# ATLAS approach to releasing likelihoods for reinterpretation

- Institution: CERN
- Department: LPCC Workshops
- Speaker: Eric Schanet
- Year: 2021
- Language: English
- Subjects: Physics
- Source: [CERN](https://videos.cern.ch/record/3019992)
- Licence: [CERN](http://copyright.web.cern.ch)
- Who can watch: Tweens profiles and older (sources give no ratings; placed by Invisible Network from the source's own words)

Full likelihoods encode the entire statistical model of an analysis and thus range among the most invaluable analysis data products for a large range of analyses, ranging from SM measurements to BSM searches. ATLAS has recently started to release the first full analysis likelihoods using a python-based implementation of HistFactory. In this talk, the JSON specification used to release the likelihoods in serialisable format is discussed and details on how process them are given. In addition, a tool to build simplified likelihoods targeted for CPU-intensive large-scale reinterpretations is presented. Finally, the current collaboration policy and future plans are discussed..

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## Lectures

1. [ATLAS approach to releasing likelihoods for reinterpretation](https://invisiblenetwork.ai/learn/cern/3019992/ee51caa10610), 38 min (CERN's player)