A Matched Filter for Cosmic Ray Detection from Eletromagnetic Wave Reflection

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. 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. Cosmic shower detection by radio-wave reflection • Particule shower generation. • Radio wave reflection. • Transmitter antenna. • Receiver antenna. • Receptor station. • 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. receptor transmitter

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

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

6. Signal detection in low signal-to-noise ratio environment Hypothese test: H0 - only noise, H1 - noise + signal. The decision is given by the likelihood ratio If the noise is gaussian, with zero mean and decorrelated This is the matched filter equation. MF l decision s The detected cosmic signal is a stochastic process. S will be aproximates by the mean of the several pre-selected signals.

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

9. Whitening W Sw S • Remove the noise mean. • Projection in a decorrelated base. • Normalize each component (σ2 = 1) W MF decision l Sw Covariance of the whitened noise - Test Covariance of the whitened noise - Train

10. ResultsThreshold x Matched Filter Test Train MF Threshold MF Threshold

11. ResultsThreshold x Matched Filter Receiver Operating Characteristic - Train Receiver Operating Characteristic - Test

12. Whitening

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

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

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

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.