Built on

and, Ix. on the problem of the most efficient tests of statistical hypotheses
1933
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N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, Dropout: A simple way to prevent neural networks from overfitting
1958
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J. Button, G. R. Kalbfleisch, G. R. Lynch, B. C. Maglić, A. H. Rosenfeld, and M. L. Stevenson, Pion-pion interaction in the reaction p ¯ + p → 2 π + + 2 π − + n π 0 \overline{p}+p\rightarrow 2{\pi}^{+}+2{\pi}^{-}+n{\pi}^{0}
1962
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Ph.D. thesis, University of California at Berkeley (2000)
B. Knuteson · 2000
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B. Knuteson, Systematic analysis of high-energy collider data
2004
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M. Pivk and F. R. Le Diberder, SPlot: A Statistical tool to unfold data distributions
2005
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T. Sjostrand, S. Mrenna, and P. Z. Skands, A Brief Introduction to PYTHIA 8.1
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2008
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M. Cacciari, G. P. Salam, and G. Soyez, The Anti-k(t) jet clustering algorithm
Original
2008
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E. Gross and O. Vitells, Trial factors for the look elsewhere effect in high energy physics
Original
2010
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arXiv:1101.0390
Original
G. Choudalakis, On hypothesis testing, trials factor, hypertests and the BumpHunter · 2011
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CMS Collaboration
2011
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O. Vitells and E. Gross, Estimating the significance of a signal in a multi-dimensional search
2011
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K. Cranmer and I. Yavin, RECAST: Extending the Impact of Existing Analyses
Original
2011
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ATLAS Collaboration
2012
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M. Cacciari, G. P. Salam, and G. Soyez, FastJet User Manual
Original
2012
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J. Thaler and K. Van Tilburg, Maximizing Boosted Top Identification by Minimizing N-subjettiness
Original
2012
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G. Cowan, K. Cranmer, E. Gross, and O. Vitells, Asymptotic formulae for likelihood-based tests of new physics
Original
2013
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Then

G. Kasieczka, T. Plehn, M. Russell, and T. Schell, Deep-learning Top Taggers or The End of QCD?
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Later among the works it cites.
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Later among the works it cites.
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2017
Later among the works it cites.
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Reading the bibliography…

2019

Extending the Bump Hunt with Machine Learning

Collins, Jack H, Howe, Kiel, Nachman, Benjamin

Understand

The oldest and most robust technique to search for new particles is to look for `bumps' in invariant mass spectra over smoothly falling backgrounds.

We present a new extension of the bump hunt that naturally benefits from modern machine learning algorithms while remaining model-agnostic.
This approach is based on the Classification Without Labels (CWoLa) method where the invariant mass is used to create two potentially mixed samples, one with little or no signal and one with a potential resonance.
Additional features that are uncorrelated with the invariant mass can be used for training the classifier.

Built on

and, Ix. on the problem of the most efficient tests of statistical hypotheses
1933
Earlier work this paper cites.
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, Dropout: A simple way to prevent neural networks from overfitting
1958
Earlier work this paper cites.
J. Button, G. R. Kalbfleisch, G. R. Lynch, B. C. Maglić, A. H. Rosenfeld, and M. L. Stevenson, Pion-pion interaction in the reaction p ¯ + p → 2 π + + 2 π − + n π 0 \overline{p}+p\rightarrow 2{\pi}^{+}+2{\pi}^{-}+n{\pi}^{0}
1962
Earlier work this paper cites.
Ph.D. thesis, University of California at Berkeley (2000)
B. Knuteson · 2000
Earlier work this paper cites.
B. Knuteson, Systematic analysis of high-energy collider data
2004
Earlier work this paper cites.
M. Pivk and F. R. Le Diberder, SPlot: A Statistical tool to unfold data distributions
2005
Earlier work this paper cites.
T. Sjostrand, S. Mrenna, and P. Z. Skands, A Brief Introduction to PYTHIA 8.1
Original
2008
Earlier work this paper cites.
M. Cacciari, G. P. Salam, and G. Soyez, The Anti-k(t) jet clustering algorithm
Original
2008
Earlier work this paper cites.
E. Gross and O. Vitells, Trial factors for the look elsewhere effect in high energy physics
Original
2010
Earlier work this paper cites.
arXiv:1101.0390
Original
G. Choudalakis, On hypothesis testing, trials factor, hypertests and the BumpHunter · 2011
Earlier work this paper cites.
CMS Collaboration
2011
Earlier work this paper cites.
O. Vitells and E. Gross, Estimating the significance of a signal in a multi-dimensional search
2011
Earlier work this paper cites.
K. Cranmer and I. Yavin, RECAST: Extending the Impact of Existing Analyses
Original
2011
Earlier work this paper cites.
ATLAS Collaboration
2012
Earlier work this paper cites.
M. Cacciari, G. P. Salam, and G. Soyez, FastJet User Manual
Original
2012
Earlier work this paper cites.
J. Thaler and K. Van Tilburg, Maximizing Boosted Top Identification by Minimizing N-subjettiness
Original
2012
Earlier work this paper cites.
G. Cowan, K. Cranmer, E. Gross, and O. Vitells, Asymptotic formulae for likelihood-based tests of new physics
Original
2013
Earlier work this paper cites.

Similar

A general search for new phenomena with the ATLAS detector in pp collisions at s = 8 \sqrt{s}=8 TeV
2014
Cited alongside, same era.
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
Original
2014
Cited alongside, same era.
DELPHES 3
Original
2014
Cited alongside, same era.
A. J. Larkoski, S. Marzani, G. Soyez, and J. Thaler, Soft Drop
Original
2014
Cited alongside, same era.
J. Cogan, M. Kagan, E. Strauss, and A. Schwarztman, Jet-Images: Computer Vision Inspired Techniques for Jet Tagging
Original

Then

G. Kasieczka, T. Plehn, M. Russell, and T. Schell, Deep-learning Top Taggers or The End of QCD?
Original
2017
Later among the works it cites.
P. T. Komiske, E. M. Metodiev, and M. D. Schwartz, Deep learning in color: towards automated quark/gluon jet discrimination
Original
2017
Later among the works it cites.
ATLAS Collaboration
2017
Later among the works it cites.
ATLAS Collaboration
2017
Later among the works it cites.
K. Datta and A. Larkoski, How Much Information is in a Jet?
Original
2017
Later among the works it cites.
L. M. Dery, B. Nachman, F. Rubbo, and A. Schwartzman, Weakly Supervised Classification in High Energy Physics

Beyond the bibliography

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

Open on alphaXiv

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2015

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L. G. Almeida, M. Backović, M. Cliche, S. J. Lee, and M. Perelstein, Playing Tag with ANN: Boosted Top Identification with Pattern Recognition

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2015

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2015

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2016

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P. Baldi, K. Bauer, C. Eng, P. Sadowski, and D. Whiteson, Jet Substructure Classification in High-Energy Physics with Deep Neural Networks

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2016

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M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, et al., Tensorflow: A system for large-scale machine learning

2016

Cited alongside, same era.

ATLAS Collaboration

2016

Cited alongside, same era.

CMS Collaboration

2016

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2017

Later among the works it cites.

E. M. Metodiev, B. Nachman, and J. Thaler, Classification without labels: Learning from mixed samples in high energy physics