Boosted Decision Trees as an Alternative to Artificial Neural Networks for Particle Identification

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.Comment: 6 pages, 5 figures; Accepted for publication in Nucl. Inst. & Meth.

2-[(4-Bromo­phenyl­imino)­meth­yl]-4,6-di­iodo­phenol

The title compound, C13H8BrI2NO, was prepared by the reaction of 3,5-diiodo­salicyl­aldehyde with 4-bromo­phenyl­amine in ethanol. There is an intra­molecular O—H⋯N hydrogen bond in the mol­ecule, which generates an S(6) ring. The dihedral angle between the benzene rings is 2.6 (3)°

2-[(2-Chloro­phen­yl)imino­meth­yl]-4,6-di­iodo­phenol

The asymmetric unit of the title compound, C13H8ClI2NO, contains half of the mol­ecule situated on a mirror plane. The hy­droxy group is involved in the formation of an intra­molecular O—H⋯N hydrogen bond. π–π inter­actions between the benzene rings of neighbouring mol­ecules [centroid–centroid distance = 3.629 (3) Å] form stacks along the b axis. In the crystal, weak C—H⋯O and C—H⋯Cl inter­actions are observed