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Developing Event Reconstruction for CTA R D Parsons (Univ. of Leeds) J Hinton (Univ. of Leicester)

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Presentation on theme: "Developing Event Reconstruction for CTA R D Parsons (Univ. of Leeds) J Hinton (Univ. of Leicester)"— Presentation transcript:

1 Developing Event Reconstruction for CTA R D Parsons (Univ. of Leeds) J Hinton (Univ. of Leicester)

2 CTA Aims CTA aims to improve sensitivity by an order of magnitude over HESS Aims to improve angular resolution by a factor of three This can be achieved by: More Telescopes Larger field of view Multiple telescope types Improved Analysis 2 2

3 Candidate Array 3 Candidate Array: 4 x Large Telescopes (23m) 23 x Medium Telescopes (12m) 32 x Small Telescopes (6m) Telescopes are single dish reflectors, with a PMT camera

4 Lookup based reconstruction Need to reconstruct core and source position (Alt, Az, X and Y) Standard direction reconstruction uses only the orientation of camera images More information can be included to improve reconstruction Extra parameters: Time Gradient Image Displacement Concentration Energy Consistency 4

5 Time Gradient 5 CTA will be able to record the trigger times on individual pixels Images move across the camera as the shower develops Produces a gradient across the integrated image This gradient is proportional to the distance from the core VERITAS Events

6 Time Gradient 6 Light from B travels further Arrives later Particles travel faster than speed of light in air Light from B arrives earlier AA BB

7 χ 2 Contributions Expected values (μ) and errors (σ exp ) of parameters (for gammas) are found from MC simulations The measured value (x) can then be compared with that expected at a trial core location Expected error can then be compared with that measured A χ 2 contribution can then be made for each parameter χ 2 = (x – μ) / σ exp 7 Measured Direction Expected Direction

8 Lookup tables Lookup tables are filled with expected values Lookups are based on dependent measurables Tables are smoothed and extended 8

9 Finding the Minimum A summed chi-squared value can be found for a trial X,Y, Alt and Az This defines a 4D Chi-squared space Best fit shower axis lies at the minimum of this surface Use Rolling function to find the minimum point 9

10 Events (Ground Plane) 10 Standard Reconstruction True Position LU based Reconstruction

11 Events (Sky Plane) 11

12 Standard Reconstruction Lookup Based Reconstruction 15-20% improvement Performance (Preliminary) 12

13 Performance (Preliminary) 13 Lookup Based Reconstruction 20% Sensitivity Gain Standard Reconstruction 5 σ Detection 50h Observation Min 10 Events 1% Background Systematics

14 Performance (Preliminary) 14 Lookup Based Reconstruction 20% Sensitivity Gain Goal Sensitivity 5 σ Detection 50h Observation Min 10 Events 1% Background Systematics

15 Maximum Likelihood For most parameters the errors are non-gaussian Hence the chi-squared value is not valid Will cause problems in estimation of error on the shower axis Instead the maximum likelihood estimator will be used Requires an extra dimension in lookups 15

16 Summary CTA will provide large improvements in both sensitivity and angular resolution The current reconstruction method is not optimised for a large array % improvements in sensitivity have been gained from improved reconstruction Even larger gains may be achievable Further refinements to reconstruction Switch to maximum likelihood estimator Introduce weighting of contributions Combine with multi-variate analysis 16

17 Simulation Chain CORSIKA Full air shower simulation, Outputs both particles and Cherenkov photons Sim_telarray Telescope and array simulation (ray tracing) Outputs in HESS data format Hessio Event reconstruction and analysis 17

18 Angular Resolution 18


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