Related papers: Tau tagging at Atlas and CMS
The extremely high rate of events that will be produced in the future Large Hadron Collider requires the triggering mechanism to take precise decisions in a few nano-seconds. We present a study which used an artificial neural network…
We study the possibility of utilising the constrained mass variable, $M_{2Cons}$, in reconstructing the semi-invisible events originated from a resonant production at the LHC. While this proposal is effective for any similar antler type…
Machine learning (ML) and tensor-based methods have been of significant interest for the scientific community for the last few decades. In a previous work we presented a novel tensor-based system identification framework to ease the…
In 2012, the LHC is operated at sqrt(s) = 8 TeV in a mode leading up to 40 inelastic pp collisions per bunch crossing. The identification and reconstruction of muons produced in hard collisions is difficult in this challenging environment.…
Collider experiments are equipped with trigger systems that rapidly inspect the physics content emerging from collisions to decide whether the resulting products are worth saving for later analysis. One crucial aspect for analyzing the…
This paper presents the results of charged particle track reconstruction in CLAS12 using artificial intelligence. In our approach, we use machine learning algorithms to reconstruct tracks, including their momentum and direction, with high…
We present a method how to detect the WH -> lnbb in the high luminosity LHC environment with the CMS detector. This study is performed with fast detector response simulation including high luminosity event pile up. The main aspects of…
Tagging systems play an essential role in various information retrieval applications such as search engines and recommender systems. Recently, Large Language Models (LLMs) have been applied in tagging systems due to their extensive world…
This note presents Monte Carlo generator comparisons of the ttbb and ttW processes at particle level. The aim is to compare the modelling of important backgrounds to ttH measurements in multi-lepton final states and in the ttH(H->bb) decay…
We develop an algorithm based on an interaction network to identify high-transverse-momentum Higgs bosons decaying to bottom quark-antiquark pairs and distinguish them from ordinary jets that reflect the configurations of quarks and gluons…
It is being proved that the neurochip \Totem{} is a viable solution for high quality and real time computational tasks in HEP, including event classification, triggering and signal processing. The architecture of the chip is based on a…
Online reconstruction is key for monitoring purposes and real time analysis in High Energy and Nuclear Physics experiments. A necessary component of reconstruction algorithms is particle identification that combines information left by a…
The class framework developed for vertex reconstruction in CMS is described. We emphasize how we proceed to develop a flexible, efficient and reliable piece of reconstruction software. We describe the decomposition of the algorithms into…
A deep-learning approach based on the transformer architecture is developed to distinguish between jets originating from quarks and gluons. The algorithm operates on jets with transverse momentum $p_{\text{T}} > 20$ and pseudorapidity…
Some of the studies performed by the ATLAS and CMS collaborations to establish the future sensitivity of the experiments to extra dimension signals are reviewed. The discrimination of those signals from other new physics signals and the…
We show that neural network classifiers can be helpful to discriminate Higgs production from background at LHC in the Higgs mass range M= 200 GeV. We employ a common feed-forward neural network trained by the backpropagation algorithm for…
Terahertz (THz) tomography is a rather novel technique for nondestructive testing that is particularly suited for the testing of plastics and ceramics. Previous publications showed a large variety of conventional algorithms adapted from…
For reconstruction of low-rank matrices from undersampled measurements, we develop an iterative algorithm based on least-squares estimation. While the algorithm can be used for any low-rank matrix, it is also capable of exploiting a-priori…
This study aims to improve the TauFinder reconstruction algorithm for the MAIA detector concept. Through this work, we seek to increase the reconstruction efficiency and identification of hadronically decaying tau leptons. Through our work,…
The expected performance of track reconstruction with LHC events using the CMS silicon tracker is presented. Track finding and fitting is accomplished with Kalman Filter techniques that achieve efficiencies above 99% on single muons with…