Acoustic Particle Detection with the ANTARES Detector
© C. Richardt et al. 2010
Received: 1 July 2009
Accepted: 5 January 2010
Published: 18 March 2010
The (Antares Modules for Acoustic Detection Under the Sea) AMADEUS system within the (Astronomy with a Neutrino Telescope and Abyss environmental RESsearch) ANTARES neutrino telescope is designed to investigate detection techniques for acoustic signals produced by particle cascades. While passing through a liquid a cascade deposits energy and produces a measurable pressure pulse. This can be used for the detection of neutrinos with energies exceeding eV. The AMADEUS setup consists of 36 hydrophones grouped in six local clusters measuring about one cubic meter each. This article focuses on acoustic particle detection, the hardware of the AMADEUS detector and techniques used for acoustic signal processing.
Amongst the goals of the AMADEUS detector are the investigation of the background for acoustic detection in the deep sea (rate of neutrino-like signals, localization of background sources, and levels of ambient noise), the study of signal and background correlations on different length scales, the development and test of filter and reconstruction algorithms, the evaluation of different sensors and sensing methods, and the study of hybrid optoacoustical detection methods. These studies will allow to evaluate the feasibility of acoustic particle detection with a future large volume detector. This paper introduces the generation of acoustical signal, describes the AMADEUS detector (Section 3), and discusses the online data handling (Sections 4 and 5). In Section 6 an example for offline analysis is given and first results are presented.
2. Signal Generation
3. The AMADEUS Detector
The acoustic modules consist of a ceramic and an amplifier glued to the inside of a glass sphere, the same spheres as used for the optical modules. This enables the investigation of the possibility to combine optical and acoustical sensing. The storey equipped with acoustic modules consists of three glass spheres mounted on a frame with an opening angle of . Each glass sphere houses two acoustic sensors with an opening angle of thus rendering a coverage in the azimuthal angle, see Figure 3(b).
4. Data Acquisition
The AMADEUS data acquisition is based on the ANTARES DAQ  consisting of an off-shore and an on-shore part. On-shore, digitization and data transfer is handled by the LCMs present on every storey. The ANTARES DAQ is capable of handling several ten Mbits/s for each storey and is thus capable of sending all data to shore in order to be processed there.
Although the AcouADC cards are capable of prefiltering the data stream currently all data is being sent to shore resulting in a data stream of 144 Mbit/s for all 36 acoustic sensors.
5. Trigger Concept
Since the raw data flow of the AMADEUS detector would result in 1.5 TByte of data per day the incoming data stream has to be filtered before storage. The data is sent to the filter processes in timeslices of 104.864 ms length. Currently 12 processes running in parallel filter the data by applying three different trigger algorithms, consisting of a minimum bias, a threshold, and a cross-correlation trigger. Once a trigger has identified a signal, a coincidence check is performed to see if a predefined number of sensors on one storey recorded the same signal. If both criteria are met a time window with raw data including the signal is stored for all triggered sensors. Following a brief overview of the filters used is given.
5.1. Minimum Bias Trigger
In order to study ambient noise in the sea a minimum bias trigger was implemented. This trigger stores the complete input stream of the sensors for about 10 seconds every hour. This results in 4 GB of noise data per day, sufficient to perform noise studies, such as noise weather correlations.
5.2. Threshold Trigger
To investigate transient signals in the vicinity of the detector a simple threshold trigger is applied. In this case an adaptive trigger is required because of varying noise conditions in the deep sea, mainly caused by sea agitation due to weather conditions. A transient signal has to exceed the RMS of the current timeslice ( 104 ms) by a predefined factor. To avoid statistical fluctuations a predefined consecutive number of samples in the timeslice have to exceed the threshold. If all criteria are fulfilled the waveform of the detected signal is stored. Advantages of a threshold trigger are that no knowledge of the signal characteristics is needed and that little computational power is required.
A known source for transient signals are the transducers (also called pingers) of the ANTARES acoustic positioning system . As only the anchors of the 13 lines are fixed to the sea floor the rest of the detector is able to move in the sea current, see Figure 1. Since precise knowledge of the storey positions within the ANTARES detector are needed for event reconstruction, an acoustic system consisting of transducers on the anchor of every line and receivers along the lines is used to triangulate their positions. The signals are high in amplitude, have a sinusoidal shape and narrow bandwidth and thus easily identified in the data stream.
5.3. Cross-Correlation Trigger
where denotes conjugation. A perfect match between the data and an expected signal would result in a -like peak with an amplitude corresponding to the template amplitude. Variations in central frequency result in a broadening of the output signal. Detected signals are analyzed for further characteristics as every bipolar signal similar to the expected signal will create a peak in the cross correlation. A measure for the quality of the signal is the ratio between the width and the height and the integral of the resulting peak. This information is recorded along with the wave form.
The operations introduced can be executed in real time, with the current system.
Although a matched filter is slightly better than the cross correlation function the increase in efficiency would cost a lot of computational power.
5.4. Further Trigger Techniques
Besides the filter methods introduced above a number of other techniques are currently being investigated. Among them are Singular Value Decomposition for noise removal and artificial neural networks for pattern recognition.
6. Offline Data Analysis
The off-line analysis consists of a wide range of activities ranging from noise weather correlation to signal source location reconstruction. Direction reconstruction is one of the off-line signal processing algorithms. It is used for source location reconstruction. To simplify reconstruction the detector was designed with clusters of hydrophones. A cluster of small size, in this case six hydrophones in a volume of about one cubic meter, greatly reduces the time window necessary to be analyzed and simplifies signal selection at high background rates. Following two methods currently employed for direction reconstruction will be discussed, beamforming and the difference method.
This method requires the knowledge of the hydrophone positions, within a cluster, synchronized data, and comparable sensor responses. Beamforming is realized by creating a sound intensity plot scanning all directions in space ( ) for the actual source direction. Given the six hydrophone coordinates , the signal of every hydrophone is shifted in time corresponding to the difference in path length of the sound wave reaching the respective hydrophone from a given direction. Hence every direction in space corresponds to a set of time differences in the data. For a direction , the beamforming output at time is given by
The beamforming output (Figure 8) shows a well-defined maximum at and . The error in is less than one degree, while the error in is about three degrees due to correction of the orientation of the storey. Local maxima and the visible patterns in Figure 8 correspond to directions where the time shift results in the constructive addition of two or more signals.
The advantage of this method is that it can be applied to untriggered data. The disadvantage is that it is time consuming compared to the method described next.
6.2. Time Difference Method
The second method described here uses time differences between signals, detected by the hydrophones in a cluster, for direction reconstruction. It requires, in addition to synchronized data and the knowledge of the hydrophone positions, a signal identification. The signal arrival times are determined once a signal passes the threshold trigger. Direction reconstruction is done by comparing the measured to the expected signal arrival times. The expected time for an arriving wave front from direction is taken from a lookup table and subtracted from the measured time. That is performed for a solid angle in the desired angular resolution, where the minimum indicates the reconstructed direction:
The time difference method is roughly a hundred time faster than the beamforming method introduced earlier.
The AMADEUS system for acoustic particle detection was introduced. The purpose of the detector is to investigate the feasibility of detecting acoustic signals produced in neutrino particle interactions. The particle interaction results in a pressure pulse detectable by acoustic sensors. To assess the feasibility, acoustic sensors were developed in form of hydrophones and acoustic modules both making use of piezo-electric sensors. All data detected by the sensors is filtered by a bandpass filter, digitized and sent to shore. On shore the data is subject to a signal processing passing a minimum bias, a threshold and a cross correlation filter. The triggered signals are classified and their wave forms recorded. Synchronization of the 36 sensors is achieved by a ns clock. Off-line data analysis includes more advanced techniques like beam forming and time delay methods for signal direction reconstruction. Triggering efficiency is currently under investigation. Direction reconstruction for source location is showing promising results. For the search of neutrino-like background signals the detected signals have to be analyzed in respect to their bipolar shape. This is an ongoing analysis.
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