Lessons for SUSY from the LHC after the first run

A review of direct searches for new particles predicted by Supersymmetry after the first run of the LHC is proposed. This review is based on the results provided by the ATLAS and CMS experiments.Comment: 31 pages, 41 figures, Appear in the special issue of the EPJ C journal entitled "SUSY after the Higgs discovery

Performance and optimization of support vector machines in high-energy physics classification problems

In this paper we promote the use of Support Vector Machines (SVM) as a machine learning tool for searches in high-energy physics. As an example for a new- physics search we discuss the popular case of Supersymmetry at the Large Hadron Collider. We demonstrate that the SVM is a valuable tool and show that an automated discovery- significance based optimization of the SVM hyper-parameters is a highly efficient way to prepare an SVM for such applications. A new C++ LIBSVM interface called SVM-HINT is developed and available on Github.Comment: 20 pages, 6 figure

Attention to Mean-Fields for Particle Cloud Generation

The generation of collider data using machine learning has emerged as a prominent research topic in particle physics due to the increasing computational challenges associated with traditional Monte Carlo simulation methods, particularly for future colliders with higher luminosity. Although generating particle clouds is analogous to generating point clouds, accurately modelling the complex correlations between the particles presents a considerable challenge. Additionally, variable particle cloud sizes further exacerbate these difficulties, necessitating more sophisticated models. In this work, we propose a novel model that utilizes an attention-based aggregation mechanism to address these challenges. The model is trained in an adversarial training paradigm, ensuring that both the generator and critic exhibit permutation equivariance/invariance with respect to their input. A novel feature matching loss in the critic is introduced to stabilize the training. The proposed model performs competitively to the state-of-art whilst having significantly fewer parameters

Point Cloud Generation using Transformer Encoders and Normalising Flows

Data generation based on Machine Learning has become a major research topic in particle physics. This is due to the current Monte Carlo simulation approach being computationally challenging for future colliders, which will have a significantly higher luminosity. The generation of collider data is similar to point cloud generation, but arguably more difficult as there are complex correlations between the points which need to be modelled correctly. A refinement model consisting of normalising flows and transformer encoders is presented. The normalising flow output is corrected by a transformer encoder, which is adversarially trained against another transformer encoder discriminator/critic. The model reaches state-of-the-art performance while yielding a stable training

Non-Simplified SUSY: Stau-Coannihilation at LHC and ILC

If new phenomena beyond the Standard Model will be discovered at the LHC, the properties of the new particles could be determined with data from the High-Luminosity LHC and from a future linear collider like the ILC. We discuss the possible interplay between measurements at the two accelerators in a concrete example, namely a full SUSY model which features a small stau_1-LSP mass difference. Various channels have been studied using the Snowmass 2013 combined LHC detector implementation in the Delphes simulation package, as well as simulations of the ILD detector concept from the Technical Design Report. We investigate both the LHC and ILC capabilities for discovery, separation and identification of various parts of the spectrum. While some parts would be discovered at the LHC, there is substantial room for further discoveries at the ILC. We finally highlight examples where the precise knowledge about the lower part of the mass spectrum which could be acquired at the ILC would enable a more in-depth analysis of the LHC data with respect to the heavier states.Comment: 42 pages, 18 figures, 12 table

JetFlow: Generating Jets with Conditioned and Mass Constrained Normalising Flows

Fast data generation based on Machine Learning has become a major research topic in particle physics. This is mainly because the Monte Carlo simulation approach is computationally challenging for future colliders, which will have a significantly higher luminosity. The generation of collider data is similar to point cloud generation with complex correlations between the points. In this study, the generation of jets with up to 30 constituents with Normalising Flows using Rational Quadratic Spline coupling layers is investigated. Without conditioning on the jet mass, our Normalising Flows are unable to model all correlations in data correctly, which is evident when comparing the invariant jet mass distributions between ground truth and generated data. Using the invariant mass as a condition for the coupling transformation enhances the performance on all tracked metrics. In addition, we demonstrate how to sample the original mass distribution by interpolating the empirical cumulative distribution function. Similarly, the variable number of constituents is taken care of by introducing an additional condition on the number of constituents in the jet. Furthermore, we study the usefulness of including an additional mass constraint in the loss term. On the \texttt{JetNet} dataset, our model shows state-of-the-art performance combined with fast and stable training

Heavy Scalar Top Quark Decays in the Complex MSSM: A Full One-Loop Analysis

We evaluate all two-body decay modes of the heavy scalar top quark in the Minimal Supersymmetric Standard Model with complex parameters (cMSSM) and no generation mixing. The evaluation is based on a full one-loop calculation of all decay channels, also including hard QED and QCD radiation. The renormalization of the complex parameters is described in detail. The dependence of the heavy scalar top quark decay on the relevant cMSSM parameters is analyzed numerically, including also the decay to Higgs bosons and another scalar quark or to a top quark and the lightest neutralino. We find sizable contributions to many partial decay widths and branching ratios. They are roughly of O(10%) of the tree-level results, but can go up to 30% or higher. These contributions are important for the correct interpretation of scalar top quark decays at the LHC and, if kinematically allowed, at the ILC. The evaluation of the branching ratios of the heavy scalar top quark will be implemented into the Fortran code FeynHiggs.Comment: 86 pages, 38 figures; minor changes, version published as Phys. Rev. D86 (2012) 03501

New Particles Working Group Report of the Snowmass 2013 Community Summer Study

This report summarizes the work of the Energy Frontier New Physics working group of the 2013 Community Summer Study (Snowmass)