am P.C. Bhat, am K. Burkett, am S. Cihangir, am O. Gutsche, am H. Jensen, am M. Johnson, am N. Luzhetskiy, am D. Mason, am T. Miao, am S. Moccia, am C. Noeding, am A. Ronzhin, am E. Skup, am W.J. Spalding, am L. Spiegel, am S. Tkaczyk, am F. Yumiceva, am A. Zatserklyaniy, am E. Zerev, am I. Anghel, an V.

The results of the CMS tracker alignment analysis are presented using the data from cosmic tracks, optical survey information, and the laser alignment system at the Tracker Integration Facility at CERN. During several months of operation in the spring and summer of 2007, about five million cosmic track events were collected with a partially active CMS Tracker. This allowed us to perform first alignment of the active silicon modules with the cosmic tracks using three different statistical approaches; validate the survey and laser alignment system performance; and test the stability of Tracker structures under various stresses and temperatures ranging from +15 °C to −15 °C. Comparison with simulation shows that the achieved alignment precision in the barrel part of the tracker leads to residual distributions similar to those obtained with a random misalignment of 50 (80) μm RMS in the outer (inner) part of the barrel.

Pulse shape discrimination (PSD) is the task of classifying electronic pulse shapes for different particle types such as gamma rays and fast neutrons interacting in scintillators and read out by photo sensitive detectors. This field has been limited in its adoption of techniques found in the statistical learning community. Methods initially employed in the 1960s for analog electronic circuitry persist in the current PSD literature describing operations performed on digitized pulses, which are amenable to statistical rigor. Despite vast amounts of data collected at low energy levels, traditional PSD methods are unable to discriminate particles below a certain threshold.In this work, Gaussian mixture models (GMMs) are used as a clustering technique for fast neutron detection in the absence of labeled data. GMMs yield improvements spanning the energy spectrum in a desirably efficient, unsupervised fashion. An extension, the Dirichlet Process GMM, provides further flexibility and classification improvement. K E Y W O R D Sclassification, clustering, mixture models, pulse shape discrimination

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