The talk presents the \texttt{pyhs3} package as a step towards a common 'inference API' and providing implementations of many mathematical probability distributions common in HEP.

‎docs/abstracts/20251011.rst‎

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Efficient Statistical Modeling for Particle Physics Using Computational Graphs in Python
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========================================================================================
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**Used for:** :cite:ps:`Stark_20251028,Smith_20251011`
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**Abstract:**
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Statistical modeling is central to discovery in particle physics, yet the tools commonly used to define, share, and evaluate these models are often complex, fragmented, or tightly coupled to legacy systems. In parallel, the scientific Python community has developed a variety of statistical modeling tools that have been widely adopted for their performance and ease of use, but remain under-utilized in particle physics. We attempt to bridge this gap with a lightweight python framework that calculates likelihood ratios through the construction and evaluation of computational graphs. With modularity, auto-differentiability, and computational efficiency in mind, we designed the framework to integrate with modern scientific computing ecosystems while providing a clean, well-documented, and extendable API. This implementation makes published particle physics results more transparent, reproducible, and accessible for reanalysis. We present the initial framework, validate its results against established calculations, examine its performance relative to existing systems, and outline future development plans.
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This work was supported by the U.S. Department of Energy (DOE) Office of High Energy Physics under Grant No. DE-SC0010107.
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.. code-block:: latex
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Statistical modeling is central to discovery in particle physics, yet the tools commonly used to define, share, and evaluate these models are often complex, fragmented, or tightly coupled to legacy systems. In parallel, the scientific Python community has developed a variety of statistical modeling tools that have been widely adopted for their performance and ease of use, but remain under-utilized in particle physics. We attempt to bridge this gap with a lightweight python framework that calculates likelihood ratios through the construction and evaluation of computational graphs. With modularity, auto-differentiability, and computational efficiency in mind, we designed the framework to integrate with modern scientific computing ecosystems while providing a clean, well-documented, and extendable API. This implementation makes published particle physics results more transparent, reproducible, and accessible for reanalysis. We present the initial framework, validate its results against established calculations, examine its performance relative to existing systems, and outline future development plans.
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This work was supported by the U.S. Department of Energy (DOE) Office of High Energy Physics under Grant No. DE-SC0010107.

‎docs/acknowledgements.rst‎

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Funding Acknowledgements
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========================
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This work was supported by the U.S. Department of Energy (DOE) Office of High Energy Physics under Grant No. DE-SC0010107.

‎docs/bib/talks.bib‎

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% NB: entries with same author-title-year are not picked up:
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% https://github.com/mcmtroffaes/sphinxcontrib-bibtex/issues/117
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@unpublished{Stark_20251028,
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title = {{Efficient Statistical Modeling for Particle Physics Using Computational Graphs in Python}},
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author = {Giordon Stark},
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year = {2025},
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month = {Oct},
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day = {28},
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note = {PyHEP 2025 - "Python in HEP" Users Workshop (hybrid), CERN},
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url = {https://indico.cern.ch/event/1566263/contributions/6736094/},
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}
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@unpublished{Smith_20251011,
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title = {{Efficient Statistical Modeling for Particle Physics Using Computational Graphs in Python}},
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author = {Allexindir Smith and Giordon Stark and Michael Hance},
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year = {2025},
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month = {Oct},
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day = {11},
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note = {2025 Annual Meeting of the APS Far West Section},
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url = {https://meetings.aps.org/Meeting/FWS25/Session/I02.1},
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}
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@unpublished{Stark_20250716,
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title = {{Graph Me If You Can: Modern Python Meets HEP Statistical Models}},
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author = {Giordon Stark},
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year = {2025},
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month = {Jul},
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day = {16},
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note = {PyHEP.dev 2025 - "Python in HEP" Developer's Workshop},
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url = {https://indico.cern.ch/event/1515852/contributions/6580473/},
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}

‎docs/conf.py‎

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"sphinx_click.ext",
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"sphinx_issues",
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"sphinxcontrib.mermaid",
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"sphinxcontrib.bibtex",
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]
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# GitHub repo
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copybutton_prompt_text = r">>> |\.\.\. |\$ "
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copybutton_prompt_is_regexp = True
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copybutton_here_doc_delimiter = "EOF"
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# bibtex configuration
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bibtex_bibfiles = [
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"bib/talks.bib",
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]
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bibtex_default_style = "unsrt"
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# Add any paths that contain custom static files (such as style sheets) here,
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# relative to this directory. They are copied after the builtin static files,
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# so a file named "default.css" will overwrite the builtin "default.css".
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html_static_path = ["_static"]
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html_css_files = [
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"css/custom.css",
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]

‎docs/index.rst‎

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defining_components
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api
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.. toctree::
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:hidden:
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:maxdepth: 2
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:caption: Academics
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acknowledgements
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abstracts
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talks
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.. toctree::
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:maxdepth: 2

‎docs/talks.rst‎

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Talks and Presentations
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=======================
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.. bibliography::
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:all:

‎pyproject.toml‎

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"intersphinx_registry>=0.2411.17",
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"sphinx-issues",
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"sphinxcontrib-mermaid",
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"sphinxcontrib-bibtex",
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]
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[project.urls]

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