Related papers: Novel Jet Observables from Machine Learning
Anomaly detection with convolutional autoencoders is a popular method to search for new physics in a model-agnostic manner. These techniques are powerful, but they are still a "black box," since we do not know what high-level physical…
In collider physics, the properties of hadronic jets are often measured as a function of their lab-frame momenta. However, jet fragmentation must occur in a particular rest frame defined by all color-connected particles. Since this frame…
We consider jet-shape observables of the type proposed recently, where the shapes of one or more high-pT jets, produced in a multi-jet event with definite jet multiplicity, may be measured leaving other jets in the event unmeasured. We…
We study the impact of selection biases on jet structure and substructure observables and separate these effects from effects caused by jet quenching. We use the angular separation $\Delta R$ of the hardest splitting in a jet as the primary…
High velocity jets are among the most prominent features of a wide class of planetary nebulae, but their origins are not understood. Several different types of physical model have been suggested to power the jets, but there is no consensus…
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 task of reconstructing particles from low-level detector response data to predict the set of final state particles in collision events represents a set-to-set prediction task requiring the use of multiple features and their correlations…
Jet radiation patterns are indispensable for the purpose of discriminating partons' with different quantum numbers. However, they are also vulnerable to various contaminations from the underlying event, pileup, and radiation of adjacent…
We present a new framework for calculating multi-jet observables. The framework is based on the factorisation of scattering amplitudes in the kinematical limit of large invariant mass between all particles. We show that by constraining the…
Many physics analyses at the LHC are looking into processes where the signal jets are originating from quarks, while jets in the background are more gluon enriched. Based on observables sensitive to fundamental differences in the…
The ability to identify jets which originated from $b$ quarks is an important tool of the physics program of the D0 experiment at the Fermilab Tevatron $p\bar{p}$ collider. This article describes a new algorithm designed to select jets…
To probe the properties of the quark-gluon plasma created in heavy-ion collisions, a very useful class of observables refers to the propagation of energetic jets. A jet is a collimated spray of particles generated via successive parton…
The wind is one of the most increasingly used renewable energy resources. Accurate and reliable forecast of wind speed is necessary for efficient power production; however, it is not an easy task because it depends upon meteorological…
For jets, with great power comes great opportunity. The unprecedented center of mass energies available at the LHC open new windows on the QGP: we demonstrate that jet shape and jet cross section measurements become feasible as a new,…
Jets can be used to probe the physical properties of the high energy density matter created in collisions at the Relativistic Heavy Ion Collider (RHIC). Measurements of strong suppression of inclusive hadron distributions and di-hadron…
Lund diagrams, a theoretical representation of the phase space within jets, have long been used in discussing parton showers and resummations. We point out that they can be created for individual jets through repeated Cambridge/Aachen…
Several observables sensitive to the fragmentation of $b$-quarks into $b$-hadrons are measured using 36 fb${}^{-1}$ of $\sqrt s = 13$ TeV proton-proton collision data collected with the ATLAS detector at the LHC. Jets containing $b$-hadrons…
Jet substructure has emerged to play a central role at the Large Hadron Collider (LHC), where it has provided numerous innovative new ways to search for new physics and to probe the Standard Model in extreme regions of phase space. In this…
Three machine learning models are used to perform jet origin classification. These models are optimized for deployment on a field-programmable gate array device. In this context, we demonstrate how latency and resource consumption scale…
To maximize the discovery potential of high-energy colliders, experimental searches should be sensitive to unforeseen new physics scenarios. This goal has motivated the use of machine learning for unsupervised anomaly detection. In this…