Related papers: Hits to Higgs: Hit-Level Higgs Classification from…
Currently, prominent Transformer architectures applied on graphs and meshes for shape analysis tasks employ traditional attention layers that heavily utilize spectral features requiring costly eigenvalue decomposition-based methods. To…
Future electron-positron colliders will play a leading role in the precision measurement of Higgs boson couplings which is one of the central interests in particle physics. Aiming at maximizing the performance to measure the Higgs couplings…
We present a novel method to discover the Higgs boson in new physics event samples at the LHC. Our technique applies to broad classes of models where the Higgs has a significant branching fraction to b-bbar. We exploit the recently…
The latest results of Higgs cross section measurements from the ATLAS and CMS experiments were presented. This proceeding focuses on the production cross section measurement under the simplified template cross section framework in multiple…
Higgs spectroscopy is a new field in which Higgs modes in nonequilibrium superconductors are analyzed to gain information about the ground state. One experimental setup in which the Higgs mode in s-wave superconductors was observed is…
Within the framework of the Standard Model, integrated and differential distributions are given for Higgs production via the $WW$-fusion mechanism and decay via the channels $H\ar b\bar b$ and $H\ar WW\ar {jjjj}$, with and without photon…
We show that weak scale vector-like fermions with order one couplings to the Higgs can lead to a novel mechanism for a strongly first-order electroweak phase transition (EWPhT), through their tendency to drive the Higgs quartic coupling…
We compute the resummed hadronic transverse-energy ($E_T$) distribution due to initial-state QCD radiation in the production of a Standard Model Higgs boson of mass 126 GeV by gluon fusion at the Large Hadron Collider, with matching to…
Transformers have revolutionized performance in Natural Language Processing and Vision, paving the way for their integration with Graph Neural Networks (GNNs). One key challenge in enhancing graph transformers is strengthening the…
The gold-plated discovery mode of a Standard Model like Higgs boson at the CERN Large Hadron Collider (LHC) is the H -> Z^0 Z^0 decay mode. To find and then measure the properties of the Higgs, it is crucial to have the most precise…
As malicious cyber threats become more sophisticated in breaching computer networks, the need for effective intrusion detection systems (IDSs) becomes crucial. Techniques such as Deep Packet Inspection (DPI) have been introduced to allow…
We study the performance of the Particle Transformer (ParT) for jet flavor tagging using ILD full simulation events (1M jets) as well as fast simulation samples (10M and 1M jets). We perform 3-category ($b/c/d$), 6-category ($b/c/d/u/s/g$),…
In high energy physics, the ability to reconstruct particles based on their detector signatures is essential for downstream data analyses. A particle reconstruction algorithm based on learning hypergraphs (HGPflow) has previously been…
New physics theories often depend on a large number of free parameters. The precise values of those parameters in some cases drastically affect the resulting phenomenology of fundamental physics processes, while in others finite variations…
We identify and study the signatures of the recently proposed Higgsless models at the Large Hadron Collider (LHC). We concentrate on tests of the mechanism of partial unitarity restoration in the longitudinal vector boson scattering, which…
Machine learning algorithms have the capacity to discern intricate features directly from raw data. We demonstrated the performance of top taggers built upon three machine learning architectures: a BDT that uses jet-level variables…
This study investigates the production of multi-top quark events final states containing up to four top quarks as a probe for new physics beyond the Standard Model (SM) within the framework of the Two Higgs Doublet Model (2HDM) Type-I.…
Current semantic segmentation models have achieved great success under the independent and identically distributed (i.i.d.) condition. However, in real-world applications, test data might come from a different domain than training data.…
Transformers have set new benchmarks in audio processing tasks, leveraging self-attention mechanisms to capture complex patterns and dependencies within audio data. However, their focus on pairwise interactions limits their ability to…
In this article, we review recent machine learning methods used in challenging particle identification of heavy-boosted particles at high-energy colliders. Our primary focus is on attention-based Transformer networks. We report the…