Built on

J. H. Collins, K. Howe, and B. Nachman, “Extending the search for new resonances with machine learning,” Phys. Rev
Original
1902
Earlier work this paper cites.
K. Datta, A. Larkoski, and B. Nachman, “Automating the Construction of Jet Observables with Machine Learning,” arXiv:1902.07180 [hep-ph]
Original
1902
Earlier work this paper cites.
A. Butter et al
Original
1902
Earlier work this paper cites.
A. Andreassen and B. Nachman, “Neural Networks for Full Phase-space Reweighting and Parameter Tuning,” arXiv:1907.08209 [hep-ph]
Original
1907
Earlier work this paper cites.
T. Sjostrand, S. Mrenna, and P. Z. Skands, “A Brief Introduction to PYTHIA 8.1,” Comput. Phys. Commun
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,” Phys. Rev
Original
2009
Earlier work this paper cites.
I. W. Stewart, F. J. Tackmann, and W. J. Waalewijn, “N-Jettiness: An Inclusive Event Shape to Veto Jets,” Phys. Rev. Lett
Original
2010
Earlier work this paper cites.
J. Thaler and K. Van Tilburg, “Identifying Boosted Objects with N-subjettiness,” JHEP
Original
2011
Earlier work this paper cites.
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python,” Journal of Machine Learning Research
2011
Earlier work this paper cites.
J. Thaler and K. Van Tilburg, “Maximizing Boosted Top Identification by Minimizing N-subjettiness,” JHEP
Original
2012
Earlier work this paper cites.
J. Stevens and M. Williams, “uBoost: A boosting method for producing uniform selection efficiencies from multivariate classifiers,” JINST
Original
2013
Earlier work this paper cites.
J. Alwall, R. Frederix, S. Frixione, V. Hirschi, F. Maltoni, O. Mattelaer, H. S. Shao, T. Stelzer, P. Torrielli, and M. Zaro, “The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations,” JHEP
Original
2014
Earlier work this paper cites.
DELPHES 3
Original
2014
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv:1412.6980
Original
2014
Earlier work this paper cites.
L. G. Almeida, M. Backović, M. Cliche, S. J. Lee, and M. Perelstein, “Playing Tag with ANN: Boosted Top Identification with Pattern Recognition,” JHEP
Original
2015
Earlier work this paper cites.
F. Chollet et al
2015
Earlier work this paper cites.

Similar

https://www.tensorflow.org/ . Software available from tensorflow.org
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-scale machine learning on heterogeneous systems,” 2015 · 2015
Cited alongside, same era.
A. Rogozhnikov, A. Bukva, V. V. Gligorov, A. Ustyuzhanin, and M. Williams, “New approaches for boosting to uniformity,” JINST
Original
2015
Cited alongside, same era.
L. de Oliveira, M. Kagan, L. Mackey, B. Nachman, and A. Schwartzman, “Jet-images — deep learning edition,” JHEP
Original
2016
Cited alongside, same era.
P. Baldi, K. Cranmer, T. Faucett, P. Sadowski, and D. Whiteson, “Parameterized neural networks for high-energy physics,” Eur. Phys. J
Original
2016
Cited alongside, same era.

Then

S. Chang, T. Cohen, and B. Ostdiek, “What is the Machine Learning?,” Phys. Rev
Original
2018
Later among the works it cites.
T. Cohen, M. Freytsis, and B. Ostdiek, “(Machine) Learning to Do More with Less,” JHEP
Original
2018
Later among the works it cites.
J. H. Collins, K. Howe, and B. Nachman, “Anomaly Detection for Resonant New Physics with Machine Learning,” Phys. Rev. Lett
Original
2018
Later among the works it cites.
K. Fraser and M. D. Schwartz, “Jet Charge and Machine Learning,” JHEP
Original
2018
Later among the works it cites.
P. T. Komiske, E. M. Metodiev, B. Nachman, and M. D. Schwartz, “Learning to classify from impure samples with high-dimensional data,” Phys. Rev

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…

2019

Mass Agnostic Jet Taggers

Bradshaw, Layne, Mishra, Rashmish K., Mitridate, Andrea et al.

Understand

Searching for new physics in large data sets needs a balance between two competing effects---signal identification vs background distortion.

In this work, we perform a systematic study of both single variable and multivariate jet tagging methods that aim for this balance.
The methods preserve the shape of the background distribution by either augmenting the training procedure or the data itself.
Multiple quantitative metrics to compare the methods are considered, for tagging 2-, 3-, or 4-prong jets from the QCD background.

Built on

J. H. Collins, K. Howe, and B. Nachman, “Extending the search for new resonances with machine learning,” Phys. Rev
Original
1902
Earlier work this paper cites.
K. Datta, A. Larkoski, and B. Nachman, “Automating the Construction of Jet Observables with Machine Learning,” arXiv:1902.07180 [hep-ph]
Original
1902
Earlier work this paper cites.
A. Butter et al
Original
1902
Earlier work this paper cites.
A. Andreassen and B. Nachman, “Neural Networks for Full Phase-space Reweighting and Parameter Tuning,” arXiv:1907.08209 [hep-ph]
Original
1907
Earlier work this paper cites.
T. Sjostrand, S. Mrenna, and P. Z. Skands, “A Brief Introduction to PYTHIA 8.1,” Comput. Phys. Commun
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,” Phys. Rev
Original
2009
Earlier work this paper cites.
I. W. Stewart, F. J. Tackmann, and W. J. Waalewijn, “N-Jettiness: An Inclusive Event Shape to Veto Jets,” Phys. Rev. Lett
Original
2010
Earlier work this paper cites.
J. Thaler and K. Van Tilburg, “Identifying Boosted Objects with N-subjettiness,” JHEP
Original
2011
Earlier work this paper cites.
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python,” Journal of Machine Learning Research
2011
Earlier work this paper cites.
J. Thaler and K. Van Tilburg, “Maximizing Boosted Top Identification by Minimizing N-subjettiness,” JHEP
Original
2012
Earlier work this paper cites.
J. Stevens and M. Williams, “uBoost: A boosting method for producing uniform selection efficiencies from multivariate classifiers,” JINST
Original
2013
Earlier work this paper cites.
J. Alwall, R. Frederix, S. Frixione, V. Hirschi, F. Maltoni, O. Mattelaer, H. S. Shao, T. Stelzer, P. Torrielli, and M. Zaro, “The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations,” JHEP
Original
2014
Earlier work this paper cites.
DELPHES 3
Original
2014
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv:1412.6980
Original
2014
Earlier work this paper cites.
L. G. Almeida, M. Backović, M. Cliche, S. J. Lee, and M. Perelstein, “Playing Tag with ANN: Boosted Top Identification with Pattern Recognition,” JHEP
Original
2015
Earlier work this paper cites.
F. Chollet et al
2015
Earlier work this paper cites.

Similar

https://www.tensorflow.org/ . Software available from tensorflow.org
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-scale machine learning on heterogeneous systems,” 2015 · 2015
Cited alongside, same era.
A. Rogozhnikov, A. Bukva, V. V. Gligorov, A. Ustyuzhanin, and M. Williams, “New approaches for boosting to uniformity,” JINST
Original
2015
Cited alongside, same era.
L. de Oliveira, M. Kagan, L. Mackey, B. Nachman, and A. Schwartzman, “Jet-images — deep learning edition,” JHEP
Original
2016
Cited alongside, same era.
P. Baldi, K. Cranmer, T. Faucett, P. Sadowski, and D. Whiteson, “Parameterized neural networks for high-energy physics,” Eur. Phys. J
Original
2016
Cited alongside, same era.