Supersymmetric alignment models for (g − 2)μ

Hierarchical masses of quarks and leptons are addressed by imposing horizontal symmetries. In supersymmetric Standard Models, the same symmetries play a role in suppressing flavor violating processes induced by supersymmetric particles. Combining the idea of spontaneous CP violation to control contributions to electric dipole moments (EDMs), the mass scale of supersymmetric particles can be lowered. We present supersymmetric models with U(1) horizontal symmetries and discuss CP and flavor constraints. Models with two U(1) symmetries are found to give a viable solution to the muon g − 2 anomaly. Interestingly, the parameter space to explain the anomaly will be probed by future electron EDM experiments.

Heavy $$Z^\prime $$ bosons in the secluded $$U(1)^\prime $$ model at hadron colliders

We study $$Z^{\prime }$$ Z ′ phenomenology at hadron colliders in an $$U(1)^{\prime }$$ U ( 1 ) ′ extended MSSM. We choose a $$U(1)^{\prime }$$ U ( 1 ) ′ model with a secluded sector, where the tension between the electroweak scale and developing a large enough mass for $$Z^{\prime }$$ Z ′ is resolved by incorporating three additional singlet superfields into the model. We perform a detailed analysis of the production, followed by decays, including into supersymmetric particles, of a $$Z^{\prime }$$ Z ′ boson with mass between 4 and 5.2 TeV, with particular emphasis on its possible discovery. We select three different scenarios consistent with the latest available experimental data and relic density constraints, and concentrate on final signals with $$2\ell +\not \! \! E_{T}$$ 2 ℓ + ⧸ E T , $$4\ell +\not \! \! E_{T}$$ 4 ℓ + ⧸ E T and $$6\ell +\not \! \! E_{T}$$ 6 ℓ + ⧸ E T . Including the SM background from processes with two, three or four vector bosons, we show the likelihood of observing a $$Z^\prime $$ Z ′ boson is not promising for the HL-LHC at 14 TeV. While at 27 and 100 TeV, the situation is more optimistic, and we devise specific benchmark scenarios which could be observed.

The order analysis for the two loop corrections to lepton MDM

The experimental data of the magnetic dipole moment(MDM) of lepton(e, $$\mu $$ μ ) is very exact. The deviation between the experimental data and the standard model prediction maybe come from new physics contribution. In the supersymmetric models, there are very many two loop diagrams contributing to the lepton MDM. In supersymmetric models, we suppose two mass scales $$M_{SH}$$ M SH and M with $$M_{SH}\gg M$$ M SH ≫ M for supersymmetric particles. Squarks belong to $$M_{SH}$$ M SH and the other supersymmetric particles belong to M. We analyze the order of the contributions from the two loop diagrams. The two loop triangle diagrams corresponding to the two loop self-energy diagram satisfy Ward-identity, and their contributions possess particular factors. This work can help to distinguish the important two loop diagrams giving corrections to lepton MDM.

Using Pyspark Environment for Solving a Big Data Problem: Searching for Supersymmetric Particles

Supersymmetry theory predicts that every particle in the standard model has a superpartner particle with a different mass. The Classification Problem of Supersymmetric Particles in High-Energy represents a major challenge for physicists. This paper aims to resolve the Big data Classification Problem in the area of Supersymmetric Particles using the Apache Spark Environment with the "MLlib" library. This contribution attempts to explore the performance of Machine Learning methods in the context of large data such as a "Susy" dataset, collected from the UCI Machine Learning repository. In this work, the performance is measured using three metrics: Accuracy, Area Under Curve (AUC), and training Computation Time (CT). The results are promising and show that the Gradient Boosted Tree (GBT) classifier achieves a high accuracy score (79%). While the Logistic Regression (LR) algorithm realizes a well AUC score (86%).

Utilizing Unsupervised Machine Learning in BSM Physics Searches At The LHC

Recent searches for supersymmetric particles at the Large Hadron Collider have been unsuccessful in detecting any BSM physics. This is partially because the exact masses of supersymmetric particles are not known, and as such, searching for them is very difficult. The method broadly used in searching for new physics requires one to optimise on the signal being searched for, potentially suppressing sensitivity to new physics which may actually be present that does not resemble the chosen signal. The problem with this approach is that, in order to detect something with this method, one must already know what to look for. I will showcase one machine-learning technique that can be used to define a “signal-agnostic” search. This is a search that does not make any assumptions about the signal being searched for, allowing it to detect a signal in a more general way. This method is applied to simulated BSM physics data and the results are explored.

Parton Branching Process with Supersymmetric Particles at LHC Energies

A 2-step parton branching model is proposed to describe the potential presence of supersymmetric particles in multiplicity distributions. This model gives a reasonable description of the data obtained at 13TeV by the ATLAS Collaboration across the maximum pseudorapidity range of η<= 2.5.