[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123388-en":3,"doc-seo-123388-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123388,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Unearthing large pseudoscalar Yukawa couplings with machine learning","With Run 3 underway, Higgs boson couplings at 125 GeV are studied with increasing precision while searches for additional scalars continue. MultiHiggs frameworks allow sizable deviations from Standard Model expectations, giving indirect access to new scalar sectors. This work examines large pseudoscalar Yukawa couplings in a softly-broken Z2 × Z2′ three-Higgs-doublet model with CP-violating coefficients. A machine-learning approach employing an evolutionary strategy with novelty reward improves sampling efficiency and uncovers parameter regions and observable consequences not found by previous techniques, demonstrated as a general Physics Beyond the Standard Model prototype.","Published for SISSA by  Springer  \nReceived: May 23, 2025  \nRevised: June 30, 2025  \nAccepted: July 2, 2025  \nPublished: July 28, 2025  \nUnearthing large pseudoscalar Yukawa couplings with machine learning  \nFernando Abreu de Souza  ,a Rafael Boto  ,b Miguel Crispim Romão  ,c,a Pedro N. Figueiredo  ,b Jorge C. Romão b and João P. Silva b  \naLIP — Laboratório de Instrumentação e Física Experimental de Partículas, Escola de Ciências, Campus de Gualtar, Universidade do Minho,  \n4701-057 Braga, Portugal  \nb Departamento de Física and CFTP, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1, P-1049-001 Lisboa, Portugal  \nc Institute for Particle Physics Phenomenology, Durham University, Durham DH1 3LE, U.K.  \nE-mail: [abreurocha@lip.pt](abreurocha@lip.pt) , [rafael.boto@tecnico.ulisboa.pt](rafael.boto@tecnico.ulisboa.pt) ,  \n[miguel.romao@durham.ac.uk](miguel.romao@durham.ac.uk) , [pedro.m.figueiredo@tecnico.ulisboa.pt](pedro.m.figueiredo@tecnico.ulisboa.pt) , [jorge.romao@tecnico.ulisboa.pt](jorge.romao@tecnico.ulisboa.pt) , [jpsilva@cftp.ist.utl.pt](jpsilva@cftp.ist.utl.pt)  \nAbstract: With the Large Hadron Collider’s Run 3 in progress, the 125GeV Higgs boson couplings are being examined in greater detail, while searching for additional scalars. MultiHiggs frameworks allow Higgs couplings to significantly deviate from Standard Model values, enabling indirect probes of extra scalars. We consider the possibility of large pseudoscalar Yukawa couplings in the softly-broken Z2 × Z2′ three-Higgs doublet model with CP violating coefficients. To explore the parameter space of the model, we employ a Machine Learning algorithm that significantly enhances sampling efficiency. Using it, we find new regions of parameter space and observable consequences, not found with previous techniques. This method leverages an Evolutionary Strategy to quickly converge towards valid regions with an additional Novelty Reward mechanism. We use this model as a prototype to illustrate the potential of the new techniques, applicable to any Physics Beyond the Standard Model scenario.  \nKeywords: Multi-Higgs Models, Specific BSM Phenomenology  \nArXiv ePrint: 2505.10625  \nOpen Access, © The Authors.  \nArticle funded by SCOAP3 . [https://doi.org/10.1007/JHEP07](https://doi.org/10.1007/JHEP07) (2025)268  \nJ HEP07(2025)268  \nContents  \n1 Introduction 1  \n2 The C3HDM 3  \n2.1 The scalar potential 3  \n2.2 Physical basis 4  \n2.3 Independent parameters 5  \n2.4 The Yukawa Lagrangian 6  \n3 Constraints 8  \n4 Black-box machine learning optimisation 10  \n4.1 Optimisation with an evolutionary strategy 11  \n4.2 Novelty reward 11  \n4.3 Simulation strategy 12  \n5 Results and discussion 13  \n5.1 b quark couplings 14  \n5.2 τ lepton couplings 16  \n5.3 Uncorrelated τ and b CP-odd couplings 16  \n5.4 Top couplings 18  \n5.5 The real limit 18  \n6 Conclusions 20  \n1 Introduction  \nThe ATLAS [1] and CMS [2] Collaborations at the Large Hadron Collider (LHC) had their first success with the discovery of the Higgs boson at 125GeV (h125 ) . This enticed two new questions: i) are the properties of this scalar consistent with those predicted by the Standard Model (SM)?; ii) are there more scalar families, just as there are extra fermion families? The first question calls for precision experiments, the second for exploratory searches. And one question informs the other; precision experiments probe the effect of extra particle through their putative virtual effects; models with extra scalars inform exploration for unusual coupling properties.  \nBoth questions are actively being pursued, and considerable new knowledge has been acquired from the LHC. One has learned that the magnitudes of the tree-level couplings of the 125GeV Higgs to all members of the third fermion family and to a pair of weak gauge boson is in accordance with the SM, to a precision of about 10% . Loop couplings with a pair of gluons and with a pair of photons have also been probed to considerable precision [3","cbCaieO0Gk8Eb3wR","https://ap.wps.com/l/cbCaieO0Gk8Eb3wR","pdf",1789508,1,26,"English","en",105,"# Introduction\n# The C3HDM\n## The scalar potential\n## Physical basis\n## Independent parameters\n## The Yukawa Lagrangian\n# Constraints\n# Black-box machine learning optimisation\n## Optimisation with an evolutionary strategy\n## Novelty reward\n## Simulation strategy\n# Results and discussion\n## b quark couplings\n## τ lepton couplings\n## Uncorrelated τ and b CP-odd couplings\n## Top couplings\n## The real limit\n# Conclusions","[{\"question\":\"What is the main goal of the study?\",\"answer\":\"To investigate the possibility of large pseudoscalar Yukawa couplings in a CP-violating three-Higgs-doublet model and to explore its parameter space efficiently using machine learning.\"},{\"question\":\"Which model framework is used to allow pseudoscalar Yukawa couplings?\",\"answer\":\"A softly-broken Z2 × Z2′ three-Higgs-doublet model with CP-violating coefficients.\"},{\"question\":\"How does the machine learning method improve exploration of the parameter space?\",\"answer\":\"It uses an evolutionary strategy to quickly converge toward valid regions and adds a novelty reward mechanism to find new regions and associated observable consequences beyond those obtained with previous techniques.\"}]","Unearthing large pseudoscalar Yukawa couplings with machine learning | 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is the main goal of the study?","Question",{"text":75,"@type":76},"To investigate the possibility of large pseudoscalar Yukawa couplings in a CP-violating three-Higgs-doublet model and to explore its parameter space efficiently using machine learning.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which model framework is used to allow pseudoscalar Yukawa couplings?",{"text":80,"@type":76},"A softly-broken Z2 × Z2′ three-Higgs-doublet model with CP-violating coefficients.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the machine learning method improve exploration of the parameter space?",{"text":84,"@type":76},"It uses an evolutionary strategy to quickly converge toward valid regions and adds a novelty reward mechanism to find new regions and associated observable consequences beyond those obtained with previous 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