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← Revision 17 as of 2018-11-15 08:49:46 ⇥
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| You will find here simulated light curves containing short-period pulsations, created with SIXTE and SRCTOOL. | 1. You will find here simulated light curves containing short-period pulsations, created with SIXTE and SRCTOOL. |
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| 1. The SIXTE event files were created for a source flux of 1e-11 erg/s, with a power-law spectrum (nH=0.5, Gamma=2). 2. The input light curves are sine functions with amplitudes varying from 0.4 to 1.0 and with periods from 0.5 to 10 s. |
* The SIXTE event files were created for a source flux of 1e-11 erg/s, with a power-law spectrum (nH=0.5, Gamma=2).<<BR>> * The input light curves for SIXTE are sine functions with amplitudes varying from 0.4 to 1.0, with periods from 0.5 to 10 s and time sampling of 0.1 s.<<BR>> * The total duration of the observations are 9e4 s, corresponding to 7 consecutive scans.<<BR>> * From the event files created by SIXTE, light curves are generated using the eSASS SRCTOOL, with 0.1 s time sampling. |
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| 3. The total duration of the light curves are 900000 s, corresponding to 7 consecutive scans. | Example of light curves (7 telescopes merged): [[attachment:sine_am0.497_pe5.9_040_LightCurve_source_1.fits]]<<BR>> [[attachment:sine_am0.499_pe9.2_040_LightCurve_source_1.fits]]<<BR>> [[attachment:sine_am0.506_pe1.1_040_LightCurve_source_1.fits]]<<BR>> [[attachment:sine_am0.732_pe1.9_040_LightCurve_source_1.fits]]<<BR>> [[attachment:sine_am0.759_pe6.6_040_LightCurve_source_1.fits]]<<BR>> [[attachment:sine_am0.904_pe1.2_040_LightCurve_source_1.fits]]<<BR>> [[attachment:sine_am0.990_pe4.5_040_LightCurve_source_1.fits]]<<BR>> [[attachment:sine_am0.992_pe7.7_040_LightCurve_source_1.fits]]<<BR>> The value of the simulated amplitude and period (s), is indicated in the file name. 2. The code to perform Lomb-Scargle period search on eROSITA/SRCTOOL light curves is available here: [[attachment:eROSITA_LS.py]]<<BR>> * It is optimized to find short period (few seconds) pulsations on either single or multiple consecutive scans.<<BR>> * It optionally calculate confidence levels using the Wild Bootstrap method (flux randomization).<<BR>> * A Gaussian function is fitted on the highest peak to retrieve the best period and estimation of the period error (1 sigma of the fitted function).<<BR>> * Output parameters are the best period, the standard deviation of the fitted Gaussian function and confidence levels values. To make it run on the ds machines, you need to setup the Anaconda environment (necessary for the {{{gatspy}}} library) by typing on a bash shell:<<BR>> {{{source /utils/anaconda-setup.sh}}} An example of how to call the above routine and give input parameters, is given here: [[attachment:call_eROSITA_LS.py]]<<BR>> For any question or comment, do not hesitate to contact me. |
1. You will find here simulated light curves containing short-period pulsations, created with SIXTE and SRCTOOL.
Some basic infos:
* The SIXTE event files were created for a source flux of 1e-11 erg/s, with a power-law spectrum (nH=0.5, Gamma=2).
* The input light curves for SIXTE are sine functions with amplitudes varying from 0.4 to 1.0, with periods from 0.5 to 10 s and time sampling of 0.1 s.
* The total duration of the observations are 9e4 s, corresponding to 7 consecutive scans.
* From the event files created by SIXTE, light curves are generated using the eSASS SRCTOOL, with 0.1 s time sampling.
Example of light curves (7 telescopes merged):
sine_am0.497_pe5.9_040_LightCurve_source_1.fits
sine_am0.499_pe9.2_040_LightCurve_source_1.fits
sine_am0.506_pe1.1_040_LightCurve_source_1.fits
sine_am0.732_pe1.9_040_LightCurve_source_1.fits
sine_am0.759_pe6.6_040_LightCurve_source_1.fits
sine_am0.904_pe1.2_040_LightCurve_source_1.fits
sine_am0.990_pe4.5_040_LightCurve_source_1.fits
sine_am0.992_pe7.7_040_LightCurve_source_1.fits
The value of the simulated amplitude and period (s), is indicated in the file name.
2. The code to perform Lomb-Scargle period search on eROSITA/SRCTOOL light curves is available here:
* It is optimized to find short period (few seconds) pulsations on either single or multiple consecutive scans.
* It optionally calculate confidence levels using the Wild Bootstrap method (flux randomization).
* A Gaussian function is fitted on the highest peak to retrieve the best period and estimation of the period error (1 sigma of the fitted function).
* Output parameters are the best period, the standard deviation of the fitted Gaussian function and confidence levels values.
To make it run on the ds machines, you need to setup the Anaconda environment (necessary for the gatspy library) by typing on a bash shell:
source /utils/anaconda-setup.sh
An example of how to call the above routine and give input parameters, is given here:
For any question or comment, do not hesitate to contact me.