# Glossary and references#

## Glossary#

1D Analysis#

1D analysis or spectral analysis where data are reduced to a simple 1D geometry along the reconstructed energy axis. In Cherenkov astronomy, this is classically performed with a OFF background measurement.

3D Analysis#

3D analysis or cube analysis, where data are reduced to a 3D cube with spatial coordinates and energy axes. In gammapy, these cube are represented by Map objects (see Sky maps (DL4)) and contained in a MapDataset object.

Aeff#

Short for effective area: it is the IRF representing the detector collection area. See Effective area.

Cash#

The cash statistic is a Poisson fit statistic usually used when signal and background can be modeled. It is defined as $$2 \times log(L)$$ See Cash : Poisson data with background model in fit statistics.

Dataset#

In Gammapy a dataset bundles the data, IRFs, model and a likelihood function. Based on the model and IRFs the predicted number of counts are computed and compared to the measured counts using the likelihood.

EDisp#

Short for energy dispersion: it is the IRF that represents the probability of measuring a given reconstructed energy as a function of the true photon energy. See Energy Dispersion

FoV#

Short for “field of view”: it indicates the angular aperture (sometimes also the solid angle) on the sky that is visible by the instrument with a single pointing.

GTI#

Short for Good Time Interval: it indicates a continuous time interval of data acquisition. In CTA, it also represents a time interval in which the IRFs are supposed to be constant.

IRF#

Short for Instrument Response Function. They are used to model the probability to detect a photon with a number of measured characteristics. See IRF Theory and Instrument Response Functions (DL3).

Joint Analysis#

A joint fit across multiple datasets implies that each dataset is handled independently during the data reduction stage, and the statistics combined during the likelihood fit. The likelihood is computed for each dataset and summed to get the total fit statistic. See Joint Analysis

MET#

Short for Mission Elapsed Time; see also Mission elapsed times (MET) in Time handling in Gammapy.

Reco Energy#

The reconstructed (or measured) energy (often written e_reco) is the energy of the measured photon by contrast with its actual true energy. Measured quantities such as counts are represented along a reco energy axis.

Reflected Background#

Background estimation method typically used for spectral analysis.

Ring Background#

Background estimation method typically used for image analysis.

RoI#

Short for “region of interest”: it indicates the spatial region in which the data are analyzed. In practice, at each energy it corresponds with the sky region in which the dataset mask is True.

Stacked Analysis#

In a stacked analysis individual observations are reduced to datasets which are then stacked to produce a single reduced dataset. The latter is then used to obtain physical information through model fitting. Some approximations must be made to perform dataset stacking (e.g. loss of individual background normalization, averaging of instrument responses, loss of information outside region of interest etc), but this can reduce very significantly the computing and memory cost. For details, see Stacking Multiple Datasets

True Energy#

The true energy (often written e_true) is the energy of the incident photon by contrast with the energy reconstructed by the instrument. Instrument response functions are represented along a true energy axis.

WStat#

The WStat is a Poisson fit statistic usually used for ON-OFF analysis. It is based on the profile likelihood method where the unknown background parameters are marginalized. See WStat : Poisson data with background measurement in fit statistics.

## References#

This is the bibliography containing the literature references for the implemented methods referenced from the Gammapy docs.

Abdalla2018

H.E.S.S. Collaboration (2018), “The H.E.S.S. Galactic plane survey”

Albert2007

Albert et al. (2007), “Unfolding of differential energy spectra in the MAGIC experiment”,

Berge2007

Berge et al. (2007), “Background modelling in very-high-energy gamma-ray astronomy”

Cash1979

Cash (1979), “Parameter estimation in astronomy through application of the likelihood ratio”

Cousins2007

Cousins et al. (2007), “Evaluation of three methods for calculating statistical significance when incorporating a systematic uncertainty into a test of the background-only hypothesis for a Poisson process”

Feldman1998

Feldman & Cousins (1998), “Unified approach to the classical statistical analysis of small signals”

Lafferty1994

Lafferty & Wyatt (1994), “Where to stick your data points: The treatment of measurements within wide bins”

LiMa1983

Li & Ma (1983), “Analysis methods for results in gamma-ray astronomy”

Meyer2010

Meyer et al. (2010), “The Crab Nebula as a standard candle in very high-energy astrophysics”

Mohrmann2019

Mohrmann et al. (2019), “Validation of open-source science tools and background model construction in γ-ray astronomy”

Naurois2012

de Naurois (2012), “Very High Energy astronomy from H.E.S.S. to CTA. Opening of a new astronomical window on the non-thermal Universe”,

Piron2001

Piron et al. (2001), “Temporal and spectral gamma-ray properties of Mkn 421 above 250 GeV from CAT observations between 1996 and 2000”,

Rolke2005

Rolke et al. (2005), “Limits and confidence intervals in the presence of nuisance parameters”,

Stewart2009

Stewart (2009), “Maximum-likelihood detection of sources among Poissonian noise”