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Novel deep learning methods for track reconstruction · Around

2018

Novel deep learning methods for track reconstruction

Farrell, Steven, Calafiura, Paolo, Mudigonda, Mayur et al.

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For the past year, the HEP.TrkX project has been investigating machine learning solutions to LHC particle track reconstruction problems.

A variety of models were studied that drew inspiration from computer vision applications and operated on an image-like representation of tracking detector data.
While these approaches have shown some promise, image-based methods face challenges in scaling up to realistic HL-LHC data due to high dimensionality and sparsity.
In contrast, models that can operate on the spacepoint representation of track measurements ("hits") can exploit the structure of the data to solve tasks efficiently.

Built on

S. Hochreiter, J. Schmidhuber, Neural Comput. 9
1997
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G. Apollinari, O. Brüning, T. Nakamoto, L. Rossi, CERN Yellow Report pp. 1–19 (2015), 1705.08830
2015
Earlier work this paper cites.

Similar

Y. LeCun, Y. Bengio, G.E. Hinton, Nature 521
2015
Cited alongside, same era.
M.M. Bronstein, J. Bruna, Y. LeCun, A. Szlam, P. Vandergheynst, CoRR abs/1611.08097
Original
2016
Cited alongside, same era.
A Common Tracking Software Project , http://acts.web.cern.ch/ACTS/index.php
Cited in the paper.

Then

P.W. Battaglia, R. Pascanu, M. Lai, D.J. Rezende, K. Kavukcuoglu, CoRR abs/1612.00222
Original
2016
Later among the works it cites.
Farrell, Steven, Anderson, Dustin, Calafiura, Paolo, Cerati, Giuseppe, Gray, Lindsey, Kowalkowski, Jim, Mudigonda, Mayur, Prabhat, Spentzouris, Panagiotis, Spiropoulou, Maria et al., EPJ Web Conf. 150
2017
Later among the works it cites.

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