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ANITA-I Analysis Progress

ANITA-I Analysis Progress. Stephen Hoover ANITA Collaboration Phone Meeting 27 April 2009. Outline. I. Thermal Noise Rejection II. 10% Data: Event Projection on Antarctica III. Anthropogenic Background Estimation IV. 100% Data (singlet & doublet blind). Introduction: Finding Neutrinos.

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ANITA-I Analysis Progress

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  1. ANITA-I Analysis Progress Stephen Hoover ANITA Collaboration Phone Meeting 27 April 2009

  2. Outline I. Thermal Noise Rejection II. 10% Data: Event Projection on Antarctica III. Anthropogenic Background Estimation IV. 100% Data (singlet & doublet blind)

  3. Introduction: Finding Neutrinos • Finding neutrinos is (at least) a 2-step process • All data  Real signals (remove thermal noise) • Real signals  Neutrino Askaryan impulses (remove anthropogenic background) • ANITA-I has ~8.2 M events – most are thermal noise! • Step 1: Get rid of thermal noise

  4. Section I Analysis Method: Interferometry • Cross-correlate waveforms in nearby antennas (then normalize the cc), and map values of the cross-correlation at each time delay to corresponding spots on the sky. • Sum cross-correlations for each pair and normalize by the total number of pairs summed. • Find the peak cross-correlation in this image

  5. Analysis Method: Coherently Summed Waveform • Once we’ve found the direction of peak cross-correlation on the sky, sum the waveforms in 10 nearest antennas, shifting each waveform by a time delay so that the signal adds coherently • Use this waveform as well as the corresponding Fourier transform and analytic signal.

  6. 0.25 Example: SignalEv# 2156850h, from unknown base on coast 0.20 0.15 0.10 Cross-correlation peak: 0.355 (Value from more finely binned image) 0.05 0 -0.05

  7. Example: SignalEv# 2156850h, Coherently Summed waveform in peak x-corr direction Waveform filtered from 220 MHz – 290 MHz Note that the analytic waveform is normalized by the average value in the start of the trace

  8. Example: NoiseEv# 2156849h Cross-correlation peak: 0.065

  9. Example: NoiseEv# 2156849h, Coherently Summed waveform in peak x-corr direction

  10. Interlude: Cuts • I filtered all events from 220 MHz – 290 MHz, then applied an adaptive filter to remove CW (narrow-band) noise. • In the next slides, I have made the following cuts on the data (in order, numbers of events refer to 10% data): • Event Quality cuts • Non RF trigger (13,149 events) • Marked sync slip (703 events) • Onboard calibration pulser (by timing, 331 events) • “Payload blast” (Large signal on all sides, 727 events) • Saturated channel(s) (37 events) • On-payload noise (By comparing vetos to Seaveys, 2815 events) • Bad waveform on read-in (79 events) • No / bad ADU5 heading information (18,521 events) • RFCMs turned off (227 events) • Ground pulser cut (1140 LDB events, 158 TD events) • Check both polarizations, keep the one with the highest “rotated cross-correlation peak” (defined later) • 744,383 events remaining for plots

  11. 10% Data: Peak cross-correlation and peak analytic signal

  12. 10% Data: Has Signal! This sample includes off-payload signals – see excess of reconstructed directions toward the hardware trigger I wish to characterize the thermal background, so for the next slide I will remove the signal by: Looking at quiet runs only (event numbers 1038300 - 2446500 or 3779600 - 6520600) Requiring that the reconstructed direction points to the ground, but not to a known base.

  13. Noise-only sample 190,188 events remaining. Now, reconstructed direction isn’t related to the hardware trigger direction 103 Note: Plot has log scale with suppressed zero. Features are artifacts of my reconstruction algorithm.

  14. Quiet data, no bases:

  15. Thermal noise-only sample:Combine cross-correlation and analytic signal Signal! Thermal noise

  16. Create Fisher Discriminant“Rotated cross-correlation peak” Rotate by -0.68 degrees: x-corr’ = cos( -0.68 )*x-corr – sin( -0.68 )*Analytic signal Thermal Noise All 10% data

  17. Rotated Cross-Correlation (x-corr’) Make an exponential fit to the trailing edge. For this distribution, I get ~ 0.089 thermal noise events with x-corr’ > 0.18 per 100,000 events ~ 0.001 thermal noise events with x-corr’ > 0.192 per 100,000 events

  18. Section II: Project Events on Antarctica! • To pick out signal events: use signal quality cuts from slide 10, plus • Rotated cross-correlation > 0.18 • Removes 1,463,052 “events” (count v-pol and h-pol as separate events) • Require that the event doesn’t reconstruct to the same side of the payload as a strong CW line • Removes 9,100 “events” • Mostly South Pole events – even with filtering, very loud CW can cause misreconstructions • Must reconstruct to the ground • Removes 866 “events”

  19. Project Events on Antarctica! 7480 events Nordenskiold Base Halley Unknown (1 event) Belgrano (1 event) AWS AGO 3 Travelers Patriot Hills South Pole Mt. Vinson Unknown Unknown x 2 WAIS Siple Dome ITASE (1 event) Unknown McMurdo

  20. 10% Data: List of Clusters 7480 events total • 6 Large clusters • 3063 events – WAIS • 3015 events – No known base • 577 events – South Pole • 527 events – McMurdo • 150 events - Nordenskiold Base (Wasa + Aboa) • 78 events – Mt. Vinson • 13 Small clusters • 16 events – Siple Dome • 14 events – Halley • 10 events – No known base • 8 events – Patriot Hills • 6 events – AWS AGO 3 • 4 events – Travelers to South Pole • 3 events – Travelers to South Pole • 2 events – Travelers to South Pole • 2 events – No known base • 2 events – No known base • 1 event – ITASE • 1 event - Belgrano • 1 event – No known base

  21. Section III: Anthropogenic Backgrounds • Most (all?) of the events on the previous slide are not physical science • We can remove events pointing to bases, or clustered together – but what about single events from unknown bases? • 4 small event groupings from locations we thought were empty! • 2 single events from known bases!

  22. What Do We Know? • Handles on events: • Grouped with other events? • Near known human activity? • Signal quality (impulsiveness)

  23. Known vs. Unknown Bases: ABCD (Warning: Don’t multiply by 10 to get 100% data values!) • Signal bin is: singlet & not known base • ABCD method: expected background = 2 * 3 / 7 = 0.86 event

  24. Signal Quality • Some base signals look impulsive, some ratty • To get a measure of impulsiveness, cross-correlate the coherently summed waveform with ANITA system impulse response from ELOG #98 • Look at two figures of merit: • Maximum absolute value cross-correlation • Impulsive signal: High • Ratty signal: Medium-High • Noise: Low • Ratio of RMS cross-correlation in 30 ns before peak to RMS cross-correlation in 30 ns after peak • Impulsive signal, noise: Near 1 • Ratty signal: Away from 1

  25. Signal Quality: Plots 10% Signal events (pass thermal noise cut) RF-triggered LDB borehole

  26. Signal Quality: ABCD • A “good” event has • maximum cross-correlation with the system impulse response > 0.40 • Ratio pre-peak x-corr RMS / post-peak x-corr RMS between 0.5 and 1.6 • Black: # of clusters, Red: # of events • Signal bin is: singlet & good signal • ABCD method: By Clusters: expected background = 2 * 3 / 7 = 0.86 event By event: expected background = 2 *25 / 42 = 1.2 events

  27. Section IV: 100% Data • Still blind! Our signal bin is events isolated from other events and from bases • Look only at clusters and events from bases! • This work: doublets also blind, just to be sure • Use the same cuts as for the 10% data

  28. BLIND: No non-base singlets or doublets plotted Section IV: 100% Data 79,128 events Near Belgrano (1 event?) N2I Traverse (1 event) Sky Blu Travelers (1 event) AWS AGO 1 AWS Lettau (1 event) AWS Swithinbank (1 event) Unknown

  29. 100% Data: List of Clusters(Blind: No non-base singlets or doublets) 79,128 events total • 10 Large clusters • 31,656 events – WAIS • 30,509 events – No known base • 6,009 events – McMurdo • 5,749 events – South Pole • 3,547 events - Nordenskiold Base (Wasa + Aboa) • 828 events – Mt. Vinson • 161 events – Halley • 139 events – AWS AGO 3 • 138 events – Siple Dome • 120 events – Patriot Hills • 15 Small clusters • 64 events (was 2) – No known base • 64 events (was 10) – No known base • 36 events (was 3) – Travelers to South Pole • 25 events – AWS AGO 1 • 23 events (was 2) – Travelers to South Pole • 23 events (was 1) - Belgrano • 15 events (was 2) – No known base • 8 events – Sky Blu • 6 events (was 1) – ITASE • 3 events – No known base • 1 event – Travelers to South Pole • 1 event – From near Belgrano • 1 event – AWS Swithinbank • 1 event – AWS Lettau • 1 event – N2I traverse

  30. 100% Data (Blind)Known vs. Unknown Bases: ABCD • Signal bin is: singlet & not known base • ABCD method: expected background = 5 * 3 / 7 = 2.1 events expected doublets = (1) * 3 / 7 = 0.4 events

  31. Signal Quality: ABCD • Define “good” events the same way as in 10% data • Remember, no non-base singles or doubles on this table! • Black: # of clusters, Red: # of events • Signal bin is: singlet & good signal • ABCD method: By Clusters: expected background = 3 * 4 / 6 = 2 events By event: expected background = 3 *149 / 118 = 3.8 events

  32. Conclusion • ANITA has a (small, non-zero) anthropogenic background • My analysis is nearing unblinding • Next steps • Unblind doublet|not-base bin • Separate out ABCD cuts by polarization • Finalize thermal noise cut • Characterize analysis efficiency • … • Unblinding!

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