First Observation of the Greisen-Zatsepin-Kuzmin Cutoff Gareth Hughes Rutgers, the State University of New Jersey Advisor: Prof. G Thomson April 2009

2 Outline Observation of the GZK –HiRes Detector –Reconstruction and Monte Carlo –Spectrum + Fits –Systematics Average Shower Profile –Method to find an average shower –Comparisons to Monte Carlo – Gaisser-Hillas –Shower shape as a function of energy TA and TALE –TA description –Physics –TALE description –FADC simulations

3 Collaboration S. BenZvi, J. Boyer, B. Connolly, C.B. Finley, B. Knapp, E.J. Mannel, A. O’Neill, M. Seman, S. Westerhoff Columbia University J.F. Amman, M.D. Cooper, C.M. Hoffman, M.H. Holzscheiter, P. Huentemeyer, C.A. Painter, J.S. Sarracino, G. Sinnis, T.N. Thompson, D. Tupa Los Alamos National Laboratory J. Belz, M. Kirn University of Montana J.A.J. Matthews, M. Roberts University of New Mexico D.R. Bergman, G. Hughes, D. Ivanov, S.R. Schnetzer, L. Scott, S. Stratton, G.B. Thomson, A. Zech Rutgers University N. Manago, M. Sasaki University of Tokyo R.U. Abbasi, T. Abu-Zayyad, G. Archbold, K. Belov, A. Blake, Z. Cao, W. Deng, W. Hanlon, C.C.H. Jui, E.C. Loh, K. Martens, J.N. Matthews, D. Rodriguez, J. Smith, P. Sokolsky, R.W. Springer, B.T. Stokes, J.R. Thomas, S.B. Thomas, L. Wiencke University of Utah

4 Introduction 1912 Cosmic Radiation discovered by Victor Hess Charged particles and Nuclei 1934 Auger discovers Extensive Air showers –Already reached eV GZK: Predicted over 40 years ago by Greisen, Zatsepin and Kuzmin –Ultra High Energy Extra Galactic Cosmic Rays –E p = eV   = –Pion Production off the Cosmic Microwave Background   +,0 takes away 20% of the proton’s energy –If the source is >50Mpc away → cutoff at 6x10 19 eV A test of Large Scale and Ultra High Energy standard physics

5 Extensive Air Shower Above eV direct detection not possible –The flux is too low Indirect detection takes advantage of the EAS –Ground Array –Fluorescence Detector

6 HiRes Location HiRes is located on the U.S. Army Dugway Proving Ground, ~2 hours from The University of Utah campus. The two detector sites are located 12.6 km apart at 5 Mile Hill and Camel’s Back Ridge Operated from

7 Detector Design Mirror: –3.72m 2 effective area –256 phototube camera –Each tube covering 1 o of the sky –UV transmitting filter HiRes-I: –Sample and hold electronics –21 Mirrors in 1 ring –3 to 17 degrees in elevation HiRes-II: –12.6km South East –42 Mirrors 2 rings –3 to 31 degrees elevation –FADC electronics (100ns)‏

8 Monocular Analysis Pattern recognition→Shower detector Plane Fit Time vs Angle HiRes-I: –Profile-constrained time fit 7 o resolution. HiRes-II: –Time fit 5 o resolution. –Gaisser-Hillas fit

9 Back of Envelope Energy Calculation Energy determination is robust. Based on center of shower, not tails. Easy to Monte Carlo.

10 Aperture Calculation Need complete simulation of the detector - create MC sample identical to the data –Inputs: Spectrum as measured by Fly’s Eye Composition HiRes-MIA, HiRes stereo experiments CORSIKA showers –Detector Simulation: Ray Tracing Atmospherics Threshold database Simulate Trigger and readout electronics Write out MC and data in the same format Analyze both using same analysis programs Compare histograms of data and MC to judge success (or failure) of simulation Excellent Simulation of Experiment

11 Spectrum Broken Power Law Fits –One Break Point  2 /DOF = 63.0/37 BP = –Two Beak Points  2 /DOF = 35.1/35 1 st BP = 18.65(5)‏ 2 nd BP = 19.75(4)‏ –Two BP with Extension Expect 51.1 events Observe 15 events Poisson probability: P(51.1;15) = 3x10 -9 (5.8  )‏ –Independent statistics: P(43.2;13)=7x10 -8 = 5.3σ -2.81(3)‏ -5.1(7)‏ -3.25(1)‏

12 Constant–Aperture Study Cut at 10 km, 15 km Flatten the aperture above eV Plot histogram of energies, weighted by E 2 to see spectral features See the “ankle”, high energy suppression, in the raw data

13 Composition Elongation rate used to measure composition Compare to pure Monte Carlo –Proton and Iron –Analyzed using full detector simulation and reconstruction Consistent with light composition –MIA result shows changing composition

14 Current Spectrum Standard Atmosphere Calibration Correction Fluorescence Yield –Kakimoto and Negano Hillas dE/dx(s)‏ Average Mirror value –0.81 reflectivity

15 Systematics Atmospheric Database –Constant Aperture –No Change in Energy Radiosonnde Database –No change in Energy –10gram X max Shift YAG Calibration –Nightly Laser calibration Mirror Reflectivity –Mirror Database –Wavelength Dependence Energy Loss dE/dX –Nerling et al Parameterization –-8% Shift In Energy Fluorescence Yield –New World Average -2% Energy –Implement FLASH Spectra GZK Input Spectra –Cutoff not an Input to Monte Carlo –Cutoff is sharper than measured

A Measurement of the Average Longitudinal Shower Development Profile

17 Motivation Highest energy interactions on Earth! We don’t see 1 st 200g/cm 2 –Future experiments will be able to see up to 1 st 100g/cm 2 (TALE)‏ Best method is Fluorescence Work first done in: –HiRes/MIA Prototype –T. Abu-Zayyad et al., A Measurement of the average Longitudinal Development Profile of CR Air showers, Astropart. Phys., 16, 1 (2001) Now: –More statistics –Improved Monte Carlo –2 orders of magnitude higher in energy range

18 Shower in x (g/cm 2 )‏ Make quality cuts  well defined showers –Standard spectrum cuts –Track length > 200g/cm 2   < 110 o –Extra Bracketing -50g/cm 2 –Cerenkov Fraction < 0.35 Locally Fit Shower Profiles Near N max –N max and X max Normalize:

19 Shower in s (age)‏ Gaisser-Hillas: With 2 free parameters: Gaussian in Age: One free parameter:   Shower Width –Symmetric about s=1

20 Black points mean of the blue –Gaussian fits in bins of age Fit to Normalized – Gaisser-Hillas – Gaussian in Age Average Shower: Data

21 Average Shower: Monte Carlo Corsika shower library –QGSJET Proton and Iron Put through detailed Detector Simulation –Resolution

22 Data – Monte Carlo Comparison Top: Good agreement between Data and Monte Carlo –Black: Data –Red: Monte Carlo Bottom: Ratio of Data/Monte Carlo –Flat from 0.6 to 1.3 in Age E > eV

23 Resolution in  Energy dependant  resolution –effects profile reconstruction Geometric bias –Top and Bottom of mirrors –Mirror edges Compare Monte Carlo reconstructed with ‘True’ value of  and R p Shows us age range we can fit 19.5 – – – – – 18.0 Log 10 (Energy)‏ Resolution (degrees)‏ Upper AgeLower Age

24 Fits to Average Showers Black points mean of the blue –Gaussian fits in bins of age Make average showers for half decade bins in energy Good fits above eV   2 /dof ~ few – – – 19.0 Gaussian in Age  2 /DOF Gaisser- Hillas  2 /DOF Log10(Energy)‏

25 Average Shower Widths , Monte Carlo CORSIKA(QGSJET)‏ –80% Proton and 20% Iron Get back what we put in Consistent across all energies

26 Data and Monte Carlo Results Good agreement –Same falling behavior –Within errors 3.5  difference in highest energy bin. What is this? –Low statistics (10 data events)‏

Telescope Array

28 Telescope Array Northern Hemisphere Hybrid Detector –Delta, Utah 507 Surface Detectors –1.2km spacing –100% duty cycle 3 Fluorescence Detectors –10% duty cycle Taking data since November 2007

29 TA Physics SD fully efficient > eV –GZK –Extra-galactic Anisotropy FD Monocular > eV –Spectrum –Large Scale Anisotropy FD Stereo > eV –Spectrum –Composition FD SD Hybrid > eV –Spectrum –Composition –Point Source Anisotropy

30 TA Physics What TA cannot do: –Ankle in Stereo FD –Energy of 2 nd Knee –Composition at 2 nd Knee –Definitive study of extra- Galactic → Galactic transition Would be interesting to look at spectrum and composition from eV –Cross calibrate with TA –Consistent measurement eV → eV TALE Need Low Energy Extension: TALE

31 TA Low Energy Extension 6km Fluorescence site –Close to Long Ridge (Stereo)‏ Infill array –400m spacing –Including buried  detectors Tower Detector –Increase low energy aperture 3x HiRes Mirrors View up to 72 0 elevation –Successfully tested with HiRes-I Summer 2007 Single mirror Ring 4 ( )‏ Events seen in Ring 1 and 4 just as expected

32 Faster FADC Low Energy→Nearby Showers →Increased Angular Speed Could a faster FADC improve  Resolution? Use Monte Carlo to compare 100ns and 25ns integration time –Use Tower Prototype Monte Carlo –Throw Standard inputs HiRes Spectrum HiRes MIA/HiRes Stereo Composition –Output MC photon number and times in 5ns bins –Reconstruct using 100 and 25ns integration times –Compare to thrown values

33 Results Below eV improved  and R p resolution –17 0 → 7 0 in  –10% → 4% in R p Reconstruction efficiency reduced Vary electronics filter time constant  –Using test 25, 32 and 50ns –Recover events without losing resolution

34 Conclusion HiRes has observed the G.Z.K. cutoff with a significance > 5  –Phys. Rev. Lett. 100, (2008) We have a developed a method to measure the Average Longitudinal Shower –Measured shower parameters as a function of energy –Good fit for both Gaisser-Hillas and Gaussian in Age. –Monte Carlo shows good agreement Using 25ns FADC and 50ns  significantly improves geometrical resolution –Will be implemented in TALE electronics