Setting Limits on Supersymmetry Using Simplified Models
November 15th, 2013
This paper demonstrates a protocol for recasting experimental simplified model limits into conservative and aggressive limits on an arbitrary new physics model. Publicly available LHC experimental results can be recast in this manner into limits on almost any new physics model with a supersymmetry-like signature.
The overall goal of this procedure is to apply existing limits on simplified models to complete new physics models. This is accomplished by first deconstructing the new physics model into its constituent processes and modes. The second step is to compile a list of simplified models that cover the processes in the new physics model.
Next, the kinematics of the chosen simplified models must be validated against the kinematics of a full point to ensure complete coverage. The final step is converting existing limits on those simplified models into limits on the new physics model. Ultimately, the estimated limits using simplified models are used to show that approximate limits can be obtained without dedicated Montecarlo studies.
The main advantage of this technique over existing methods is that no detector simulation need to be validated or run in order to obtain a useful limit. This method gives theorists a new way to use experimental results Individuals new to beyond understand a model. Physics generally struggle with the apparent complexity of new physics models.
However, with this method, we are able to almost fully reproduce the kinematics of the complete model, which is the small number of simplified models, which makes life a lot easier. The first step in exploring minimal super gravity studied in this video or any new physics model is to generate proton proton collision events covering a plane in its parameter space. To do this, use a collection of software that produces events with Parton showers and incorporates a patronization model.
Pass the events through the pretty good simulation PGS software package with a large Hadron Collider detector parameter card, and extract final state objects. Next, use the PGS event results and the generator event record to classify spart production in decay modes. Keep track of all particle masses, production mechanisms, decay chains, and their respective counts, and use these to calculate branching fractions.
Calculate the best production cross sections for the model of interest. Start model reconstruction by selecting a point in the parameter space of the new physics. Model the M zero M1 half plane in minimal super gravity.
Determine the production modes for this point, and note the important ones for the same point in parameter space. Determine the important decay modes Scan parameter space and repeat these steps until there is a dictionary of simplified models that cover at least 50%of the open production and decay modes of the new physics model. Next, begin testing the quality of the simplified model.
Choose a representative point of the new physics model and construct the relevant simplified model there using the appropriate masses. Repeat this for several points resulting in several simplified models. Begin with one simplified model and weight it with a factor that is proportional to its production fraction times its branching fraction.
Next, add a second weighted model to the first. Continue to do the same for each of the other models to form a sum over all the models. Next, calculate the kinematic distributions for the representative points of minimal super gravity using the event generation procedure and compare it with those of the combined simplified model.
If the kinematics differ by more than 30%include additional simplified models to improve coverage for the most conservative limit. Begin limit construction by considering the expression for the expected number of events shown here. Obtain the relevant products of acceptance and efficiency.
Choose a parameter space point and use this equation to test the new physics model behavior when no assumptions are made about events not explicitly included in the simplified model. To obtain a more realistic limit for the same parameter space point. Test the new physics model under the assumption that the efficiency for associated production is not significantly different from that of pair production.
For a more aggressive limit test the parameter space point with the assumption that the production modes not represented by explicitly included. Simplified models are comparable to those that are included. To obtain the most aggressive feasible limit, add the assumption that the decay modes not represented by the explicitly included.
Simplified models are comparable to those of the models that are included. Assuming no information on correlations, use the limit set by the signal region with the best expected performance. This plot shows an example zero lept on exclusion limit for minimal super gravity models with a ratio of Higgs vacuum expectation values of 10, alinear coupling of zero and a positive mass parameter.
The combined limits are obtained by using the signal region, which generates best expected limit at each point in the parameter space. The dash blue line shows the expected 95%confidence level limit. No theoretical systematic uncertainties are taken into account.
The solid red line is the observed limit results from previous searches with different parameter choices are also shown. Here are the exclusion limits obtained using only simplified models for each of the successively more aggressive assumptions made in the analysis. The limits are labeled by their manuscript equation number.
To make a comparison with the Atlas experiment, the product of the acceptance ratio and the efficiency is interpolated. The most conservative exclusion limit follows the limit of the dedicated search in regions that are well covered by the simplified models, the most aggressive limit overestimates exclusion by up to 40 gig electron volts in the squawk dominated region and up to 100 gig electron volts in the Gino dominated region. Note that even for the small number of simplified models used, the conservative limits set are close to the correct result.
After watching this video, you should have a good understanding of how to use existing experimental limits to set a limit on any new physics model. While attempting this procedure, it is important to exactly remember what assumptions were being made on the final states and whether those assumptions are physical and valid.
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Overview
This paper presents a protocol for translating experimental limits from simplified models into conservative and aggressive limits applicable to new physics models. The methodology allows for the use of existing LHC experimental results to derive limits on various new physics models with signatures similar to supersymmetry.
Key Study Components
Area of Science
- Particle Physics
- Experimental Physics
- Theoretical Physics
Background
- New physics models often require validation against existing experimental data.
- Simplified models serve as a bridge to understand complex new physics scenarios.
- Limits on simplified models can inform broader theoretical frameworks.
- Existing LHC results provide a rich source of data for analysis.
Purpose of Study
- To apply existing limits on simplified models to new physics models.
- To ensure comprehensive coverage of processes in new physics models.
- To demonstrate that approximate limits can be derived without extensive Monte Carlo simulations.
Methods Used
- Deconstruction of new physics models into constituent processes.
- Compilation of a list of relevant simplified models.
- Validation of kinematics between simplified and full models.
- Conversion of existing limits into new physics model limits.
Main Results
- Established a systematic approach to recast limits from simplified models.
- Demonstrated the feasibility of using existing data for new physics constraints.
- Provided a framework for future studies on new physics models.
- Showed that limits can be approximated without dedicated Monte Carlo studies.
Conclusions
- The protocol effectively bridges simplified and new physics models.
- Existing LHC results can be leveraged for broader theoretical implications.
- This approach enhances the understanding of new physics signatures.
Frequently Asked Questions
Executive Industry Relevance
Setting experimental limits on supersymmetry and related theories is a critical challenge due to vast parameter spaces and complex model structures. The use of simplified models enables more interpretable and transferable constraints, supporting robust hypothesis testing and portfolio triage in early-stage discovery. This approach enhances predictive confidence and accelerates decision-making across theoretical and translational research pipelines.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Enables systematic interrogation of theoretical hypotheses by mapping complex models to tractable simplified representations.
- Supports biological de-risking by clarifying which parameter regions are excluded by current data.
- Facilitates functional target validation through quantitative exclusion limits derived from experimental results.
- Improves predictive confidence for advancing or deprioritizing candidate mechanisms.
Screening & Assay Development
- Prepares validated model systems for downstream quantitative analysis by leveraging acceptance and efficiency tables.
- Standardizes the process of limit-setting, ensuring reproducibility and comparability across studies.
- Enables scalable screening of theoretical models against experimental constraints without bespoke simulations.
- Supports reliable evaluation of new candidate models for further investigation.
Translational & Preclinical Research
- Aligns theoretical exclusion limits with experimental observables, supporting translational continuity.
- Provides a framework for risk-adjusted advancement decisions based on quantitative model coverage.
- Enhances mechanistic de-risking by clarifying the impact of physical assumptions on exclusion boundaries.
Pipeline & Workflow Integration
This methodology integrates from early discovery through lead identification by enabling rapid recasting of experimental results into new theoretical frameworks.
- Discovery Biology: Supports hypothesis testing and pathway clarification by mapping complex models to simplified analogs.
- Screening: Delivers reproducible, quantitative outputs using standardized acceptance and efficiency metrics.
- Analytics: Provides statistical readouts and exclusion limits that facilitate direct comparison of model conditions.
- Translational Research: Maintains continuity from theoretical modeling to experimental validation by leveraging LHC data.
- Enterprise Reuse: Establishes a reusable protocol for recasting experimental limits across diverse theoretical models.
Operational & Enterprise Impact
- Scientific Value: Increases predictive confidence and reduces mechanistic ambiguity in model evaluation.
- Operational Value: Streamlines standardization, reproducibility, and scalability of limit-setting workflows.
- Strategic Value: Enables more informed go/no-go decisions and capital-efficient portfolio management.
- Portfolio Impact: Supports risk-adjusted prioritization and advancement of theoretical models.
Implementation Considerations
- Requires expertise in theoretical modeling, event generation, and statistical analysis.
- Depends on access to event simulation software and validated detector parameterizations.
- Necessitates cross-team agreement on physical assumptions and model coverage thresholds.
- May require adaptation for models with unique or poorly covered decay modes.
- Assumptions about production and decay efficiencies must be explicitly tracked and justified.
