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Step: a tool to perform tests of smoothness on differential distributions based on expansion of polynomials
by Patrick L. S. Connor, Radek Žlebčík
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Submission summary
| Authors (as registered SciPost users): | Patrick Connor |
| Submission information | |
|---|---|
| Preprint Link: | https://arxiv.org/abs/2111.09968v3 (pdf) |
| Code repository: | https://gitlab.cern.ch/step/library |
| Data repository: | https://gitlab.cern.ch/step/library |
| Date submitted: | 2022-06-13 11:36 |
| Submitted by: | Connor, Patrick |
| Submitted to: | SciPost Physics Core |
| Ontological classification | |
|---|---|
| Academic field: | Physics |
| Specialties: |
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| Approach: | Computational |
Abstract
We motivate and describe a method based on fits with polynomials to test the smoothness of differential distributions. As a demonstration, we apply the method to several measurements of inclusive jet double-differential cross section in the jet transverse momentum and rapidity at the Tevatron and LHC. This method opens new possibilities to test the quality of differential distributions used for the extraction of physics quantities such as the strong coupling.
Author comments upon resubmission
thank you for your invitation to resubmit.
We have carefully considered all the comments from the referees and applied changes to the paper draft accordingly. In addition, we have applied changes according to feedback on the pre-print from the community.
Best regards,
Patrick Connor & Radek Žlebčík
List of changes
- The technique has been checked successfully with alternative bases, e.g. Legendre polynomials.
- Two additional early-stopping criteria have been implemented, namely F-test and cross validation on replicas.
- The fit performance of the Step algorithm has been comparsed with that of an alternative function suggested by one of the referees.
- A comparison of the fit performance with Asimov data sets to show expectation has been added.
- Additional uncertainties on the ATLAS data coming from the in-situ calibration have been included in the fit.
- Distributions of pulls have been included for the Tevatron measurements.
- Fit probability tables are now shown.
- Global chi2/ndf values for the LHC measurement considering also bin-to-bin correlations have been provided.
- In general, we have significantly extended the discussions.
- The figure with the comparison of the chi2/ndf per rapidity bin and per experiment has been removed.
Current status:
Reports on this Submission
Report #1 by Anonymous (Referee 3) on 2022-9-1 (Invited Report)
- Cite as: Anonymous, Report on arXiv:2111.09968v3, delivered 2022-09-01, doi: 10.21468/SciPost.Report.5619
Strengths
1) These paper presents a new, complementary to the existing ones, approach of i) assessing the smoothness of QCD differential distributions and the appropriateness of bin-by-bin uncertainties together with their correlations, and ii) performing a high-quality smooth fits using empirical parametrizations and an iterative method, determining the number of needed free parameters using several different statistical methods.
2) This approach is automated, requires less tuning of initial parameter and parameter ranges, and is easily accessible by both the experimental Collaborations, and the phenomenology and theory community.
3) To validate this approach and show its performance several tests have been performed and are presented in this paper, using both Tevatron and LHC data, showing how successful the method is in describing differential jet pT distributions, and in assessing whether the uncertainties accompanying them are conservative or optimistic. Also, comparisons with existing methods are also presented showing complementary and in some cases advantageous results.
Report
These paper presents a new, complementary to the existing ones, approach of i) assessing the smoothness of QCD differential distributions and the appropriateness of bin-by-bin uncertainties together with their correlations, and ii) performing a high-quality smooth fits using empirical parametrizations and an iterative method, determining the number of needed free parameters using several different statistical methods.
This approach is automated, requires less tuning of initial parameter and parameter ranges, and is easily accessible by both the experimental Collaborations, and the phenomenology and theory community.
To validate this approach and show its performance several tests have been performed and are presented in this paper, using both Tevatron and LHC data, showing how successful the method is in describing differential jet pT distributions, and in assessing whether the uncertainties accompanying them are conservative or optimistic. Also, comparisons with existing methods are also presented showing complementary and in some cases advantageous results.
Requested changes
1)Please define in the text what the Y axis on Figures 2,4,6,8, by clarifying what sigma and smooth(sigma) are.
2) If possible, please improve the markers on Figures 2a,4a, 6a, 8a, using, for example, different colours for the statistical and systematic components.