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Enhancing Event Reconstruction Techniques for the Cherenkov Telescope Array (CTA)

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rhonda-graham
18 diapositives · 299 Views · Nov 30, 2014

This presentation by R.D. Parsons (University of Leeds) and J. Hinton (University of Leicester) discusses innovative methods for improving event reconstruction in the Cherenkov Telescope Array (CTA). The CTA aims to significantly boost sensitivity and angular resolution compared to existing systems. Key strategies include utilizing more telescopes, expanding the field of view, and enhancing analytical approaches. The use of maximum likelihood estimators and multi-variate analysis could lead to substantial gains in sensitivity. This presentation will explore cutting-edge reconstruction techniques and their implications for future cosmic observations.

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  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

  3. Candidate Array 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

  5. Time Gradient • 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 A A Light from B travels further Arrives later Particles travel faster than speed of light in air Light from B arrives earlier B B

  7. χ2 Contributions • Expected values (μ) and errors (σexp) 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 χ2 contribution can then be made for each parameter • χ2 = (x – μ) / σexp Expected Direction Measured Direction

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

  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

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

  11. Events (Sky Plane)

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

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

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

  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

  16. Summary • CTA will provide large improvements in both sensitivity and angular resolution • The current reconstruction method is not optimised for a large array • 15-20 % 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

  17. Simulation Chain

  18. Angular Resolution