Built on

S. Carrazza and F. A. Dreyer, “Jet grooming through reinforcement learning,” Phys. Rev
Original
1903
Earlier work this paper cites.
S. Höche, S. Prestel, and H. Schulz, “Simulation of Vector Boson Plus Many Jet Final States at the High Luminosity LHC,” Phys. Rev. D
Original
1905
Earlier work this paper cites.
A. Andreassen and B. Nachman, “Neural Networks for Full Phase-space Reweighting and Parameter Tuning,” Phys. Rev. D
Original
1907
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. B
1993
Earlier work this paper cites.
S. D. Ellis and D. E. Soper, “Successive combination jet algorithm for hadron collisions,” Phys. Rev. D
1993
Earlier work this paper cites.
E. Dale and J. Moon, “The permuted analogues of three Catalan sets,” 1993
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.
S. Catani, F. Krauss, R. Kuhn, and B. Webber, “QCD matrix elements + parton showers,” JHEP
2001
Earlier work this paper cites.
T. Sørlie, C. M. Perou, R. Tibshirani, T. Aas, S. Geisler, H. Johnsen, T. Hastie, M. B. Eisen, M. Van De Rijn, S. S. Jeffrey, et al
2001
Earlier work this paper cites.
L. Lonnblad, “Correcting the color dipole cascade model with fixed order matrix elements,” JHEP
2002
Earlier work this paper cites.
C. S. Greenberg, S. Macaluso, N. Monath, J.-A. Lee, P. Flaherty, K. Cranmer, A. McGregor, and A. McCallum, “Data Structures \& Algorithms for Exact Inference in Hierarchical Clustering,” arXiv:2002.11661 [cs.DS]
Original
2002
Earlier work this paper cites.

Similar

A. Kraskov, H. Stögbauer, R. G. Andrzejak, and P. Grassberger, “Hierarchical clustering using mutual information,” EPL (Europhysics Letters)
2005
Cited alongside, same era.
M. Cacciari, G. P. Salam, and G. Soyez, “The anti- k t k_{t} jet clustering algorithm,” JHEP
Original
2008
Cited alongside, same era.
T. Gleisberg, S. Hoeche, F. Krauss, M. Schonherr, S. Schumann, F. Siegert, and J. Winter, “Event generation with SHERPA 1.1,” JHEP
Original
2009
Cited alongside, same era.
D. Callan, “A combinatorial survey of identities for the double factorial,” 2009
2009
Cited alongside, same era.
A. Buckley, H. Hoeth, H. Lacker, H. Schulz, and J. E. von Seggern, “Systematic event generator tuning for the LHC,” Eur. Phys. J. C
Original
2010

Then

D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, and Others, “Mastering the game of go with deep neural networks and tree search,” nature
2016
Later among the works it cites.
D. Ferreira de Lima, P. Petrov, D. Soper, and M. Spannowsky, “Quark-Gluon tagging with Shower Deconstruction: Unearthing dark matter and Higgs couplings,” Phys. Rev. D
Original
2017
Later among the works it cites.
D. Silver, J. Schrittwieser, K. Simonyan, I. Antonoglou, A. Huang, A. Guez, T. Hubert, L. Baker, M. Lai, A. Bolton, and Others, “Mastering the game of go without human knowledge,” nature
2017
Later among the works it cites.
D. Silver, T. Hubert, J. Schrittwieser, I. Antonoglou, M. Lai, A. Guez, M. Lanctot, L. Sifre, D. Kumaran, T. Graepel, and Others, “Mastering chess and shogi by self-play with a general reinforcement learning algorithm,” arXiv:1712.01815
Original
2017
Later among the works it cites.

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

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Reading the bibliography…

2021

Reframing Jet Physics with New Computational Methods

Cranmer, Kyle, Drnevich, Matthew, Macaluso, Sebastian et al.

Understand

We reframe common tasks in jet physics in probabilistic terms, including jet reconstruction, Monte Carlo tuning, matrix element - parton shower matching for large jet multiplicity, and efficient event generation of jets in complex, signal-like regions of phase space.

We also introduce Ginkgo, a simplified, generative model for jets, that facilitates research into these tasks with techniques from statistics, machine learning, and combinatorial optimization.
We review some of the recent research in this direction that has been enabled with Ginkgo.
We show how probabilistic programming can be used to efficiently sample the showering process, how a novel trellis algorithm can be used to efficiently marginalize over the enormous number of clustering histories for the same observed particles, and how dynamic programming, A* search, and reinforcement learning can be used to find the maximum likelihood clustering in this enormous search space.

Built on

S. Carrazza and F. A. Dreyer, “Jet grooming through reinforcement learning,” Phys. Rev
Original
1903
Earlier work this paper cites.
S. Höche, S. Prestel, and H. Schulz, “Simulation of Vector Boson Plus Many Jet Final States at the High Luminosity LHC,” Phys. Rev. D
Original
1905
Earlier work this paper cites.
A. Andreassen and B. Nachman, “Neural Networks for Full Phase-space Reweighting and Parameter Tuning,” Phys. Rev. D
Original
1907
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. B
1993
Earlier work this paper cites.
S. D. Ellis and D. E. Soper, “Successive combination jet algorithm for hadron collisions,” Phys. Rev. D
1993
Earlier work this paper cites.
E. Dale and J. Moon, “The permuted analogues of three Catalan sets,” 1993
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.
S. Catani, F. Krauss, R. Kuhn, and B. Webber, “QCD matrix elements + parton showers,” JHEP
2001
Earlier work this paper cites.
T. Sørlie, C. M. Perou, R. Tibshirani, T. Aas, S. Geisler, H. Johnsen, T. Hastie, M. B. Eisen, M. Van De Rijn, S. S. Jeffrey, et al
2001
Earlier work this paper cites.
L. Lonnblad, “Correcting the color dipole cascade model with fixed order matrix elements,” JHEP
2002
Earlier work this paper cites.
C. S. Greenberg, S. Macaluso, N. Monath, J.-A. Lee, P. Flaherty, K. Cranmer, A. McGregor, and A. McCallum, “Data Structures \& Algorithms for Exact Inference in Hierarchical Clustering,” arXiv:2002.11661 [cs.DS]
Original
2002
Earlier work this paper cites.

Similar

A. Kraskov, H. Stögbauer, R. G. Andrzejak, and P. Grassberger, “Hierarchical clustering using mutual information,” EPL (Europhysics Letters)
2005
Cited alongside, same era.
M. Cacciari, G. P. Salam, and G. Soyez, “The anti- k t k_{t} jet clustering algorithm,” JHEP
Original
2008
Cited alongside, same era.
T. Gleisberg, S. Hoeche, F. Krauss, M. Schonherr, S. Schumann, F. Siegert, and J. Winter, “Event generation with SHERPA 1.1,” JHEP
Original
2009
Cited alongside, same era.
D. Callan, “A combinatorial survey of identities for the double factorial,” 2009
2009
Cited alongside, same era.
A. Buckley, H. Hoeth, H. Lacker, H. Schulz, and J. E. von Seggern, “Systematic event generator tuning for the LHC,” Eur. Phys. J. C
Original
2010

Then

D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, and Others, “Mastering the game of go with deep neural networks and tree search,” nature
2016
Later among the works it cites.
D. Ferreira de Lima, P. Petrov, D. Soper, and M. Spannowsky, “Quark-Gluon tagging with Shower Deconstruction: Unearthing dark matter and Higgs couplings,” Phys. Rev. D
Original
2017
Later among the works it cites.
D. Silver, J. Schrittwieser, K. Simonyan, I. Antonoglou, A. Huang, A. Guez, T. Hubert, L. Baker, M. Lai, A. Bolton, and Others, “Mastering the game of go without human knowledge,” nature
2017
Later among the works it cites.
D. Silver, T. Hubert, J. Schrittwieser, I. Antonoglou, M. Lai, A. Guez, M. Lanctot, L. Sifre, D. Kumaran, T. Graepel, and Others, “Mastering chess and shogi by self-play with a general reinforcement learning algorithm,” arXiv:1712.01815
Original
2017
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…

Cited alongside, same era.

D. E. Soper and M. Spannowsky, “Finding physics signals with shower deconstruction,” Phys. Rev. D

Original

2011

Cited alongside, same era.

11, 2020

Original

J. Brehmer, S. Macaluso, D. Pappadopulo, and K. Cranmer, “Hierarchical clustering in particle physics through reinforcement learning,” in 34th Conference on Neural Information Processing Systems · 2011

Cited alongside, same era.

D. E. Soper and M. Spannowsky, “Finding top quarks with shower deconstruction,” Phys. Rev. D

Original

2013

Cited alongside, same era.

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,” Comput. Phys. Commun

Original

2015

Cited alongside, same era.

J. Bellm et al

Original

2016