Fetching the paper…
Reading the bibliography…
The growing role of data science (DS) and machine learning (ML) in high-energy physics (HEP) is well established and pertinent given the complex detectors, large data, sets and sophisticated analyses at the heart of HEP research.
A. Butter et al., The Machine Learning landscape of top taggers , SciPost Phys. 7
1902
Earlier work this paper cites.
1903
Earlier work this paper cites.
1909
Earlier work this paper cites.
1909
Earlier work this paper cites.
D. Bourilkov, Machine and Deep Learning Applications in Particle Physics , Int. J. Mod. Phys. A 34
1912
Earlier work this paper cites.
J.D. Hunter, Matplotlib: A 2d graphics environment , Computing in Science & Engineering 9
2007
Earlier work this paper cites.
A. Butter and T. Plehn, Generative Networks for LHC events , 2008.08558
2008
Earlier work this paper cites.
S. Forte and S. Carrazza, Parton distribution functions , 2008.12305
2008
Earlier work this paper cites.
2008
Earlier work this paper cites.
B. Nachman, Anomaly Detection for Physics Analysis and Less than Supervised Learning , 2010.14554
2010
Earlier work this paper cites.
2010
Earlier work this paper cites.
Wes McKinney, Data Structures for Statistical Computing in Python , in Proceedings of the 9th Python in Science Conference , Stéfan van der Walt and Jarrod Millman, eds., pp. 56 – 61, 2010, DOI
2010
Earlier work this paper cites.
2010
Earlier work this paper cites.
2011
Earlier work this paper cites.
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel et al., Scikit-learn: Machine learning in Python , J. of Mach. Learn. Res. 12
2011
Earlier work this paper cites.
2012
Earlier work this paper cites.
V. Dhar, Data science and prediction , Communications of the ACM 56
2013
Earlier work this paper cites.
S. Shukla Shubhendu and J. Vijay, Applicability of artificial intelligence in different fields of life , International Journal of Scientific Engineering and Research 1
2013
Earlier work this paper cites.
The CMS Collaboration, Observation of a new boson with mass near 125 gev in pp collisions at s = 7 \sqrt{s}=7 and 8 tev , Journal of High Energy Physics 2013
2013
Earlier work this paper cites.
L. Lyons, Bayes and frequentism: a particle physicist’s perspective , Contemporary Physics 54
2013
Earlier work this paper cites.
O. Behnke, K. Kröninger, G. Schott and T. Schörner-Sadenius, Data analysis in high energy physics: a practical guide to statistical methods , John Wiley & Sons (2013)
2013
Earlier work this paper cites.
S. Bach, A. Binder, G. Montavon, F. Klauschen, K.-R. Müller and W. Samek, On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation , PloS one 10
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
M.D. Wilkinson, M. Dumontier, I.J. Aalbersberg, G. Appleton, M. Axton, A. Baak et al., The FAIR guiding principles for scientific data management and stewardship , Sci. Data 3
2016
Earlier work this paper cites.
S.M. Lundberg and S.-I. Lee, A unified approach to interpreting model predictions , Advances in neural information processing systems 30
2017
Cited alongside, same era.
A. Shrikumar, P. Greenside and A. Kundaje, Learning important features through propagating activation differences , in International conference on machine learning , pp. 3145–3153, PMLR, 2017
2017
Cited alongside, same era.
L. Lista, Statistical methods for data analysis in particle physics , vol. 941, Springer (2017)
2017
Cited alongside, same era.
2018
Cited alongside, same era.
P. Virtanen, R. Gommers, T.E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau et al., SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python , Nature Methods 17
2020
Later among the works it cites.
2020
Later among the works it cites.
A. Roy, N. Nikiforou, N. Castro and T. Andeen, Novel interpretation strategy for searches of singly produced vectorlike quarks at the lhc , Physical Review D 101
2020
Later among the works it cites.
D. Turvill, L. Barnby, B. Yuan and A. Zahir, A survey of interpretability of machine learning in accelerator-based high energy physics , in 2020 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT) , p. 77, IEEE, 2020
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018
Cited alongside, same era.
Alexander Radovic, Mike Williams, David Rousseau, Michael Kagan, Daniele Bonacorsi, Alexander Himmel, Adam Aurisano, Kazuhiro Terao, and Taritree Wongjirad, Machine learning at the energy and intensity frontiers of particle physics , Nature 560
2018
Cited alongside, same era.
2018
Cited alongside, same era.
M. Paganini, L. de Oliveira and B. Nachman, Accelerating science with generative adversarial networks: an application to 3d particle showers in multilayer calorimeters , Physical review letters 120
2018
Cited alongside, same era.
“ Theoretical Advanced Study Institute Summer School 2018 ”Theory in an Era of Data”
2018
Cited alongside, same era.
The 2019 US-ATLAS Computing Bootcamp website. https://sammeehan.com/2019-08-19-usatlas-computing-bootcamp
2019
Cited alongside, same era.
2019
Cited alongside, same era.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan et al., Pytorch: An imperative style, high-performance deep learning library , in Advances in Neural Information Processing Systems 32 , H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox and R. Garnett, eds., pp. 8024–8035, Curran Associates, Inc. (2019), http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf
2019
Cited alongside, same era.
A.-L. Lamprecht, L. Garcia, M. Kuzak, C. Martinez, R. Arcila, E. Martin Del Pico et al., Towards FAIR principles for research software , Data Sci. J. 3
2020
Later among the works it cites.
S. Samuel, F. Löffler and B. König-Ries, Machine learning pipelines: provenance, reproducibility and fair data principles , in Provenance and Annotation of Data and Processes , p. 226, Springer (2020)
2020
Later among the works it cites.
M.D. Schwartz, Modern Machine Learning and Particle Physics , Harv. Data Sci. Rev. 3
2021
Later among the works it cites.
“ 2021 Machine Learning and the Physical Sciences Workshop
2021
Later among the works it cites.
M. Erdmann, J. Glombitza, G. Kasieczka and U. Klemradt, Deep Learning for Physics Research , World Scientific (2021), 10.1142/12294 , [ https://www.worldscientific.com/doi/pdf/10.1142/12294 ]
2021
Later among the works it cites.
M.L. Waskom, seaborn: statistical data visualization , Journal of Open Source Software 6
2021
Later among the works it cites.
E. Van Dusen, Jupyter for teaching data science , in SIGCSE ’21: Proceedings of the 52nd ACM Technical Symposium on Computer Science Education , p. 1359, 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
“ 2021 CERN-Fermilab HCP Summer School
2021
Later among the works it cites.
G. Cowan, “Statistics for Particle Physicists.” https://cds.cern.ch/record/2773595 , 2021
2021
Later among the works it cites.
D.S. Katz, F. Psomopoulos and L. Castro, Working towards understanding the role of FAIR for machine learning , DaMaLOS@ ISWC (2021) 1
2021
Later among the works it cites.
2021
Later among the works it cites.
2022
Closest in time.
2022
Closest in time.
P. Calafiura, D. Rousseau and K. Terao, Artificial Intelligence for High Energy Physics , World Scientific (2022), 10.1142/12200
2022
Closest in time.
2022
Closest in time.
E. Stanfield, C.D. Slown, Q. Sedlacek and S.E. Worcester, A course-based undergraduate research experience (CURE) in biology: Developing systems thinking through field experiences in restoration ecology , CBE—Life Sciences Education 21
2022
Closest in time.