Developing Event Reconstruction for CTA R D Parsons (Univ. of Leeds) J Hinton (Univ. of Leicester)
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Transcript of Developing Event Reconstruction for CTA R D Parsons (Univ. of Leeds) J Hinton (Univ. of Leicester)
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Developing Event Reconstruction for CTA R D Parsons (Univ. of Leeds)
J Hinton (Univ. of Leicester)
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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
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Candidate Array3
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
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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
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Time Gradient5
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
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Time Gradient6
Light from B travels furtherArrives later
Particles travel faster than speed of light in airLight from B arrives earlier
A A
BB
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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
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MeasuredDirection
ExpectedDirection
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Lookup tables
Lookup tables are filled with expected values
Lookups are based on dependent measurables
Tables are smoothed and extended
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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
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Events (Ground Plane)10
StandardReconstruction
TruePosition
LU based Reconstruction
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Events (Sky Plane)11
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Standard Reconstruction
Lookup Based Reconstruction15-20% improvement
Performance (Preliminary)12
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Performance (Preliminary)13
Lookup Based Reconstruction20% Sensitivity Gain
Standard Reconstruction
5 σ Detection50h ObservationMin 10 Events1% Background Systematics
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Performance (Preliminary)14
Lookup Based Reconstruction20% Sensitivity Gain
Goal Sensitivity
5 σ Detection50h ObservationMin 10 Events1% Background Systematics
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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
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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
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Simulation Chain17
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Angular Resolution 18