Reconstruction (reco)

Introduction

ctapipe.reco contains functions and classes to reconstruct physical shower parameters, using either stereo (multiple images of a shower) or mono (single telescope) information.

All shower reconstruction algorithms should be subclasses of Reconstructor which defines some common functionality.

Currently Implemented Algorithms

Moment-based Stereo Reconstruction

Moment-base reconstruction uses the moments of each shower image (the Hillas Parameters to estimate the shower axis for each camera, and combines them geometrically to estimate the true shower direction.

The implementation is in the HillasReconstructor class.

Machine Learning-based Reconstruction

This module also provides Reconstructor implementations using machine learning algorithms.

At the moment, these are based on algorithms from scikit-learn and make use of DL1b and DL2 information.

Template-based Stereo Reconstruction

Moment-base reconstruction uses the a fit of the full camera images to an expected image model to find the best fit shower axis, energy and depth of maximum. The implementation is in the ImPACTReconstructor class.

• Reconstructors Based On Scikit-Learn (sklearn)
  • Introduction
  • Reference/API
    • ctapipe.reco.sklearn Module
      • Classes
        • SKLearnReconstructor
          • SKLearnReconstructor
            • SKLearnReconstructor.features
            • SKLearnReconstructor.instrument_table
            • SKLearnReconstructor.load_path
            • SKLearnReconstructor.model_cls
            • SKLearnReconstructor.model_config
            • SKLearnReconstructor.prefix
            • SKLearnReconstructor.property
            • SKLearnReconstructor.stereo_combiner_cls
            • SKLearnReconstructor.target
            • SKLearnReconstructor.__call__()
            • SKLearnReconstructor.fit()
            • SKLearnReconstructor.predict_table()
            • SKLearnReconstructor.write()
        • SKLearnRegressionReconstructor
          • SKLearnRegressionReconstructor
            • SKLearnRegressionReconstructor.log_target
            • SKLearnRegressionReconstructor.model_cls
        • SKLearnClassificationReconstructor
          • SKLearnClassificationReconstructor
            • SKLearnClassificationReconstructor.invalid_class
            • SKLearnClassificationReconstructor.model_cls
            • SKLearnClassificationReconstructor.positive_class
        • EnergyRegressor
          • EnergyRegressor
            • EnergyRegressor.property
            • EnergyRegressor.target
            • EnergyRegressor.__call__()
            • EnergyRegressor.predict_table()
        • ParticleClassifier
          • ParticleClassifier
            • ParticleClassifier.positive_class
            • ParticleClassifier.property
            • ParticleClassifier.target
            • ParticleClassifier.__call__()
            • ParticleClassifier.predict_table()
        • DispReconstructor
          • DispReconstructor
            • DispReconstructor.features
            • DispReconstructor.instrument_table
            • DispReconstructor.load_path
            • DispReconstructor.log_target
            • DispReconstructor.norm_cls
            • DispReconstructor.norm_config
            • DispReconstructor.prefix
            • DispReconstructor.sign_cls
            • DispReconstructor.sign_config
            • DispReconstructor.stereo_combiner_cls
            • DispReconstructor.target
            • DispReconstructor.__call__()
            • DispReconstructor.fit()
            • DispReconstructor.predict_table()
            • DispReconstructor.read()
            • DispReconstructor.write()
        • CrossValidator
          • CrossValidator
            • CrossValidator.n_cross_validations
            • CrossValidator.output_path
            • CrossValidator.rng_seed
            • CrossValidator.__call__()
            • CrossValidator.close()
• Stereo Combination
  • Introduction
  • Reference/API
    • ctapipe.reco.stereo_combination Module
      • Classes
        • StereoCombiner
          • StereoCombiner
            • StereoCombiner.prefix
            • StereoCombiner.property
            • StereoCombiner.__call__()
            • StereoCombiner.predict_table()
        • StereoMeanCombiner
          • StereoMeanCombiner
            • StereoMeanCombiner.log_target
            • StereoMeanCombiner.weights
            • StereoMeanCombiner.__call__()
            • StereoMeanCombiner.predict_table()
• Analysis With ImPACT
  • What Is ImPACT?
    • Image Templates
    • Image Prediction
    • Calculating Image Likelihood
    • Maximum Likelihood Fitting
    • Performance
  • Running an ImPACT Analysis
    • Choosing Your Minimiser

Reference/API

ctapipe.reco Package

Classes

Reconstructor(**kwargs)	This is the base class from which all reconstruction algorithms should inherit from
HillasGeometryReconstructor(**kwargs)	Base class for algorithms predicting only the shower geometry using Hillas Based methods
ReconstructionProperty(value)	Primary particle properties estimated by a Reconstructor
ShowerProcessor(**kwargs)	Run the stereo event reconstruction on the input events.
HillasReconstructor(**kwargs)	class that reconstructs the direction of an atmospheric shower using a simple hillas parametrisation of the camera images it provides a direction estimate in two steps and an estimate for the shower's impact position on the ground.
ImPACTReconstructor(**kwargs)	This class is an implementation if the impact_reco Monte Carlo Template based image fitting method from parsons14.
HillasIntersection(**kwargs)	This class is a simple re-implementation of Hillas parameter based event reconstruction.
EnergyRegressor(**kwargs)	Use a scikit-learn regression model per telescope type to predict primary energy.
ParticleClassifier(**kwargs)	Predict dl2 particle classification.
DispReconstructor(**kwargs)	Predict absolute value and sign for disp origin reconstruction and convert to altitude and azimuth prediction for each telescope.
StereoCombiner(**kwargs)	Base Class for algorithms combining telescope-wise predictions to common prediction.
StereoMeanCombiner(**kwargs)	Calculate array-event prediction as (weighted) mean of telescope-wise predictions.
CrossValidator(**kwargs)	Class to train sklearn based reconstructors in a cross validation.

ctapipe.reco.reconstructor Module

Classes

Reconstructor(**kwargs)	This is the base class from which all reconstruction algorithms should inherit from
HillasGeometryReconstructor(**kwargs)	Base class for algorithms predicting only the shower geometry using Hillas Based methods
TooFewTelescopesException	Less valid telescope events than required in an array event.
InvalidWidthException	Hillas width is 0 or nan
ReconstructionProperty(value)	Primary particle properties estimated by a Reconstructor

ctapipe.reco.hillas_intersection Module

TODO: - Speed tests, need to be certain the looping on all telescopes is not killing performance - Introduce new weighting schemes - Make intersect_lines code more readable

Classes

HillasIntersection(**kwargs)

This class is a simple re-implementation of Hillas parameter based event reconstruction.

ctapipe.reco.hillas_reconstructor Module

Line-intersection-based fitting for reconstruction of direction and core position of a shower.

Classes

HillasReconstructor(**kwargs)

class that reconstructs the direction of an atmospheric shower using a simple hillas parametrisation of the camera images it provides a direction estimate in two steps and an estimate for the shower's impact position on the ground.

ctapipe.reco.impact Module

Implementation of the ImPACT reconstruction algorithm

Classes

ImPACTReconstructor(**kwargs)

This class is an implementation if the impact_reco Monte Carlo Template based image fitting method from parsons14.