Light Curves¶
lightcurve() fits the
charateristics of a source (flux, TS, etc.) in a sequence of time
bins. This method uses the data selection and model of a baseline
analysis (e.g. the full mission) and is therefore restricted to
analyzing time bins that are encompassed by the time selection of the
baseline analysis. In general when using this method it is
recommended to use a baseline time selection of at least several years
or more to ensure the best characterization of background sources in
the ROI.
When fitting a time bin the method will initialize the model to the
current parameters of the baseline analysis. The parameters to be
refit in each time bin may be controlled with free_background,
free_sources, free_radius, free_params, and
shape_ts_threshold options.
Examples¶
# Generate a lightcurve with two bins
lc = gta.lightcurve('sourceA', nbins=2)
# Generate a lightcurve with 1-week binning
lc = gta.lightcurve('sourceA', binsz=86400.*7.0)
# Generate a lightcurve freeing sources within 3 deg of the source
# of interest
lc = gta.lightcurve('sourceA', binsz=86400.*7.0, free_radius=3.0)
# Generate a lightcurve with arbitrary MET binning
lc = gta.lightcurve('sourceA', time_bins=[239557414,242187214,250076614],
free_radius=3.0)
Optimizing Computation Speed¶
By default the lightcurve method will run an end-to-end analysis
in each time bin using the same processing steps as the baseline analysis.
Depending on the data selection and ROI size each time bin may take
10-15 minutes to process. There are several options which can be used
to reduce the lightcurve computation time. The multithread option splits the
analysis of time bins across multiple cores:
# Split lightcurve across all available cores
lc = gta.lightcurve('sourceA', nbins=2, multithread=True)
# split lightcurve across 2 cores
lc = gta.lightcurve('sourceA', nbins=2, multithread=True, nthread=2)
Note that when using the multithread option in a computing cluster
environment one should reserve the appropriate number of cores when
submitting the job.
The use_scaled_srcmap option generates an approximate source map
for each time bin by scaling the source map of the baseline analysis
by the relative exposure.
# Enable scaled source map
lc = gta.lightcurve('sourceA', nbins=2, use_scaled_srcmap=True)
Enabling this option can speed up the lightcurve calculation by at least a factor of 2 or 3 at the cost of slightly reduced accuracy in the model evaluation. For point-source analysis on medium to long timescales (days to years) the additional systematic uncertainty incurred by using scaled source maps should be no more than 1-2%. For analysis of diffuse sources or short time scales (< day) one should verify the systematic uncertainty is less than the systematic uncertainty of the IRFs.
Output¶
The following tables describe the contents of the method output:
Key |
Type |
Description |
|---|---|---|
|
Name of Source, |
|
|
Lower edge of time bin in MET. |
|
|
Upper edge of time bin in MET. |
|
|
Did the likelihood fit converge? True if yes. |
|
|
Copy of the input configuration to this method. |
|
|
TS of variability. Should be distributed as \(\chi^2\) with \(n-1\) degrees of freedom, where \(n\) is the number of time bins. |
Key |
Type |
Description |
|---|---|---|
|
Names of spectral parameters. |
|
|
Spectral parameter values. |
|
|
Spectral parameters errors. |
|
|
Source test statistic. |
|
|
Log-likelihood of the model evaluated at the best-fit normalization of the source. |
|
|
Log-likelihood values for scan of source normalization. |
|
|
Delta Log-likelihood values for scan of source normalization. |
|
|
Energy flux values for scan of source normalization. |
|
|
Flux values for scan of source normalization. |
|
|
Normalization parameters values for scan of source normalization. |
|
|
Number of predicted counts from this source integrated over the analysis energy range. |
|
|
Number of predicted counts from this source integrated over the analysis energy range. |
|
|
Decorrelation energy in MeV. |
|
|
Photon flux (\(\mathrm{cm}^{-2}~\mathrm{s}^{-1}\)) integrated over analysis energy range |
|
|
Photon flux (\(\mathrm{cm}^{-2}~\mathrm{s}^{-1}\)) integrated from 100 MeV to 316 GeV. |
|
|
Photon flux (\(\mathrm{cm}^{-2}~\mathrm{s}^{-1}\)) integrated from 1 GeV to 316 GeV. |
|
|
Photon flux (\(\mathrm{cm}^{-2}~\mathrm{s}^{-1}\)) integrated from 10 GeV to 316 GeV. |
|
|
Photon flux uncertainty (\(\mathrm{cm}^{-2}~\mathrm{s}^{-1}\)) integrated over analysis energy range |
|
|
Photon flux uncertainty (\(\mathrm{cm}^{-2}~\mathrm{s}^{-1}\)) integrated from 100 MeV to 316 GeV. |
|
|
Photon flux uncertainty (\(\mathrm{cm}^{-2}~\mathrm{s}^{-1}\)) integrated from 1 GeV to 316 GeV. |
|
|
Photon flux uncertainty (\(\mathrm{cm}^{-2}~\mathrm{s}^{-1}\)) integrated from 10 GeV to 316 GeV. |
|
|
95% CL upper limit on the photon flux (\(\mathrm{cm}^{-2}~\mathrm{s}^{-1}\)) integrated over analysis energy range |
|
|
95% CL upper limit on the photon flux (\(\mathrm{cm}^{-2}~\mathrm{s}^{-1}\)) integrated from 100 MeV to 316 GeV. |
|
|
95% CL upper limit on the photon flux (\(\mathrm{cm}^{-2}~\mathrm{s}^{-1}\)) integrated from 1 GeV to 316 GeV. |
|
|
95% CL upper limit on the photon flux (\(\mathrm{cm}^{-2}~\mathrm{s}^{-1}\)) integrated from 10 GeV to 316 GeV. |
|
|
Energy flux (\(\mathrm{MeV}~\mathrm{cm}^{-2}~\mathrm{s}^{-1}\)) integrated over analysis energy range |
|
|
Energy flux (\(\mathrm{MeV}~\mathrm{cm}^{-2}~\mathrm{s}^{-1}\)) integrated from 100 MeV to 316 GeV. |
|
|
Energy flux (\(\mathrm{MeV}~\mathrm{cm}^{-2}~\mathrm{s}^{-1}\)) integrated from 1 GeV to 316 GeV. |
|
|
Energy flux (\(\mathrm{MeV}~\mathrm{cm}^{-2}~\mathrm{s}^{-1}\)) integrated from 10 GeV to 316 GeV. |
|
|
Energy flux uncertainty (\(\mathrm{MeV}~\mathrm{cm}^{-2}~\mathrm{s}^{-1}\)) integrated over analysis energy range |
|
|
Energy flux uncertainty (\(\mathrm{MeV}~\mathrm{cm}^{-2}~\mathrm{s}^{-1}\)) integrated from 100 MeV to 316 GeV. |
|
|
Energy flux uncertainty (\(\mathrm{MeV}~\mathrm{cm}^{-2}~\mathrm{s}^{-1}\)) integrated from 1 GeV to 316 GeV. |
|
|
Energy flux uncertainty (\(\mathrm{MeV}~\mathrm{cm}^{-2}~\mathrm{s}^{-1}\)) integrated from 10 GeV to 316 GeV. |
|
|
95% CL upper limit on the energy flux (\(\mathrm{MeV}~\mathrm{cm}^{-2}~\mathrm{s}^{-1}\)) integrated over analysis energy range |
|
|
95% CL upper limit on the energy flux (\(\mathrm{MeV}~\mathrm{cm}^{-2}~\mathrm{s}^{-1}\)) integrated from 100 MeV to 316 GeV. |
|
|
95% CL upper limit on the energy flux (\(\mathrm{MeV}~\mathrm{cm}^{-2}~\mathrm{s}^{-1}\)) integrated from 1 GeV to 316 GeV. |
|
|
95% CL upper limit on the energy flux (\(\mathrm{MeV}~\mathrm{cm}^{-2}~\mathrm{s}^{-1}\)) integrated from 10 GeV to 316 GeV. |
|
|
Differential photon flux (\(\mathrm{cm}^{-2}~\mathrm{s}^{-1}~\mathrm{MeV}^{-1}\)) evaluated at the pivot energy. |
|
|
Differential photon flux (\(\mathrm{cm}^{-2}~\mathrm{s}^{-1}~\mathrm{MeV}^{-1}\)) evaluated at 100 MeV. |
|
|
Differential photon flux (\(\mathrm{cm}^{-2}~\mathrm{s}^{-1}~\mathrm{MeV}^{-1}\)) evaluated at 1 GeV. |
|
|
Differential photon flux (\(\mathrm{cm}^{-2}~\mathrm{s}^{-1}~\mathrm{MeV}^{-1}\)) evaluated at 10 GeV. |
|
|
Differential photon flux uncertainty (\(\mathrm{cm}^{-2}~\mathrm{s}^{-1}~\mathrm{MeV}^{-1}\)) evaluated at the pivot energy. |
|
|
Differential photon flux uncertainty (\(\mathrm{cm}^{-2}~\mathrm{s}^{-1}~\mathrm{MeV}^{-1}\)) evaluated at 100 MeV. |
|
|
Differential photon flux uncertainty (\(\mathrm{cm}^{-2}~\mathrm{s}^{-1}~\mathrm{MeV}^{-1}\)) evaluated at 1 GeV. |
|
|
Differential photon flux uncertainty (\(\mathrm{cm}^{-2}~\mathrm{s}^{-1}~\mathrm{MeV}^{-1}\)) evaluated at 10 GeV. |
|
|
Logarithmic slope of the differential photon spectrum evaluated at the pivot energy. |
|
|
Logarithmic slope of the differential photon spectrum evaluated at 100 MeV. |
|
|
Logarithmic slope of the differential photon spectrum evaluated evaluated at 1 GeV. |
|
|
Logarithmic slope of the differential photon spectrum evaluated at 10 GeV. |
Reference/API¶
- GTAnalysis.lightcurve(name, **kwargs)
Generate a lightcurve for the named source. The function will complete the basic analysis steps for each bin and perform a likelihood fit for each bin. Extracted values (along with errors) are Integral Flux, spectral model, Spectral index, TS value, pred. # of photons. Note: successful calculation of TS:subscript:
varrequires at least one free background parameter and a previously optimized ROI model.