Benjamin Nachman, Natalie Roe
Matt Schwartz (Harvard)
Markus Stoye (CERN)
Francesco Rubbo (SLAC)
Matt Dolan (The University of Melbourne)
Taoli Cheng (University of Chinese Academy of Sciences)
I am writing to propose a talk based on the recent paper https://arxiv.org/abs/1711.02633. The main topic is exploring the performance of Recursive Neural Networks in quark/gluon tagging.
Wojciech Fedorko (UBC) , Dr Wojciech Fedorko (University of British Columbia)
Multivariate techniques based on engineered features have found wide adoption in the identification of jets resulting from hadronic top decays at the Large Hadron Collider (LHC). Recent Deep Learning developments in this area include the treatment of the calorimeter activation as an image or supplying a list of jet constituent momenta to a fully connected network. This latter approach lends...
6. Deep(Boosted)Jet: Boosted jet identification using particle-level convolutional neural networks (15'+5')
Qu Huilin (UCSB)
dentification of boosted top quarks from their hadronic decays can play an important role in searches for new physics at the LHC. We present DeepBoostedJet, a new approach for boosted jet identification using particle-flow jets at CMS. One dimensional convolutional neural networks are utilized to classify a jet directly from its reconstructed constituent particles. The new method shows...
Gregor Kasieczka (Uni Hamburg)
Distinguishing hadronic top quark decays from light quark and gluon jets (top tagging) is an important tool for new physics searches at the LHC and allows the comparison of different machine learning approaches. We present results on using convolutional neural networks as well as recent studies employing a physics motivated network architecture based on Lorentz Invariance (and not much else)...
Isaac Henrion (NYU)
Yang-Ting Chien (MIT)
Frederic Dreyer (MIT)
We introduce a novel representation for emission patterns inside a jet, by declustering a Cambridge-Aachen jet and using the primary-emission Lund plane coordinates. We present several possible variations of this method, and show how it can be used to construct either an n by n pixel image or a graph, which can be used as inputs for neural networks. Using W tagging as an example, we show...
Mr Eric Metodiev (MIT)
In this talk, I will present Energy Flow Polynomials (EFPs), a novel class of jet substructure observables that form a discrete, linear basis of all infrared- and collinear-safe information in a jet. The EFPs are multiparticle energy correlators with a powerful graph-theoretic interpretation which encompass and generalize the analytic structures present in many existing classes of jet...
Mr Patrick Komiske (MIT)
In this talk, I will demonstrate the linear power of Energy Flow Polynomials (EFPs) by applying linear classification methods to quark/gluon discrimination, boosted W tagging, and boosted top tagging, achieving performance that compares favorably to other jet representations and modern machine learning approaches. I will briefly describe novel algorithms that make use of the graph-theoretic...
Anders ANDREASSEN (Harvard)
Many early applications of Machine Learning in jet physics are classifiers that use Convolutional Neural Networks trained on jet images. We will present a work-in-progress custom probabilistic model, tailored to learning the physics of jet production in an unsupervised way. Our model is built on a Recurrent Neural Network suited to modeling the approximate sequential splitting of a tree, which...
Frye Chris (Harvard)
The initial state fluctuations in relativistic heavy ion collisions are converted to the final state correlations of soft particles in momentum space, through strong collective expansion of the quark gluon plasma (QGP) and the QCD transition from QGP to hadrons. The patterns (equations of state) encoded in the relativistic hydrodynamic evolution are extracted from the final particle...
5. Probing heavy ion collisions using quark and gluon jet substructure with machine learning (15'+5')
Dr Raghav Kunnawalkam Elayavalli (Wayne State University)
We study the classification of quark-initiated jets and gluon-initiated jets in proton-proton and heavy ion collisions using modern machine learning techniques. We train the deep convolutional neutral network on discretized jet images. The classification performance is compared with the multivariate analysis of several physically-contructed jet observables including the jet mass, the $p_T^D$,...
Taylor Childers (ANL)
Several studies have had success applying deep convolutional neural nets (CNNs) to a subset of the calorimeter for individual jet classification / tagging. We explore approaches that use the entire calorimeter, combined with track information, for directly conducting multi-jet physics analyses, without the need for any jet reconstruction. We use an existing RPV-Susy analysis as a case study...
Mr Alexx Perloff (TAMU) , Dr Raghav Kunnawalkam Elayavalli (Rutgers University)
Understanding and appropriately correcting for the detector response on any observable of interest is an important chore for experimentalists. Such a procedure is ultimately necessary to remove the impact of the finite detector and to facilitate direct comparisons with theoretical predictions. All current experiments take on this major task by generating Monte Carlo samples and running them...
Marat Freytsis (University of Oregon)
Bryan Ostdiek (University of Oregon)
Applications of machine learning tools to problems of physical interest are often criticized for producing sensitivity at the expense of transparency. In this talk, I explore a procedure for identifying combinations of variables -- aided by physical intuition -- that can discriminate signal from background. Weights are introduced to smooth away the features in a given variable(s). New networks...
Eric Metodiev (MIT)
Jack Collins (University of Maryland)
Kyle Cranmer (NYU)
We use an adversarial neural network to train a jet classifier that remains largely uncorrelated with the jet mass --- a nuisance parameter that is highly correlated with the observed features. This adversarial training strategy balances the dual objectives of classification accuracy and decorrelation, reducing the deleterious effect of systematic uncertainties in the background modeling. The...
Isaac Henrion (NYU)