# Source Finding¶

find_sources() is an iterative source-finding algorithm that uses peak detection on a TS map to find new source candidates. The procedure for adding new sources at each iteration is as follows:

• Generate a TS map for the test source model defined with the model argument.
• Identify peaks with sqrt(TS) > sqrt_ts_threshold and an angular distance of at least min_separation from a higher amplitude peak in the map.
• Order the peaks by TS and add a source at each peak starting from the highest TS peak. Set the source position by fitting a 2D parabola to the log-likelihood surface around the peak maximum. After adding each source, re-fit its spectral parameters.
• Add sources at the N highest peaks up to N = sources_per_iter.

Source finding is repeated up to max_iter iterations or until no peaks are found in a given iteration. Sources found by the method are added to the model and given designations PS JXXXX.X+XXXX according to their position in celestial coordinates.

## Examples¶

model = {'Index' : 2.0, 'SpatialModel' : 'PointSource'}
srcs = gta.find_sources(model=model, sqrt_ts_threshold=5.0,
min_separation=0.5)


The method for generating the TS maps can be controlled with the tsmap_fitter option. TS maps can be generated with either tsmap() or tscube().

## Reference/API¶

GTAnalysis.find_sources(prefix='', **kwargs)

An iterative source-finding algorithm that uses likelihood ratio (TS) maps of the region of interest to find new sources. After each iteration a new TS map is generated incorporating sources found in the previous iteration. The method stops when the number of iterations exceeds max_iter or no sources exceeding sqrt_ts_threshold are found.

Parameters: {options} – tsmap (dict) – Keyword arguments dictionary for tsmap method. tscube (dict) – Keyword arguments dictionary for tscube method. peaks (list) – List of peak objects. sources (list) – List of source objects.