Matt Dolan
(The University of Melbourne)
12/11/17, 12:30 PM
Taoli Cheng
(University of Chinese Academy of Sciences)
12/11/17, 12:50 PM
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)
12/11/17, 1:10 PM
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...
Gregor Kasieczka
(Uni Hamburg)
12/11/17, 1:50 PM
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)
12/11/17, 2:10 PM
Yang-Ting Chien
(MIT)
12/12/17, 9:00 AM
Mr
Patrick Komiske
(MIT)
12/12/17, 10:00 AM
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)
12/12/17, 10:20 AM
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)
12/12/17, 10:35 AM
Dr
Raghav Kunnawalkam Elayavalli
(Wayne State University)
12/12/17, 12:10 PM
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)
12/12/17, 3:00 PM
Mr
Alexx Perloff
(TAMU), Dr
Raghav Kunnawalkam Elayavalli
(Rutgers University)
12/12/17, 3:40 PM
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...
Michela Paganini
12/12/17, 4:00 PM
William Mccormack
12/12/17, 4:20 PM
Marat Freytsis
(University of Oregon)
12/13/17, 9:00 AM
Bryan Ostdiek
(University of Oregon)
12/13/17, 9:20 AM
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)
12/13/17, 9:40 AM
Jack Collins
(University of Maryland)
12/13/17, 10:00 AM
