Related papers: Probing stop pair production at the LHC with graph…
Message-Passing Neural Networks (MPNNs) are extensively employed in graph learning tasks but suffer from limitations such as the restricted scope of information exchange, by being confined to neighboring nodes during each round of message…
The High-Luminosity LHC (HL-LHC) will reach luminosities up to 7 times higher than the previous run, yielding denser events and larger occupancies. Next generation trigger algorithms must retain reliable selection within a strict latency…
In high-energy particle collisions, the primary collision products usually decay further resulting in tree-like, hierarchical structures with a priori unknown multiplicity. At the stable-particle level all decay products of a collision form…
Anomaly detection offers a promising strategy for discovering new physics at the Large Hadron Collider (LHC). This paper investigates AutoEncoders built using neuromorphic Spiking Neural Networks (SNNs) for this purpose. One key application…
Phasor measurement units (PMUs) are being widely installed on power systems, providing a unique opportunity to enhance wide-area situational awareness. One essential application is the use of PMU data for real-time event identification.…
Designing model-independent anomaly detection algorithms for analyzing LHC data remains a central challenge in the search for new physics, due to the high dimensionality of collider events. In this work, we develop a graph autoencoder as an…
So far the squarks have not been detected at the LHC indicating that they are heavier than a few hundred GeVs, if they exist. The lighter stop can be considerably lighter than the other squarks. We study the possibility that a…
The message passing-based graph neural networks (GNNs) have achieved great success in many real-world applications. However, training GNNs on large-scale graphs suffers from the well-known neighbor explosion problem, i.e., the exponentially…
The Large Hadron Collider at CERN produces immense volumes of complex data from high-energy particle collisions, demanding sophisticated analytical techniques for effective interpretation. Neural Networks, including Graph Neural Networks,…
Supervised artificial neural networks with the rapidity-mass matrix (RMM) inputs were studied using several Monte Carlo event samples for various pp collision processes. The study shows the usability of this approach for general event…
Graph Neural Networks (GNNs) have emerged as powerful tools for learning representations of graph-structured data. In addition to real-valued GNNs, quaternion GNNs also perform well on tasks on graph-structured data. With the aim of…
Naturalness arguments suggest that the stop sector is within reach of the Large Hadron Collider (LHC). We investigate how the observation of a third generation squark signal could predict masses and discovery modes of other supersymmetric…
The results of a search for the stop, the supersymmetric partner of the top quark, in final states with one isolated electron or muon, jets, and missing transverse momentum are reported. The search uses the 2015 LHC $pp$ collision data at a…
This paper presents a search for the pair production of top squarks in events with a single isolated electron or muon, jets, large missing transverse momentum, and large transverse mass. The data sample corresponds to an integrated…
Motivated by the recent progress of direct search for the productions of stop pair and sbottom pair at the LHC, we examine the constraints of the search results on the stop ($\tilde{t}_1$) mass in natural SUSY. We first scan the parameter…
Knowledge graphs (KGs) facilitate a wide variety of applications. Despite great efforts in creation and maintenance, even the largest KGs are far from complete. Hence, KG completion (KGC) has become one of the most crucial tasks for KG…
We are considering a possibility for detecting non-perturbative effect in process of top pair production in association with a high $p_T$ photon. Starting from previous results on two solutions for a spontaneous generation of wouldbe…
In particle detectors at the Large Hadron Collider, tens of terabytes of data are produced every second from proton-proton collisions occurring at a rate of 40 megahertz. This data rate is reduced to a sustainable level by a real-time event…
Detecting anomalies in dynamic graphs is a vital task, with numerous practical applications in areas such as security, finance, and social media. Previous network embedding based methods have been mostly focusing on learning good node…
We describe a method for searching for a light stop squark [M(stop)+M(LSP)<M(t)] at the Fermilab Tevatron. Traditional searches rely upon stringent background-reducing cuts which, unfortunately, leave very few signal events given the…