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Louie Corpe, UCL ([email protected]) 1
Reinterpretation theory & practice
RivetFest, Lunga House May 2019
L. Corpe (UCL)
Motivation and introduction
2
• 7th year of LHC operation, in total over ~150/fb @13 TeV... ...but no BSM physics so far
• In last half-century, HEP has been in lucky position to be in theory-driven ‘top-down’ mode... Searching for particles with a theoretical roadmap:
• W, Z, top, Higgs: knew what to look for, designed searches to find it
• A little data goes a long way when you know what you are looking for!
• Now all pieces of SM are found: we don’t have a roadmap to guide us
• Enter new era of data-driven ‘bottom-up’ physics.
• So how to proceed?
• Rivet (+its ecosystem) could play an important role in this paradigm shift
• BSM physics likely to manifest as tiny deviations from SM...
• Renewed emphasis on precision measurements of SM observablesWhat ingredients are needed? The obvious ones:
• reduce experimental uncertainties
• better MC modelling of processes
• improved accuracy of theory predictions
• What more can be done? Improve efficiency of feedback loop!
• ensure we make best use of experimental data to tune MC
• make the most of searches: re-interpretability and fiducial measurements
• Take stock of results to determine which models are actually viable
• Market Survey: What is the current state of ‘re-interpretation’ ?
Motivation and introduction
3
Theory
Re-interpretation/Preservation tools
4
Search Measurement
Approx/Exact Limits
RECAST
Data
BDT/Non-standard(eg LLPs) Cut-based
Analysis logic preservation
Some BSM model
Reco-level Particle-level(unfolded)
Rivet Routine
Data preservationinc Errors, Covariance...
Observed data as yields
Efficiency maps+ detector smearing
Data as YODA/YAML:
Yields/diff XS/Err Breakdowns
Data as YODA/YAML: Yields/Histos/Err
Breakdowns
RIVET/YODA HEPData
Theory/Models eg FeynRules
Suboptimal or Missing Links / Inputs (LC bias)
Original Observations
Reco-level measurements
Unfolded measurements
CONTUR
SM predictions
CR/SR Unfolding
Rivet Routine
Full analysisWorkflow in Gitlab
New BSM Model
KeyTopFitter
Re-interpretation/Preservation tools
Bank of models
5
Search Measurement
Approx/Exact Limits
RECAST
Data
BDT/Non-standard(eg LLPs) Cut-based
Analysis logic preservation
Some BSM model
Reco-level Particle-level(unfolded)
Rivet Routine
Data preservationinc Errors, Covariance...
Observed data as yields
Efficiency maps+ detector smearing
Data as YODA/YAML:
Yields/diff XS/Err Breakdowns
Data as YODA/YAML: Yields/Histos/Err
Breakdowns
RIVET/YODA HEPData
Theory/Models eg FeynRules
Suboptimal or Missing Links / Inputs (LC bias)
Original Observations
Cut-based searches
Reco-level measurements
Unfolded measurements
CONTUR
SM predictions
CR/SR Unfolding
Rivet Routine
(Simplified) Likelihood Generator
Full analysisWorkflow in Gitlab
New BSM Model
KeyTopFitter
Slow, cumbersome.Sample generation load?Sustainable? Permanence? MC predictionsCov Matrix Searches
MC predictionsCov Matrix
Radial effs for LLPs?
6
Looking past RECAST
• RECAST is currently the only way to preserve analyses which depend strongly on detector response or BDTs. But...
• Concerns about how useable the RECAST paradigm would be
• Simulation of new signals: heavy connoting load
• Will theorists really be able to request new grids of signals
• etc..
• Could RIVET handle this instead?
• 3D efficiency (add eff vs Lxy or time for LLPs)
• What to do about BDTs?
• Event getting the eff within 50% would be good enough
7
Correlation Fixation
• Many re-interpretation schemes rely on having correlation matrix preserved ...
• If preserved as error breakdown: we already have most elements in place since last year.
• Switch to column representation rather than annotation?
• For correlation matrices, both HEPData and YODA need a new format, and need to be able to make sure they can comunicate:
• 2D-histo like object, which is aware of which rows/columns correspond to bins of other histograms
• YODA: annotation on top of a Histo2D? Binned how? Annotation just a python dict?
• Or some dedicated new object/class?
8
Prescription for storing MC predictions
• If we are to do proper limit setting, need to store the MC bkg prediction from the analysis.
• How to distinguish this from data in HEPData / YODA?
• Extra label?
• How to use it automatically ?
9
Miscellanea
• What’s needed to make TopFitter and Rivet/HEPData communicate?
• Bank of simplified models
• Using smeared routines for searches in CONTUR
• Simplified Likelihood Generator
• Automatically use cov/correlation or error breakdown to add a chi2 value in new matplotlib make-plots?
10
Backup
11Search
Measurement
CR/SR Unfold
RIVET/YODA HEPData
Approx/Exact Limits
RECASTMC predictions
Bank of simplified models
Data
BDT/Non-standard(eg LLPs) Cut-based
Some BSM model
Reco-level Particle-level(unfolded)
Rivet Routine
Observed data as yields
Efficiency maps+ detector smearing
Data as YODA/YAML:
Yields/diff XS/Err Breakdowns
Data as YODA/YAML: Yields/Histos/Err
Breakdowns
Cov Matrix
Cut-based searches
reco measurementsUnfolded
measurements
SearchesCONTUR
SM predictions
SM predictions
What about non-resonant signals?
Cov MatrixMC predictions
New BSM Model
Full analysisWorkflow in Gitlab
Slow, cumbersome.Sample generation load?Sustainable? Permanence?
Likelihood Builder