The REINFORCE citizen-science project and the search for new long-lived particles at the LHC

The REINFORCE project engages and supports citizens to cooperate with researchers and actively contribute to the development of new knowledge for the needs of science and society. The overall aim is to bridge the gap between them, and reinforce society’s science capital. Within this context, the demonstrator titled “New Particle Search at CERN” will engage citizen-scientists in searches for new elementary particles produced in the high-energy proton-proton collisions at the LHC. To make this possible, the demonstrator adopts a three-stage architecture. The first two stages use simulated data from the ATLAS detector to train citizens, but also to allow for a quantitative assessment of their performance and a comparison with machine learning algorithms. The third stage, on the other hand, uses real data from the ATLAS Open-Data subset, providing two research paths: (a) study of Higgs boson decays to two photons, one of which could be converted to an electron-positron pair by interaction with detector material, and (b) search for yet undiscovered long-lived particles predicted by certain theories Beyond-the-Standard-Model.

Does SUSY have friends? A new approach for LHC event analysis

We present a novel technique for the analysis of proton-proton collision events from the ATLAS and CMS experiments at the Large Hadron Collider. For a given final state and choice of kinematic variables, we build a graph network in which the individual events appear as weighted nodes, with edges between events defined by their distance in kinematic space. We then show that it is possible to calculate local metrics of the network that serve as event-by-event variables for separating signal and background processes, and we evaluate these for a number of different networks that are derived from different distance metrics. Using a supersymmetric electroweakino and stop production as examples, we construct prototype analyses that take account of the fact that the number of simulated Monte Carlo events used in an LHC analysis may differ from the number of events expected in the LHC dataset, allowing an accurate background estimate for a particle search at the LHC to be derived. For the electroweakino example, we show that the use of network variables outperforms both cut-and-count analyses that use the original variables and a boosted decision tree trained on the original variables. The stop example, deliberately chosen to be difficult to exclude due its kinematic similarity with the top background, demonstrates that network variables are not automatically sensitive to BSM physics. Nevertheless, we identify local network metrics that show promise if their robustness under certain assumptions of node-weighted networks can be confirmed.

A Performance Study of Moving Particle Semi-Implicit Method for Incompressible Fluid Flow on GPU

The aim of moving particle semi-implicit (MPS) is to simulate the incompressible flow of fluids in free surface. MPS, when implemented, consumes a lot of time and thus, needs a very powerful computing system. Instead of using parallel computing system, the performance level of the MPS model can be improved by using graphics processing units (GPUs). The aim is to have a computing system that is capable of performing at high levels thereby enhancing the speed of processing the numerical computations required in MPS. The primary aim of the study is to build a GPU-accelerated MPS model using CUDA aimed at reducing the time taken to perform the search for neighboring particles. In order to increase the GPU processing speed, specific consideration is given towards the optimization of a neighboring particle search process. The numerical model of MPS is performed using the governing equations, notably the Navier-Stokes equation. The simulation model indicates that using GPU based MPS produce better performance compared to the traditional arrangement of using CPUs.