Highlights from the “Waveforms” Workshop

1. Highlights from the “Waveforms” Workshop Spencer Klein, LBNL • Co-organized by SK and Steve Barwick • This morning, 9:00 to 11:00 • Goals: • Discuss methods of waveform compression • How to choose the best one? • Discuss methods of feature extraction • Commonality between IceCube and AMANDA TWR • Look at problems with waveform analysis • ATWD combiner with saturation

2. Agenda • [10] Introduction Spencer/Steve [20] IceCube Waveform compression with a Road-grader algorithm - Dave Nygren / Joshua  Sopher [20] IceCube Waveform compression with Wavelets - Dawn Williams [20] Iterative pe finder  - Dave Seckel [20] IceCube Feature Extraction - Dima Chirkin [15]  TWR Analysis - Dipo Fadiran [10] Conclusions; plans for future work Steve/Spencer

3. Waveform Compression • Single p.e. rate * DOMReadout size > cable bandwidth • Possible Solutions • Deadtime – lose data • Partial Readout • Fewer time bins • Local Coincidence – lose isolated photons • Compress the data

4. Data Compression Algorithms • Road Grader • Zero suppress samples below a programmable threshold • Wavelet Transform • Transform ATWD waveform using wavelets, and save principal coefficients. • Dave Seckel Approach • Fit data to a pulse structure, save fit results

5. Road Grader approach • Select samples above a (programmable) threshold • Apply run-length encoding • String of same value  length, #occurrences • Huffman Lite Encoding • ‘0’ byte  ‘0’ bit; • other byte -> ‘1+byte’ • Conceptually simple • Issues when baseline shifts Dave Nygren/Joshua Sopher

6. Wavelet Transform • Wavelet transform data • Save coefficients above threshold Dawn Williams

7. Iterative p.e. finder • Feature extraction in the DOM • Transmit features • Similar to approach planned for IceTop, where significant compression is required • IceTop will fit to muon pulses • wider than pe’s • Current version uses floating-point arithmetic • Work ongoing to modify for FPGA use Dave Seckel

8. Compression – what now? • Road grader is baseline • Will be used for year 1 • Need a mechanism (scorecard) to compare algorithms • Physics performance • implementation cost, • FPGA area, etc.

9. Feature Extraction • From waveforms to photon arrival times 5 peak waveform from String 21 With fit from FeatureExtractor Dima Chirkin

10. undershooting: 1 mV for 50 mV pulse (Christopher Wendt): +~(exp(-(t-t0)/dt)-1) Waveforms – the good, the bad and the ugly • possible extra late pulse (PMT anode configuration artifact) (Shigeru Yoshida) fit independetly • pedestal drift (corrected for by the fat-reader) Dima Chirkin

11. Multi-peak fit • find first peak using the existing algorithm (refined with the root fit) • construct difference with the fitted function, weight by 1/F(waveform) to emphasize all peaks (big and small) • find maximum and add SPE fit function with t0 close to it Dima Chirkin

12. In-Ice Fits (low PEs) Dima Chirkin

13. Combining ATWD Waveforms • When an ATWD channel saturates, the pulse widens • ATWD0 is wider than ATWD1 • Creates a jagged trailing edge when ATWD waveforms are combined • Artificial feature • Width matching? • Feature Extraction on ATWD by ATWD basis Dima Chirkin/Tom McCauley

14. TWR analysis • Use Ralice/IcePack to compare TWR to muon DAC • Good amplitude correlations • Better for electrical channels Dipo Fadiran

15. Conclusions • Several attractive approaches to waveform compression • Which is optimal? • Feature Extraction does a good job of extracting photon arrival times • Good fits to complex waveforms • The waveforms have several ‘features’ that have yet to be fully appreciated • What is the best way to deal with the elastic scattering peak? • AMANDA TWR and muon DAC data agree quite well