Source Detection¶
fermipy provides several methods for source detection that can be used to look for unmodeled sources as well as evaluate the fit quality of the model. These methods are
 TS Map:
tsmap()
generates a test statistic (TS) map for a new source centered at each spatial bin in the ROI.  TS Cube:
tscube()
generates a TS map using thegttscube
ST application. In addition to generating a TS map this method can also extract a test source likelihood profile as a function of energy and position over the whole ROI.  Residual Map:
residmap()
generates a residual map by evaluating the difference between smoothed data and model maps (residual) at each spatial bin in the ROI.  Source Finding:
find_sources()
is an iterative sourcefinding algorithim that adds new sources to the ROI by looking for peaks in the TS map.
Additional information about using each of these methods is provided in the sections below.
TS Map¶
tsmap()
performs a likelihood
ratio test for an additional source at the center of each spatial bin
of the ROI. The methodology is similar to that of the gttsmap
ST
application but with a simplified source fitting implementation that
significantly speeds up the calculation. For each spatial bin the
method calculates the maximum likelihood test statistic given by
where the summation index k runs over both spatial and energy bins,
μ is the test source normalization parameter, and θ represents the
parameters of the background model. Unlike gttsmap
, the likelihood
fitting implementation used by
tsmap()
only fits for the
normalization of the test source and does not refit parameters of the
background model. The properties of the test source (spectrum and
spatial morphology) are controlled with the model
dictionary
argument. The syntax for defining the test source properties follows
the same conventions as
add_source()
as illustrated in
the following examples.
# Generate TS map for a powerlaw point source with Index=2.0
model = {'Index' : 2.0, 'SpatialModel' : 'PointSource'}
maps = gta.tsmap('fit1',model=model)
# Generate TS map for a powerlaw point source with Index=2.0 and
# restricting the analysis to E > 3.16 GeV
model = {'Index' : 2.0, 'SpatialModel' : 'PointSource'}
maps = gta.tsmap('fit1_emin35',model=model,erange=[3.5,None])
# Generate TS maps for a powerlaw point source with Index=1.5, 2.0, and 2.5
model={'SpatialModel' : 'PointSource'}
maps = []
for index in [1.5,2.0,2.5]:
model['Index'] = index
maps += [gta.tsmap('fit1',model=model)]
If running interactively, the multithread
option can be enabled to
split the calculation across all available cores. However it is not
recommended to use this option when running in a cluster environment.
>>> maps = gta.tsmap('fit1',model=model,multithread=True)
tsmap()
returns a maps
dictionary containing Map
representations of the TS
and NPred of the bestfit test source at each position.
>>> model = {'Index' : 2.0, 'SpatialModel' : 'PointSource'}
>>> maps = gta.tsmap('fit1',model=model)
>>> print(maps.keys())
[u'file', u'name', u'sqrt_ts', u'ts', u'src_dict', u'npred', u'amplitude']
The contents of the output dictionary are described in the following table.
Key  Type  Description 

amplitude  Map 
Bestfit test source amplitude expressed in terms of the spectral prefactor. 
npred  Map 
Bestfit test source amplitude expressed in terms of the total model counts (Npred). 
ts  Map 
Test source TS (twice the logLike difference between null and alternate hypothese). 
sqrt_ts  Map 
Squareroot of the test source TS. 
file  str  Path to a FITS file containing the maps (TS, etc.) generated by this method. 
src_dict  dict  Dictionary defining the properties of the test source. 
Maps are also written as both FITS and rendered image files to the
analysis working directory. All output files are prepended with the
prefix
argument. Sample images for sqrt_ts
and npred
generated
by tsmap()
are shown below. A
colormap threshold for the sqrt_ts
image is applied at 5 sigma with
iscontours at 2 sigma intervals (3,5,7,9, ...) indicating values above
this threshold.
Sqrt(TS)  NPred 

Reference/API¶

GTAnalysis.
tsmap
(prefix=u'', **kwargs) Generate a spatial TS map for a source component with properties defined by the
model
argument. The TS map will have the same geometry as the ROI. The output of this method is a dictionary containingMap
objects with the TS and amplitude of the bestfit test source. By default this method will also save maps to FITS files and render them as image files.This method uses a simplified likelihood fitting implementation that only fits for the normalization of the test source. Before running this method it is recommended to first optimize the ROI model (e.g. by running
optimize()
).Parameters:  prefix (str) – Optional string that will be prepended to all output files (FITS and rendered images).
 model (dict) – Dictionary defining the properties of the test source.
 exclude (str or list of str) – Source or sources that will be removed from the model when computing the TS map.
 loge_bounds (list) – Restrict the analysis to an energy range (emin,emax) in log10(E/MeV) that is a subset of the analysis energy range. By default the full analysis energy range will be used. If either emin/emax are None then only an upper/lower bound on the energy range wil be applied.
 max_kernel_radius (float) – Set the maximum radius of the test source kernel. Using a smaller value will speed up the TS calculation at the loss of accuracy. The default value is 3 degrees.
 make_plots (bool) – Write image files.
 write_fits (bool) – Write a FITS file.
 write_npy (bool) – Write a numpy file.
Returns: maps – A dictionary containing the
Map
objects for TS and source amplitude.Return type:
Residual Map¶
residmap()
calculates the
residual between smoothed data and model maps. Whereas
tsmap()
fits for positive
excesses with respect to the current model,
residmap()
is sensitive to
both positive and negative residuals and therefore can be useful for
assessing the model goodnessoffit. The significance of the
data/model residual at map position (i, j) is given by
where n and m are the data and model maps and k is the
convolution kernel. The spatial and spectral properties of the
convolution kernel are defined with the model
argument. All source
models are supported as well as a gaussian kernel (defined by setting
SpatialModel to Gaussian). The following examples illustrate how
to run the method with different spatial kernels.
# Generate residual map for a Gaussian kernel with Index=2.0 and
# radius (R_68) of 0.3 degrees
model = {'Index' : 2.0,
'SpatialModel' : 'Gaussian', 'SpatialWidth' : 0.3 }
maps = gta.residmap('fit1',model=model)
# Generate residual map for a powerlaw point source with Index=2.0 for
# E > 3.16 GeV
model = {'Index' : 2.0, 'SpatialModel' : 'PointSource'}
maps = gta.residmap('fit1_emin35',model=model,erange=[3.5,None])
# Generate residual maps for a powerlaw point source with Index=1.5, 2.0, and 2.5
model={'SpatialModel' : 'PointSource'}
maps = []
for index in [1.5,2.0,2.5]:
model['Index'] = index
maps += [gta.residmap('fit1',model=model)]
residmap()
returns a maps
dictionary containing Map
representations of the
residual significance and amplitude as well as the smoothed data and
model maps. The contents of the output dictionary are described in
the following table.
Key  Type  Description 

sigma  Map 
Residual significance in sigma. 
excess  Map 
Residual amplitude in counts. 
data  Map 
Smoothed counts map. 
model  Map 
Smoothed model map. 
files  dict  File paths of the FITS image files generated by this method. 
src_dict  dict  Source dictionary with the properties of the convolution kernel. 
Maps are also written as both FITS and rendered image files to the
analysis working directory. All output files are prepended with the
prefix
argument. Sample images for sigma
and excess
generated
by residmap()
are shown below. A
colormap threshold for the sigma
image is applied at both 5 and 5
sigma with iscontours at 2 sigma intervals (5, 3, 3, 5, 7, 9, ...)
indicating values above and below this threshold.
Sigma  Excess Counts 

Reference/API¶

GTAnalysis.
residmap
(prefix=u'', **kwargs) Generate 2D spatial residual maps using the current ROI model and the convolution kernel defined with the
model
argument.Parameters:  prefix (str) – String that will be prefixed to the output residual map files.
 model (dict) – Dictionary defining the properties of the convolution kernel.
 exclude (str or list of str) – Source or sources that will be removed from the model when computing the residual map.
 loge_bounds (list) – Restrict the analysis to an energy range (emin,emax) in log10(E/MeV) that is a subset of the analysis energy range. By default the full analysis energy range will be used. If either emin/emax are None then only an upper/lower bound on the energy range wil be applied.
 make_plots (bool) – Write image files.
 write_fits (bool) – Write FITS files.
Returns: maps – A dictionary containing the
Map
objects for the residual significance and amplitude.Return type:
TS Cube¶
Warning
This method is experimental and is not supported by the current public release of the Fermi STs.

GTAnalysis.
tscube
(prefix=u'', **kwargs) Generate a spatial TS map for a source component with properties defined by the
model
argument. This method uses thegttscube
ST application for source fitting and will simultaneously fit the test source normalization as well as the normalizations of any background components that are currently free. The output of this method is a dictionary containingMap
objects with the TS and amplitude of the bestfit test source. By default this method will also save maps to FITS files and render them as image files.Parameters:  prefix (str) – Optional string that will be prepended to all output files (FITS and rendered images).
 model (dict) – Dictionary defining the properties of the test source.
 do_sed (bool) – Compute the energy binbybin fits.
 nnorm (int) – Number of points in the likelihood v. normalization scan.
 norm_sigma (float) – Number of sigma to use for the scan range.
 tol (float) – Critetia for fit convergence (estimated vertical distance to min < tol ).
 tol_type (int) – Absoulte (0) or relative (1) criteria for convergence.
 max_iter (int) – Maximum number of iterations for the Newton’s method fitter
 remake_test_source (bool) – If true, recomputes the test source image (otherwise just shifts it)
 st_scan_level (int) –
 make_plots (bool) – Write image files.
 write_fits (bool) – Write a FITS file with the results of the analysis.
Returns: maps – A dictionary containing the
Map
objects for TS and source amplitude.Return type:
Source Finding¶
Warning
This method is experimental and still under development. API changes are likely to occur in future releases.
find_sources()
is an iterative sourcefinding
algorithm that uses peak detection on the TS map to find the locations
of new sources.

GTAnalysis.
find_sources
(prefix=u'', **kwargs) An iterative sourcefinding algorithm.
Parameters:  model (dict) – Dictionary defining the properties of the test source. This is the model that will be used for generating TS maps.
 sqrt_ts_threshold (float) – Source threshold in sqrt(TS). Only peaks with sqrt(TS) exceeding this threshold will be used as seeds for new sources.
 min_separation (float) – Minimum separation in degrees of sources detected in each iteration. The source finder will look for the maximum peak in the TS map within a circular region of this radius.
 max_iter (int) – Maximum number of source finding iterations. The source finder will continue adding sources until no additional peaks are found or the number of iterations exceeds this number.
 sources_per_iter (int) – Maximum number of sources that will be added in each iteration. If the number of detected peaks in a given iteration is larger than this number, only the N peaks with the largest TS will be used as seeds for the current iteration.
 tsmap_fitter (str) –
Set the method used internally for generating TS maps. Valid options:
 tsmap
 tscube
 tsmap (dict) – Keyword arguments dictionary for tsmap method.
 tscube (dict) – Keyword arguments dictionary for tscube method.
Returns:  peaks (list) – List of peak objects.
 sources (list) – List of source objects.