- Physical Review Journals
All JournalsPhysics Magazine

Physical Review D

Reuse & Permissions

It is not necessary to obtain permission to reuse this article or its components as it is available under the terms of the Creative Commons Attribution 4.0 International license. This license permits unrestricted use, distribution, and reproduction in any medium, provided attribution to the author(s) and the published article's title, journal citation, and DOI are maintained. Please note that some figures may have been included with permission from other third parties. It is your responsibility to obtain the proper permission from the rights holder directly for these figures.

Export citation

Export citation

Choose format for download:

Download Citation
  • Open Access

Optimizing the cut and count method in phenomenological studies

Baradhwaj Coleppa1,*, Gokul B. Krishna1,†, Agnivo Sarkar2,‡, and Sujay Shil3,§

  • *Contact author: baradhwaj@iitgn.ac.in
  • †Contact author: gokulb@iitgn.ac.in
  • ‡Contact author: agnivosarkar@hri.res.in
  • §Contact author: sujayshil1@gmail.com
PDF

Phys. Rev. D 113, 095020 – Published 13 May, 2026

DOI: https://doi.org/10.1103/zdy9-yndh

Abstract

We introduce an optimization technique to discriminate signal and background in any phenomenological study based on the cut and count-based method. The core ideas behind this technique are the introduction of a ranking scheme that can quantitatively assess the relative importance of various observables involved in a new physics process, and a more methodical way of choosing what cuts to impose. The technique is an iterative process that works with the help of the madanalysis5 interface. Working in the context of a beyond the Standard Model scenario where we carry out a signal search of singly charged Higgs in the context of the two Higgs doublet model, we demonstrate how automating the cut and count process in this specific way results in an enhanced discovery potential compared with the more traditional way of imposing cuts.

View figure in article

Physics Subject Headings (PhySH)

Article Text

References (38)

  1. S. L. Glashow, Partial symmetries of weak interactions, Nucl. Phys. 22, 579 (1961).
  2. Steven Weinberg, A model of leptons, Phys. Rev. Lett. 19, 1264 (1967).
  3. Abdus Salam, Weak and electromagnetic interactions, Conf. Proc. C 680519, 367 (1968).
  4. Dan Guest, Kyle Cranmer, and Daniel Whiteson, Deep learning and its application to LHC physics, Annu. Rev. Nucl. Part. Sci. 68, 161 (2018).
  5. Luke de Oliveira, Michael Kagan, Lester Mackey, Benjamin Nachman, and Ariel Schwartzman, Jet-images—deep learning edition, J. High Energy Phys. 07 (2016) 069.
  6. J. Alwall, R. Frederix, S. Frixione, V. Hirschi, F. Maltoni, O. Mattelaer, H. S. Shao, T. Stelzer, P. Torrielli, and M. Zaro, The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations, J. High Energy Phys. 07 (2014) 079.
  7. Adam Alloul, Neil D. Christensen, Céline Degrande, Claude Duhr, and Benjamin Fuks, feynrules2.0—A complete toolbox for tree-level phenomenology, Comput. Phys. Commun. 185, 2250 (2014).
  8. Torbjörn Sjöstrand, Stefan Ask, Jesper R. Christiansen, Richard Corke, Nishita Desai, Philip Ilten, Stephen Mrenna, Stefan Prestel, Christine O. Rasmussen, and Peter Z. Skands, An introduction to pythia 8.2, Comput. Phys. Commun. 191, 159 (2015).
  9. J. de Favereau, C. Delaere, P. Demin, A. Giammanco, V. Lemaître, A. Mertens, and M. Selvaggi (delphes 3 Collaboration), delphes 3, A modular framework for fast simulation of a generic collider experiment, J. High Energy Phys. 02 (2014) 057.
  10. Eric Conte, Benjamin Fuks, and Guillaume Serret, madanalysis 5, A User-Friendly framework for collider phenomenology, Comput. Phys. Commun. 184, 222 (2013).
  11. Kyle Cranmer, Juan Pavez, and Gilles Louppe, Approximating likelihood ratios with calibrated discriminative classifiers, arXiv:1506.02169.
  12. Pierre Baldi, Kyle Cranmer, Taylor Faucett, Peter Sadowski, and Daniel Whiteson, Parameterized neural networks for high-energy physics, Eur. Phys. J. C 76, 235 (2016).
  13. Eric M. Metodiev, Benjamin Nachman, and Jesse Thaler, Classification without labels: Learning from mixed samples in high energy physics, J. High Energy Phys. 10 (2017) 174.
  14. Amit Chakraborty, Sung Hak Lim, and Mihoko M. Nojiri, Interpretable deep learning for two-prong jet classification with jet spectra, J. High Energy Phys. 07 (2019) 135.
  15. Taylor Faucett, Jesse Thaler, and Daniel Whiteson, Mapping machine-learned physics into a human-readable space, Phys. Rev. D 103, 036020 (2021).
  16. Jonathan Shlomi, Peter Battaglia, and Jean-Roch Vlimant, Graph neural networks in particle physics, Mach. Learn. 2, 021001 (2021).
  17. Vasileios Vatellis, Advancing physics data analysis through machine learning and Physics-Informed neural networks, arXiv:2410.14760.
  18. Aishik Ghosh, Benjamin Nachman, and Daniel Whiteson, Uncertainty-aware machine learning for high energy physics, Phys. Rev. D 104, 056026 (2021).
  19. Sven Bollweg, Manuel Haußmann, Gregor Kasieczka, Michel Luchmann, Tilman Plehn, and Jennifer Thompson, Deep-learning jets with uncertainties and more, SciPost Phys. 8, 006 (2020).
  20. Vladimir Chekhovsky et al. (CMS Collaboration), Development of systematic uncertainty-aware neural network trainings for binned-likelihood analyses at the LHC, Eur. Phys. J. C 85, 1360 (2025).
  21. Lisa Benato, Cristina Giordano, Claudius Krause, Ang Li, Robert Schöfbeck, Dennis Schwarz, Maryam Shooshtari, and Daohan Wang, Unbinned inclusive cross-section measurements with machine-learned systematic uncertainties, Phys. Rev. D 112, 052006 (2025).
  22. Matthew Feickert and Benjamin Nachman, A living review of machine learning for particle physics, arXiv:2102.02770.
  23. Li-Gang Xia, Understanding the boosted decision tree methods with the weak-learner approximation, arXiv:1811.04822.
  24. Yann Coadou, Boosted decision trees, in Artificial Intelligence for High Energy Physics (World Scientific, Singapore, 2022).
  25. Byron P. Roe, Hai-Jun Yang, Ji Zhu, Yong Liu, Ion Stancu, and Gordon McGregor, Boosted decision trees, an alternative to artificial neural networks, Nucl. Instrum. Methods Phys. Res., Sect. A 543, 577 (2005).
  26. G. C. Branco, P. M. Ferreira, L. Lavoura, M. N. Rebelo, Marc Sher, and Joao P. Silva, Theory and phenomenology of two-Higgs-doublet models, Phys. Rep. 516, 1 (2012).
  27. Anirban Karan, Víctor Miralles, and Antonio Pich, Global fit of the aligned two-Higgs-doublet model, Proc. Sci. EPS-HEP2023 (2024) 053 [arXiv:2312.00514].
  28. Heather E. Logan, TASI 2013 lectures on Higgs physics within and beyond the standard model, arXiv:1406.1786.
  29. Majid Hashemi and Laleh Roushandel, Charged Higgs decay to W±H at a high energy lepton collider, Eur. Phys. J. C 84, 966 (2024).
  30. Georges Aad et al. (ATLAS Collaboration), Search for charged Higgs bosons decaying via H±→τ±ν in fully hadronic final states using pp collision data at s=8  TeV with the ATLAS detector, J. High Energy Phys. 03 (2015) 088.
  31. Vardan Khachatryan et al. (CMS Collaboration), Search for a charged Higgs boson in pp collisions at s=8  TeV, J. High Energy Phys. 11 (2015) 018.
  32. Georges Aad et al. (ATLAS Collaboration), Search for charged Higgs bosons in the H±→tb decay channel in pp collisions at s=8  TeV using the ATLAS detector, J. High Energy Phys. 03 (2016) 127.
  33. Debtosh Chowdhury and Otto Eberhardt, Update of global two-Higgs-doublet model fits, J. High Energy Phys. 05 (2018) 161.
  34. Glen Cowan, Kyle Cranmer, Eilam Gross, and Ofer Vitells, Asymptotic formulae for likelihood-based tests of new physics, Eur. Phys. J. C 71, 1554 (2011); 73, 2501(E) (2013).
  35. Li-Gang Xia, QBDT, a new boosting decision tree method with systematical uncertainties into training for high energy physics, Nucl. Instrum. Methods Phys. Res., Sect. A 930, 15 (2019).
  36. A. Bhattacharyya, On a measure of divergence between two multinomial populations, Sankhyā: Indian J. Stat. (1933–1960) 7, 401 (1946), https://www.jstor.org/stable/25047882.
  37. S. Kullback and R. A. Leibler, On information and sufficiency, Ann. Math. Stat. 22, 79 (1951).
  38. Arghya Choudhury, Arpita Mondal, and Subhadeep Sarkar, Searches for the BSM scenarios at the LHC using decision tree-based machine learning algorithms: A comparative study and review of random forest, AdaBoost, XGBoost and LightGBM frameworks, Eur. Phys. J. Special Topics 233, 2425 (2024).

Outline

Information

Phys. Rev. D 113, 095020– Published 13 May, 2026
  • Received 6 November 2025
  • Accepted 22 April 2026
Crossmark - Check for updates button
Creative Commons Logo

Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. Funded by SCOAP3.

Published by the American Physical Society

Sign In to Your Journals Account

Filter

Filter

Article Lookup

Enter a citation