ImPACTReconstructor#

class ctapipe.reco.ImPACTReconstructor(**kwargs: Any)[source]#

Bases: HillasGeometryReconstructor

This class is an implementation if the impact_reco Monte Carlo Template based image fitting method from parsons14. This method uses a comparison of the predicted image from a library of image templates to perform a maximum likelihood fit for the shower axis, energy and height of maximum.

Besides the image information, there is also the option to use the time gradient of the pixels across the image as additional information in the fit. This requires an additional set of templates

Because this application is computationally intensive the usual advice to use astropy units for all quantities is ignored (as these slow down some computations), instead units within the class are fixed:

Angular units in radians
Distance units in metres
Energy units in TeV

Parameters:

subarrayctapipe.instrument.SubarrayDescription: The telescope subarray to use for reconstruction
atmosphere_profilectapipe.atmosphere.AtmosphereDensityProfile: Density vs. altitude profile of the local atmosphere
dummy_reconstructorbool, optional: Option to use a set of dummy templates. This can be used for testing the algorithm, but for any actual reconstruction should be set to its default False

References

[parsons14]

Parsons & Hinton, Astroparticle Physics 56 (2014), pp. 26-34

Attributes Summary

`ped_table`
`property`
`root_dir`	Directory containing ImPACT tables
`spe`
`use_time_gradient`	Use time gradient in ImPACT reconstruction.

Methods Summary

`__call__`(event)	Perform the full shower geometry reconstruction on the input event.
`get_hillas_mean`()	This is a simple function to find the peak position of each image in an event which will be used later in the Xmax calculation.
`get_likelihood`(source_x, source_y, core_x, ...)	Get the likelihood that the image predicted at the given test position matches the camera image.
`get_shower_max`(source_x, source_y, core_x, ...)	Function to calculate the depth of shower maximum geometrically under the assumption that the shower maximum lies at the brightest point of the camera image.
`image_prediction`(tel_type, zenith, azimuth, ...)	Creates predicted image for the specified pixels, interpolated from the template library.
`initialise_templates`(tel_type)	Check if templates for a given telescope type has been initialised and if not do it and add to the dictionary
`minimise`(params, step, limits)	Parameters:
`predict`(hillas_dict, subarray, ...[, ...])	Predict method for the ImPACT reconstructor.
`predict_time`(tel_type, zenith, azimuth, ...)	Creates predicted image for the specified pixels, interpolated from the template library.
`reset_interpolator`()	This function is needed in order to reset some variables in the interpolator at each new event.
`set_event_properties`(hillas_dict, ...)	The setter class is used to set the event properties within this class before minimisation can take place.

Attributes Documentation

ped_table = {'ASTRICam': 0.5, 'CHEC': 0.5, 'FlashCam': 1.3, 'LSTCam': 1.4, 'NectarCam': 1.3, 'SST-Camera': 0.5, 'UNKNOWN-960PX': 1.0, 'dummy': 0.01}#

property = 3#

root_dir#: Directory containing ImPACT tables

spe = 0.6#

use_time_gradient#: Use time gradient in ImPACT reconstruction. Requires an extra set of time gradient templates

Methods Documentation

__call__(event)[source]#

Perform the full shower geometry reconstruction on the input event.

Parameters:

eventcontainer: ctapipe.containers.ArrayEventContainer

get_hillas_mean()[source]#: This is a simple function to find the peak position of each image in an event which will be used later in the Xmax calculation. Peak is found by taking the average position of the n hottest pixels in the image.

get_likelihood(source_x, source_y, core_x, core_y, energy, x_max_scale, goodness_of_fit=False)[source]#

Get the likelihood that the image predicted at the given test position matches the camera image.

Parameters:

source_x: float: Source position of shower in the nominal system (in deg)
source_y: float: Source position of shower in the nominal system (in deg)
core_x: float: Core position of shower in tilted telescope system (in m)
core_y: float: Core position of shower in tilted telescope system (in m)
energy: float: Shower energy (in TeV)
x_max_scale: float: Scaling factor applied to geometrically calculated Xmax
goodness_of_fit: boolean: Determines whether expected likelihood should be subtracted from result
Returns
——-
float: Likelihood the model represents the camera image at this position

get_shower_max(source_x, source_y, core_x, core_y, zen)[source]#

Function to calculate the depth of shower maximum geometrically under the assumption that the shower maximum lies at the brightest point of the camera image.

Parameters:

source_x: float: Event source position in nominal frame
source_y: float: Event source position in nominal frame
core_x: float: Event core position in telescope tilted frame
core_y: float: Event core position in telescope tilted frame
zen: float: Zenith angle of event

Returns:

float: Depth of maximum of air shower

image_prediction(tel_type, zenith, azimuth, energy, impact, x_max, pix_x, pix_y)[source]#

Creates predicted image for the specified pixels, interpolated from the template library.

Parameters:

tel_type: string: Telescope type specifier
energy: float: Event energy (TeV)
impact: float: Impact diance of shower (metres)
x_max: float: Depth of shower maximum (num bins from expectation)
pix_x: ndarray: X coordinate of pixels
pix_y: ndarray: Y coordinate of pixels

Returns:

ndarray: predicted amplitude for all pixels

initialise_templates(tel_type)[source]#

Check if templates for a given telescope type has been initialised and if not do it and add to the dictionary

Parameters:

tel_type: dictionary: Dictionary of telescope types in event

Returns:

boolean: Confirm initialisation

minimise(params, step, limits)[source]#

Parameters:

params: ndarray: Seed parameters for fit
step: ndarray: Initial step size in the fit
limits: ndarray: Fit bounds
Returns
——-
tuple: best fit parameters and errors

predict(hillas_dict, subarray, array_pointing, shower_seed, energy_seed, telescope_pointings=None, image_dict=None, mask_dict=None, time_dict=None)[source]#

Predict method for the ImPACT reconstructor. Used to calculate the reconstructed ImPACT shower geometry and energy.

Parameters:

shower_seed: ReconstructedShowerContainer: Seed shower geometry to be used in the fit
energy_seed: ReconstructedEnergyContainer: Seed energy to be used in fit

Returns:

ReconstructedShowerContainer, ReconstructedEnergyContainer:

predict_time(tel_type, zenith, azimuth, energy, impact, x_max)[source]#

Creates predicted image for the specified pixels, interpolated from the template library.

Parameters:

tel_type: string: Telescope type specifier
energy: float: Event energy (TeV)
impact: float: Impact diance of shower (metres)
x_max: float: Depth of shower maximum (num bins from expectation)

Returns:

ndarray: predicted amplitude for all pixels

reset_interpolator()[source]#: This function is needed in order to reset some variables in the interpolator at each new event. Without this reset, a new event starts with information from the previous event.

set_event_properties(hillas_dict, image_dict, time_dict, mask_dict, subarray, array_pointing, telescope_pointing)[source]#

The setter class is used to set the event properties within this class before minimisation can take place. This simply copies a bunch of useful properties to class members, so that we can use them later without passing all this information around.

Parameters:

hillas_dict: dict: dictionary with telescope IDs as key and HillasParametersContainer instances as values
image_dict: dict: Amplitude of pixels in camera images
time_dict: dict: Time information per each pixel in camera images
mask_dict: dict: Event image masks
subarray: dict: Type of telescope
array_pointing: SkyCoord[AltAz]: Array pointing direction in the AltAz Frame
telescope_pointing: SkyCoord[AltAz]: Telescope pointing directions in the AltAz Frame
Returns
——-
None