Neutrino mass from M Theory SO(10)

We study the origin of neutrino mass from $SO(10)$ arising from $M$ Theory compactified on a $G_2$-manifold. This is linked to the problem of the breaking of the extra $U(1)$ gauge group, in the $SU(5)\times U(1)$ subgroup of $SO(10)$, which we show can achieved via a (generalised) Kolda-Martin mechanism. The resulting neutrino masses arise from a combination of the seesaw mechanism and induced R-parity breaking contributions. The rather complicated neutrino mass matrix is analysed for one neutrino family and it is shown how phenomenologically acceptable neutrino masses can emerge.Comment: 32 pages, 12 figure

SO(10) Grand Unification in M theory on a G2 manifold

We consider Grand Unified Theories based on $SO(10)$ which originate from string/$M$ theory on $G_2$ manifolds or Calabi-Yau spaces with discrete symmetries. In this framework we are naturally led to a novel solution of the doublet-triplet splitting problem previously considered by Dvali which involves an extra vector-like Standard Model family and light, but weakly coupled colour triplets. These additional states are predicted to be accessible at the LHC and also induce R-parity violation. Gauge coupling unification occurs with a larger GUT coupling.Comment: 5 pages, added references, revised argument on equation 18, results unchanged, a new example is given in equation 24, agrees with published version in Physical Review

R-Parity violation in F-Theory

We discuss R-parity violation (RPV) in semi-local and local F-theory constructions. We first present a detailed analysis of all possible combinations of RPV operators arising from semi-local F-theory spectral cover constructions, assuming an $SU(5)$ GUT. We provide a classification of all possible allowed combinations of RPV operators originating from operators of the form $10\cdot \bar 5\cdot \bar 5$, including the effect of $U(1)$ fluxes with global restrictions. We then relax the global constraints and perform explicit computations of the bottom/tau and RPV Yukawa couplings, at an $SO(12)$ local point of enhancement in the presence of general fluxes subject only to local flux restrictions. We compare our results to the experimental limits on each allowed RPV operator, and show that operators such as $LLe^c$, $LQd^c$ and $u^cd^cd^c$ may be present separately within current bounds, possibly on the edge of observability, suggesting lepton number violation or neutron-antineutron oscillations could constrain F-theory models.Comment: 40 pages, 13 figures, minor correction

MSSM from F-theory SU(5) with Klein Monodromy

We revisit a class of $SU(5)$ SUSY GUT models which arise in the context of the spectral cover with Klein Group monodromy $V_4=Z_2\times Z_2$. We show that $Z_2$ matter parities can be realised via new geometric symmetries respected by the spectral cover. We discuss a particular example of this kind, where the low energy effective theory below the GUT scale is just the MSSM with no exotics and standard matter parity, extended by the seesaw mechanism with two right-handed neutrinos

Exploring parameter spaces with artificial intelligence and machine learning black-box optimisation algorithms

Constraining Beyond the Standard Model theories usually involves scanning highly multi-dimensional parameter spaces and check observable predictions against experimental bounds and theoretical constraints. Such task is often timely and computationally expensive, especially when the model is severely constrained and thus leading to very low random sampling efficiency. In this work we tackled this challenge using Artificial Intelligence and Machine Learning search algorithms used for Black-Box optimisation problems. Using the cMSSM and the pMSSM parameter spaces, we consider both the Higgs mass and the Dark Matter Relic Density constraints to study their sampling efficiency and parameter space coverage. We find our methodology to produce orders of magnitude improvement of sampling efficiency whilst reasonably covering the parameter space.We thank José Santiago Pérez and Jorge Romão for the careful reading of the paper draft and for the useful discussions. This work is supported by FCT - Fundação para a Ciência e a Tecnologia, I.P. under project CERN/FIS-PAR/0024/2019. FAS is also supported by FCT under the research grant with reference UI/BD/153105/2022. The computational work was partially done using the resources made available by RNCA and INCD under project CPCA/A1/401197/2021info:eu-repo/semantics/publishedVersio

Starobinsky-like inflation in no-scale supergravity Wess-Zumino model with Polonyi term

We propose a simple modification of the no-scale supergravity Wess-Zumino model of Starobinsky-like inflation to include a Polonyi term in the superpotential. The purpose of this term is to provide an explicit mechanism for supersymmetry breaking at the end of inflation. We show how successful inflation can be achieved for a gravitino mass satisfying the strict upper bound $m_{3/2}< 10^3$ TeV, with favoured values $m_{3/2}\lesssim\mathcal{O}(1)$ TeV. The model suggests that SUSY may be discovered in collider physics experiments such as the LHC or the FCC.Comment: 13 pages, 4 figure

Jet substructure observables for jet quenching in quark gluon plasma: A machine learning driven analysis

We present a survey of a comprehensive set of jet substructure observables commonly used to study the modifications of jets resulting from interactions with the Quark Gluon Plasma in Heavy Ion Collisions. The JEWEL event generator is used to produce simulated samples of quenched and unquenched jets. Three distinct analyses using Machine Learning techniques on the jet substructure observables have been performed to identify both linear and non-linear relations between the observables, and to distinguish the Quenched and Unquenched jet samples. We find that most of the observables are highly correlated, and that their information content can be captured by a small set of observables. We also find that the correlations between observables are resilient to quenching effects and that specific pairs of observables exhaust the full sensitivity to quenching effects. The code, the datasets, and instructions on how to reproduce this work are also provided

Fitting a collider in a quantum computer: tackling the challenges of quantum machine learning for big datasets

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/frai.2023.1268852/full#supplementary-materialCódigo computacional disponível em: https://github.com/mcpeixoto/QML-HEPCurrent quantum systems have significant limitations affecting the processing of large datasets with high dimensionality, typical of high energy physics. In the present paper, feature and data prototype selection techniques were studied to tackle this challenge. A grid search was performed and quantum machine learning models were trained and benchmarked against classical shallow machine learning methods, trained both in the reduced and the complete datasets. The performance of the quantum algorithms was found to be comparable to the classical ones, even when using large datasets. Sequential Backward Selection and Principal Component Analysis techniques were used for feature's selection and while the former can produce the better quantum machine learning models in specific cases, it is more unstable. Additionally, we show that such variability in the results is caused by the use of discrete variables, highlighting the suitability of Principal Component analysis transformed data for quantum machine learning applications in the high energy physics context.The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by Fundação para a Ciência e a Tecnologia, Portugal, through project CERN/FIS-COM/0004/2021 (“Exploring quantum machine learning as a tool for present and future high energy physics colliders”). IO was supported by the fellowship LCF/BQ/PI20/11760025 from La Caixa Foundation (ID 100010434) and by the European Union Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 847648.info:eu-repo/semantics/publishedVersio