Built on

M. Cacciari and G. P. Salam, Pileup subtraction using jet areas
Original
2008
Earlier work this paper cites.
J. M. Butterworth, A. R. Davison, M. Rubin and G. P. Salam, Jet substructure as a new Higgs search channel at the LHC
Original
2008
Earlier work this paper cites.
M. Cacciari, G. P. Salam and G. Soyez, The Anti- k t k_{t} jet clustering algorithm
Original
2008
Earlier work this paper cites.
S. D. Ellis, C. K. Vermilion and J. R. Walsh, Techniques for improved heavy particle searches with jet substructure
2009
Earlier work this paper cites.
D. Krohn, J. Thaler and L.-T. Wang, Jet Trimming
Original
2010
Earlier work this paper cites.
S. D. Ellis, C. K. Vermilion and J. R. Walsh, Recombination algorithms and jet substructure: pruning as a tool for heavy particle searches
2010
Earlier work this paper cites.
J. e. a. Bergstra, Theano: A cpu and gpu math compiler in python
2010
Earlier work this paper cites.
M. Cacciari, G. P. Salam and G. Soyez, FastJet User Manual
Original
2012
Earlier work this paper cites.
A. J. Larkoski, G. P. Salam and J. Thaler, Energy Correlation Functions for Jet Substructure
Original
2013
Earlier work this paper cites.
A. J. Larkoski, S. Marzani, G. Soyez and J. Thaler, Soft Drop
Original
2014
Earlier work this paper cites.
D. Krohn, M. D. Schwartz, M. Low and L.-T. Wang, Jet Cleansing: Pileup Removal at High Luminosity
Original
2014
Earlier work this paper cites.

Similar

D. Bertolini, P. Harris, M. Low and N. Tran, Pileup Per Particle Identification
Original
2014
Cited alongside, same era.
P. Berta, M. Spousta, D. W. Miller and R. Leitner, Particle-level pileup subtraction for jets and jet shapes
Original
2014
Cited alongside, same era.
D. Bertolini, T. Chan and J. Thaler, Jet Observables Without Jet Algorithms
Original
2014
Cited alongside, same era.
D. Kingma and J. Ba, Adam: A method for stochastic optimization
Original
2014
Cited alongside, same era.
Z. Marshall, A. Collaboration et. al
2014
Cited alongside, same era.

Then

F. Chollet, Keras
2015
Later among the works it cites.
K. He, X. Zhang, S. Ren and J. Sun, Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
2015
Later among the works it cites.
T. Sjöstrand, S. Ask, J. R. Christiansen, R. Corke, N. Desai, P. Ilten, S. Mrenna, S. Prestel, C. O. Rasmussen and P. Z. Skands, An Introduction to PYTHIA 8.2
Original
2015
Later among the works it cites.
L. de Oliveira, M. Kagan, L. Mackey, B. Nachman and A. Schwartzman, Jet-images — deep learning edition
Original
2016
Later among the works it cites.
P. Baldi, K. Bauer, C. Eng, P. Sadowski and D. Whiteson, Jet Substructure Classification in High-Energy Physics with Deep Neural Networks
Original
2016
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Reading the bibliography…

2017

Pileup Mitigation with Machine Learning (PUMML)

Komiske, Patrick T., Metodiev, Eric M., Nachman, Benjamin et al.

Understand

Pileup involves the contamination of the energy distribution arising from the primary collision of interest (leading vertex) by radiation from soft collisions (pileup).

We develop a new technique for removing this contamination using machine learning and convolutional neural networks.
The network takes as input the energy distribution of charged leading vertex particles, charged pileup particles, and all neutral particles and outputs the energy distribution of particles coming from leading vertex alone.
The PUMML algorithm performs remarkably well at eliminating pileup distortion on a wide range of simple and complex jet observables.