Explore the vast range of titles available.

Finding and fitting circles from a set of points is a frequent problem in the data analysis of high-energy physics experiments. In a tracker immersed in a homogeneous magnetic field, tracks are close to perfect circles if projected to the bending plane. In a ring-imaging Cherenkov (RICH) detector, circles of photons around the crossing point of charged particles have to be found and their radii estimated. In both cases, non-negligible background may be present that tends to complicate the pattern recognition and to bias the circle fit. In this contribution we present a robust circle fit based on a modified Riemann fit that removes or significantly reduces the effect of background points. As in the standard Riemann fit, the measured points are projected to the Riemann sphere or paraboloid, and a plane is fitted to the projected points. The fit is made robust by replacing the usual least-squares regression by a least median of squares (LMS) regression. Because of the high breakdown point of the LMS estimator, the fit is insensitive to background points. The LMS plane is used to initialize the weights of an M-estimator that refits the plane in order to suppress eventual remaining outliers and to obtain the final circle parameters. The method is demonstrated on three sets of artificial data: points on a circle plus a comparable number of background points; points on two overlapping circles with additional background; and points obtained by the simulation of tracks in a drift chamber with mirror points and additional background. The results show high circle finding efficiency and small contamination of the final fitted circles.

We present in this contribution a new Riemann track fit which operates on centered and scaled measurements. With these transformations, the fit becomes invariant under translations and similarity transforms of the measurements. We show in a simulation study in a generic, cylindrical detector that the modified Riemann fit is more precise than the standard Riemann fit, in particular if the hit resolution is large.

Non-normally distributed data are ubiquitous in many areas of science, including high-energy physics. We present a general formalism for constrained fits, also called data reconciliation, with data that are not normally distributed. It is based on Bayesian reasoning and implemented via MCMC sampling. We show how systems of both linear and non-linear constraints can be efficiently treated. We also show how the fit can be made robust against outlying observations. The method is demonstrated on a couple of examples ranging from material flow analysis to the combination of non-normal measurements. Finally, we discuss possible applications in the field of event reconstruction, such as vertex fitting and kinematic fitting with non-normal track errors.

The application of sparse model-based clustering to the problem of primary vertex finding is discussed. The observed z-positions of the charged primary tracks in a bunch crossing are modeled by a Gaussian mixture. The mixture parameters are estimated via Markov Chain Monte Carlo (MCMC). Sparsity is achieved by an appropriate prior on the mixture weights. The results are shown and compared to clustering by the expectation-maximization (EM) algorithm.

We present the concept of a track trigger for the Belle II experiment, based on a neural network approach, that is able to reconstruct the z (longitudinal) position of the event vertex within the latency of the first level trigger. The trigger will thus be able to suppress a large fraction of the dominating background from events outside of the interaction region. The trigger uses the drift time information of the hits from the Central Drift Chamber (CDC) of Belle II within narrow cones in polar and azimuthal angle as well as in transverse momentum (sectors), and estimates the z-vertex without explicit track reconstruction. The preprocessing for the track trigger is based on the track information provided by the standard CDC trigger. It takes input from the 2D (r - ) track finder, adds information from the stereo wires of the CDC, and finds the appropriate sectors in the CDC for each track in a given event. Within each sector, the z-vertex of the associated track is estimated by a specialized neural network, with a continuous output corresponding to the scaled z-vertex. The input values for the neural network are calculated from the wire hits of the CDC.

One of the main components of the CMS experiment is the Silicon Tracker. This device, designed to measure the trajectories of charged particles, is composed of approximately 16,000 planar silicon detector modules, which makes it the biggest of its kind. However, systematic measurement errors, caused by unavoidable inaccuracies in the construction and assembly phase, reduce the precision of the measurements significantly. The geometrical corrections that are therefore required have to be known to an accuracy that is better than the intrinsic resolution of the detector modules. The Kalman Alignment Algorithm is a novel approach to extract a set of alignment constants from a large collection of recorded particle tracks, and is applicable for a system even as big as the CMS Tracker. To show that the method is functional and well understood, and thus suitable for the data-taking period of the CMS experiment, two case studies are presented and discussed here.

The Kalman alignment algorithm has been specifically developed to cope with the demands that arise from the specifications of the CMS Tracker. The algorithmic concept is based on the Kalman filter formalism and is designed to avoid the inversion of large matrices. Most notably, the algorithm strikes a balance between conventional global and local track-based alignment algorithms, by restricting the computation of alignment parameters not only to alignable objects hit by the same track, but also to all other alignable objects that are significantly correlated. Nevertheless, this feature also comes with various trade-offs: Mechanisms are needed that affect which alignable objects are significantly correlated and keep track of these correlations. Due to the large amount of alignable objects involved at each update (at least compared to local alignment algorithms), the time spent for retrieving and writing alignment parameters as well as the required user memory becomes a significant factor. The large-scale test presented here applies the Kalman alignment algorithm to the (misaligned) CMS Tracker barrel, and demonstrates the feasibility of the algorithm in a realistic scenario. It is shown that both the computation time and the amount of required user memory are within reasonable bounds, given the available computing resources, and that the obtained results are satisfactory.

LiC is a simple but powerful and flexible software tool, written in MatLab, for basic detector design studies (geometries, material budgets) by determining the resolution of reconstructed track parameters. It is based on a helix track model including multiple scattering, and consists of a simplified simulation of the detector followed by track reconstruction using the Kalman filter. After a short description of LiC's main characteristics, we demonstrate its capabilities by applying this tool in a performance study of the LDC and SiD detector concepts at the International Linear Collider (ILC).

Stringent limits are set on the long-lived lepton-like sector of the phenomenological minimal supersymmetric standard model (pMSSM) and the anomaly-mediated supersymmetry breaking (AMSB) model. We derived the limits from the results presented in a recent search for long-lived charged particles in proton–proton collisions, based on data collected by the CMS detector at a centre-of-mass energy of 8 TeV at the Large Hadron Collider. In the pMSSM parameter sub-space considered, 95.9 % of the points predicting charginos with a lifetime of at least 10 ns are excluded. Furthermore, these constraints on the pMSSM are the first obtained at the LHC. Charginos with a lifetime greater than 100 ns and masses up to about 800 GeV in the AMSB model are also excluded. Furthermore, the method described can also be used to set constraints on other models.

An analysis of dijet events in PbPb and pp collisions is performed to explore the properties of energy loss by partons traveling in a quark-gluon plasma. Data are collected at a nucleon-nucleon center-of-mass energy of 2.76 TeV at the LHC. The distribution of transverse momentum (p sub(T)) surrounding dijet systems is measured by selecting charged particles in different ranges of p sub(T) and at different angular cones of pseudorapidity and azimuth. The measurement is performed as a function of centrality of the PbPb collisions, the p sub(T) asymmetry of the jets in the dijet pair, and the distance parameter R used in the anti-k sub(T) jet clustering algorithm. In events with unbalanced dijets, PbPb collisions show an enhanced multiplicity in the hemisphere of the subleading jet, with the p sub(T) imbalance compensated by an excess of low-p sub(T) particles at large angles from the jet axes. [Figure not available: see fulltext.]