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.
Reference/API¶
ctapipe.reco Package¶
Classes¶
|
This is the base class from which all reconstruction algorithms should inherit from |
|
Base class for algorithms predicting only the shower geometry using Hillas Based methods |
|
Primary particle properties estimated by a |
|
Run the stereo event reconstruction on the input events. |
|
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. |
|
This class is an implementation if the impact_reco Monte Carlo Template based image fitting method from parsons14. |
|
This class is a simple re-implementation of Hillas parameter based event reconstruction. |
|
Use a scikit-learn regression model per telescope type to predict primary energy |
|
Predict dl2 particle classification |
|
Predict absolute value and sign for disp origin reconstruction for each telescope. |
|
Base Class for algorithms combining telescope-wise predictions to common prediction |
|
Calculate array-event prediction as (weighted) mean of telescope-wise predictions |
|
Class to train sklearn based reconstructors in a cross validation |
ctapipe.reco.reconstructor Module¶
Classes¶
|
This is the base class from which all reconstruction algorithms should inherit from |
|
Base class for algorithms predicting only the shower geometry using Hillas Based methods |
Less valid telescope events than required in an array event. |
|
Hillas width is 0 or nan |
|
|
Primary particle properties estimated by a |
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¶
|
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¶
|
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¶
Functions¶
|
|
|
|
|
Simple estimation of depth of shower max based on the expected gamma-ray elongation rate. |
Classes¶
|
This class is an implementation if the impact_reco Monte Carlo Template based image fitting method from parsons14. |