Related papers: JEDI-net: a jet identification algorithm based on …
We assess the performance of different jet-clustering algorithms, in the presence of different resolution parameters and reconstruction procedures, in resolving fully hadronic final states emerging from the chain decay of the discovered…
We apply gradient boosting machine learning techniques to the problem of hadronic jet substructure recognition using classical subjettiness variables available within a common parameterized detector simulation package DELPHES. Per-jet…
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…
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…
We present the RODEM Jet Datasets, a comprehensive collection of simulated large-radius jets designed to support the development and evaluation of machine-learning algorithms in particle physics. These datasets encompass a diverse range of…
In the past years significant progress has been made toward achieving a quantitative understanding of jets and their substructure in high-energy proton-proton collisions from first principles in QCD. Precise measurements have become…
The jets are the final state manifestation of the hard parton scattering. Since at LHC energies the production of hard processes in proton-proton collisions will be copious and varied, it is important to develop methods to identify them…
We study the efficiency of a neural-net filter and deconvolution method for estimating jet energies and spectra in high-background reactions such as nuclear collisions at the relativistic heavy-ion collider and the large hadron collider.…
Embedding symmetries in the architectures of deep neural networks can improve classification and network convergence in the context of jet substructure. These results hint at the existence of symmetries in jet energy depositions, such as…
New particles at the TeV scale can decay hadronically with strongly collimated jets, thus the standard reconstruction methods based on invariant-masses of well-separated jets can fail. We discuss how to identify such particles in pp…
We argue that dark matter particles which have strong interactions with the Standard Model particles are not excluded by current astrophysical constraints. These dark matter particles have unique signatures at colliders; instead of missing…
High P_T measurements of hard hadrons or jets at RHIC and LHC appear contradictory and in some cases counter-intuitive, but upon closer investigation they represent a coherent picture of jet-medium interaction physics which can be…
Jet quenching is a phenomenon in heavy-ion collisions arising from jet interactions with the quark-gluon plasma (QGP). Its study is complicated by the interplay of multiple physics processes that affect jet observables. In addition,…
Low-energy strong interactions are a major source of background at hadron colliders, and methods of subtracting the associated energy flow are well established in the field. Traditional approaches treat the contamination as diffuse, and…
Modern machine learning techniques, such as convolutional, recurrent and recursive neural networks, have shown promise for jet substructure at the Large Hadron Collider. For example, they have demonstrated effectiveness at boosted top or W…
The mechanisms underlying hadronization are not well understood, both in vacuum and in hot QCD matter. Precise characterization of jet fragmentation to hadrons in p-p collisions will help elucidate the fundamental process of hadronization,…
This paper presents the application of a variety of techniques to study jet substructure. The performance of various modified jet algorithms, or jet grooming techniques, for several jet types and event topologies is investigated for jets…
Machine Learning algorithms have played an important role in hadronic jet classification problems. The large variety of models applied to Large Hadron Collider data has demonstrated that there is still room for improvement. In this context…
The identification of jets originating from b quarks is crucial both for the searches for new physics and for the measurement of standard model processes. The Compact Muon Solenoid (CMS) collaboration at the Large Hadron Collider (LHC) has…
Real-time jet tagging is critical for identifying short-lived particle decays in the high-throughput detectors of the Large Hadron Collider, where real-time trigger systems responsible for deciding which collision events to store impose…