matthewfeickert/main.tex

## main.tex
Detailed preservation of data and analyses enables their re-use beyond the scope of the original analyses.
This includes reinterpreting the results in combination with the outputs of other analyses (e.g. in global fits), reusing one or several existing analyses for testing new theoretical ideas (recasting), or reusing experimental or simulation data for a completely new analysis.
Designing and implementing datasets and analyses with this reuse in mind helps guide the pragmatic choices for where preservation effort is best spent.

Effective reinterpretation and recasting requires the preservation of both analysis data products and analyses, though the goals of the reinterpretation may require different levels of fidelity of the preservation.
For example, ATLAS has implemented full fidelity analysis reinterpretations internally using the RECAST framework and fully preserved analysis workflows.
CMS has similarly implemented much more lightweight solutions, with a smaller scope focus of statistically combining analyses that explore complementary final states.~\cite{REFERENCE 2, LHCReinterpretationForum:2020xtr}

Extensive efforts are being pursued outside of the collaborations to develop public software packages for the task of reinterpretation and/or recasting.
They heavily rely on the amount and quality of public information from the experiments (on all levels from analysis logic to analysis data products).
Aiming at statistical statements about the results, e.g. in setting limits on the parameters of new models or in performing global fits, they strongly benefit from extended information on the statistical modeling~\cite{Cranmer:2021urp}.

Public reinterpretation frameworks typically supply a database of implemented analyses.
Different tools provide distinct analyses coverage and sometimes different implementations of the same analysis.
However, the proliferation of analyses and tools, and the lack of interoperability between the tools, can make the complete coverage of a physics case in reinterpretation studies difficult.

While a unified file format for analysis implementation (a longstanding challenge) would be a significant boon for reinterpretation workflows, in the near term improvements could be made by the creation of a centralized (meta)database where the analyses available in the specific tools and the corresponding validation material can be acquired.
Additionally, creating standards and specifications for input and output formats, as well as statistical treatments would simplify adoption of the various tools.
Reinterpretation and recasting also motivates extensive exploration of the complementarities between collider results and other experimental results with global fits.
These fits expose limitations in coverage by the experiments and can identify which un(der)covered physics searches are most viable.

Robust reinterpretation and reuse encourages applying reproducible principles early in the analysis design and creating high quality analysis data products, as well as the FAIR-ification of code and analysis data products from (theory) reinterpretation studies outside the experimental collaborations at the same level as experimental analyses.
	Detailed preservation of data and analyses enables their re-use beyond the scope of the original analyses.
	This includes reinterpreting the results in combination with the outputs of other analyses (e.g. in global fits), reusing one or several existing analyses for testing new theoretical ideas (recasting), or reusing experimental or simulation data for a completely new analysis.
	Designing and implementing datasets and analyses with this reuse in mind helps guide the pragmatic choices for where preservation effort is best spent.

	Effective reinterpretation and recasting requires the preservation of both analysis data products and analyses, though the goals of the reinterpretation may require different levels of fidelity of the preservation.
	For example, ATLAS has implemented full fidelity analysis reinterpretations internally using the RECAST framework and fully preserved analysis workflows.
	CMS has similarly implemented much more lightweight solutions, with a smaller scope focus of statistically combining analyses that explore complementary final states.~\cite{REFERENCE 2, LHCReinterpretationForum:2020xtr}

	Extensive efforts are being pursued outside of the collaborations to develop public software packages for the task of reinterpretation and/or recasting.
	They heavily rely on the amount and quality of public information from the experiments (on all levels from analysis logic to analysis data products).
	Aiming at statistical statements about the results, e.g. in setting limits on the parameters of new models or in performing global fits, they strongly benefit from extended information on the statistical modeling~\cite{Cranmer:2021urp}.

	Public reinterpretation frameworks typically supply a database of implemented analyses.
	Different tools provide distinct analyses coverage and sometimes different implementations of the same analysis.
	However, the proliferation of analyses and tools, and the lack of interoperability between the tools, can make the complete coverage of a physics case in reinterpretation studies difficult.

	While a unified file format for analysis implementation (a longstanding challenge) would be a significant boon for reinterpretation workflows, in the near term improvements could be made by the creation of a centralized (meta)database where the analyses available in the specific tools and the corresponding validation material can be acquired.
	Additionally, creating standards and specifications for input and output formats, as well as statistical treatments would simplify adoption of the various tools.
	Reinterpretation and recasting also motivates extensive exploration of the complementarities between collider results and other experimental results with global fits.
	These fits expose limitations in coverage by the experiments and can identify which un(der)covered physics searches are most viable.

	Robust reinterpretation and reuse encourages applying reproducible principles early in the analysis design and creating high quality analysis data products, as well as the FAIR-ification of code and analysis data products from (theory) reinterpretation studies outside the experimental collaborations at the same level as experimental analyses.