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We present an open-source Mathematica importer for CERN ROOT files. Taking advantage of Mathematicas import/export plug-in mechanism, the importer offers a simple, unified interface that cleanly wraps around its MathLink-based core that links the ROOT libraries with Mathematica. Among other tests for accuracy and efficiency, the importer has also been tested on a large (~5 Gbyte) file structure, D3PD, used by the ATLAS experiment for offline analysis without problems. In addition to describing the installation and usage of the importer, we discuss how the importer may be further improved and customized. A link to the package can be found at: http://library.wolfram.com/infocenter/Articles/7793/ and a related presentation is at: http://cd-docdb.fnal.gov/cgi-bin/DisplayMeeting?conferenceid=522
We discuss the traditional criterion for discovery in Particle Physics of requiring a significance corresponding to at least 5 sigma; and whether a more nuanced approach might be better.
The many ways in which machine and deep learning are transforming the analysis and simulation of data in particle physics are reviewed. The main methods based on boosted decision trees and various types of neural networks are introduced, and cutting-
Differential measurements of particle collisions or decays can provide stringent constraints on physics beyond the Standard Model of particle physics. In particular, the distributions of the kinematical and angular variables that characterise heavy m
A method for correcting for detector smearing effects using machine learning techniques is presented. Compared to the standard approaches the method can use more than one reconstructed variable to infere the value of the unsmeared quantity on event b
We investigate whether state-of-the-art classification features commonly used to distinguish electrons from jet backgrounds in collider experiments are overlooking valuable information. A deep convolutional neural network analysis of electromagnetic