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Register a new userImplementation of GENFIT2 as an experiment independent track-fitting framework
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Added by
Elisabetta Prencipe
Publication date
2019
fields
Physics
and research's language is
English
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The GENFIT toolkit, initially developed at the Technische Universitaet Muenchen, has been extended and modified to be more general and user-friendly. The new GENFIT, called GENFIT2, provides track representation, track-fitting algorithms and graphic visualization of tracks and detectors, and it can be used for any experiment that determines parameters of charged particle trajectories from spacial coordinate measurements. Based on general Kalman filter routines, it can perform extrapolations of track parameters and covariance matrices. It also provides interfaces to Millepede II for alignment purposes, and RAVE for the vertex finder. Results of an implementation of GENFIT2 in basf2 and PandaRoot software frameworks are presented here.
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A general method is presented to explicitly compute autocovariance functions for non-Poisson dichotomous noise based on renewal theory. The method is specialized to a random telegraph signal of Mittag-Leffler type. Analytical predictions are compared to Monte Carlo simulations. Non-Poisson dichotomous noise is non-stationary and standard spectral methods fail to describe it properly as they assume stationarity.
For a high source activity experiment, such as HOLMES, non-constant baseline pulses could constitute a great fraction of the data-set. We test the optimal filter matrix technique, proposed to process these pulses, on simulated responses of HOLMES microcalorimeters.
For experiments with high arrival rates, reliable identification of nearly-coincident events can be crucial. For calorimetric measurements to directly measure the neutrino mass such as HOLMES, unidentified pulse pile-ups are expected to be a leading source of experimental error. Although Wiener filtering can be used to recognize pile-up, it suffers errors due to pulse-shape variation from detector nonlinearity, readout dependence on sub-sample arrival times, and stability issues from the ill-posed deconvolution problem of recovering Dirac delta-functions from smooth data. Due to these factors, we have developed a processing method that exploits singular value decomposition to (1) separate single-pulse records from piled-up records in training data and (2) construct a model of single-pulse records that accounts for varying pulse shape with amplitude, arrival time, and baseline level, suitable for detecting nearly-coincident events. We show that the resulting processing advances can reduce the required performance specifications of the detectors and readout system or, equivalently, enable larger sensor arrays and better constraints on the neutrino mass.
The spatial resolution achieved by recent synchrotron radiation microtomographs should be estimated from the modulation transfer function (MTF) on the micrometer scale. Step response functions of a synchrotron radiation microtomograph were determined by the slanted edge method by using high-precision surfaces of diamond crystal and ion-milled aluminum wire. Tilted reconstruction was introduced to enable any edge to be used as the slanted edge by defining the reconstruction pixel matrix in an arbitrary orientation. MTFs were estimated from the step response functions of the slanted edges. The obtained MTFs coincided with MTF values estimated from square-wave patterns milled on the aluminum surface. Although x-ray refraction influences should be taken into account to evaluate MTFs, any flat surfaces with nanometer roughness can be used to determine the spatial resolutions of microtomographs.
I re-examine a recent work by G. Landi and G. E. Landi. [arXiv:1808.06708 [physics.ins-det]], in which the authors claim that the resolution of a tracker ca vary linearly with the number of detection layers, $N$, that is, faster than the commonly known $sqrt{N}$ variation, for a tracker of fixed length, in case the precision of the position measurement is allowed to vary from layer to layer, i.e. heteroscedasticity, and an appropriate analysis method, a weighted least squares fit, is used.
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