# ML-based reweighting of BSM signal grids: an example in Rivet

- Institution: CERN
- Department: Workshops
- Speaker: Tomasz Procter
- Year: 2025
- Language: English
- Subjects: Physics
- Source: [CERN](https://videos.cern.ch/record/3021436)
- 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)

Following on from discussions at previous reinterpretation forums on the reinterpretation of ML-based searches; we look at how one of these (ATLAS-SUSY-2018-30) has been used as a test-bed for studies of ML-based reweighting, which can reduce the computational load of studying large parameter spaces. Some new features of YODA 2 which allowed the project to run in Rivet will also be highlighted..

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## Lectures

1. [ML-based reweighting of BSM signal grids: an example in Rivet](https://invisiblenetwork.ai/learn/cern/3021436/1d70d13fd2d6), 22 min (CERN's player)