Current Physics Results Gordon Thomson Rutgers University

Outline HiRes1 and HiRes2 mono spectra. –Monte Carlo development –HiRes2 mono spectrum –HiRes1 mono spectrum –Systematic uncertainties –Fitting the result Stereo Resolution. Composition. Anisotropy. –Search for point sources –Event to event correlations Searches. Future plans.

Monte Carlo Development (for HiRes2 Monocular Spectrum). Inputs: –Library of Corsika/QGSJet showers (protons, Fe). QGSJet is tested at 2x10 15 eV for protons, 1x10 17 eV for Fe. –Fly’s Eye spectrum, composition. –Atmospheric effects. –Electronics, trigger, and DAQ. Day-by-day adjustment of: –Live time, working mirrors, trigger gains and threshholds. Output in same format as data; analyze using same programs.

Comparisons between Data and Monte Carlo Events Distance to mean of shower. Zenith angle.

Data – MC Comparisons Photoelectrons per degree of track. Ring number.

Data – MC Comparisons Chisquared of time fit. Energy.

HiRes2 Monocular Spectrum Dec., 1999 – May, 2000 (first stable HiRes2 running). ~30% of data. Consistent trigger (big change after May). Cuts: –Clear weather. –Downward going track. –Track length > 7 degrees –Linear fit chisquared/tube < 20 –Pseudodistance > 1.5 km –.85 < tubes/degree < 3. –Photoelectrons/degree > 25 –Zenith angle < 60 degrees –Shower max in view

Spectrum Results J(E) (correct for resolution) E 3 J(E)

HiRes1 Monocular Spectrum Period: June, 1997 – May, mirror hours. Cuts: –Clear weather. –Downward going track. –Track length > 7.9 degrees –Pseudodistance > 5 km –.85 < tubes/degree < 4. –Photoelectrons/degree > 25 –Constrained fit converges. –Shower max in view Minimum energy is 3x10 18 eV due to shorter tracks.

Profile-Constrained Fit Shorter tracks in HiRes1 have less curvature. Use constraint that profile must fit G-H function: leads to bias, same in MC and stereo.

HiRes1 Data-MC Comparison R p, 18.4<log(E/eV)<18.6

Systematic Uncertainties PMT calibration: 10% Fluorescence yield: 10% Unobserved energy: 5% Atmospheric absorption: most sensitive to vertical aerosol optical depth (VAOD) –Mean VAOD = 0.04 –VAOD RMS = 0.02 –VAOD systematic is smaller. –Modify MC and analysis programs to use VAOD = 0.02 and 0.06, reanalyze. –J(E) changes by 15% Total systematic uncertainty = 21%

HiRes Mono Spectra Fit: E -2.8 from 18.7 to 19.8; Predicts 19.1 events, logE>19.8; See 5. Probability = 1.4 x 10 -4

HiRes and AGASA; Fit to galactic+extragalactic model Galactic motivated by composition. Extragalactic sources: constant density. P = 0.15

AGASA Energy Rescaled by 0.79

Evolution of Sources Density ~ (uniform) x (1+z) m m=3 for star formation, GRB’s P = 0.25

Measured Galaxy Density (x2, r < 30 Mpc) P = 0.37 Fits improve with each refinement in model.

Single-Source Model CEN A = closest AGN (4 Mpc), using best point in preferred parameter region.

Highest Energy Event in HiRes1 Mono Spectrum

Highest Energy Stereo Event (as seen from HiRes2)

Conclusions from Measurement of Mono Spectra The two HiRes detectors are collecting data smoothly. Calibration is under control. Measured flux agrees with Fly’s Eye experiment. In our energy range we see four spectral features. The GZK cutoff and pileup seem to exist. We see evidence for e + e - pair production. One model accounts for four spectral features of the data. Need more statistics: –Run for 5 years! –Build a third detector!

Stereo Resolution

Energy Resolution, Stereo

Energy Histogram, Stereo

Composition All-Energy X max Distribution Protons Iron Nuclei Solid Line: Data. Dotted: QGSJet. Dot-dashed: SIBYLL Dot-dashed: SIBYLL

Data and Fe MC prediction in three energy bins

HiRes Stereo Composition Measurement

Anisotropy: Search for Point Sources in HiRes1 Mono Data

All Energies

Point Source Results: HiRes1 Monocular For all energies (3,115 events above eV), we rule out the existence of point sources at >90% confidence level. Source strength ~ 50 events. For events of E > eV, we rule out point sources at the >90% confidence level. Source strength ~ 16 events.

Anisotropy (cont’d.) Autocorrelation Autocorrelation is the distribution of space angles between pairs of events –An autocorrelation function is created by doing the following: Take any pair of events Calculate the cosine of the space angle between the events Enter into a histogram of the cosine of the space angle Repeat until every possible pair in the event sample has been considered If we have point sources, we will see enhancements in our autocorrelation function at small space angles.

What would an autocorrelation function from a sample resulting from point sources look like?

The Autocorrelation function for HiRes-1 events above eV

Searches for CR Neutrinos and Gamma Rays Neutrinos = upward- going events –E min = 2x10 17 eV. –Earth is opaque, even for tau neutrinos. For regeneration need E min ~ eV. –Factor of 500 away from seeing neutrinos (if same flux as charged CRs). Gamma rays: showers develop late due to LPM effect.

Future Plans Run for 5 years. –Better statistics at GZK cutoff. –Good statistics at GZK pileup. –Excellent statistics at e + e - pair- production region: second knee and ankle. Build a third detector. –Improve signal/noise by x 20. –Search for CR neutrinos down to 1 x eV. –Increase data rate x 2 for high energy events. –Preparing NSF MRI proposal.