1. Point Sources Jacob Feintzeig WIPAC − May 21, 2014

2. A few slides on neutrino astronomy/origin of cosmic rays/examples of potential sources (including galactic center) – these slides can be copied/repeated from an introductory talk they get earlier?

3. A simulated skymap Explain: -What a skymap is -Coordinate system
-Event locations
-Plusses (+) are cascades, crosses (x) are tracks
-This is a simulated “random” dataset, ie. no sources

4. A simulated skymap with a point source
Explain:
-Injected 7 events from a point source
Ask:
-Where is the point source?
-How do you know?
-Why are the 7 events not all coming from the exact same place?
-Concept of angular resolution

5. A simulated skymap with a point source
Ask:
-What is our signal? (events clustering around one specific point)
-What is our background? (events distributed evenly across the sky, either atmospheric muons, atmospheric neutrinos, or astrophysical neutrinos not from source)
-How can we quantitatively figure out if there is a point source in our data?
(instructors lead discussion on counting # of events in a bin)
Main points to get across:
-A simple analysis is to count events in an on-source/signal bin
-Background expectation can be estimated from skymaps with data events scrambled in right ascension
-To estimate the p-value, we will count the # of events in the signal bin in real data, repeat this for many scrambled skymaps, and determine the proportion of random skymaps with equal or greater # of events in the signal bin compared to the data.
-The size of the bin should be about the size of the angular resolution (we will use 15 degrees)

6. A simulated skymap with the signal region
Demonstrate:
-Counting # of events in signal bin
(Red region is a 15 degree circle around the galactic center – our on-source/signal region)
Instructor can also illustrate the analysis more by using this map to start making a sample histogram on the board of the TS for the scrambled distribution, and demonstrate how to calculate a p-value if the data had 3 or 4 events in the region, etc.

7. Break up into groups, hand out scrambled maps

8. Histogram of Analysis for Scrambled Trials
This is a template for the histogram that the class will create. I will either draw this on the board, or this will be projected onto the whiteboard and the students can draw on top of it.
Each group will come up and mark tallies in each bin for how the scrambled maps they analyzed
Instructor will then use tallies to draw a standard histogram on the axes

9. And here comes the real data…

10. And here comes the real data…
Have each student at their desk count the # of signal events (2) and calculate the p-value from the histogram on the board. Then, instructor does the same for everyone to see.
Discussion:
-What does the p-value mean? Is this significant? (Can include concept of a “sigma”, description of 3,5 sigma, instructor draws a gaussian on the board…)
-Can we say there is a neutrino point source at the galactic center?
-Do you see any other point sources in this map?
Discussion: What are the problems with this analysis?
-There are some events near the signal region but not in it? Should these be included (flip to next slide, plot with angular uncertainties)
-What if you moved the signal region to the left? You could get 5 events! How come we didn’t do that?
-What if there was a track nearby? If you have 1 degree angular resolution, you don’t need a 15 degree signal region
-What if there were many events clustering around other spots in the sky?
Discussion: What would a better analysis include? (Classroom-wide brainstorming session, can write these on the board)
-Knowledge of the shape/size of the angular uncertainties for each event
-Some sort of adjustable binning/ un-binned method to avoid events falling in/out of bins
-Some sort of scan across the sky to look for point sources everywhere

11. And here comes the real data…
Same map, with angular uncertainties, to aid discussion

12. Point Source Analysis in the Science Paper
Explain:
-Color scale denotes how much “clustering” there is, how in-compatible the observed events are compared to the background expectation
-Darker colors mean more clustering, more likely to be a point source
-Analysis knows about size/shape of angular resolution for each event – muons have very narrow hotspots, galactic center cluster is much wider
-Analysis has no bins – near galactic center, all 5 events are contributing, according to how close/far away they are and how big their angular uncertainties are
-Analysis looks over the entire sky
-P-value calculated in same way as we did: analysis repeated on scrambled datasets
-P-value for galactic center alone: 5%
Should we go into more details with the math? I don’t think its necessary. Maybe instructors, together with the class, can read through the point source section of the Science paper and decode/explain what it says. This could be a good 10 min activity…
Discussion:
-How can we figure out if this is a point source or not? Wait for more data, look for correlations with data from other experiments…

13. Point Source Results with 3 Years of Data
Discuss locations of new events. Do we think there’s a point source at the galactic center? Do we think there’s a source anywhere else?

14. Point Source Results with 3 Years of Data
Discuss locations of new events. Do we think there’s a point source at the galactic center? Do we think there’s a source anywhere else?
This map shows LLH with location of source list overplotted. Source list contains 76 astrophysical objects that other telescopes observe and could be candidates for high-energy neutrino emission