background - Background modeling¶

Introduction¶

gammapy.background contains methods to estimate and model background for specral, image based and cube analyses.

Most of the methods implemented are described in [Berge2007]. Section 7.3 “Background subtraction” and Section 7.4 “Acceptance determination and predicted background” in [Naurois2012] describe mostly the same methods as [Berge2007], except for the “2D acceptance model” described in Section 7.4.3.

Getting Started¶

TODO: example how to read and use a 2D and A 3D background model

Using `gammapy.background`¶

If you’d like to learn more about using gammapy.background, read the following sub-pages:

Reflected regions

Reference/API¶

gammapy.background Package¶

Background estimation and modeling methods.

Functions¶

profile_background_estimate(pos, on_radius, …) Simple profile background estimation

Classes¶

`AdaptiveRingBackgroundEstimator`(r_in, …[, …])	Adaptive ring background algorithm.
`BackgroundEstimate`(on_region, on_events, …)	Container class for background estimate.
`PhaseBackgroundEstimator`(on_region, …)	Background estimation with on and off phases.
`ReflectedRegionsBackgroundEstimator`(…)	Reflected Regions background estimator.
`ReflectedRegionsFinder`(region, center[, …])	Find reflected regions.
`RingBackgroundEstimator`(r_in, width)	Ring background method for cartesian coordinates.