The TA Energy Scale Douglas Bergman Rutgers University Aspen UHECR Workshop April 2007

Introduction TA is just now being deployed –Can’t say honestly what we will do –Can only say what I think we should do TA is a Hybrid Detector –Use fluorescence detector (FD) to calibrate surface detector (SD) –This limits model dependence in SD reconstruction

Outline Working from what we know to what we wish to know –Fluorescence Detector Energy Scale –Surface Detector Energy Calibration using the Fluorescence Detector –Using the Surface Detector at the High Energies

Outline Working from what we know to what we wish to know –Fluorescence Detector Energy Scale –Surface Detector Energy Calibration using the Fluorescence Detector –Using the Surface Detector at the High Energies

The FD Energy Scale From the shower to the data (and back again!) –Missing energy and the EM portion of the shower –Depositing shower energy in the atmosphere –Fluorescence yield –Atmospheric losses –Light collection –Photometric scale –Shower parameterization and fitting –Verification with MC (Data/MC comparisons) –Finding the aperture (more Data/MC comparisons)

Missing Energy Only the EM portion of the EAS is accessible to FD –Calculate using Corsika and QGSJet –Depends on composition Composition from X max distribution in Data/MC Song et al, APP 14 (2000) 7

Missing Energy Only the EM portion of the EAS is accessible to FD –Calculate using Corsika and QGSJet –Depends on composition Composition from X max distribution in Data/MC –There are measurements Do we believe Yakutsk? Knurenko et al, NPB(ps) 151 (2006) 92

Energy Deposited in Atmosphere The energy deposited by an electron depends on its energy –The average electron energy depends on the age, but also on the model –At any age have given average energy Song et al, APP 14 (2000) 7

Energy Deposited in Atmosphere The energy deposited by an electron depends on its energy –The average electron energy depends on the age, but also on the model –At any age have given average energy –Average over all ages New version of QGSJet gives 10% different Song et al, APP 14 (2000) 7

Fluorescence Yield Each MeV deposited in the atmosphere gives some number of fluorescence photons –Use fit to FLASH, Nagano and Kakimoto results This is Kakimoto * (1.00±0.06)

Atmospheric Losses Not all fluorescence photons reach the detector –Rayleigh scattering Slowly varying over small range –Aerosol scattering Varies dramatically Have to measure the VAOD Will use a measurement scheme similar to the in HiRes Abbasi et al, APP 23 (2005) 157

Atmospheric Losses Not all fluorescence photons reach the detector –Rayleigh scattering Slowly varying over small range –Aerosol scattering Varies dramatically Have to measure the VAOD Will use a measurement scheme similar to the in HiRes Abbasi et al, APP inpress

Light Collection We have to measure the amount of light reflected by the mirrors, and the amount lost at the filters

Photometric Calibration The response of each PMT must be adjusted and calibrated. Can calibrate many mirrors with one source at Middle Drum! –Will include mirror and filter losses

Photometric Calibration The response of each PMT must be adjusted and calibrated. Can calibrate many mirrors with one source at Middle Drum! –Will include mirror and filter losses Will also have an electron beam! –End-to-end calibration with a known N e

Fitting Showers Have to fit profile in data to a parametric form –Gaussian in “age” –Gaisser-Hillas

Fitting Showers Have to fit profile in data to a parametric form –Gaussian in “age” –Gaisser-Hillas Gaisser-Hillas works well for HiRes, we’ll use it in TA too

Data/MC Comparisons Have to put all of the above into the detector simulation How does one know that the simulation is right? –Any and all distributions observed in the data should be able to be reproduced by MC –Any distribution that does not agree between data and MC is a systematic error –To the level one can tell that distributions agree lets one assign systematic uncertainties

The Aperture Calculation For FD, MC is needed to calculate the aperture –How can we be confident in the calculation? Data/MC comparisons –Which distributions are most important? How far away (R P ) What directions (θ or Ψ) How much light (shower brightness) X max (if cutting on shower shape parameters) Abbasi et al, APP inpress

Outline Working from what we know to what we wish to know –Fluorescence Detector Energy Scale –Surface Detector Energy Calibration using the Fluorescence Detector –Using the Surface Detector at the High Energies

From the FD to the SD Energy Geometric Reconstruction –Hybrid reconstruction gives both FD and SD reconstruction resolution SD Energy Reconstruction –MIP normalization of counter response –S(1000) vs E FD –Attenuation correction –LDF slope compared to X max –Rise time compared to X max (no muons!)

Finding S(1000) Have to fit Lateral Distribution Function to find S(1000) or S(600) –Use NKG parameterization Can use FD energy to check linearity of S(x) vs E relation Yoshida et al APP 3 (1995) 105

Attenuation Corrections Can get attenuation correction from data using hybrid without making constant intensity cuts –But only at energies with sufficient hybrid data Sakaki et al 27 th ICRC (2001) 333

Rise time compared to Xmax TA can measure the rise time of the shower front (and how it changes with distance from core) Can be used to measure composition Walker & Watson JPG 7 (1981) 1297

Outline Working from what we know to what we wish to know –Fluorescence Detector Energy Scale –Surface Detector Energy Calibration using the Fluorescence Detector –Using the Surface Detector at the High Energies

SD at the Highest Energies Highest half-decade in energy is just SD –10% FD duty cycle Extrapolations –Extrapolate S(1000) vs E and attenuation –Base the extrapolation on MC –Check MC by Data/MC comparisons LDF Slopes –If linearity changes differently between data and MC, will LDF slope distribution show it? Rise times –Showers only accurately modeled if rise times agree in data and MC Zenith Angle Distribution