Built on

S. Marzani, G. Soyez, and M. Spannowsky, “Looking inside jets: an introduction to jet substructure and boosted-object phenomenology,” arXiv:1901.10342 [hep-ph]
Original
1901
Earlier work this paper cites.
H. Qu and L. Gouskos, “ParticleNet: Jet Tagging via Particle Clouds,” arXiv:1902.08570 [hep-ph]
Original
1902
Earlier work this paper cites.
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.
A. Butter et al
Original
1902
Earlier work this paper cites.
S. D. Ellis, A. Hornig, T. S. Roy, D. Krohn, and M. D. Schwartz, “Qjets: A Non-Deterministic Approach to Tree-Based Jet Substructure,” Phys. Rev. Lett
Original
1914
Earlier work this paper cites.
R. Prescott Adams and R. S. Zemel, “Ranking via Sinkhorn Propagation,” arXiv e-prints
Original
1925
Earlier work this paper cites.
S. D. Ellis and D. E. Soper, “Successive combination jet algorithm for hadron collisions,” Phys. Rev
1993
Earlier work this paper cites.
S. Catani, Y. L. Dokshitzer, M. H. Seymour, and B. R. Webber, “Longitudinally invariant K t K_{t} clustering algorithms for hadron hadron collisions,” Nucl. Phys
1993
Earlier work this paper cites.
Y. L. Dokshitzer, G. D. Leder, S. Moretti, and B. R. Webber, “Better jet clustering algorithms,” JHEP
1997
Earlier work this paper cites.
R. Brun and F. Rademakers, “Root — an object oriented data analysis framework,” Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
1997
Earlier work this paper cites.
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE
1998
Earlier work this paper cites.
Hahnloser Richard H. R., Sarpeshkar Rahul, Mahowald Misha A., Douglas Rodney J., and Seung H. Sebastian, “Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit,” Nature
2000
Earlier work this paper cites.
T. Sjostrand, S. Mrenna, and P. Z. Skands, “PYTHIA 6.4 Physics and Manual,” JHEP
2006
Earlier work this paper cites.
A. Hoecker, P. Speckmayer, J. Stelzer, J. Therhaag, E. von Toerne, and H. Voss, “TMVA: Toolkit for Multivariate Data Analysis,” PoS
2007
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,” Phys. Rev. Lett
Original
2008
Earlier work this paper cites.
G. F. Sterman, “Some Basic Concepts of Perturbative QCD,” Acta Phys. Polon
Original
2008
Earlier work this paper cites.
M. Cacciari, G. P. Salam, and G. Soyez, “The anti- k t k_{t} jet clustering algorithm,” JHEP
Original
2008
Earlier work this paper cites.
D. E. Kaplan, K. Rehermann, M. D. Schwartz, and B. Tweedie, “Top Tagging: A Method for Identifying Boosted Hadronically Decaying Top Quarks,” Phys. Rev. Lett
Original
2008
Earlier work this paper cites.
G. P. Salam, “Towards Jetography,” Eur. Phys. J
Original
2010
Earlier work this paper cites.
D. Krohn, J. Thaler, and L.-T. Wang, “Jet Trimming,” JHEP
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,” Phys. Rev
Original
2010
Earlier work this paper cites.
T. Plehn, M. Spannowsky, M. Takeuchi, and D. Zerwas, “Stop Reconstruction with Tagged Tops,” JHEP
Original
2010
Earlier work this paper cites.

Similar

T. Plehn, G. P. Salam, and M. Spannowsky, “Fat Jets for a Light Higgs,” Phys. Rev. Lett
Original
2010
Cited alongside, same era.
J. Gallicchio and M. D. Schwartz, “Seeing in Color: Jet Superstructure,” Phys. Rev. Lett
Original
2010
Cited alongside, same era.
I. W. Stewart, F. J. Tackmann, and W. J. Waalewijn, “N-Jettiness: An Inclusive Event Shape to Veto Jets,” Phys. Rev. Lett
Original
2010
Cited alongside, same era.
J. Thaler and K. Van Tilburg, “Identifying Boosted Objects with N-subjettiness,” JHEP
Original
2011
Cited alongside, same era.
CMS Collaboration
Original
2012

Then

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

A robust anomaly finder based on autoencoders

Roy, Tuhin S., Vijay, Aravind H.

Understand

We propose a robust method to identify anomalous jets by vetoing QCD-jets.

The robustness of this method ensures that the distribution of the proposed discriminating variable (which allows us to veto QCD-jets) remains unaffected by the phase space of QCD-jets, even if they were different from the region on which the model was trained.
This suggests that our method can be used to look for anomalous jets in high m/p T bins by simply training on jets from low m/p T bins, where sufficient background-enriched data is available.
The robustness follows from combining an autoencoder with a novel way of pre-processing jets.