Related papers: How to Improve Top Tagging
The CMS experiment makes use of a large variety of algorithms to identify the origin of particle jets measured in the detector. Through the study of jet substructure properties, jets originating from quarks, gluons, W/Z/Higgs bosons, top…
Experiments in the high-luminosity runs at the Large Hadron Collider face the challenges of very large pile-up. Primary techniques to deal with this are based on precise vertex and track reconstruction. Outside tracker acceptances, however,…
The success of machine learning on a given task dependson, among other things, which learning algorithm is selected and its associated hyperparameters. Selecting an appropriate learning algorithm and setting its hyperparameters for a given…
Studying heavy-flavor jets in pp collision is important since they can test pQCD calculations and be used as a reference for heavy-ion collisions. Jets in this analysis are reconstructed from charged particles using the…
Jets with a large radius $R\gtrsim 1$ and grooming algorithms are widely used to fully capture the decay products of boosted heavy particles at the Large Hadron Collider (LHC). Unlike most discriminating variables used in such studies, the…
Jet substructure techniques are playing an essential role in exploring the TeV scale at the Large Hadron Collider (LHC), since they facilitate the efficient reconstruction and identification of highly-boosted objects. Both for the LHC and…
We examine the problem of jet reconstruction at heavy-ion colliders using jet-area-based background subtraction tools as provided by FastJet. We use Monte Carlo simulations with and without quenching to study the performance of several jet…
We introduce a novel search strategy for heavy top-philic resonances that induce new contributions to four-top production at the LHC. We capitalize on recent advances in top-tagging performance to demonstrate that the final state, that is…
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…
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…
A technique for detecting errors made by Hidden Markov Model taggers is described, based on comparing observable values of the tagging process with a threshold. The resulting approach allows the accuracy of the tagger to be improved by…
While deep learning has reduced the prevalence of manual feature extraction, transformation of data via feature engineering remains essential for improving model performance, particularly for underwater acoustic signals. The methods by…
These proceedings highlight a selection of recent results by the ATLAS, CMS and LHCb collaborations. The majority of the featured analyses make use of the large set of $\sqrt{s}=13$ TeV proton-proton collision data collected during the…
We present algorithms for efficiently learning regularizers that improve generalization. Our approach is based on the insight that regularizers can be viewed as upper bounds on the generalization gap, and that reducing the slack in the…
The most natural supersymmetric solution to the hierarchy problem prefers the scalar top partner to be close in mass to the top quark. Experimental searches exclude top squarks across a wide range of masses, but a gap remains when the…
This paper describes an innovative way to optimize a multivariate classifier, in particular a Support Vector Machine algorithm, on a problem characterized by a biased training sample. This is possible thanks to the feedback of a…
QCD-jets at the LHC are described by simple physics principles. We show how super-resolution generative networks can learn the underlying structures and use them to improve the resolution of jet images. We test this approach on massless…
In this overview article we will consider the deliberate restarting of algorithms, a meta technique, in order to improve the algorithm's performance, e.g., convergence rates or approximation guarantees. One of the major advantages is that…
We discuss recent progress in understanding the issues essential to the development of better cone jet algorithms.
One of the major challenges for the LHC will be to extract precise information from hadronic final states in the presence of the large number of additional soft pp collisions, pileup, that occur simultaneously with any hard interaction in…