1. 1 A Matched Filter for Cosmic Ray Detection from Eletromagnetic Wave Reflection Luciano Andrade Thiago Ciodaro José Seixas Federal University of Rio de Janeiro/COPPE

2. 2 Outline Cosmic shower detection by radio-wave reflection. The detector setup. Signal detection in low signal-to-noise ratio environments → The Matched-Filter (MF). – Whitening – Detection efficiency Free-running. Conclusions.

3. 3 Cosmic shower detection by radio-wave reflection 1.Particule shower generation. 2.Radio wave reflection. 3.Transmitter antenna. 4.Receiver antenna. 5.Receptor station. 6.Scintilators.  Well known approach for metheor detection.  Very High Frequency (VHF) waves – 30 to 300 MHz.  Commercial DTV - channel 2 (55.25 MHz) and 4 (67.25 MHz).  Scintilators - high efficiency, but small area. Only for test. transmitterreceptor

4. 4 The Detector Setup antenna GPS – to synchronize several stations sound board (80 kHz) radio receivers NIM crate for the scintilators hard disc (high capacity)

5. 5 Raw data and typical signal shape 3 seconds recorded data (only noise) Typical cosmic signal Volts # sample Volts MARIACHI in Brookhaven. DRACON in Rio. Only one antenna. No coincidence with scintilator. Data selected by hand. To test the matched filter method. Automatic detection – event filter.

6. 6 Signal detection in low signal-to-noise ratio environment Hypothese test: H 0 - only noise, H 1 - noise + signal. If the noise is gaussian, with zero mean and decorrelated The detected cosmic signal is a stochastic process. S will be aproximates by the mean of the several pre-selected signals. The decision is given by the likelihood ratio s l MF decision This is the matched filter equation.

7. 7 Data Set  Tipical signal length – 36 samples.  480 signals selected.  3600 noise segments (36 samples).  50 % train set, 50% test set.  S = mean signal.

8. 8 Noise characterization Gaussian fit Chi-square = 1.475 Mean = 0.032 mVolts Noise distribution Noise covariance matrix Samples are correlated. It will be considered white for the first tests. Whitening pre-processing should be done.

9. 9 Whitening WMF l Sw W S Covariance of the whitened noise - Train Covariance of the whitened noise - Test Remove the noise mean. Projection in a decorrelated base. Normalize each component (σ 2 = 1) decision

10. 10 Results Threshold x Matched Filter Train MFThreshold Test MFThreshold

11. 11 Results Threshold x Matched Filter Receiver Operating Characteristic - Train Receiver Operating Characteristic - Test

12. 12 Whitening

13. 13 Free-Running algorithm MF > threshold step output memory = 0 step memory = output MF > memory output memory = output back Find index with max correlation memory = 0 index yes no yes no Input – raw data. Output – index of signal candidates. Need two parameters: step and threshold. raw data scan in step samples. until output > threshold. Looking for the best index for the signal

14. 14 Free-Running Find optimal parameters Noise and signal concatenated in a known sequency. If index output belongs to any signal sample – signal found.

15. 15 Free Running results (step = 6, threshold = 0.5) Receiver Operating Characteristic - Train Receiver Operating Characteristic - Test

16. 16 Conclusion and To-Do list New approach in cosmic shower detection. Low cust environment. Free-running matched filter → stored data reduction. Next steps – Whitening pre-processing + free-running. – Implement stochastic signal detection. – Coincidence with scintilator.