Taking the energy dispersion into accountΒΆ
What you will learn
You will learn how to take the energy dispersion (or energy redistribution) into account.
Although the effect of the energy dispersion is often neglegible there may be cases where you want to consider energy dispersion in an analysis, for example if you are analysing the data down to very low energies.
Warning
Energy dispersion is fully implemented in ctools but is computationally intensive. So be aware that the tools and scripts will take a substantial amount of computing time if energy dispersion is considered.
You may not have recognised, but the examples you have exercised so far have neglected the impact of the energy dispersion on the analysis. In reality, however, the reconstructed event energy will differ from the true photon energy, and this effect will become particularily important at low energies. There are therefore cases where you want to take the energy dispersion into account.
To simulate events taking the energy dispersion into account you run the
ctobssim tool with edisp=yes parameter:
$ ctobssim edisp=yes
RA of pointing (degrees) (0-360) [83.63]
Dec of pointing (degrees) (-90-90) [22.51]
Radius of FOV (degrees) (0-180) [5.0]
Start time (UTC string, JD, MJD or MET in seconds) [2020-01-01T00:00:00]
Stop time (UTC string, JD, MJD or MET in seconds) [2020-01-01T01:00:00]
Lower energy limit (TeV) [0.03]
Upper energy limit (TeV) [150.0]
Calibration database [prod2]
Instrument response function [South_0.5h]
Input model definition XML file [$CTOOLS/share/models/crab.xml]
Output event data file or observation definition XML file [events.fits] events_edisp.fits
You then select events from the simulated data as before using the ctselect tool:
$ ctselect
Input event list or observation definition XML file [events.fits] events_edisp.fits
Radius of ROI around pointing or specified RA/DEC (degrees) (0-180) [NONE] 3.0
Start time (UTC string, JD, MJD or MET in seconds) [2020-01-01T00:10:00]
Stop time (UTC string, JD, MJD or MET in seconds) [2020-01-01T00:40:00]
Lower energy limit (TeV) [NONE] 0.1
Upper energy limit (TeV) [100.0]
Output event list or observation definition XML file [selected_events.fits] selected_events_edisp.fits
Then you bin as before the selected events into a counts cube using the ctbin tool:
$ ctbin
Input event list or observation definition XML file [selected_events.fits] selected_events_edisp.fits
Coordinate system (CEL - celestial, GAL - galactic) (CEL|GAL) [CEL]
Projection method (AIT|AZP|CAR|GLS|MER|MOL|SFL|SIN|STG|TAN) [CAR]
First coordinate of image center in degrees (RA or galactic l) (0-360) [83.63]
Second coordinate of image center in degrees (DEC or galactic b) (-90-90) [22.51]
Image scale (in degrees/pixel) [0.02]
Size of the X axis in pixels [200]
Size of the Y axis in pixels [200]
Algorithm for defining energy bins (FILE|LIN|LOG|POW) [LOG]
Lower energy limit (TeV) [0.1]
Upper energy limit (TeV) [100.0]
Number of energy bins (1-200) [20]
Output counts cube file or observation definition XML file [cntcube.fits] cntcube_edisp.fits
As next step you need to compute the energy dispersion cube using the ctexpcube tool. You run the tool as follows:
$ ctedispcube
Input event list or observation definition XML file [NONE] selected_events_edisp.fits
Calibration database [prod2]
Instrument response function [South_0.5h]
Input counts cube file to extract energy dispersion cube definition [NONE]
Coordinate system (CEL - celestial, GAL - galactic) (CEL|GAL) [CEL]
Projection method (AIT|AZP|CAR|GLS|MER|MOL|SFL|SIN|STG|TAN) [CAR]
First coordinate of image center in degrees (RA or galactic l) (0-360) [83.63]
Second coordinate of image center in degrees (DEC or galactic b) (-90-90) [22.51]
Image scale (in degrees/pixel) [1.0]
Size of the X axis in pixels [10]
Size of the Y axis in pixels [10]
Algorithm for defining energy bins (FILE|LIN|LOG|POW) [LOG]
Lower energy limit (TeV) [0.1]
Upper energy limit (TeV) [100.0]
Number of energy bins (1-1000) [20]
Output energy dispersion cube file [edispcube.fits]
Now you are ready to perform a binned maximum likelihood analysis taking the
energy dispersion into account. You do this by running the ctlike tool
with the edisp=yes parameter. The ctlike tool will now query for the
energy dispersion cube:
$ ctlike edisp=yes
Input event list, counts cube or observation definition XML file [selected_events.fits] cntcube_edisp.fits
Input exposure cube file [expcube.fits]
Input PSF cube file [psfcube.fits]
Input background cube file [bkgcube.fits]
Input energy dispersion cube file [NONE] edispcube.fits
Input model definition XML file [$CTOOLS/share/models/crab.xml] models.xml
Output model definition XML file [crab_results.xml] crab_results_edisp.xml
And here is the output in the log file:
2019-04-02T14:20:38: +=================================+
2019-04-02T14:20:38: | Maximum likelihood optimisation |
2019-04-02T14:20:38: +=================================+
2019-04-02T14:21:49: >Iteration 0: -logL=57889.195, Lambda=1.0e-03
2019-04-02T14:22:51: >Iteration 1: -logL=57887.419, Lambda=1.0e-03, delta=1.776, step=1.0e+00, max(|grad|)=1.111374 [Index:3]
2019-04-02T14:23:54: >Iteration 2: -logL=57887.417, Lambda=1.0e-04, delta=0.002, step=1.0e+00, max(|grad|)=-0.005923 [Index:7]
2019-04-02T14:24:57:
2019-04-02T14:24:57: +=========================================+
2019-04-02T14:24:57: | Maximum likelihood optimisation results |
2019-04-02T14:24:57: +=========================================+
2019-04-02T14:24:57: === GOptimizerLM ===
2019-04-02T14:24:57: Optimized function value ..: 57887.417
2019-04-02T14:24:57: Absolute precision ........: 0.005
2019-04-02T14:24:57: Acceptable value decrease .: 2
2019-04-02T14:24:57: Optimization status .......: converged
2019-04-02T14:24:57: Number of parameters ......: 10
2019-04-02T14:24:57: Number of free parameters .: 4
2019-04-02T14:24:57: Number of iterations ......: 2
2019-04-02T14:24:57: Lambda ....................: 1e-05
2019-04-02T14:24:57: Maximum log likelihood ....: -57887.417
2019-04-02T14:24:57: Observed events (Nobs) ...: 19137.000
2019-04-02T14:24:57: Predicted events (Npred) ..: 19136.996 (Nobs - Npred = 0.00354148293627077)
2019-04-02T14:24:57: === GModels ===
2019-04-02T14:24:57: Number of models ..........: 2
2019-04-02T14:24:57: Number of parameters ......: 10
2019-04-02T14:24:57: === GModelSky ===
2019-04-02T14:24:57: Name ......................: Crab
2019-04-02T14:24:57: Instruments ...............: all
2019-04-02T14:24:57: Observation identifiers ...: all
2019-04-02T14:24:57: Model type ................: PointSource
2019-04-02T14:24:57: Model components ..........: "PointSource" * "PowerLaw" * "Constant"
2019-04-02T14:24:57: Number of parameters ......: 6
2019-04-02T14:24:57: Number of spatial par's ...: 2
2019-04-02T14:24:57: RA .......................: 83.6331 [-360,360] deg (fixed,scale=1)
2019-04-02T14:24:57: DEC ......................: 22.0145 [-90,90] deg (fixed,scale=1)
2019-04-02T14:24:57: Number of spectral par's ..: 3
2019-04-02T14:24:57: Prefactor ................: 5.52559284054621e-16 +/- 9.88229994960437e-18 [1e-23,1e-13] ph/cm2/s/MeV (free,scale=1e-16,gradient)
2019-04-02T14:24:57: Index ....................: -2.48163444213634 +/- 0.015305403980771 [-0,-5] (free,scale=-1,gradient)
2019-04-02T14:24:57: PivotEnergy ..............: 300000 [10000,1000000000] MeV (fixed,scale=1000000,gradient)
2019-04-02T14:24:57: Number of temporal par's ..: 1
2019-04-02T14:24:57: Normalization ............: 1 (relative value) (fixed,scale=1,gradient)
2019-04-02T14:24:57: Number of scale par's .....: 0
2019-04-02T14:24:57: === GCTAModelCubeBackground ===
2019-04-02T14:24:57: Name ......................: BackgroundModel
2019-04-02T14:24:57: Instruments ...............: CTA, HESS, MAGIC, VERITAS
2019-04-02T14:24:57: Observation identifiers ...: all
2019-04-02T14:24:57: Model type ................: "PowerLaw" * "Constant"
2019-04-02T14:24:57: Number of parameters ......: 4
2019-04-02T14:24:57: Number of spectral par's ..: 3
2019-04-02T14:24:57: Prefactor ................: 1.00540991217377 +/- 0.0157241034891596 [0.01,100] ph/cm2/s/MeV (free,scale=1,gradient)
2019-04-02T14:24:57: Index ....................: 0.00380886384530723 +/- 0.00942814666809632 [-5,5] (free,scale=1,gradient)
2019-04-02T14:24:57: PivotEnergy ..............: 1000000 MeV (fixed,scale=1000000,gradient)
2019-04-02T14:24:57: Number of temporal par's ..: 1
2019-04-02T14:24:57: Normalization ............: 1 (relative value) (fixed,scale=1,gradient)
You can also perform an unbinned maximum likelihood analysis taking the energy dispersion into account. In that case the energy dispersion information will be directly determined from the instrument response functions and no energy dispersion cube is required:
$ ctlike edisp=yes
Input event list, counts cube or observation definition XML file [cntcube_edisp.fits] selected_events_edisp.fits
Calibration database [prod2]
Instrument response function [South_0.5h]
Input model definition XML file [models.xml] $CTOOLS/share/models/crab.xml
Output model definition XML file [crab_results_edisp.xml]
Here the output in the log file:
2019-04-02T14:26:58: +=================================+
2019-04-02T14:26:58: | Maximum likelihood optimisation |
2019-04-02T14:26:58: +=================================+
2019-04-02T14:27:00: >Iteration 0: -logL=143060.165, Lambda=1.0e-03
2019-04-02T14:27:02: >Iteration 1: -logL=143059.529, Lambda=1.0e-03, delta=0.636, step=1.0e+00, max(|grad|)=-0.938341 [Prefactor:6]
2019-04-02T14:27:04: >Iteration 2: -logL=143059.529, Lambda=1.0e-04, delta=0.000, step=1.0e+00, max(|grad|)=-0.002461 [Index:7]
2019-04-02T14:27:05:
2019-04-02T14:27:05: +=========================================+
2019-04-02T14:27:05: | Maximum likelihood optimisation results |
2019-04-02T14:27:05: +=========================================+
2019-04-02T14:27:05: === GOptimizerLM ===
2019-04-02T14:27:05: Optimized function value ..: 143059.529
2019-04-02T14:27:05: Absolute precision ........: 0.005
2019-04-02T14:27:05: Acceptable value decrease .: 2
2019-04-02T14:27:05: Optimization status .......: converged
2019-04-02T14:27:05: Number of parameters ......: 10
2019-04-02T14:27:05: Number of free parameters .: 4
2019-04-02T14:27:05: Number of iterations ......: 2
2019-04-02T14:27:05: Lambda ....................: 1e-05
2019-04-02T14:27:05: Maximum log likelihood ....: -143059.529
2019-04-02T14:27:05: Observed events (Nobs) ...: 22407.000
2019-04-02T14:27:05: Predicted events (Npred) ..: 22406.999 (Nobs - Npred = 0.000615512442891486)
2019-04-02T14:27:05: === GModels ===
2019-04-02T14:27:05: Number of models ..........: 2
2019-04-02T14:27:05: Number of parameters ......: 10
2019-04-02T14:27:05: === GModelSky ===
2019-04-02T14:27:05: Name ......................: Crab
2019-04-02T14:27:05: Instruments ...............: all
2019-04-02T14:27:05: Observation identifiers ...: all
2019-04-02T14:27:05: Model type ................: PointSource
2019-04-02T14:27:05: Model components ..........: "PointSource" * "PowerLaw" * "Constant"
2019-04-02T14:27:05: Number of parameters ......: 6
2019-04-02T14:27:05: Number of spatial par's ...: 2
2019-04-02T14:27:05: RA .......................: 83.6331 [-360,360] deg (fixed,scale=1)
2019-04-02T14:27:05: DEC ......................: 22.0145 [-90,90] deg (fixed,scale=1)
2019-04-02T14:27:05: Number of spectral par's ..: 3
2019-04-02T14:27:05: Prefactor ................: 5.61701723486666e-16 +/- 1.00237442767986e-17 [1e-23,1e-13] ph/cm2/s/MeV (free,scale=1e-16,gradient)
2019-04-02T14:27:05: Index ....................: -2.48356027289941 +/- 0.0152626158555975 [-0,-5] (free,scale=-1,gradient)
2019-04-02T14:27:05: PivotEnergy ..............: 300000 [10000,1000000000] MeV (fixed,scale=1000000,gradient)
2019-04-02T14:27:05: Number of temporal par's ..: 1
2019-04-02T14:27:05: Normalization ............: 1 (relative value) (fixed,scale=1,gradient)
2019-04-02T14:27:05: Number of scale par's .....: 0
2019-04-02T14:27:05: === GCTAModelIrfBackground ===
2019-04-02T14:27:05: Name ......................: CTABackgroundModel
2019-04-02T14:27:05: Instruments ...............: CTA
2019-04-02T14:27:05: Observation identifiers ...: all
2019-04-02T14:27:05: Model type ................: "PowerLaw" * "Constant"
2019-04-02T14:27:05: Number of parameters ......: 4
2019-04-02T14:27:05: Number of spectral par's ..: 3
2019-04-02T14:27:05: Prefactor ................: 1.0079566950951 +/- 0.0133706835965654 [0.001,1000] ph/cm2/s/MeV (free,scale=1,gradient)
2019-04-02T14:27:05: Index ....................: 0.00303753293882809 +/- 0.00807470190154737 [-5,5] (free,scale=1,gradient)
2019-04-02T14:27:05: PivotEnergy ..............: 1000000 [10000,1000000000] MeV (fixed,scale=1000000,gradient)
2019-04-02T14:27:05: Number of temporal par's ..: 1
2019-04-02T14:27:05: Normalization ............: 1 (relative value) (fixed,scale=1,gradient)