Built on

J. S. Speagle, “DYNESTY: a dynamic nested sampling package for estimating Bayesian posteriors and evidences,” mnras 493
Original
1904
Earlier work this paper cites.
S.-M. Udrescu and M. Tegmark, “AI Feynman: a Physics-Inspired Method for Symbolic Regression,” Sci. Adv. 6
Original
1905
Earlier work this paper cites.
M. D. Cranmer, R. Xu, P. W. Battaglia, and S. Ho, “Learning symbolic physics with graph networks,” CoRR abs/1909.05862
Original
1909
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A. Lewis, “GetDist: a Python package for analysing Monte Carlo samples,” [arXiv:1910.13970 [astro-ph.IM]] . https://getdist.readthedocs.io
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1910
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MIT Press, Cambridge, MA, USA, 1992
J. R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection · 1992
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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
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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
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J. Skilling, “Nested Sampling,” AIP Conf. Proc. 735
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J. Skilling, “Nested sampling for general Bayesian computation,” Bayesian Analysis 1
2006
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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
Original
2006
Earlier work this paper cites.

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2006
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Original
2008
Cited alongside, same era.
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
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2017
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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 · 2020
Cited alongside, same era.
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Then

https://arxiv.org/abs/2304.01117
Original
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 · 2023
Later among the works it cites.
https://doi.org/10.48550/arXiv.2305.01582
M. Cranmer, “Interpretable machine learning for science with pysr and symbolicregression.jl,” 2023 · 2023
Later among the works it cites.
https://cds.cern.ch/record/2871702
CMS · 2023
Later among the works it cites.
Muon g-2
Original
2023
Later among the works it cites.
J. C. Criado, R. Kogler, and M. Spannowsky, “Quantum fitting framework applied to effective field theories,” Phys. Rev. D 107
Original

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2024

Symbolic Regression for Beyond the Standard Model Physics

AbdusSalam, Shehu, Abel, Steve, Romao, Miguel Crispim

Understand

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.

Built on

J. S. Speagle, “DYNESTY: a dynamic nested sampling package for estimating Bayesian posteriors and evidences,” mnras 493
Original
1904
Earlier work this paper cites.
S.-M. Udrescu and M. Tegmark, “AI Feynman: a Physics-Inspired Method for Symbolic Regression,” Sci. Adv. 6
Original
1905
Earlier work this paper cites.
M. D. Cranmer, R. Xu, P. W. Battaglia, and S. Ho, “Learning symbolic physics with graph networks,” CoRR abs/1909.05862
Original
1909
Earlier work this paper cites.
A. Lewis, “GetDist: a Python package for analysing Monte Carlo samples,” [arXiv:1910.13970 [astro-ph.IM]] . https://getdist.readthedocs.io
Original
1910
Earlier work this paper cites.
MIT Press, Cambridge, MA, USA, 1992
J. R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection · 1992
Earlier work this paper cites.
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
Earlier work this paper cites.
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
Earlier work this paper cites.
J. Skilling, “Nested Sampling,” AIP Conf. Proc. 735
2004
Earlier work this paper cites.
J. Skilling, “Nested sampling for general Bayesian computation,” Bayesian Analysis 1
2006
Earlier work this paper cites.
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
Original
2006
Earlier work this paper cites.

Similar

T. Aoyama et al
Original
2006
Cited alongside, same era.
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
Original
2008
Cited alongside, same era.
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
Original
2017
Cited alongside, same era.
Association for Computing Machinery, internet, July 8-12, 2020
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 · 2020
Cited alongside, same era.
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

Then

https://arxiv.org/abs/2304.01117
Original
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 · 2023
Later among the works it cites.
https://doi.org/10.48550/arXiv.2305.01582
M. Cranmer, “Interpretable machine learning for science with pysr and symbolicregression.jl,” 2023 · 2023
Later among the works it cites.
https://cds.cern.ch/record/2871702
CMS · 2023
Later among the works it cites.
Muon g-2
Original
2023
Later among the works it cites.
J. C. Criado, R. Kogler, and M. Spannowsky, “Quantum fitting framework applied to effective field theories,” Phys. Rev. D 107
Original

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

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Original

2021

Cited alongside, same era.

G. Ashton et al

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Cited alongside, same era.

A. Hammad, M. Park, R. Ramos, and P. Saha, “Exploration of parameter spaces assisted by machine learning,” Comput. Phys. Commun. 293

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2023

Cited alongside, same era.

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

Original

2023

Cited alongside, same era.

S. A. Abel, A. Constantin, T. R. Harvey, and A. Lukas, “Cosmic Inflation and Genetic Algorithms,” Fortsch. Phys. 71

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Cited alongside, same era.

S. M. Koksbang, “Cosmological parameter constraints using phenomenological symbolic expressions: On the significance of symbolic expression complexity and accuracy,” Phys. Rev. D 108

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2023

Cited alongside, same era.

J. C. Romão and M. Crispim Romão, “Combining Evolutionary Strategies and Novelty Detection to go Beyond the Alignment Limit of the Z 3 Z_{3} 3HDM,” [arXiv:2402.07661 [hep-ph]]

Original

Cited in the paper.

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 − L B-L )SSM,” [arXiv:2404.18653 [hep-ph]]

Original

Cited in the paper.

2023

Later among the works it cites.

T. Sousa, D. J. Bartlett, H. Desmond, and P. G. Ferreira, “Optimal inflationary potentials,” Phys. Rev. D 109

Original

2024

Closest in time.

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

Original

2024

Closest in time.

https://gitlab.com/miguel.romao/symbolic-regression-bsm

S. AbdusSalam, S. Abel, and M. Crispim Romão, “Symbolically Regressing Beyond the Standard Model Physics ,” May, 2024 · 2024

Closest in time.

https://doi.org/10.5281/zenodo.11366471

S. AbdusSalam, S. Abel, and M. Crispim Romão, “1 Million cMSSM parameter space points with low- energy predictions from SPheno and microOMEGAS,” May, 2024 · 2024

Closest in time.