Related papers: Exploring jets: substructure and flavour tagging i…
Building on the notion of a particle physics detector as a camera and the collimated streams of high energy particles, or jets, it measures as an image, we investigate the potential of machine learning techniques based on deep learning…
We present a concise review of the plans of the ATLAS and CMS collaborations to search for new physics using dijet events. The inclusive cross section as a function of jet p$_{T}$, the dijet mass distribution and the dijet ratio's CMS…
Top quark physics measurements performed using data from the ATLAS detector at the LHC rely on efficient reconstruction and precise calibration of leptons, jets and missing transverse energy. A review of the techniques used to reconstruct…
Recent studies in ATLAS and CMS experiments for the reconstruction and identification of electrons and photons using full Monte Carlo and testbeam data are reported
The efficiency to identify jets containing $b$-hadrons ($b$-jets) is measured using a high purity sample of dileptonic top quark-antiquark pairs ($t\bar{t}$) selected from the 36.1 fb$^{-1}$ of data collected by the ATLAS detector in 2015…
Deep learning techniques have the power to identify the degree of modification of high energy jets traversing deconfined QCD matter on a jet-by-jet basis. Such knowledge allows us to study jets based on their initial, rather than final…
Interest in deep learning in collider physics has been growing in recent years, specifically in applying these methods in jet classification, anomaly detection, particle identification etc. Among those, jet classification using neural…
Over the past decade, a large number of jet substructure observables have been proposed in the literature, and explored at the LHC experiments. Such observables attempt to utilize the internal structure of jets in order to distinguish those…
The appearance of monojets is among the most striking signature of jet quenching in the context of ultrarelativistic heavy-ion collisions. Experimentally, the disappearance of jets has been quantified by the ATLAS and CMS collaborations in…
The past few years have seen a rapid development of machine-learning algorithms. While surely augmenting performance, these complex tools are often treated as black-boxes and may impair our understanding of the physical processes under…
Recent work on activation and latent steering has demonstrated that modifying internal representations can effectively guide large language models (LLMs) toward improved reasoning and efficiency without additional training. However, most…
A brief review of the discovery potential of the ATLAS and CMS experiments to search for signals from extra dimensions in different luminosity scenarios is given.
A review on recent top quark properties measurements by the ATLAS and CMS Collaborations in pp collisions at the LHC is presented.
Highly energetic jets and photons are complementary probes for the kinematics and the topology of nuclear collisions. Jets are collimated sprays of charged and neutral particles, which are produced in the fragmentation of hard scattered…
A measurement of jet substructure variables is presented using data collected in 2016 by the ATLAS experiment at the LHC with proton-proton collisions at $\sqrt{s}=13$ TeV. Large-radius jets groomed with the trimming and soft-drop…
The identification of jets resulting from the fragmentation and hadronization of b quarks is an important part of high-pT collider physics. The methods used by the CDF and DO collaborations to perform this identification are described,…
We describe the construction of end-to-end jet image classifiers based on simulated low-level detector data to discriminate quark- vs. gluon-initiated jets with high-fidelity simulated CMS Open Data. We highlight the importance of precise…
During the first years of the LHC operation a large amount of jet data was recorded by the ATLAS and CMS experiments. In this review several measurements of jet-related observables are presented, such as multi-jet rates and cross sections,…
Understanding the inner working of the quark-gluon plasma requires complete and precise jet substructure studies in heavy ion collisions. In this proceeding we discuss the use of quark and gluon jets as independent probes, and how their…
Deep learning techniques have shown the capability to identify the degree of energy loss of high-energy jets traversing hot QCD medium on a jet-by-jet basis. The average amount of quenching of quark and gluon jets in hot QCD medium actually…