spectrum - 1D spectrum analysis

Introduction

gammapy.spectrum holds functions and classes related to 1D region based spectral analysis. This includes also simulation tools.

The basic of 1D spectral analysis are explained in this talk. A good reference for the forward-folding on-off likelihood fitting methods is Section 7.5 “Spectra and Light Curves” in [Naurois2012], in publications usually the reference [Piron2001] is used. A standard reference for the unfolding method is [Albert2007].

Getting Started

The following code snippet demonstrates how to load an observation stored in OGIP format and fit a spectral model.

from gammapy.spectrum import SpectrumDatasetOnOff
from gammapy.modeling import Fit
from gammapy.modeling.models import PowerLaw

filename = '$GAMMAPY_DATA/joint-crab/spectra/hess/pha_obs23523.fits'
dataset = SpectrumDatasetOnOff.from_ogip_files(filename)

model = PowerLaw(
    index=2,
    amplitude='1e-12 cm-2 s-1 TeV-1',
    reference='1 TeV',
)

dataset.model = model

fit = Fit([dataset])
result = fit.run()
model.parameters.covariance = result.parameters.covariance
print(model)

It will print the following output to the console:

PowerLaw

Parameters:

       name     value     error        unit      min max frozen
    --------- --------- --------- -------------- --- --- ------
        index 2.817e+00 1.496e-01                nan nan  False
    amplitude 5.142e-11 6.423e-12 cm-2 s-1 TeV-1 nan nan  False
    reference 1.000e+00 0.000e+00            TeV nan nan   True

Covariance:

       name     index   amplitude reference
    --------- --------- --------- ---------
        index 2.239e-02 6.160e-13 0.000e+00
    amplitude 6.160e-13 4.126e-23 0.000e+00
    reference 0.000e+00 0.000e+00 0.000e+00

Using gammapy.spectrum

For more advanced use cases please go to the tutorial notebooks:

The following pages describe gammapy.spectrum in more detail:

Reference/API

gammapy.spectrum Package

1D spectrum analysis.

Classes

BackgroundEstimate(on_region, on_events, …) Container class for background estimate.
CountsSpectrum(energy_lo, energy_hi[, data, …]) Generic counts spectrum.
FluxPoints(table) Flux points container.
FluxPointsDataset(model, data[, mask_fit, …]) Fit a set of flux points with a parametric model.
FluxPointsEstimator(datasets, e_edges[, …]) Flux points estimator.
PhaseBackgroundEstimator(on_region, …) Background estimation with on and off phases.
ReflectedRegionsBackgroundEstimator(…[, binsz]) Reflected Regions background estimator.
ReflectedRegionsFinder(region, center[, …]) Find reflected regions.
SensitivityEstimator(arf, rmf, bkg, livetime) Estimate differential sensitivity.
SpectrumDataset([model, counts, livetime, …]) Spectrum dataset for likelihood fitting.
SpectrumDatasetOnOff([model, counts, …]) Spectrum dataset for on-off likelihood fitting.
SpectrumDatasetOnOffStacker(obs_list) Stack a list of homogeneous datasets.
SpectrumEvaluator(model[, aeff, edisp, …]) Calculate number of predicted counts (npred).
SpectrumExtraction(observations, bkg_estimate) Creating input data to 1D spectrum fitting.