estimators - High level estimators

Introduction

The gammapy.estimators submodule contains algorithms and classes for high level flux and significance estimation. This includes estimation flux points, flux maps, flux points, flux profiles and flux light curves. All estimators feature a common API and allow to estimate fluxes in bands of reconstructed energy.

The core of any estimator algorithm is hypothesis testing: a reference model or counts excess is tested against a null hypothesis. From the best fit reference model a flux is derived and a corresponding \(\Delta TS\) value from the difference in fit statistics to the null hypothesis. Assuming one degree of freedom, \(\sqrt{\Delta TS}\) represents an approximation (Wilk’s theorem) of the “classical significance”. In case of a negative best fit flux, e.g. when the background is overestimated, the significance is defined as \(-\sqrt{\Delta TS}\) by convention.

In general the flux can be estimated using two methods:

  1. Based on model fitting: given a (global) best fit model with multiple model components, the flux of the component of interest is re-fitted in the chosen energy, time or spatial region. The new flux is given as a norm with respect to the global reference model. Optionally other component parameters in the global model can be re-optimised. This method is also named forward folding.

  2. Based on excess: in the case of having one energy bin, neglecting the PSF and not re-optimising other parameters, one can estimate the significance based on the analytical solution by [LiMa1983]. In this case the “best fit” flux and significance are given by the excess over the null hypothesis. This method is also named backward folding.

Uniformly for both methods most estimators compute the same basic quantities:

Quantity

Definition

norm

Best fit norm with respect to the reference spectral model

norm_err

Symmetric error on the norm derived from the Hessian matrix

stat

Fit statistics value of the best fit hypothesis

stat_null

Fit statistics value of the null hypothesis

ts

Difference in fit statistics (stat - stat_null )

sqrt_ts

Square root of ts time sign(norm), in case of one degree of freedom, corresponds to significance (Wilk’s theorem)

npred

Predicted counts of the best fit hypothesis, equivalent to correlated counts for backward folding

npred_null

Predicted counts of the null hypothesis, equivalent to correlated null counts for backward folding

npred_excess

Predicted counts of the excess over npred_null, equivalent to (npred - npred_null), equivalent to correlated counts for backward folding

In addition the following optional quantities can be computed:

Quantity

Definition

norm_errp

Positive error of the norm

norm_errn

Negative error of the norm

norm_ul

Upper limit of the norm

norm_scan

Norm scan

stat_scan

Fit statistics scan

To compute the error, assymetric errors as well as upper limits one can specify the arguments n_sigma and n_sigma_ul. The n_sigma arguments are translated into a TS difference assuming ts = n_sigma ** 2.

In addition to the norm values a reference spectral model and energy ranges are given. Using this reference spectral model the norm values can be converted to the following different SED types:

Quantity

Definition

e_ref

Reference energy

e_min

Minimum energy

e_max

Maximum energy

dnde

Differential flux at e_ref

flux

Integrated flux between e_min and e_max

eflux

Integrated energy flux between e_min and e_max

e2dnde

Differential energy flux between e_ref

The same can be applied for the error and upper limit information. More information can be found on the likelihood SED type page.

The FluxPoints and FluxMaps objects can optionally define meta data with the following valid keywords:

Name

Definition

n_sigma

Number of sigma used for error estimation

n_sigma_ul

Number of sigma used for upper limit estimation

ts_threshold_ul

TS threshold to define the use of an upper limit

Getting started

Flux maps

This how to compute flux maps with the ExcessMapEstimator:

import numpy as np
from gammapy.datasets import MapDataset
from gammapy.estimators import ExcessMapEstimator
from astropy import units as u

dataset = MapDataset.read("$GAMMAPY_DATA/cta-1dc-gc/cta-1dc-gc.fits.gz")

estimator = ExcessMapEstimator(
    correlation_radius="0.1 deg", energy_edges=[0.1, 1, 10] * u.TeV
)

maps = estimator.run(dataset)
print(maps["flux"])
WcsNDMap
<BLANKLINE>
    geom  : WcsGeom
    axes  : ['lon', 'lat', 'energy']
    shape : (320, 240, 2)
    ndim  : 3
    unit  : 1 / (cm2 s)
    dtype : float64
<BLANKLINE>

Flux points

This is how to compute flux points:

from astropy import units as u
from gammapy.datasets import SpectrumDatasetOnOff, Datasets
from gammapy.estimators import FluxPointsEstimator
from gammapy.modeling.models import PowerLawSpectralModel, SkyModel

path = "$GAMMAPY_DATA/joint-crab/spectra/hess/"
dataset_1 = SpectrumDatasetOnOff.read(path + "pha_obs23523.fits")
dataset_2 = SpectrumDatasetOnOff.read(path + "pha_obs23592.fits")

datasets = Datasets([dataset_1, dataset_2])

pwl = PowerLawSpectralModel(index=2, amplitude='1e-12  cm-2 s-1 TeV-1')

datasets.models = SkyModel(spectral_model=pwl, name="crab")

estimator = FluxPointsEstimator(
    source="crab", energy_edges=[0.1, 0.3, 1, 3, 10, 30, 100] * u.TeV
)

# this will run a joint fit of the datasets
fp = estimator.run(datasets)
table = fp.to_table(sed_type="dnde", formatted=True)
# print(table[["e_ref", "dnde", "dnde_err"]])

# or stack the datasets
# fp = estimator.run(datasets.stack_reduce())
table = fp.to_table(sed_type="dnde", formatted=True)
# print(table[["e_ref", "dnde", "dnde_err"]])

Reference/API

gammapy.estimators Package

Estimators.

Classes

FluxPoints(data, reference_model[, meta, gti])

Flux points container.

LightCurve(table)

Lightcurve container.

ImageProfile(table)

Image profile class.

Estimator

Abstract estimator base class.

ExcessMapEstimator([correlation_radius, …])

Computes correlated excess, significance and error maps from a map dataset.

TSMapEstimator([model, kernel_width, …])

Compute TS map from a MapDataset using different optimization methods.

ASmoothMapEstimator([scales, kernel, …])

Adaptively smooth counts image.

FluxPointsEstimator([energy_edges])

Flux points estimator.

LightCurveEstimator([time_intervals, atol])

Estimate light curve.

SensitivityEstimator([spectrum, n_sigma, …])

Estimate differential sensitivity.

ImageProfileEstimator([x_edges, method, …])

Estimate profile from image.

ExcessProfileEstimator(regions[, …])

Estimate profile from a DataSet.

Variables

ESTIMATOR_REGISTRY

Registry of estimator classes in Gammapy.