Boosted decision trees as an alternative to artificial neural networks for particle identification

By B. Roe, Hai-Jun Yang, Ji Zhu, Yong Liu, Ionut Stancu, G. Mcgregor. 2004

600 citations recorded by the source corpus.

The efficacy of particle identification is compared using artificial neutral networks and boosted decision trees. The comparison is performed in the context of the MiniBooNE, an experiment at Fermilab searching for neutrino oscillations. Based on studies of Monte Carlo samples of simulated data, particle identification with boosting algorithms has better performance than that with artificial neural networks for the MiniBooNE experiment. Although the tests in this paper were for one experiment, it is expected that boosting algorithms will find wide application in physics.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Nuclear and High Energy Physics

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Citations in the corpus

Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data (2016, background): “, detecting particles in Large Hadron Collider experiments [5], [6]) to providing full-fledged knowledge discovery frameworks (e.”
Why Normalizing Flows Fail to Detect Out-of-Distribution Data (2020, methodology): “In this experiment, we use 2 UCI classiﬁcation datasets which were used for unsupervised modeling in prior works on normalizing ﬂows [30, 11, 13]: HEPMASS [2] and MINIBOONE [34].”
CatBoost: unbiased boosting with categorical features (2017, methodology): “For a number of years, it has remained the primary method for learning problems with heterogeneous features, noisy data, and complex dependencies: web search, recommendation systems, weather forecasting, and many others [5, 19, 21, 23].”
CatBoost: gradient boosting with categorical features support (2018, methodology): “For a number of years, it has remained the primary method for learning problems with heterogeneous features, noisy data, and complex dependencies: web search, recommendation systems, weather forecasting, and many others [2, 15, 17, 18].”
Search for diphoton resonances in the mass range from 150 to 850 GeV in pp collisions at $s =$ 8 TeV (2015, methodology): “due to conversions in the material upstream of the calorimeter are corrected using a multivariate regression technique based on a boosted decision tree (BDT) [27, 28], which uses as input a collection of shower shape and kinematic variables, together with the energy measured in the preshower for eve”
Machine learning and the physical sciences (2019, methodology): “Neural networks were largely displaced by Boosted Decision Trees (BDTs) as the go-to for classification and regression tasks for more than a decade (Breiman et al., 1984; Freund and Schapire, 1997; Roe et al., 2005).”