Slide title

Louie Corpe, UCL (l.corpe@ucl.ac.uk) 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