IceTop DOM Feature Extraction

1. IceTop DOM Feature Extraction Paul Evenson David Seckel University of Delaware

2. IceTop Science Goals • InIce Veto • Retain all hits for readout • InIce Calibration • Trigger for vertical and horizontal showers • Cosmic ray composition • Shower reconstruction over large dynamic range: 300 TeV –1 EeV

3. IceTop Raw Data • Single Muons 1200/second • Horizontal showers + background • Soft Component (>30 MeV) 1000/second • Vertical showers • Waveforms 100/second • Showers: dcore > 300 m, muon id within core • Calibration + monitoring

4. IceTop Data Return Strategy • Single Muons • Characterize, identify as muon, return short report • Soft Component • Characterize, identify as soft, check for local coincidence, return short report • Waveform • Feature recognition fails • Scaled selection of minimum bias and event triggers • Compress and return complete waveform

5. Constraints 100 kilobyte/sec uplink rate 5000 raw events/second 200 microseconds per event 6600 cpu clock cycles Strategy Use Altera code to characterize event and compress waveforms IceTop Feature Recognition

6. Altera FPGA • Good at addition, subtraction, multiplication • Bad at long division • Therefore: Work toward feature recognition and characterization based on fitting with orthogonal functions • One pass does everything • Expansions always truncate

7. Eight Function Orthogonal Fitter(Thanks to Andrew McDermott, Holger Leich and Gerald Przybylski)

8. Eight Function Fitter Operation • For ease of simulation, the input waveform in the example is just encoded in a ROM • Outputs are likewise just put in a FIFO • Fits eight orthogonal functions • Uses about 1/3 of the “real estate” of the current chip in the DOM • Runs in about 80 microseconds at 25 MHz clock rate

9. Eight Function Fitter Operation • First Pass (128 clock cycles): • Clock waveform into (recirculating) Waveform FIFO • Dot product waveform with first fit function to get coefficient 1 • Enter first fit function into Fit FIFO • Multiply waveform by (negative) normalization constant and enter into the Quality FIFO • Passes 2 to 8 (fit function “N”) • Dot product of waveform with fit function “N” to get coefficient N • Multiply fit function N-1 (in Fit FIFO) by coefficient N-1 and add to the contents of the Quality FIFO • Enter fit function N into Fit FIFO • Pass 9 • Sum absolute values of Quality FIFO (This could be done in Pass 8, but this seems to be overall much faster.

10. Which Orthogonal Functions?Chebyshevs are sort of OK Chebyshevs Discrete functions: 128 samples Reconstruction Powers of t

11. Basis Function Based on Data(Hey, it works!) Trial 1 Use mean m for u1 Reconstruction Mean m + powers of t

12. Tailored Functions: PMT model • Three parameter PMT pulse: g, t0, t0 • model: • expand: • data: • orthoganalize:

13. Data + PMT Model Data based trial set Reconstruction m0 + dm/da

14. Trim noise + Offset parameter Best trial set Reconstruction

15. Summary • IceTop goals require efficient use of bandwidth between DOM and Hub • FPGA development effort at UD • Feature extraction • orthogonal functions • use data + model to make orthogonal basis • Estimated bandwidth 10 B per hit • Need to validate on actual DOM

16. STOP This slide intentionally left blank.

17. RMS pnt by pnt residual This slide was not shown at Berkeley. It illustrates the average improvement in fitting over the whole data set of 81 events. RMS noise is at about 5 mV Black –Chebyshev Blue - Data 1+ powers Red - Best Best trial (expanded view)