1. Cosmic Ray Composition from the 40-string IceCube/IceTop Detectors For the IceCube Collaboration: Katherine Rawlins University of Alaska Anchorage Karen Andeen University of Madison – Wisconsin* Tom Feusels University of Gent * (graduated)

2. Proton-iron separation Signals at the surface Signals at depth Plot by: R. Engel 8/13/20112ICRC 2011: Beijing, China

3. The IceCube Observatory Signals at depth from IceCube Signals at the surface from IceTop 40-string array 125-meter spacing 8/13/20113ICRC 2011: Beijing, China

4. How the IceTop reconstruction works Find a first-guess shower core using COG Find a first-guess shower direction using a plane wave Likelihood fit: signals (S) expected to follow a lateral distribution function (LDF) with respect to distance from the core: Evaluated at reference distance Rref = 125 meters is called “S125” 8/13/20114ICRC 2011: Beijing, China

5. How the In-ice reconstruction works Use the IceTop reconstruction as first guess track Likelihood fit: signals N PE expected to follow a lateral distribution function (LDF) with respect to distance from the distance from center of the bundle Evaluated at reference distance d = 70 meters is called “K70” This term takes into account range-out of muons (as a function of slant depth X) The effective attenuation length of ice is corrected for dust (as a function of depth z) 8/13/20115ICRC 2011: Beijing, China Free parameters: A and 0

6. Composition separation in K-S space A nonlinear mapping between the two observables (K70 and S125) and (mass and energy). 8/13/20116ICRC 2011: Beijing, China Blue: pure iron Red: pure protons Purple: sample contains both Low energy High energy

7. Analyzing: the neural network Two inputs S125, K70 Two outputs log(E), ln(A) 5 x 2 hidden nodes “Weights” connect the nodes to each other, forming a nonlinear function translating (or mapping) inputs into outputs. The weights are set by “training” on simulations where both inputs and outputs are known. Trained using five nuclear species: H, He, O, Si, Fe 8/13/20117ICRC 2011: Beijing, China

8. To measure composition Within each energy bin, each nuclear species has a “template histogram” of NN mass output 8/13/20118ICRC 2011: Beijing, China Take a slice in energy:

9. Using template histograms Try mixture: 70% p 30 % O+He 0 % Fe Not a good fit to the data histogram 8/13/20119ICRC 2011: Beijing, China

10. Using template histograms Try mixture: 50% p 20 % O+He 30 % Fe Better fit to the data histogram 8/13/201110ICRC 2011: Beijing, China

11. Using template histograms Ask Minuit to find best-fit mixture: 20 % p 19 % O+He 61 % Fe Best fit to the data histogram! 8/13/201111ICRC 2011: Beijing, China

12. Systematic shifts in IceTop signal S (S125 left or right) or in In-ice signal N PE (K70 up or down) can make cosmic rays look heavier or lighter Estimating systematic uncertainties log(K70) log(S125) Black: some model Yellow: a different model protons iron Find all of these differences (bin by bin), for p and for Fe. 128/13/2011ICRC 2011: Beijing, China

13. Summary of magnitudes of various sources of systematic uncertainty 13 This curve used to shift the K70’s of data events, to see how final results might turn out differently 8/13/2011ICRC 2011: Beijing, China

14. Result: Energy Spectrum 8/13/201114ICRC 2011: Beijing, China PRELIMINARY

15. Result: Composition 8/13/201115ICRC 2011: Beijing, China PRELIMINARY

16. Want to know more? Icetop overview: Talk #807 (Aug. 12 evening) Reconstructing energy loss: Talk #85 (Aug 13 afternoon) and Poster #345 Air shower parameters: Poster #336 VEM calibration and snow: Poster #899 Seasonal variations: Poster #662 Studying 79-station array (73 IceTop stations): Poster #838 PhD thesis of Karen Andeen: http://www.icecube.wisc.edu/~kandeen/work/dox/Thesis/an deen_thesis_final.pdf 8/13/201116ICRC 2011: Beijing, China

17. BACKUP SLIDES 8/13/201117ICRC 2011: Beijing, China

18. Compare to only other mu/e experiments 8/13/201118ICRC 2011: Beijing, China PRELIMINARY

19. A test: when you feed the analysis a known mixture, do you get the same mixture back out? (Answer: yes) 8/13/201119ICRC 2011: Beijing, China

20. Response of MC to the neural network Energy output response: a composition-independent measurement of E Mass output response: one particular (true) energy bin …Use reconstructed energy (from data!) to make an energy spectrum… 8/13/201120ICRC 2011: Beijing, China

21. Baseline: SIBYLL Alternate: EPOS 21 Log10(S125) 8/13/2011ICRC 2011: Beijing, China

22. 22 Baseline: SIBYLL Alternate: QGSJET Log10(S125) 8/13/2011ICRC 2011: Beijing, China

23. Baseline: no snow simulated Alternate: snow simulated, and then removed in reconstruction 23 Log10(S125) 8/13/201123ICRC 2011: Beijing, China

24. AHA vs. SPICE ice Log10(Etrue) Log10(S125) Based on in-ice MC only! Must convert the curve from a function of energy to a function of S125… (a cheap solution) 24 Baseline: simulated with AHA, and reconstructed with AHA Alternatives explored: Spice simulations reconstructed with AHA, AHA simulations reconstructed with Spice 8/13/2011ICRC 2011: Beijing, China

25. Snow buildup on the tanks? Data falls differently in K-S space from the “old half” (with lots of snow) and “new half” (with little snow) of the detector Accounted for in reconstruction, but since two “halves” of the detector have different energy thresholds, safest to confine analysis to the “new half” (X < 200 m) 8/13/201125ICRC 2011: Beijing, China

26. Reconstructing snow The idea: snow attenuates electrons. Let’s assume it attenuates exponentially through the slant depth of snow between the surface and the tank, which is: exp(-snowdepth/cos(zenith) / d0) Incorporate this into S_expected at every tank “d0” is a characteristic attenuation length in snow… what is it? A “zeroth order” reconstruction: assume this exp() affects the whole signal, and find the best d0 from data.* *Not true, but assume for now…

27. Does it work? From IT-73 data: The S125’s go up more in the “old array” where there is more snow

28. The best d0? Ask toprec to fit this as a free parameter, and you get this:

29. Changing atmosphere? Data falls differently in K-S space at different times of year Difficult to correct for! Solution: confine analysis to one month of data (August), which should match simulated Atm. 12 8/13/201129ICRC 2011: Beijing, China

30. Estimating systematic uncertainties CHANGE in log(K70) log(S125) Red: proton differences Blue: iron differences Green points : average of blue and red Green line: fit of some polynomial to green points 308/13/2011ICRC 2011: Beijing, China

31. Livetime: data from August 2008 8/13/201131ICRC 2011: Beijing, China

32. Choosing the best “K” distance 8/13/201132ICRC 2011: Beijing, China

33. Choosing the best “K” distance 8/13/201133ICRC 2011: Beijing, China

34. Choosing the best “S” distance 8/13/201134ICRC 2011: Beijing, China

35. Choosing the best “S” distance 8/13/201135ICRC 2011: Beijing, China

36. Choosing the best “K” 8/13/201136ICRC 2011: Beijing, China

37. Man vs. Machine Mass: Energy: Human: Neural Network: 8/13/201137ICRC 2011: Beijing, China

38. Man vs. Machine Mass: Energy: Human: Neural Network: 8/13/201138ICRC 2011: Beijing, China

39. Training the Neural Network 8/13/201139ICRC 2011: Beijing, China

40. Detector efficiency and aperture All events generated Surviving events after cuts  = their ratio  A = aperture Fitted to a curve 8/13/201140ICRC 2011: Beijing, China

41. To make an energy spectrum Detector efficiency as a function of energy (measured using surviving events divided by events generated) Solid angle of generated events = about 2.5 radians Area (on the surface) of generated events = 1200 meters at all energies Livetime (about 30 days for August 2008 data) Observed distribution of energies in data (as reconstructed by the neural network) “Aperture” (steradian-meters) 8/13/201141ICRC 2011: Beijing, China

42. How good is the energy measurement? Example: an energy range of 1-10 PeV 8/13/201142ICRC 2011: Beijing, China

43. How good is the energy measurement? Over a range of different energies… widths: offsets: 8/13/201143ICRC 2011: Beijing, China

44. Backup slides about CUTS 8/13/2011ICRC 2011: Beijing, China44

45. Applying cuts Look for cuts which – Improve angular resolution – Improve proton-iron separation This is a composition analysis, so check each cut (in simulations) to make sure that it does not “prefer” protons over iron (or vice versa) 8/13/201145ICRC 2011: Beijing, China Step 1:Enough hits to survive filters and reconstruct successfully in both detectors Step 2:Containment of reconstructed event inside both detectors Step 3:Separate IceTop and IceCube fits agree with each other Step 4:Long reconstructed track length in IceCube Step 5 Fit 0 in IceCube came out reasonable Step 6:Get rid of uncorrelated coincidences using timing

46. No cuts at all: lots of junk 8/13/201146ICRC 2011: Beijing, China

47. After cuts: now much cleaner 8/13/201147ICRC 2011: Beijing, China

48. The cuts: details Step 1: enough hits to survive filters and reconstruct successfully – Good COG status – Good PlaneFit status – Time Window Cleaning – Good ShowerCombined status – Good ShowerCombined_2 status – 3 Stations must trigger – NCh > 5 8/13/201148ICRC 2011: Beijing, China

49. The cuts: details Step 2: containment (at the surface, and depth) of both 1 st iteration fits and 2 nd iteration fits – LateralFit: IcetopSiz < 1 and IniceSiz < 1 – LateralFit, 2nd Iter: IcetopSiz < 1 and IniceSiz < 1 – MuonBundleReco, 2nd Iter: IcetopSiz < 1 and IniceSiz < 1 – LineFit: IcetopSiz < 1 8/13/201149ICRC 2011: Beijing, China

50. The cuts: details Step 3: IceTop and IceCube fits within 1 degree of each other in direction 8/13/201150ICRC 2011: Beijing, China

51. The cuts: details Step 4: nice long track length in IceCube 8/13/201151ICRC 2011: Beijing, China

52. The cuts: details Step 5: fit 0 in IceCube came out reasonable 8/13/201152ICRC 2011: Beijing, China

53. The cuts: details Step 6: get rid of uncorrelated coincidences (using expected travel time from surface to depth) 8/13/201153ICRC 2011: Beijing, China