1. Update on Rolling Cascade Search Brennan Hughey UW-Madison 3-26-04

2. Overview of Rolling Cascade Search Rolling search scans entire year (2001) of data for a significant clumping of events (inconsistent with poissonian background) Utilitzes Cascade channel (and high-energy cascade-like muons), so is not directionally dependent and has effective volumes greater than the volume of the detector at high energies Broken power law spectrum for signal Monte Carlo and 15- second bins selected to be consistent with expectations for Gamma Ray Bursts

3. Rolling Search Time

4. Data Reduction Step 1: High Energy Filter (~1% of data remains) Step 2: Cut on ratio of Ndird/Nhits Step 3: Support Vector Machine output cut based on 8 variable input and using SVM light (Replaces PawMLP neural network cut) Goal of data reduction: obtain greatest chance of observing a signal, Signal is defined as a number of events in a bin such that there is a less than 5% chance of that number of events ocurring in 300 days given poissonian background

5. Ndird shows good separation, but there is a significant tail of signal events with a large number of total hits _____ real data (background) _ _ _ _TEA Monte Carlo (cascade signal)............dCorsika (background MC)

6. _____ real data (background) _ _ _ _TEA Monte Carlo (cascade signal)............dCorsika (background MC) Dividing by nhits removes the high energy tail. Initial hard cut taken at.18 to reduce data but stay well away from signal, since MC and data show some significant disagreement

7. _____ real data (background) _ _ _ _TEA Monte Carlo (cascade signal)............dCorsika (background MC) _._._._.E-1 Nusim (high energy muons) Cut Variables in Support Vector Machine

8. Cut Variables in Support Vector Machine _____ real data (background) _ _ _ _TEA Monte Carlo (cascade signal)............dCorsika (background MC) _._._._.E-1 Nusim (high energy muons)

9. Cut Variables in Support Vector Machine _____ real data (background) _ _ _ _TEA Monte Carlo (cascade signal)............dCorsika (background MC) _._._._.E-1 Nusim (high energy muons)

10. Cut Variables in Support Vector Machine _____ real data (background) _ _ _ _TEA Monte Carlo (cascade signal)............dCorsika (background MC) _._._._.E-1 Nusim (high energy muons)

11. Cut Variables in Support Vector Machine _____ real data (background) _ _ _ _TEA Monte Carlo (cascade signal)............dCorsika (background MC) _._._._.E-1 Nusim (high energy muons)

12. Cut Variables in Support Vector Machine _____ real data (background) _ _ _ _TEA Monte Carlo (cascade signal)............dCorsika (background MC) _._._._.E-1 Nusim (high energy muons)

13. Positive values are events the Support Vector Machine identified as signal. Plot shows test data, not original training data. Cost factor (favoring identification as background or signal) and constant in mathematical kernel function can be varied _____ real data (background) _ _ _ _TEA Monte Carlo (cascade signal)............dCorsika (background MC)

14. Assuming Poissonian background, there is less than a 5% chance of seeing 3 events in 15 seconds during 300 days without signal at this background level Assuming Poissonian distribution of signal events, odds of seeing 3 signal events in a 15 second window at this cut level are better than is possible for any situation requiring 4 or more events for a significant detection. Average remaining background events around 11 per day total acceptance rate on the order of ~10 -6 Retention of signal MC is around 55%.

15. _____ real data (background) One can obtain nearly identical results from the Paw neural network by cutting at.98, but this requires cutting into the sharply spiked signal peak, which means considerable systematic uncertainty _ _ _ _TEA Monte Carlo (cascade signal)............dCorsika (background MC)

16. Cascade Effective Volume 10 Energy (GeV) Effective Volume (km 3 ) physical volume of detector

17. Neutrino Effective Area Pre-filter events: circles: Cascade channel squares: Muon channel Energy (GeV) Effective Area (cm 2 ) 10 Muons blocked by Earth increasingly at higher energies (Earth shadowing effect) Also, range of muon does not increase linearly due to Bremstrahlung Cascade range does continue to increase – change in slope can be attributed to interaction cross sections

18. 2 event coincidence for signal detection? Possibility #1: further background rejection - requires approximately 1 event every 5 days - too much training data needed - reduces signal retention Possibility #2: shortening the time window of search - on the order of 15 ms at current cut level - difficult to demonstrate that data is poissonian at this level - deadtime becomes a significant factor......

19. Deadtime Nch>160 (high energy events) All Nch Number of events vs.  t (in milliseconds) between event and next event for all events and events with Nchannel > 160 time (milliseconds)