Fetching the paper…

Reading the bibliography…

Machine learning challenges in theoretical HEP · Around

2017

Machine learning challenges in theoretical HEP

Carrazza, Stefano

Original
PDF
Post on X

Understand

In these proceedings we perform a brief review of machine learning (ML) applications in theoretical High Energy Physics (HEP-TH).

We start the discussion by defining and then classifying machine learning tasks in theoretical HEP.
We then discuss some of the most popular and recent published approaches with focus on a relevant case study topic: the determination of parton distribution functions (PDFs) and related tools.
Finally, we provide an outlook about future applications and developments due to the synergy between ML and HEP-TH.

Built on

P. Nogueira, J. Comput. Phys. 105
1993
Earlier work this paper cites.
T. Hahn and M. Perez-Victoria, Comput. Phys. Commun. 118
1999
Earlier work this paper cites.
Likas, Aristidis Computer physics communications, Vol. 135, 2, 167–175, 2001
2001
Earlier work this paper cites.
P. Nason, JHEP 0411
2004
Earlier work this paper cites.
T. Sjostrand, S. Mrenna and P. Z. Skands, JHEP 0605
2006
Earlier work this paper cites.
G. Altarelli, R. D. Ball and S. Forte, Nucl. Phys. B 799
Original
2008
Earlier work this paper cites.
M. Bahr et al
Original
2008
Earlier work this paper cites.
S. Catani, L. Cieri, G. Ferrera, D. de Florian and M. Grazzini, Phys. Rev. Lett. 103
Original
2009
Earlier work this paper cites.
T. Carli, D. Clements, A. Cooper-Sarkar, C. Gwenlan, G. P. Salam, F. Siegert, P. Starovoitov and M. Sutton, Eur. Phys. J. C 66
Original
2010
Earlier work this paper cites.
Pedregosa, F. et al
2011
Earlier work this paper cites.
A. van Hameren, Comput. Phys. Commun. 182
Original
2011
Earlier work this paper cites.

Similar

G. Watt and R. S. Thorne, JHEP 1208
Original
2012
Cited alongside, same era.
J. Alwall et al
Original
2014
Cited alongside, same era.
P. Skands, S. Carrazza and J. Rojo, Eur. Phys. J. C 74
Original
2014
Cited alongside, same era.
E. R. Nocera et al
Original
2014
Cited alongside, same era.
TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
M. Abadi, et al · 2015
Cited alongside, same era.

Then

S. Carrazza, R. K. Ellis and G. Zanderighi, Comput. Phys. Commun. 209
Original
2016
Later among the works it cites.
J. Butterworth et al
Original
2016
Later among the works it cites.
S. Dulat et al
Original
2016
Later among the works it cites.
S. Carrazza, S. Forte, Z. Kassabov and J. Rojo, Eur. Phys. J. C 76
Original
2016
Later among the works it cites.
S. Carrazza and Z. Kassabov, PoS PP @LHC2016
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…

C. Anastasiou, C. Duhr, F. Dulat, F. Herzog and B. Mistlberger, Phys. Rev. Lett. 114

2015

Cited alongside, same era.

R. D. Ball et al

2015

Cited alongside, same era.

L. A. Harland-Lang, A. D. Martin, P. Motylinski and R. S. Thorne, Eur. Phys. J. C 75

2015

Cited alongside, same era.

S. Carrazza, J. I. Latorre, J. Rojo and G. Watt, Eur. Phys. J. C 75

2015

Cited alongside, same era.

S. Carrazza, S. Forte, Z. Kassabov, J. I. Latorre and J. Rojo, Eur. Phys. J. C 75

Original

2015

Cited alongside, same era.

Theano Development Team [arXiv:1605.02688], May 2016,

2016

Cited alongside, same era.

T. Kluge, K. Rabbertz and M. Wobisch, doi:10.1142/9789812706706_0110 hep-ph/0609285

Cited in the paper.

R. Boughezal, X. Liu and F. Petriello, JHEP 1703

2017

Closest in time.

R. Boughezal, J. M. Campbell, R. K. Ellis, C. Focke, W. Giele, X. Liu, F. Petriello and C. Williams, Eur. Phys. J. C 77

2017

Closest in time.

R. D. Ball et al

2017

Closest in time.

V. Bertone et al

2017

Closest in time.

P. T. Komiske, E. M. Metodiev and M. D. Schwartz, JHEP 1701

2017

Closest in time.

S. Carrazza, Acta Phys. Polon. B 48

2017

Closest in time.