Seeing in Color: Jet Superstructure

A new class of observables is introduced which aims to characterize the superstructure of an event, that is, features, such as color flow, which are not determined by the jet four-momenta alone. Traditionally, an event is described as having jets which are independent objects; each jet has some energy, size, and possible substructure such as subjets or heavy flavor content. This description discards information connecting the jets to each other, which can be used to determine if the jets came from decay of a color singlet object, or if they were initiated by quarks or gluons. An example superstructure variable, pull, is presented as a simple handle on color flow. It can be used on an event-by-event basis as a tool for distinguishing previously irreducible backgrounds at the Tevatron and the LHC.Comment: 4 pages, 5 figures. Published version. Some clarifications and references adde

Quark and Gluon Tagging at the LHC

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 substructure observables, we find that a multivariate approach can filter out over 95% of the gluon jets while keeping more than half of the light-quark jets. Moreover, a combination of two simple variables, the charge track multiplicity and the $p_T$-weighted linear radial moment (girth), can achieve similar results. While this pair appears very promising, our study is only Monte Carlo based, and other discriminants may work better with real data in a realistic experimental environment. To that end, we explore many other observables constructed using different jet sizes and parameters, and highlight those that deserve further theoretical and experimental scrutiny. Additional information, including distributions of around 10,000 variables, can be found on this website http://jets.physics.harvard.edu/qvg .Comment: 5 pages, 3 figures. v2 published versio

Hierarchical Temporal Representation in Linear Reservoir Computing

Recently, studies on deep Reservoir Computing (RC) highlighted the role of layering in deep recurrent neural networks (RNNs). In this paper, the use of linear recurrent units allows us to bring more evidence on the intrinsic hierarchical temporal representation in deep RNNs through frequency analysis applied to the state signals. The potentiality of our approach is assessed on the class of Multiple Superimposed Oscillator tasks. Furthermore, our investigation provides useful insights to open a discussion on the main aspects that characterize the deep learning framework in the temporal domain.Comment: This is a pre-print of the paper submitted to the 27th Italian Workshop on Neural Networks, WIRN 201

Pure Samples of Quark and Gluon Jets at the LHC

Having pure samples of quark and gluon jets would greatly facilitate the study of jet properties and substructure, with many potential standard model and new physics applications. To this end, we consider multijet and jets+X samples, to determine the purity that can be achieved by simple kinematic cuts leaving reasonable production cross sections. We find, for example, that at the 7 TeV LHC, the pp {\to} {\gamma}+2jets sample can provide 98% pure quark jets with 200 GeV of transverse momentum and a cross section of 5 pb. To get 10 pb of 200 GeV jets with 90% gluon purity, the pp {\to} 3jets sample can be used. b+2jets is also useful for gluons, but only if the b-tagging is very efficient.Comment: 19 pages, 16 figures; v2 section on formally defining quark and gluon jets has been adde

Quark and Gluon Jet Substructure

Distinguishing quark-initiated jets from gluon-initiated jets has the potential to significantly improve the reach of many beyond-the-standard model searches at the Large Hadron Collider and to provide additional tests of QCD. To explore whether quark and gluon jets could possibly be distinguished on an event-by-event basis, we perform a comprehensive simulation-based study. We explore a variety of motivated and unmotivated variables with a semi-automated multivariate approach. General conclusions are that at 50% quark jet acceptance efficiency, around 80%-90% of gluon jets can be rejected. Some benefit is gained by combining variables. Different event generators are compared, as are the effects of using only charged tracks to avoid pileup. Additional information, including interactive distributions of most variables and their cut efficiencies, can be found at http://jets.physics.harvard.edu/qvg.Physic

Deep Tree Transductions - A Short Survey

The paper surveys recent extensions of the Long-Short Term Memory networks to handle tree structures from the perspective of learning non-trivial forms of isomorph structured transductions. It provides a discussion of modern TreeLSTM models, showing the effect of the bias induced by the direction of tree processing. An empirical analysis is performed on real-world benchmarks, highlighting how there is no single model adequate to effectively approach all transduction problems.Comment: To appear in the Proceedings of the 2019 INNS Big Data and Deep Learning (INNSBDDL 2019). arXiv admin note: text overlap with arXiv:1809.0909

Reservoir Topology in Deep Echo State Networks

Deep Echo State Networks (DeepESNs) recently extended the applicability of Reservoir Computing (RC) methods towards the field of deep learning. In this paper we study the impact of constrained reservoir topologies in the architectural design of deep reservoirs, through numerical experiments on several RC benchmarks. The major outcome of our investigation is to show the remarkable effect, in terms of predictive performance gain, achieved by the synergy between a deep reservoir construction and a structured organization of the recurrent units in each layer. Our results also indicate that a particularly advantageous architectural setting is obtained in correspondence of DeepESNs where reservoir units are structured according to a permutation recurrent matrix.Comment: Preprint of the paper published in the proceedings of ICANN 201

Richness of Deep Echo State Network Dynamics

Reservoir Computing (RC) is a popular methodology for the efficient design of Recurrent Neural Networks (RNNs). Recently, the advantages of the RC approach have been extended to the context of multi-layered RNNs, with the introduction of the Deep Echo State Network (DeepESN) model. In this paper, we study the quality of state dynamics in progressively higher layers of DeepESNs, using tools from the areas of information theory and numerical analysis. Our experimental results on RC benchmark datasets reveal the fundamental role played by the strength of inter-reservoir connections to increasingly enrich the representations developed in higher layers. Our analysis also gives interesting insights into the possibility of effective exploitation of training algorithms based on stochastic gradient descent in the RC field.Comment: Preprint of the paper accepted at IWANN 201

Discretizing Gravity in Warped Spacetime

We investigate the discretized version of the compact Randall-Sundrum model. By studying the mass eigenstates of the lattice theory, we demonstrate that for warped space, unlike for flat space, the strong coupling scale does not depend on the IR scale and lattice size. However, strong coupling does prevent us from taking the continuum limit of the lattice theory. Nonetheless, the lattice theory works in the manifestly holographic regime and successfully reproduces the most significant features of the warped theory. It is even in some respects better than the KK theory, which must be carefully regulated to obtain the correct physical results. Because it is easier to construct lattice theories than to find exact solutions to GR, we expect lattice gravity to be a useful tool for exploring field theory in curved space.Comment: 17 pages, 4 figures; references adde