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Register a new userAlgorithms for Identification of Nearly-Coincident Events in Calorimetric Sensors
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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.
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A principal component analysis (PCA) of clean microcalorimeter pulse records can be a first step beyond statistically optimal linear filtering of pulses towards a fully non-linear analysis. For PCA to be practical on spectrometers with hundreds of sensors, an automated identification of clean pulses is required. Robust forms of PCA are the subject of active research in machine learning. We examine a version known as coherence pursuit that is simple, fast, and well matched to the automatic identification of outlier records, as needed for microcalorimeter pulse analysis.
We present a novel technique, called DSVP (Discrimination through Singular Vectors Projections), to discriminate spurious events within a dataset. The purpose of this paper is to lay down a general procedure which can be tailored for a broad variety of applications. After describing the general concept, we apply the algorithm to the problem of identifying nearly coincident events in low temperature microcalorimeters in order to push the time resolution close to its intrinsic limit. In fact, from simulated datasets it was possible to achieve an effective time resolution even shorter than the sampling time of the system considered. The obtained results are contextualized in the framework of the HOLMES experiment, which aims at directly measuring the neutrino mass with the calorimetric approach, allowing to significally improve its statistical sensitivity.
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.
We present a Bayesian estimation analysis for a particular trace gas detection technique with species separation provided by differential diffusion. The proposed method collects a sample containing multiple gas species into a common volume, and then allows it to diffuse across a linear array of optical absorption detectors, using, for example, high-finesse Fabry-Perot cavities. The estimation procedure assumes that all gas parameters (e.g. diffusion constants, optical cross sections) are known except for the number population of each species, which are determined from the time-of-flight absorption profiles in each detector.
The overall design of the Integrated Spectral Analysis Workbench (ISAW), being developed at Argonne, provides for an extensible, highly interactive, collaborating set of viewers for neutron scattering data. Large arbitrary collections of spectra from multiple detectors can be viewed as an image, a scrolled list of individual graphs, or using a 3D representation of the instrument showing the detector positions. Data from an area detector can be displayed using a contour or intensity map as well as an interactive table. Selected spectra can be displayed in tables or on a conventional graph. A unique characteristic of these viewers is their interactivity and coordination. The position pointed at by the user in one viewer is sent to other viewers of the same DataSet so they can track the position and display relevant information. Specialized viewers for single crystal neutron diffractometers are being developed. A proof-of-concept viewer that directly displays the 3D reciprocal lattice from a complete series of runs on a single crystal diffractometer has been implemented.
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