Related papers: Probing stop pair production at the LHC with graph…
Graph representation learning for hypergraphs can be used to extract patterns among higher-order interactions that are critically important in many real world problems. Current approaches designed for hypergraphs, however, are unable to…
An unconventional solution for finding the location of event creation is presented. It is based on two feed-forward neural networks with fixed architecture, whose parameters are chosen so as to reach a high accuracy. The interaction point…
Graph Neural Networks (GNNs) that are based on the message passing (MP) paradigm generally exchange information between 1-hop neighbors to build node representations at each layer. In principle, such networks are not able to capture…
We describe the outcome of a data challenge conducted as part of the Dark Machines Initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenged aims at detecting signals of new physics at the LHC using…
With a high instantaneous luminosity and the large top quark pair production cross section, the Large Hadron Collider (LHC) will be a "top factory" allowing the analysis of millions of top events. After a short description of the top quark…
A measurement of the production cross-section for top quark pairs($\ttbar$) in $pp$ collisions at $\sqrt{s}=7 \TeV$ is presented using data recorded with the ATLAS detector at the Large Hadron Collider. Events are selected in two different…
This paper presents a Temporal Graph Neural Network (TGNN) framework for detection and localization of false data injection and ramp attacks on the system state in smart grids. Capturing the topological information of the system through the…
We estimate the production rates of supersymmetric particles in central heavy-ion collisions at LHC. The parton cascade model is used to seek for possible collective phenomena which enlarge the production probability of very heavy…
The Standard Model of particle physics successfully describes the elementary particles and their interactions at low energies, up to 100 GeV. Beyond this scale lies the realm of new physics needed to remedy problems that arise at higher…
We consider the signals arising from top partner pair production at the LHC as a probe of theories of Neutral Naturalness. We focus on scenarios in which top partners carry electroweak charges, such as Folded SUSY or the Quirky Little…
Evidence for the production of top quarks in heavy ion collisions is reported in a data sample of lead-lead collisions recorded in 2018 by the CMS experiment at a nucleon-nucleon center-of-mass energy of $\sqrt{s_{_{\mathrm{NN}}}} =$ 5.02…
Detecting the Maximum Common Subgraph (MCS) between two input graphs is fundamental for applications in drug synthesis, malware detection, cloud computing, etc. However, MCS computation is NP-hard, and state-of-the-art MCS solvers rely on…
Subgraph pattern detection aims to uncover complex interaction structures in graphs. However, state-of-the-art graph neural network (GNN)-based solutions assume centralized access to the entire graph. When graphs are instead distributed…
Low energy supersymmetric models provide a solution to the hierarchy problem and also have the necessary ingredients to solve two of the most outstanding issues in cosmology: the origin of the baryon asymmetry and the source of dark matter.…
Recent results in the search for supersymmetry in pp collisions at sqrt(s) = 7 TeV and sqrt(s) = 8 TeV by the ATLAS and CMS experiments at the LHC are reviewed. After discussing features of inclusive analyses and the presentation of…
In the semi-supervised setting where labeled data are largely limited, it remains to be a big challenge for message passing based graph neural networks (GNNs) to learn feature representations for the nodes with the same class label that is…
This paper introduces a new stochastic hybrid system (SHS) framework for contingency detection in modern power systems (MPS). The framework uses stochastic hybrid system representations in state space models to expand and facilitate…
We present an exclusion process based approach for sampling densest $k$-sub-graphs from regular graphs $L$ with connected complement. By interpreting an exclusion process as a Markov chain on a corresponding Token Graph $\mathfrak{L}_k$, we…
Stop squarks with a mass just above the top's and which decay to a nearly massless LSP are difficult to probe because of the large SM di-top background. Here we discuss search strategies which could be used to set more stringent bounds in…
Reliable data quality monitoring is a key asset in delivering collision data suitable for physics analysis in any modern large-scale High Energy Physics experiment. This paper focuses on the use of artificial neural networks for supervised…