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Symbolic Regression for Beyond the Standard Model Physics (2405.18471v2)

Published 28 May 2024 in hep-ph, cs.AI, cs.LG, hep-th, and physics.comp-ph

Abstract: We propose symbolic regression as a powerful tool for studying Beyond the Standard Model physics. As a benchmark model, we consider the so-called Constrained Minimal Supersymmetric Standard Model, which has a four-dimensional parameter space defined at the GUT scale. We provide a set of analytical expressions that reproduce three low-energy observables of interest in terms of the parameters of the theory: the Higgs mass, the contribution to the anomalous magnetic moment of the muon, and the cold dark matter relic density. To demonstrate the power of the approach, we employ the symbolic expressions in a global fits analysis to derive the posterior probability densities of the parameters, which are obtained extremely rapidly in comparison with conventional methods.

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References (33)
  1. J. Skilling, “Nested Sampling,” AIP Conf. Proc. 735 no. 1, (2004) 395.
  2. J. Skilling, “Nested sampling for general Bayesian computation,” Bayesian Analysis 1 no. 4, (2006) 833–859.
  3. G. Ashton et al., “Nested sampling for physical scientists,” Nature 2 (2022) , [arXiv:2205.15570 [stat.CO]].
  4. F. Feroz and M. P. Hobson, “Multimodal nested sampling: an efficient and robust alternative to MCMC methods for astronomical data analysis,” Mon. Not. Roy. Astron. Soc. 384 (2008) 449, [arXiv:0704.3704 [astro-ph]].
  5. S. Caron, J. S. Kim, K. Rolbiecki, R. Ruiz de Austri, and B. Stienen, “The BSM-AI project: SUSY-AI–generalizing LHC limits on supersymmetry with machine learning,” Eur. Phys. J. C 77 no. 4, (2017) 257, [arXiv:1605.02797 [hep-ph]].
  6. A. Hammad, M. Park, R. Ramos, and P. Saha, “Exploration of parameter spaces assisted by machine learning,” Comput. Phys. Commun. 293 (2023) 108902, [arXiv:2207.09959 [hep-ph]].
  7. F. A. de Souza, M. Crispim Romão, N. F. Castro, M. Nikjoo, and W. Porod, “Exploring parameter spaces with artificial intelligence and machine learning black-box optimization algorithms,” Phys. Rev. D 107 no. 3, (2023) 035004, [arXiv:2206.09223 [hep-ph]].
  8. J. C. Romão and M. Crispim Romão, “Combining Evolutionary Strategies and Novelty Detection to go Beyond the Alignment Limit of the Z3subscript𝑍3Z_{3}italic_Z start_POSTSUBSCRIPT 3 end_POSTSUBSCRIPT 3HDM,” [arXiv:2402.07661 [hep-ph]].
  9. M. A. Diaz, G. Cerro, S. Dasmahapatra, and S. Moretti, “Bayesian Active Search on Parameter Space: a 95 GeV Spin-0 Resonance in the (B−L𝐵𝐿B-Litalic_B - italic_L)SSM,” [arXiv:2404.18653 [hep-ph]].
  10. J. R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA, USA, 1992. https://www.bibsonomy.org/bibtex/27573e564bc5369e1a853e74b3ac62607/emanuel.
  11. S.-M. Udrescu and M. Tegmark, “AI Feynman: a Physics-Inspired Method for Symbolic Regression,” Sci. Adv. 6 no. 16, (2020) eaay2631, [arXiv:1905.11481 [physics.comp-ph]].
  12. A. Butter, T. Plehn, N. Soybelman, and J. Brehmer, “Back to the Formula – LHC Edition,” [arXiv:2109.10414 [hep-ph]].
  13. S. A. Abel, A. Constantin, T. R. Harvey, and A. Lukas, “Cosmic Inflation and Genetic Algorithms,” Fortsch. Phys. 71 no. 1, (2023) 2200161, [arXiv:2208.13804 [hep-th]].
  14. D. J. Bartlett, H. Desmond, and P. G. Ferreira, “Exhaustive Symbolic Regression,” [arXiv:2211.11461 [astro-ph.CO]].
  15. S. M. Koksbang, “Cosmological parameter constraints using phenomenological symbolic expressions: On the significance of symbolic expression complexity and accuracy,” Phys. Rev. D 108 no. 4, (2023) 043539, [arXiv:2307.16468 [astro-ph.CO]].
  16. T. Sousa, D. J. Bartlett, H. Desmond, and P. G. Ferreira, “Optimal inflationary potentials,” Phys. Rev. D 109 no. 8, (2024) 083524, [arXiv:2310.16786 [astro-ph.CO]].
  17. Tsoi, Ho Fung, Pol, Adrian Alan, Loncar, Vladimir, Govorkova, Ekaterina, Cranmer, Miles, Dasu, Sridhara, Elmer, Peter, Harris, Philip, Ojalvo, Isobel, and Pierini, Maurizio, “Symbolic regression on fpgas for fast machine learning inference,” EPJ Web of Conf. 295 (2024) 09036, [arXiv:2305.04099]. https://doi.org/10.1051/epjconf/202429509036.
  18. S. AbdusSalam, S. Abel, and M. Crispim Romão, “Symbolically Regressing Beyond the Standard Model Physics ,” May, 2024. https://gitlab.com/miguel.romao/symbolic-regression-bsm.
  19. S. AbdusSalam, S. Abel, and M. Crispim Romão, “1 Million cMSSM parameter space points with low- energy predictions from SPheno and microOMEGAS,” May, 2024. https://doi.org/10.5281/zenodo.11366471.
  20. F. O. de Franca, M. Virgolin, M. Kommenda, M. S. Majumder, M. Cranmer, G. Espada, L. Ingelse, A. Fonseca, M. Landajuela, B. Petersen, R. Glatt, N. Mundhenk, C. S. Lee, J. D. Hochhalter, D. L. Randall, P. Kamienny, H. Zhang, G. Dick, A. Simon, B. Burlacu, J. Kasak, M. Machado, C. Wilstrup, and W. G. L. Cava, “Interpretable symbolic regression for data science: Analysis of the 2022 competition,” 2023. https://arxiv.org/abs/2304.01117.
  21. M. D. Cranmer, R. Xu, P. W. Battaglia, and S. Ho, “Learning symbolic physics with graph networks,” CoRR abs/1909.05862 (2019) , [1909.05862]. http://arxiv.org/abs/1909.05862.
  22. M. D. Cranmer, A. Sanchez-Gonzalez, P. W. Battaglia, R. Xu, K. Cranmer, D. N. Spergel, and S. Ho, “Discovering symbolic models from deep learning with inductive biases,” CoRR abs/2006.11287 (2020) , [2006.11287]. https://arxiv.org/abs/2006.11287.
  23. W. G. L. Cava, P. Orzechowski, B. Burlacu, F. O. de França, M. Virgolin, Y. Jin, M. Kommenda, and J. H. Moore, “Contemporary symbolic regression methods and their relative performance,” CoRR abs/2107.14351 (2021) , [2107.14351]. https://arxiv.org/abs/2107.14351.
  24. M. Cranmer, “Interpretable machine learning for science with pysr and symbolicregression.jl,” 2023. https://doi.org/10.48550/arXiv.2305.01582.
  25. B. Burlacu, G. Kronberger, and M. Kommenda, “Operon c++: An efficient genetic programming framework for symbolic regression,” in Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, R. Allmendinger et al., eds., GECCO ’20, pp. 1562–1570. Association for Computing Machinery, internet, July 8-12, 2020. https://doi.org/10.1145/3377929.3398099.
  26. W. Porod, “SPheno, a program for calculating supersymmetric spectra, SUSY particle decays and SUSY particle production at e+ e- colliders,” Comput. Phys. Commun. 153 (2003) 275–315, [arXiv:hep-ph/0301101].
  27. G. Belanger, F. Boudjema, A. Pukhov, and A. Semenov, “MicrOMEGAs: A Program for calculating the relic density in the MSSM,” Comput. Phys. Commun. 149 (2002) 103–120, [arXiv:hep-ph/0112278].
  28. CMS Collaboration, “Measurement of the Higgs boson mass and width using the four leptons final state,” tech. rep., CERN, Geneva, 2023. https://cds.cern.ch/record/2871702.
  29. Planck Collaboration, N. Aghanim et al., “Planck 2018 results. VI. Cosmological parameters,” Astron. Astrophys. 641 (2020) A6, [arXiv:1807.06209 [astro-ph.CO]]. [Erratum: Astron.Astrophys. 652, C4 (2021)].
  30. T. Aoyama et al., “The anomalous magnetic moment of the muon in the Standard Model,” Phys. Rept. 887 (2020) 1–166, [arXiv:2006.04822 [hep-ph]].
  31. Muon g-2 Collaboration, D. P. Aguillard et al., “Measurement of the Positive Muon Anomalous Magnetic Moment to 0.20 ppm,” Phys. Rev. Lett. 131 no. 16, (2023) 161802, [arXiv:2308.06230 [hep-ex]].
  32. A. Lewis, “GetDist: a Python package for analysing Monte Carlo samples,” [arXiv:1910.13970 [astro-ph.IM]]. https://getdist.readthedocs.io.
  33. J. C. Criado, R. Kogler, and M. Spannowsky, “Quantum fitting framework applied to effective field theories,” Phys. Rev. D 107 no. 1, (2023) 015023, [arXiv:2207.10088 [hep-ph]].
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