Related papers: Jet Flavour Classification Using DeepJet
The suppression and modification of high-energy objects, like jets, in heavy-ion collisions provide an important window to access the degrees of freedom of the quark-gluon plasma on different length scales. Despite increasingly precise and…
In collider physics at the TeV scale, there are many important processes which involve six or more jets. The sensitivity of the physics analysis depends critically on the performance of the jet clustering algorithm. We present a full…
We describe a method to obtain point and dispersion estimates for the energies of jets arising from b quarks produced in proton-proton collisions at an energy of $\sqrt{s} =$ 13 TeV at the CERN LHC. The algorithm is trained on a large…
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…
An $s$-jet tagging approach to determine the Cabibbo-Kobayashi-Maskawa matrix component $|V_{ts}|$ directly in the dileptonic final state events of the top pair production in proton-proton collisions has been previously studied by measuring…
Collimated streams of particles produced in high energy physics experiments are organized using clustering algorithms to form jets. To construct jets, the experimental collaborations based at the Large Hadron Collider (LHC) primarily use…
At the LHC, tagging boosted heavy particle resonances which decay hadronically, such as top quarks and Higgs bosons, can play an essential role in new physics searches. In events with high multiplicity, however, the standard approach to tag…
A new algorithm for the identification of boosted, hadronically decaying, heavy particles at the LHC is presented. The algorithm is based on the known procedure of jet clustering with variable distance parameter $R$ and adapts the jet size…
Attention-based transformer models have become increasingly prevalent in collider analysis, offering enhanced performance for tasks such as jet tagging. However, they are computationally intensive and require substantial data for training.…
This paper presents a new tool to perform various steps in jet tagger development in an efficient and comprehensive way. A common data structure is used for training, as well as for performance evaluation in data. The introduction of this…
We present a new method to expose the dead cone effect at colliders using iterative declustering techniques. Iterative declustering allows to unwind the jet clustering and to access the subjets or branches at different depths of the jet…
An intuitive definition of the partonic flavor of a jet in quantum chromodynamics is often only well-defined in the deep ultraviolet, where the strong force becomes a free theory and a jet consists of a single parton. However, measurements…
Jet measurements in heavy ion collisions at low jet momentum can provide constraints on the properties of the quark gluon plasma but are overwhelmed by a significant, fluctuating background. We build upon our previous work which…
Being able to distinguish light-quark jets from gluon jets on an event-by-event basis could significantly enhance the reach for many new physics searches at the Large Hadron Collider. Through an exhaustive search of existing and novel jet…
Latent representations are an important theme in modern machine learning. Any network training with the notion of locality introduces a latent geometry which we can analyze with the help of differential geometry, specifically information…
Heavy-flavour quarks are important to probe Quark-Gluon Plasma(QGP) properties. Cold Nuclear Matter(CNM) effects can be accessed by $p$Pb collisions. LHCb is a heavy-flavour precision experiment and has collected large collision data…
A key question for machine learning approaches in particle physics is how to best represent and learn from collider events. As an event is intrinsically a variable-length unordered set of particles, we build upon recent machine learning…
Recently machine learning algorithms based on deep layered artificial neural networks (DNNs) have been applied to a wide variety of high energy physics problems such as jet tagging or event classification. We explore a simple but effective…
Heavy flavor jets provide ideal tools to probe the mass effect on jet substructure in both vacuum and quark-gluon plasma (QGP). Energy-energy correlator (EEC) is an excellent jet substructure observable owning to its strong sensitivity to…
Jet tagging is a crucial classification task in high energy physics. Recently the performance of jet tagging has been significantly improved by the application of deep learning techniques. In this study, we introduce a new architecture for…