m (→Detecting stars) |
m |
||
| Line 45: | Line 45: | ||
Files containing the absolute and relative astrometry are also placed in the directory ./astrom while the individual files will have an extension of *.atrans and *.dtrans, respectively. These transformation files are suitable as an input for the tasks grtrans and fitrans, the latter one will also be used in the next step (registration). | Files containing the absolute and relative astrometry are also placed in the directory ./astrom while the individual files will have an extension of *.atrans and *.dtrans, respectively. These transformation files are suitable as an input for the tasks grtrans and fitrans, the latter one will also be used in the next step (registration). | ||
| − | <bash> | + | <syntaxhighlight lang="bash"> |
#!/bin/bash | #!/bin/bash | ||
| Line 72: | Line 72: | ||
done | done | ||
| − | </ | + | </syntaxhighlight> |
The absoulte astrometric transformations (in the files *.atrans) can be used to precisely estimate the plate scale of the images and therefore the size of the field-of-view. Since the coordinates in the reference catalogue varuna.cat are expressed in degrees, the linear coefficiens (in practice, the square root of the determinant of the matrix for the linear coefficients) directly give us the plate scale in pixels/degree units. This is approximately 3508.5 for this setup, thus the field-of-view has a size of 4096/3508.5 = 1.167 degrees. | The absoulte astrometric transformations (in the files *.atrans) can be used to precisely estimate the plate scale of the images and therefore the size of the field-of-view. Since the coordinates in the reference catalogue varuna.cat are expressed in degrees, the linear coefficiens (in practice, the square root of the determinant of the matrix for the linear coefficients) directly give us the plate scale in pixels/degree units. This is approximately 3508.5 for this setup, thus the field-of-view has a size of 4096/3508.5 = 1.167 degrees. | ||
| Line 84: | Line 84: | ||
The output of this process step are placed in the directory ./reg. Note that since the registered images are also stored in FITS format, these share the same extension (*.fits) as the original images in the ./fits directory. | The output of this process step are placed in the directory ./reg. Note that since the registered images are also stored in FITS format, these share the same extension (*.fits) as the original images in the ./fits directory. | ||
| − | <bash> | + | <syntaxhighlight lang="bash"> |
#!/bin/bash | #!/bin/bash | ||
| Line 100: | Line 100: | ||
done | done | ||
| − | </ | + | </syntaxhighlight> |
=== Flux level scaling === | === Flux level scaling === | ||
| Line 108: | Line 108: | ||
Since the FOV of these images is relatively large (1.167 × 1.167 degrees), both the background removal and the flux level scaling is done in a linear (first) order. To fit a certain background level or a flux scale level, the task lfit is used to obtain the coefficients. In order to subtract the background and scale the flux levels, the task fiarith is used to evaluate the coefficients obtained by lfit. | Since the FOV of these images is relatively large (1.167 × 1.167 degrees), both the background removal and the flux level scaling is done in a linear (first) order. To fit a certain background level or a flux scale level, the task lfit is used to obtain the coefficients. In order to subtract the background and scale the flux levels, the task fiarith is used to evaluate the coefficients obtained by lfit. | ||
| − | <bash> | + | <syntaxhighlight lang="bash"> |
#!/bin/bash | #!/bin/bash | ||
| Line 125: | Line 125: | ||
done | done | ||
| − | </ | + | </syntaxhighlight> |
| − | <bash> | + | <syntaxhighlight lang="bash"> |
#!/bin/bash | #!/bin/bash | ||
| Line 154: | Line 154: | ||
done | done | ||
| − | </ | + | </syntaxhighlight> |
=== Astrometry of the moving target Varuna === | === Astrometry of the moving target Varuna === | ||
| Line 164: | Line 164: | ||
Some notes. The filter of `head -n -2` in the script expunges the last two frames of the basename list, since due to twilight, these frames are rather noisy and Varuna can only barely be detected. In addition, Varuna might not be detected in some of the frames, thus the condition `if [ -n "$l" ] ; then ... ; fi` is used to print only those lines where there is a detection for this TNO. | Some notes. The filter of `head -n -2` in the script expunges the last two frames of the basename list, since due to twilight, these frames are rather noisy and Varuna can only barely be detected. In addition, Varuna might not be detected in some of the frames, thus the condition `if [ -n "$l" ] ; then ... ; fi` is used to print only those lines where there is a detection for this TNO. | ||
| − | <bash> | + | <syntaxhighlight lang="bash"> |
#!/bin/bash | #!/bin/bash | ||
| Line 182: | Line 182: | ||
n=$((n+1)) | n=$((n+1)) | ||
done > varuna.star | done > varuna.star | ||
| − | </ | + | </syntaxhighlight> |
By inspecting the output file varuna.star, it can be seen that the ''individual'' centroid coordinates have a scatter of 0.3 pixels. However, there are numerous (89) frames that can be used for astrometry, so the initializing step of the photometry is to perform a regression on this file, by fitting a ``straight line'' to the individual X and Y pixel coordinates, independently. In this process, we silently exploit the fact that all of the 89 frames were taken with exactly the same cadence, so using the frame sequence number for this linear interpolation is as good as the time for an independent value of this linear fit. Of course, if cadence varies in time, this frame number $n must be replaced by the time (for instance, Julian Day). | By inspecting the output file varuna.star, it can be seen that the ''individual'' centroid coordinates have a scatter of 0.3 pixels. However, there are numerous (89) frames that can be used for astrometry, so the initializing step of the photometry is to perform a regression on this file, by fitting a ``straight line'' to the individual X and Y pixel coordinates, independently. In this process, we silently exploit the fact that all of the 89 frames were taken with exactly the same cadence, so using the frame sequence number for this linear interpolation is as good as the time for an independent value of this linear fit. Of course, if cadence varies in time, this frame number $n must be replaced by the time (for instance, Julian Day). | ||
| Line 192: | Line 192: | ||
These interpolated pixel coordinates are then simply used as an input for the photometry, in parallel with the respective scaled images. The flux scaling allows us to treat the output of the photometry directly as the magnitude of Varuna. The individual calls of fiphot print the results to the standard output, that is collected by the shell loop itself by feeding the file varuna-a2.0.phot. This file contains the Julian Day as well, (that is derived using the appropriate FITS keywords with some simple tricks), so the final output file can directly be used by any plotting or analysis tool. | These interpolated pixel coordinates are then simply used as an input for the photometry, in parallel with the respective scaled images. The flux scaling allows us to treat the output of the photometry directly as the magnitude of Varuna. The individual calls of fiphot print the results to the standard output, that is collected by the shell loop itself by feeding the file varuna-a2.0.phot. This file contains the Julian Day as well, (that is derived using the appropriate FITS keywords with some simple tricks), so the final output file can directly be used by any plotting or analysis tool. | ||
| − | <bash> | + | <syntaxhighlight lang="bash"> |
#!/bin/bash | #!/bin/bash | ||
| Line 217: | Line 217: | ||
n=$((n+1)) | n=$((n+1)) | ||
done > varuna-a2.0.phot | done > varuna-a2.0.phot | ||
| − | </ | + | </syntaxhighlight> |
This example demonstrates a data reduction process in which the optical lightcurve of the fastly rotating trans-Neptunian object (TNO), (20000) Varuna has been measured. Observations were made in the night of 2010 November 05/06, between 23:34 and 04:27 UT using the 60/90/180 cm Schmidt telescope located on the Piszkés-tető Mountain Station of the Konkoly Observatory, Hungary. Varuna orbits the Sun: through this set of observations, it has an apparent porper motion of approximately 4", that is not negligible. Therefore, photometry should be done by correcting to the motion of the target itself, however, due to the relatively short time scale, a linear motion is a rather good approximation for the apprrent motion.
All in all, scientific 89 frames have been acquired with an exposure time of 180 seconds and without any interruption, using a 4K × 4K CCD camera mounted on the primary focal plane of the telescope. With this configuration, the effective field-of-view (FOV) of the images were approximately 1.2 × 1.2 degrees (see later on for a more accurate estimation on the size of the FOV that is based on the analysis of auxiliary data yielded by the data reduction process).
Throughout the reduction process, we assume that the calibrated scientific frames (89 in total) are stored in a sub-directory named ./fits (see other examples related to calibration). In this example, the FITS files are named as vf_20101105-0001-E.fits ... vf_20101105-0089-E.fits. Let us suppose that the ``basename of these files (i.e. the name of each FITS file without the *.fits extension) is stored in a file named base.list. In the following, all file products of the reduction process that is related to the individual scientific frames are 1/ stored in some sub-directories depending on the context (e.g. data related to astrometry will be placed into the directory ./astrom) and 2/ named using the above mentioned ``basename with an appropriate extension. For instance, the list of stars detected on the first frame is found in the file ./astrom/vf_20101105-0001-E.stars.
In principle, the series of scripts presented in this example do the following:
We have to note here that the above listed steps of data reduction process is only one of the many possibilities. Of course, there are many alternatives, which might be implemented as well with approximately the same complexity, for example:
Contents |
This script uses the task fistar to detect the stars on each image. The flux threshold is set to 50000: this is a relatively high value, however, it is enough to detect approximately 2500 stars on each image (since the field itself is in the Milky Way). Such a number of individual stars are sufficient for both absoulte and relative astrometry.
Files containing the list of stars are placed in the directory ./astrom while the individual files will have an extension of *.stars. This list of detected stars contain 8 columns in the respective order of: internal identifier (a simple integer), X and Y coordinates, three profile shape parameters (S, D and K), and an estimation for the brightness (both in flux units and magnitudes).
#!/bin/bash FITS=./fits ASTR=./astrom cat base.list | \ while read base dummy ; do fistar $FITS/$base.fits \ --flux-threshold 50000 --model elliptic \ --format id,x,y,s,d,k,flux,mag \ --output $ASTR/$base.stars echo "$base: done." >> /dev/stderr done
This script uses two subsequent calls of the task grmatch to obtain absoulte and relative astrometry, respectively. The first call for grmatch uses the reference catalogue found in the file varuna.cat, that is an excerpt from the USNO-B1.0 catalogue for this field. The relative astrometry (done by the second call of grmatch) derives a spatial transformation which moves the frame to one of the images that has been selected for being a reference (vf_20101105-0045-E, in practice, this is the middle frame for this series of observations).
Files containing the absolute and relative astrometry are also placed in the directory ./astrom while the individual files will have an extension of *.atrans and *.dtrans, respectively. These transformation files are suitable as an input for the tasks grtrans and fitrans, the latter one will also be used in the next step (registration).
#!/bin/bash ASTR=./astrom ref=vf_20101105-0045-E cat base.list | \ while read base dummy ; do grmatch --reference varuna.cat --col-ref 2,3 --col-ref-ordering -5 \ --input $ASTR/$base.stars --col-inp 2,3 --col-inp-ordering -8 \ --max-distance 1 \ --order 3 --triangulation auto,unitarity=0.01,maxref=1000,maxinp=1000 \ --weight reference,column=5,magnitude,power=2 \ --comment --output-transformation $ASTR/$base.atrans grmatch --reference $ASTR/$ref.stars --col-ref 2,3 --col-ref-ordering -8 \ --input $ASTR/$base.stars --col-inp 2,3 --col-inp-ordering -8 \ --max-distance 1 \ --order 3 --triangulation auto,unitarity=0.01,maxref=1000,maxinp=1000 \ --weight reference,column=8,magnitude,power=2 \ --comment --output-transformation $ASTR/$base.dtrans echo "$base: done." >> /dev/stderr done
The absoulte astrometric transformations (in the files *.atrans) can be used to precisely estimate the plate scale of the images and therefore the size of the field-of-view. Since the coordinates in the reference catalogue varuna.cat are expressed in degrees, the linear coefficiens (in practice, the square root of the determinant of the matrix for the linear coefficients) directly give us the plate scale in pixels/degree units. This is approximately 3508.5 for this setup, thus the field-of-view has a size of 4096/3508.5 = 1.167 degrees.
We should note here that regarding to the reducition of astronomical images, ``astrometry might mean slightly different things, depending on the context. The default interpretation of the term ``astrometry is to obtain the celestial corrdinates (α: right ascension, δ:declination) of an object by analyzing the images. The most simple way to do this is to fit the (x,y) pixel coordinates of the centroid of the given object (star, minor planet, ...), and then do an (x,y) ↔ (α,δ) transformation. In the practice of the FITS package, this transformation is done in two steps. See the example related to astrometry for more details.
This script employs the task fitrans to register the original calibrated frames to the same system as the reference image (vf_20101105-0045-E). Since the transformation files (*.dtrans) describe how the reference system should be transformed to the individual frames, here we should use the reverse of this transformation. The command line switch -c for fitrans imply a spline-based inter-pixel interpolation instead of the default linear interpolation.
The output of this process step are placed in the directory ./reg. Note that since the registered images are also stored in FITS format, these share the same extension (*.fits) as the original images in the ./fits directory.
#!/bin/bash FITS=./fits ASTR=./astrom REG=./reg cat base.list | \ while read base dummy ; do fitrans $FITS/$base.fits --input-transformation $ASTR/$base.dtrans \ -c --reverse --output $REG/$base.fits echo "$base: done." >> /dev/stderr done
The flux level scaling is done in two distinct steps. First, aperture photometry is performed on a pre-selected list of stars (found in the file varuna.reg). The results of this aperture photometry are stored in the directory ./phot. These photometric results are used in the second step, where the background infromation is used to subtract the background of the image to a nearly zero level while the flux information is used to scale the intensity level of the images to a previously selected reference level (that is the same as the astrometric reference, vf_20101105-0045-E).
Since the FOV of these images is relatively large (1.167 × 1.167 degrees), both the background removal and the flux level scaling is done in a linear (first) order. To fit a certain background level or a flux scale level, the task lfit is used to obtain the coefficients. In order to subtract the background and scale the flux levels, the task fiarith is used to evaluate the coefficients obtained by lfit.
#!/bin/bash REG=./reg PHOT=./phot cat base.list | \ while read base dummy ; do fiphot --input $REG/$base.fits \ --input-list varuna.reg --col-xy 2,3 --col-id 1 \ --aperture 8:15:25 --format IXY,FfBb \ --output $PHOT/$base.phot echo "$base: done." >> /dev/stderr done
#!/bin/bash REG=./reg PHOT=./phot SCALED=./scaled ref=vf_20101105-0045-E cat base.list | \ while read base dummy ; do A=($( lfit $PHOT/$base.phot \ -c x:2,y:3,bg:6 -v a,b,c -n 3 -r 3 \ -f "a*(x-2048)/2048+b*(y-2040)/2048+c" -y bg )) B=($( paste $PHOT/$ref.phot $PHOT/$base.phot | \ lfit \ -c x:2,y:3,ri:4,ii:11 -v a,b,c -n 3 -r 3 \ -f "a*(x-2048)/2048+b*(y-2040)/2048+c" -y "ri/ii" )) fiarith "['$REG/$base.fits']((a-((${A[0]})*x+(${A[1]})*y+(${A[2]})))*((${B[0]})*x+(${B[1]})*y+(${B[2]})))" \ --output $SCALED/$base.fits echo "$base: done." >> /dev/stderr done
This script also empolys the task fistar to do astrometry on Varuna itself. A simple visual inspection of the registered (and scaled) frames gives us that Varuna is the only one prominent source in the region of [2021,2034] × [1835,1852]. Thus, we use this constraint for fistar to search stellar profiles only in this section of the scaled images.
This preliminary astrometry for Varuna is stored in a single file named varuna.star. Each line of this file contains the sequence number of the frame (i.e. an integer between 1 and 89) and the same 8 fields as it was described above in the very first reduction step of detecting stars.
Some notes. The filter of `head -n -2` in the script expunges the last two frames of the basename list, since due to twilight, these frames are rather noisy and Varuna can only barely be detected. In addition, Varuna might not be detected in some of the frames, thus the condition `if [ -n "$l" ] ; then ... ; fi` is used to print only those lines where there is a detection for this TNO.
#!/bin/bash SCALED=./scaled n=1 cat base.list | \ head -n -2 | \ while read base dummy ; do l=$(fistar $SCALED/$base.fits \ --only-candidates --format id,x,y,s,d,k,flux,mag \ --flux-threshold 2000 --section 2021:2034,1835:1852) if [ -n "$l" ] ; then echo $n $l fi n=$((n+1)) done > varuna.star
By inspecting the output file varuna.star, it can be seen that the individual centroid coordinates have a scatter of 0.3 pixels. However, there are numerous (89) frames that can be used for astrometry, so the initializing step of the photometry is to perform a regression on this file, by fitting a ``straight line to the individual X and Y pixel coordinates, independently. In this process, we silently exploit the fact that all of the 89 frames were taken with exactly the same cadence, so using the frame sequence number for this linear interpolation is as good as the time for an independent value of this linear fit. Of course, if cadence varies in time, this frame number $n must be replaced by the time (for instance, Julian Day).
The final step of the data processing is performing the aperture photometry on Varuna itself. The centroid coordinates for each frames are derived by a linear interpolation where the linear coefficients are computed by lfit, by fitting a ``straight line to the individual pixel coordinates. The results of these two fits (for X and Y coordinates) are stored in the shell arrays $FX and $FY, respectively, and subsequently used inside the shell loop to obtain the interpolated pixel coordinates.
These interpolated pixel coordinates are then simply used as an input for the photometry, in parallel with the respective scaled images. The flux scaling allows us to treat the output of the photometry directly as the magnitude of Varuna. The individual calls of fiphot print the results to the standard output, that is collected by the shell loop itself by feeding the file varuna-a2.0.phot. This file contains the Julian Day as well, (that is derived using the appropriate FITS keywords with some simple tricks), so the final output file can directly be used by any plotting or analysis tool.
#!/bin/bash FITS=./fits SCALED=./scaled FX=($(lfit varuna.star -c n:1,x:3 -v x0,dx -f "x0+dx*(n-45)" -y x)) FY=($(lfit varuna.star -c n:1,y:4 -v y0,dy -f "y0+dy*(n-45)" -y y)) n=1 cat base.list | \ while read base dummy ; do A=($(fiheader $FITS/$base.fits --get date-obs | tr "T:-" " ")) ymd=(`echo ${A[*]} | awk '{ print $1,$2,$3+($4*3600+$5*60+$6)/86400; }'`) jd=`echo ${ymd[*]} | awk '{ printf("%.5f\n",2455500.5+$3); }'` x=`echo $n | awk -v x0=${FX[0]} -v dx=${FX[1]} '{ n=$1; printf("%.2f\n",x0+dx*(n-45)); }'` y=`echo $n | awk -v y0=${FY[0]} -v dy=${FY[1]} '{ n=$1; printf("%.2f\n",y0+dy*(n-45)); }'` echo $jd $x $y | \ fiphot --input $SCALED/$base.fits \ --input-list - --col-id 1 --col-xy 2,3 \ --aperture 2.0:5:10 --sky-fit mode \ --format IXY,MmBbFf --output - n=$((n+1)) done > varuna-a2.0.phot