arXiv subjects

C. Richardt

Publications and source records attributed to C. Richardt.

4 recordsLinked to original sources

arXiv2011

Signal Classification for Acoustic Neutrino Detection

This article focuses on signal classification for deep-sea acoustic neutrino detection. In the deep sea, the background of transient signals is very diverse. Approaches like matched filtering are not sufficient to distinguish between neutrino-like signals and other transient signals with similar signature, which are forming the acoustic background for neutrino detection in the deep-sea environment. A classification system based on machine learning algorithms is analysed with the goal to find a robust and effective way to perform this task. For a well-trained model, a testing error on the level of one percent is achieved for strong classifiers like Random Forest and Boosting Trees using the extracted features of the signal as input and utilising dense clusters of sensors instead of single sensors.

astro-ph.IM↗

arXiv2011

Development of Combined Opto-Acoustical Sensor Modules

The faint fluxes of cosmic neutrinos expected at very high energies require large instrumented detector volumes. The necessary volumes in combination with a sufficient shielding against background constitute forbidding and complex environments (e.g. the deep sea) as sites for neutrino telescopes. To withstand these environments and to assure the data quality, the sensors have to be reliable and their operation has to be as simple as possible. A compact sensor module design including all necessary components for data acquisition and module calibration would simplify the detector mechanics and ensures the long term operability of the detector. The compact design discussed here combines optical and acoustical sensors inside one module, therefore reducing electronics and additional external instruments for calibration purposes. In this design the acoustical sensor is primary used for acoustic positioning of the module. The module may also be used for acoustic particle detection and marine science if an appropriate acoustical sensor is chosen. First tests of this design are promising concerning the task of calibration. To expand the field of application also towards acoustic particle detection further improvements concerning electromagnetic shielding and adaptation of the single components are necessary.

astro-ph.IM↗

arXiv2009

Reconstruction methods for acoustic particle detection in the deep sea using clusters of hydrophones

This article focuses on techniques for acoustic noise reduction, signal filters and source reconstruction. For noise reduction, bandpass filters and cross correlations are found to be efficient and fast ways to improve the signal to noise ratio and identify a possible neutrino-induced acoustic signal. The reconstruction of the position of an acoustic point source in the sea is performed by using small-volume clusters of hydrophones (about 1 cubic meter) for direction reconstruction by a beamforming algorithm. The directional information from a number of such clusters allows for position reconstruction. The algorithms for data filtering, direction and position reconstruction are explained and demonstrated using simulated data.

astro-ph.IM↗

arXiv2009

Position reconstruction of acoustic sources with the AMADEUS Detector

This article focuses on techniques for position reconstruction of acoustic point sources with the AMADEUS setup consisting of 36 acoustic sensors in the Mediterranean Sea. The direction reconstruction of an acoustic point source utilizes the information of the 6 small-volume hydrophone clusters of AMADEUS individually. Source position reconstruction is then done by combining the directional information of each cluster. The algorithms for direction and position reconstruction are explained and demonstrated using data taken in the deep sea.

astro-ph.IM↗