1. ARENA08 Roma 25-27 June 2008 Francesco Simeone (francesco.simeone@roma1.infn.it) (Francesco Simeone INFN Roma) Beam-forming and matched filter techniques for the underwater acoustic detection of UHE neutrino

2. ARENA08 Roma 25-27 June 2008 Francesco Simeone (francesco.simeone@roma1.infn.it) High energy proton (50 Mpc) Neutrinos Astrophysical source Low energy proton (deflected) High energy photon (10 Mpc) Why using neutrinos?

3. ARENA08 Roma 25-27 June 2008 Francesco Simeone (francesco.simeone@roma1.infn.it) Propagation : sound velocity and density (T,S) Production: thermodynamic parameters (T,S) Input : Energy density (UHE Particle showers) Output : Pressure (signal) E = 5 ·10 20 eV R =1 km Vandenbroucke et al.,2004 The hydro-acoustic technique is under study to detect >PeV neutrinos. The technique is based on the detection of acoustic waves produced by energy dissipation, in water, following a UHE neutrino induced shower. Due to long absorption length of sound in water (1-10 km) large volumes can be observed using a small number of detectors deployed in a huge volume of water (>10 km 3 ). Neutrinos interaction and signal generation

4. ARENA08 Roma 25-27 June 2008 Francesco Simeone (francesco.simeone@roma1.infn.it) The knowledge of ambient noise at large depth as a function of time is poorly known. One of the best resources for this kind of information is the data set acquired by ONDE. (NEMO-ONDE : a submarine station for real-time monitoring of acoustic background installed at 2000 m depth in the Mediterranean Sea ) The maximum of PSD of the expected signal is expected in the 10-30 kHz band. In this range most of the diffuse noise is due to surface agitation (sea state), loud impuslive signals are expected by biological sources Ambient noise

5. ARENA08 Roma 25-27 June 2008 Francesco Simeone (francesco.simeone@roma1.infn.it) Average SPD measured at 2000 m depth with OnDE compared with bibliographic data (shallow depth). The monthly variability is of the order of 10 uPa 2 /Hz. If we integrate the PSD in frequency region of interest we obtain an average noise level of tens of mPa. Noise spectrum measured by ONDE

6. ARENA08 Roma 25-27 June 2008 Francesco Simeone (francesco.simeone@roma1.infn.it) Analysis strategy The expected neutrino signal, even at UHE, is comparable with the ambient noise. It’s necessary to detect signals even smaller. This can be obtained combining two strategies: Analize data from a single hydrophone to obtain the maximun signal to noise ratio.(Matched filter) Analize data from an array of hydrophones to reject ambient noise.(Beamforming)

7. ARENA08 Roma 25-27 June 2008 Francesco Simeone (francesco.simeone@roma1.infn.it) The matched filter is the optimal linear filter to maximize the signal to noise ratio (SNR) in presence of additive stochastic noise. Its transfer function is the complex coniugate of the spectrum of the signal to which it is matched. A typical matched filter application is shown: Matched filter S(t)X(t) N(t) Y(t) In this sample the SNR is about -3.5dB. Matched filter part I

8. ARENA08 Roma 25-27 June 2008 Francesco Simeone (francesco.simeone@roma1.infn.it) Matched filter part II In case of not-white noise the common approach is to weight the matched filter with the noise spectral density. One can convince himself of this intuitively in the following manner: Input signal(with spectrum S(i2πf)) plus non white noise of known power density spectrum |N(i2πf)| 2 Filter with transfer function 1/ N(i*2πf) The noise component at this stage will be white; however, the signal componet will be distorted with spectrum S(i2πf) / N(i2πf) Matched filter for non white(but known) noise This filter must match the distorted signal so its transfer function will be the original(white one) divided by |N(i2πf)| 2 If the noise spectrum is known there is no loss of performance.

9. ARENA08 Roma 25-27 June 2008 Francesco Simeone (francesco.simeone@roma1.infn.it) Matched filter part III Performance results for the applied matched filter in presence of white noise. Time window simulated 1ms Number of windows 1000. Signal sampled at 1MS/s

10. ARENA08 Roma 25-27 June 2008 Francesco Simeone (francesco.simeone@roma1.infn.it) The beamforming is a technique used to analize an array of sensors to provide a versatile form of spatial filtering. Beamforming In its simplest form the beamforming is a multi input single output algorithm implemented as “delay and sum”. Using the correct delays, signals from different sensors are summed coherently.

11. ARENA08 Roma 25-27 June 2008 Francesco Simeone (francesco.simeone@roma1.infn.it) Hydrophone calibration Match Filter V Pa Hydrophone calibration Match Filter Threshold Beamforming The threshold is function of the noise level. This means that this algorithm must be adaptive. Analysis set-up The delay are calculated using wave plane approximation. Time of arrival Direction of arrival …

12. ARENA08 Roma 25-27 June 2008 Francesco Simeone (francesco.simeone@roma1.infn.it) Test geometry

13. ARENA08 Roma 25-27 June 2008 Francesco Simeone (francesco.simeone@roma1.infn.it) Beamforming plus matched filter performance SNR=0dBSNR=-3.5dBSNR=-6dBSNR=-9.5dBSNR=-12dB The beamforming can, in principle, increase the SNR by a factor equal to the square root of the hydrophones number.

14. ARENA08 Roma 25-27 June 2008 Francesco Simeone (francesco.simeone@roma1.infn.it) How to use the beamforming? The beamforming can be used with any number of hydrophones at any distant. The only requirements is the ability to correlate the signals of different hydrophones. If we try to correlate “near” hydrophones we need to assume one direction of arrival of a plane wave (two free parameters). If we want to correlate “distant” hydrophones we need a propagation model for underwater sound. In the approximation of constant sound gradient we need to assume the shower position and its orientation (5 free parameters).

15. ARENA08 Roma 25-27 June 2008 Francesco Simeone (francesco.simeone@roma1.infn.it) Computational power This set-up is very expensive in terms of computational power: Each matched filter requires, for each samples, a number of additions(add) and multiplications(mul) equal to the signal length (in samples). For instance applying the matched filter to an hydrophone sampled at 200kHz requires 2Madd/s and 2Mmul/s. To build the beamforming output, for each samples, a number of add equals to the number of hydrophones minus one are required. For instance if the number of hydrophones is 4 and the sampling rate is 200kHz we require 600kadd/s for each set of delays.

16. ARENA08 Roma 25-27 June 2008 Francesco Simeone (francesco.simeone@roma1.infn.it) Conclusion The matched filter give the best results that can be obtained by a linear filter on a single channel. The beam-forming is the only way to further increase the SNR. It can be used on near hydophone to recover the direction of incident sound waves. It needs an underwater sound propagation model if we want to apply it on distant hydrophones. It needs a lots of computational power.