TS Cube¶
Warning
This method requires Fermi Science Tools version 110400 or later.
tscube()
can be used generate
both test statistic (TS) maps and binbybin scans of the test source
likelihood as a function of spatial pixel and energy bin (likelihood cubes).
The implemention is based on the gttscube
ST application which uses
an efficient newton optimization algorithm for fitting the test source at
each pixel in the ROI.
The TS map output has the same format as TS maps produced by
tsmap()
(see TS Map for
further details). However while
tsmap()
fixes the background
model, tscube()
can also fit
background normalization parameters when scanning the test source
likelihood. This method makes no approximations in the
evaluation of the likelihood and may be somewhat slower than
tsmap()
depending on the ROI
dimensions and energy bounds.
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. Normalization parameters of the background model are refit at every test source position if they are free in the model. All other spectral parameters (indices etc.) are kept fixed.
Examples¶
The method is executed by providing a model
dictionary argument that
defines the spectrum and spatial morphology of the test source:
# Generate TS cube for a powerlaw point source with Index=2.0
model = {'Index' : 2.0, 'SpatialModel' : 'PointSource'}
cube = gta.tscube('fit1',model=model)
# Generate TS cube for a powerlaw point source with Index=2.0 and
# restricting the analysis to E > 3.16 GeV
model = {'Index' : 2.0, 'SpatialModel' : 'PointSource'}
cube = gta.tscube('fit1_emin35',model=model,erange=[3.5,None])
# Generate TS cubes for a powerlaw point source with Index=1.5, 2.0, and 2.5
model={'SpatialModel' : 'PointSource'}
cubes = []
for index in [1.5,2.0,2.5]:
model['Index'] = index
cubes += [gta.tsmap('fit1',model=model)]
In addition to generating a TS map, this method can also extract a
test source likelihood profile as a function of energy at every
position in the ROI (likelihood cube). This information is saved to
the SCANDATA
HDU of the output FITS file:
from astropy.table import Table
cube = gta.tscube('fit1',model=model, do_sed=True)
tab_scan = Table.read(cube['file'],'SCANDATA')
tab_ebounds = Table.read(cube['file'],'EBOUNDS')
eflux_scan = tab_ebounds['REF_EFLUX'][None,:,None]*tab_scan['norm_scan']
# Plot likelihood for pixel 400 and energy bin 2
plt.plot(eflux_scan[400,2],tab_scan['dloglike_scan'][400,2])
The likelihood profile cube can be used to evaluate the likelihood for
a test source with an arbitrary spectral model at any position in the
ROI. The TSCube
and CastroData
classes can be used to analyze a TS cube:
from fermipy.castro import TSCube
tscube = TSCube.create_from_fits('tscube.fits')
cd = tscube.castroData_from_ipix(400)
# Fit the likelihoods at pixel 400 with different spectral models
cd.test_spectra()
Configuration¶
The default configuration of the method is controlled with the tscube section of the configuration file. The default configuration can be overriden by passing the option as a kwargs argument to the method.
Option  Default  Description 

cov_scale 
1.0  Scale factor to apply to broadband fitting cov. matrix in binbybin fits ( < 0 > fixed ) 
cov_scale_bb 
1.0  Scale factor to apply to global fitting cov. matrix in broadband fits. ( < 0 > no prior ) 
do_sed 
True  Compute the energy binbybin fits 
exclude 
None  List of sources that will be removed from the model when computing the TS map. 
init_lambda 
0  Initial value of damping parameter for newton step size calculation. A value of zero disables damping. 
max_iter 
30  Maximum number of iterations for the Newtons method fitter. 
model 
None  Dictionary defining the spatial/spectral properties of the test source. If model is None the test source will be a PointSource with an Index 2 powerlaw spectrum. 
nnorm 
10  Number of points in the likelihood v. normalization scan 
norm_sigma 
5.0  Number of sigma to use for the scan range 
remake_test_source 
False  If true, recomputes the test source image (otherwise just shifts it) 
st_scan_level 
0  Level to which to do STbased fitting (for testing) 
tol 
0.001  Critetia for fit convergence (estimated vertical distance to min < tol ) 
tol_type 
0  Absoulte (0) or relative (1) criteria for convergence. 
Reference/API¶

GTAnalysis.
tscube
(prefix='', **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: