Advancement to Candidacy Presentation

1. Advancement to Candidacy Presentation Kirk Bays UCI Oct 5. 2007

2. Outline Super-K Detector Review: General info, history Triggering, DAQ Calibration What I’ve done: Water Transparency from Decay-e Reactor Neutrino Feasibility Study IDEAL and fitter tuning What I Want to Do: SN Relic Neutrinos Previous study What I want to improve

3. THE SUPER-KAMIOKANDE DETECTOR

4. Radon system • 99.98% effective at reducing radon • reduces background, worker health The Super-K Detector Purpose: Proton decay and neutrino studies ‘Inner Detector’, ID • ~11,150 inward facing photomultiplier tubes • single photon sensitivity • ~40% surface coverage 41.4 m • ‘Outer Detector’, OD • Optically separate from ID • 1885 outward facing PMT’s. 39.3 m filtration, degasification, water flow water entering tank 18.2 MΩ*cm leaving tank 11 MΩ*cm 35-70 tons/hour water flow ID fiducial volume two meters from PMT’s; 32 ktons -> 22.5 Ktons 26 Helmholtz coils reduce Earth’s magnetic field by factor of 9

5. Detector History • April 1 1996 – July 15 2001: SK-I data taking (11,146 PMT’s, 40% coverage) • July 2001: SK-I ends, begin upgrades for SK-II • Nov 11 2001: Accident destroys ~60% of PMT’s • Dec 6. 2002: Surviving PMT’s repositioned, start of SK-II data taking (5,182 PMT’s, 19% coverage) • Late 2005: End SK-II, begin full repair • Mid 2006: Kirk joins the team! • June 2006: Full repairs complete, begin SK-III data taking (11,129 PMT’s, 40% coverage)

6. .3 p.e. Triggering, DAQ • Dark noise: 3-5 KHz. • PMT timing resolution ~3 ns • Lower trigger thresholds can detect lower energy events • Limited by computing power • 2 channels per PMT keep detector mostly dead-time free • New upgrade planned next year -11 mV The Super-Kamiokande Detector The Super-Kamiokande Collaboration, Nucl. Instrum. Meth. A501(2003)418-462 Final trigger records data in 1.3 μs range (one ‘event’)

7. Some LowE fitters Have: Timing, positions, signal of hit PMT’s Need: Particle type, position/path, energy, direction How: software fitters Fitting • LowE fitters: • electrons/positrons • doesn’t use PMT charge info • assume 1 p.e. per hit PMT • path length < vertex resolution • vertex fit: grid search with goodness function • direction fit: uses ‘likelihood’ function • energy from % PMT’s hit, with correction • BONSAI best, slowest; Hayai worst, fastest M.B. Smy, Low Energy Challenges in Super- Kamiokande-III, Nuc. Phy. B, 168, pg 118-121 (2007) • muon fitters: • must fit entire track • uses charge info from PMT’s • finds exit point as ‘hot spot’ • looks for earliest and latest hit PMT’s • some muons stop in detector, decay • muboy program

8. Calibration • LINAC: • Most important calibration source • Old medical linear accelerator, on site • Shoots mono-energetic electrons ( 5 – 16 MeV) into known positions • Energy known to within 20 KeV • Primary calibration of absolute energy scale (accurate to within 1%) • Also useful for energy resolution, angular resolution, spatial resolution, and detection efficiency of LE events • Xe source and scintillator ball: • Helps fine tune high voltage to regulate individual PMT gain • N2 laser and diffuser ball: • Relative PMT timing, ‘tq map’ • Deuterium-tritium neutron generator (DT): • double check absolute energy scale, trigger efficiency • Decay electrons: • determines water transparency for LE group (more detail later), checks energy scale at higher energies, stability of energy scale

9. atmospheric Event types and spectrums • Atmospheric neutrinos up to TeV • Reactor, solar, spallation all under 21 MeV solar

10. WHAT I’VE DONE:Water transparency from decay electrons

11. Method • Decay electrons from stopping muons • Geometrically, PMT’s projected onto a sphere surrounding the event vertex, divided up azimuthally into 36 segments • PMT angle from event constrained to between 32 and 52 degrees (to lie on Cherenkov ring) • PMT’s used from a 50 ns time window (after time of flight subtracted out)

12. Method log(intensity) • Each angular segment is a certain distance from the detector wall for any particular event • The charge intensity of each segment is put in an appropriate distance bin • As each event occurs the intensities are added into distance bins in a sum • At the end, a properly normalized log(intensity) is plotted for each distance bin in a histogram, and the inverse of the slope is the water transparency. distance of PMT from event (m)

13. My Work • Was leftover code from SK-I and SK-II but they didn’t agree, had bugs, so I re-wrote the code. • I added error bars to the water transparency tables • Did away with the running average method, which hid problems • I made the binning of the water transparency variable (right now I use six days) • I’ve maintained the water transparency table for the LowE group since. cm 10 day gap, new hardware test days since july 12 2006

14. mean of Neff distribution Trustworthiness 140 130 assuming calculated wt • Stability of the Neff distribution is measure of trustworthiness • Neff is a reconstruction variable directly proportionate to the energy of the decay electron • The calculation of Neff requires knowledge of water transparency • The spectrum of the decay electrons shouldn’t be changing in time; therefore the Neff distribution should be constant in time • If you assume constant water transparency, Neff is not stable • My results gives Neff stable to within 1% • Also, my results are comparable to results from through going muons to within a small scaling factor 120 assuming constant 90 m wt 110 m 100 90 80 water transparency (in units of .5 m) 70 days since July 12 2006

15. WHAT I’VE DONE:Feasibility study of Reactor Neutrinos with Gadolinium

16. Motivation • With gadolinium, could use neutron tagging to spot events and calculate neutrino oscillation parameters • To test this and motivate gadolinium, I wrote a detailed Monte Carlo • Highest concentration of nuclear reactors anywhere around Super-K • MC includes 15 nearby reactors, most importantly the Kashiwazaki reactor, which accounts for 31% of flux • Takes into account the four different radioactive isotopes in reactor fuel • Can simulate all the reactors at once with the correct flux and event direction for each. • Neutrinos interact via inverse beta decay, model with cross section from Beacom and Vogel Super-K concentration of reactors around Super-K

17. Generated X Direction Reactor MC • Neutron capture simulated by more Monte Carlo. Not yet perfected, right now gadolinium spectrum is approximated • Beacom and Vogel show an expected bias of positron events pointed away from the incoming neutrino direction; this is clearly visible in the MC • We keep only good events by cutting on: • fiducial volume • energy • goodness • vertex correlation between reconstructed positrons and the reconstructed gammas from the neutron capture a large fraction of neutrinos come from Kashiwazaki, which coincidentally lies almost exactly it the +x direction from Super-K Positron Energy generated, no cuts reconstructed, all cuts

18. Plots and Cuts (vertex resolution) No neutron capture With neutron capture fiducial only (green) fiducial cut only (black) fiducial + goodness**2-dirks**2 cut (red) fiducial + vertex correlation (red) fiducial + goodness + energy > 2.5 MeV (blue) fiducial + v.corr. + energy>3MeV (green) fiducial + goodness + energy > 3 MeV (black) fiducial + v.corr. + energy + goodness**2-dirks**2>.25 (blue)

19. To predict the event rate at super-k, I started from first principles. Before oscillation, the event rate is determined by: Event Rate Calculation Where the sum is over reactors, P is the power output, d is distance to super-k, σis the cross section Where dN/dE comes from a KamLAND paper by Murayama and Pierce, and C is the % of fuel made up by that isotope • Also taken into account is: • the density of free protons in water • 20% loss due to spallation cut • Parameters calculated to be the same as the Monte Carlo, then I make many years of data, and see what % of events survive all the cuts and oscillation. I multiply my event rate by this % to get my final event rate. My prediction: ~2800 events/year

20. I assumed the real life best fit parameters of ∆m^2 = 8E-5 ev and sin^2(2θ) = .86. Then I oscillated the positron spectrum from the Monte Carlo. Oscillation and Fake Data I wanted to make a data set that would approximate what we might really see in an experiment. So, I took the oscillated spectrum (blue) and I applied random Poisson fluctuations to it to create a ‘fake data’ set (red). unoscillated spectrum from Monte Carlo MeV

21. Made oscillation predictions for 6,400 different combinations of oscillation parameters from MC • Made χ2map of these predictions vs. the fake data set. Repeated 1,000 times, averaged χ2 – χ2minmaps to make a final χ2 map, which I used to make contours • I included a general event suppression error (σα) and the energy scale error (σE) • I minimize α analytically, but the δ parameter affects spectrum, so I can’t do this. Instead I also consider 100 different values of δ and apply them as I oscillate the spectrum, then minimize χ2 Analysis N = fake data set t = oscillation predictions

22. Results • The latest KamLAND results improve on shown world limit, but we could still beat it by at least a factor of 2 • Angle parameter not as good but still competitive with world limit • Not only is my flux rate conservative, but we should be able to do better then the 1% energy scale error. The real results could be better my 95% Δm^2 current PDG best limit 95% sin^2(2θ) All reactors, 5 years, energy scale error 1% event suppression error 2%

23. Kashiwazaki off • The recent earthquake in Japan has taken Kashiwazaki temporarily offline • If we took gadolinium data with Kashiwazaki both off and on, could we subtract the data sets and get the equivalent of Kashiwazaki only data? • Knowing exactly the direction and distance of the reactor should improve our analysis significantly • Unfortunately, the MC doesn’t show any improvement over just having all reactors. It may be that the loss in flux counteracts the gain. • This is surprising, so I’ve performed many checks on the study, but found no error all reactors 5 years 2.5 yrs all reactors, 2.5 yrs Kashi off all reactors (normalized spectrum) all but Kashiwazaki the subtracted spectrum is sharpest MeV

24. WHAT I’VE DONE: Everything Else

25. IDEAL • Irvine Device for Evaluating the Attenuation of Light • Being constructed virtually from scratch here at UCI • I’ve been doing grunt work, helping make and design holders, dark boxes, etc. Fitter tuning • My first job here was to help tune the LowE fitters • I improved the speed and effectiveness of Bonsai, Clusfit, the two fitters that are actually used for everything

26. SN RELIC NEUTRINOS(not big bang relics)

27. Original Motivation 100x bigger • Can do more then wait for a galactic supernova • Should be a diffuse signal from all supernovas throughout history • Useful for Cosmology • Detectable at Super-K, unfortunately flux is small (~5/year) • Irreducible backgrounds make the study difficult Assuming Super-K • Study was undertaken by Matthew Malek, a grad student at Stony Brook, NY • In 2003, produced the worlds best limit on SN relic flux • Previous best limit was from original Kamiokande, was much poorer.

28. Discussion of Backgrounds Super-K is sensitive to many different types of events, many of which overlap with the SN relic energy regime. Examples of non-relic events include: • Electronic noise • Solar neutrinos • Reactor neutrinos • Atmospheric neutrinos • Cosmic ray muons • Spallation event (~600/day) • Radioactive backgrounds Spallation is the most difficult to deal with The spallation products decay with energies as high as 20.8 MeV and lifetimes up to 13.8 s Spallation limits our energy threshold for the study, the spallation cuts cut much signal Reactor (dashed), 8B solar νe(solid), hep solar νe (dotted), and atmospheric (dot-dashed)

29. Original Analysis: Reduction steps • The data reduction is the application of many cuts to eliminate unwanted events. The cuts include: • Detector deadened 50 μs after every clear event eliminates some decay electron events and electronic noise • Fiducial Volume cut. Standard 22.5 Kton fiducial vol. was used. • Deff < 450 cm cut. Deff defined in picture. Vertex resolution of the fitters is worst along the event direction, so some events from outside the f.v. seep in. • Calibration and problematic events cut • Events with OD triggers cut • Noise cuts: The fitters construct variables that test for noise • Goodness cut: this fitter variable tests how ‘good’ the fit was. Low values indicate poor fits • Decay electron cuts: decay electrons are linked to muons, some even occur in same 1.3 μs trigger window

30. Spallation cuts Most tricky, most loss in signal. Previous analysis required all spallation events gone, so cuts are harsh • Likelihood function cut: A likelihood function tests each event for the ‘likelihood’ of being a spallation event. Is a function of three parameters, assumed to be independent: • shortest distance to muon track • muon residual charge • time difference to previous muons. Estimated to eliminate 21% of the signal • Time correlation cut: The plot shows the time difference between the candidate event and preceding muon events (up to the previous 200 muons). This was used as justification to implement a .15 s cut after every muon. MC estimates showed this cut approximately 19% of the signal. Overall efficiency was 64% No spallation events remained > 18 MeV. Thus the energy threshold for the study was 18 MeV. The cuts were applied up to 34 MeV (5 sigma past the highest energy expected spallation events).

31. Further cuts • Cherenkov angle cut: Low energy muons (momentum < ~350 MeV) can look like electrons. Due to the momentum difference, the speed is not the same, so the Cherenkov angle is different. The cut limits the Cherenkov angle to between 38 and 50 degrees. The lower limit cuts out muons, the upper limit cuts out multiple γ events. • Solar angle cut: Above 18 MeV is only the tail of the spectrum, but it is still nontrivial compared to the expected handful of SN relic events. Solar events are easily filtered out because we known our angular resolution (~20 deg) and we know where the sun is. The analysis cuts out data within ~30 degrees around the solar angle. This eliminates all solar neutrinos and ~7% of the SN relic signal. • Final efficiency: It was estimated that 47% of the original SN relic signal was retained after all cuts. The final sample was 271 events.

32. Irreducible backgrounds Unfortunately, after all cuts anyone could think of were applied, there were still two irreducible backgrounds: • Decay electrons from invisible muons: • Atmospheric muon neutrinos can undergo charged current quasi-elastic scattering to form low energy (< 120 MeV) muons. • Are so low energy, they don’t produce Cherenkov radiation. Hence, ‘invisible’. • However, some still stop in the detector and decay into electrons. No way to correlate these to the muon. • Low energy atmospheric νe: • E < ~200 MeV • also interact primarily via inverse beta decay • Also isotropic • no way to tag them or distinguish them from SN relic events. MC simulated atmospheric electron neutrino spectrum (left), and spectrum of invisible muon decay e’s (right)

33. χ2 Analysis • Irreducible backgrounds modeled with Monte Carlo. Both spectrums well known, flux less so • Once modeled, χ2 analysis performed to look for any excess of signal beyond expected background: dotted: decay e’s dashed: atmospheric ν’s solid: sum blue: what expected SN relic signal would look like • Nl: total number of events in energy bin l • α, β, γ: free parameters representing fraction of events in bin l that are SN relic events, • invisible muon decay electrons, or atmospheric ν’s. • Correspondingly, A, B, and C are the expected • number of events per bin, B and C from the MC • generated spectrums, and A being model dependent. • The uncertainty is dominated by σdata, as the sample size is small. • The MC error is negligible since the MC was generated in such large number. • The systematic uncertainty was ~6%

34. Where F90 is the 90% C.L. flux limit for any model Results model insensitive model sensitive

35. SN RELIC NEUTRINOS:Proposed work

36. [1]: L. Strigari, M. Kaplinghat, G. Steigman, T. Walker, The Supernova Relic Neutrino Backgrounds at KamLAND and Super-Kamiokande, JCAP 0403 (2004) 007 Motivation for a new study • The 2003 limit ~ 200x better than previous limit, actually had useful cosmological implications • This result prompted a team including our resident experts to do a more detailed study • They conclude that ‘Our best estimate for the flux at Super-K is slightly below, but very close to the current Super-K upper limit. …We estimate that the SRN background should be detected (at 1σ) at Super-K with a total of about 9 years (including the existing 4 years) of data.’ [1] • They predict the flux to be . • We are more pessimistic in our hopes of discovering the full signal, but even seeing a hint could motivate new efforts (gadolinium). • Even just improving the limit is significant • We can improve the limit appreciably now; it will require ~10 more years of Super-K data to improve the limit appreciably again. Now is the time for this study! lower limit

37. Proposed Analysis Improvements: I believe we can improve on the previous analysis as follows: • More live time. • SK-II + SK-III data, doubles live time • Improving the spallation cuts: • Estimated that the spallation cuts only retained 64% of the signal • The largest improvement to the analysis we can hope for comes from improving the spallation cuts • Hope to retune the cuts to make them more efficient • Time correlation cut: • Previously, the detector was deadened for .15 s after every muon. This is too much! Want to reduce this and look for other ways of eliminating spallation events that bleed through • Spallation likelihood function: • Idea is to add another parameter, the energy of the LowE event, to the likelihood • Almost certainly correlations between this parameter and the others, independence assumption breaks down • Maybe just look at other potential parameters (instead of residual charge, for instance) and retune the whole thing • Note that this function is from the solar analysis, and was never tuned for SN relic study!

38. More analysis improvements: • Better fitters • Better quality fitters developed since SK-I • Newer fitters are slower, but we have both time and computing power • Fully reprocessing SK-I reduces the number of misfit events, improves resolution • Helps many analysis steps, but most importantly the spallation cuts • Lowering the energy threshold • The SN relic neutrino signal is stronger at lower energies • Pushing back the threshold from 18 MeV to 15 MeV or better could increase our signal significantly • Difficulty is spallation background • Should be able to get something out of 15-18 MeV region, if we impose stronger cuts there • If it turns out we can’t fully eliminate the spallation signal, we can try fitting the spallation in the χ2 along with the irreducible backgrounds, but this is undesirable

39. Proposed Improvements cont’d • Lastly, we can try expanding the fiducial volume. • 22.5 Ktonf.v. not tuned for this study • Difficulty comes from higher backgrounds, Monte Carlo less trustworthy in that region, and we have to rely on calibrations done 10 years ago if any questions arise • PMT flashers are a big difficulty • However, expanding the fiducial volume has been done in other analyses • Can try doing it in a time dependent fashion, making it smaller or excluding areas for flasher heavy time periods • The increase in live time alone should give us twice as much exposure, which should allow us to improve the limit by √2 • Significant improvements in the rest of the analysis can at best get another factor of √2 improvement So most optimistically we hope for a factor of 2 improvement in the world’s best limit to come from this. The new limit should be in this region; not quite discounting any models, unfortunately 2003 limit

40. SUPPLEMENTAL SLIDES

41. P. Vogel, J.F. Beacom, Angular Distribution of Neutron Inverse Beta Decay, Phys. Rev. D 60 053003 Inverse Beta Decay

42. DAQ

43. Water Filtration System

44. Radon System

45. Spallation Products Shortest lived, Highest E General correlation: Smaller lifetimes, higher E Longest lived

46. DT generator, Xe lamp

47. N2 laser

48. μ-e event (decayed within 1.3 μs)

49. LE muon electron

50. Multiple γ event, 20 MeV reconstruction energy