Документ взят из кэша поисковой машины. Адрес оригинального документа : http://www.stsci.edu/~borders/training/report_II.pdf Дата изменения: Mon Nov 10 17:10:29 2008 Дата индексирования: Sun Feb 3 15:30:08 2013 Кодировка: Поисковые слова: 47 tuc

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RIA Training Report 2008 Part I I

HST ACS Photometry of of 47 Tucanae
Tiffany Michelle Borders Space Telescope Science Institute November 10, 2008

A

BSTRACT

This paper presents Part II of my RIA training data project. This paper discusses the results of HST ACS photometry of 47 Tuc as observed in F435W (B), F606W (V) and F814W (I).

Intro duction
47 Tuc (NGC 104) is the second largest and second brightest globular cluster in the skies (Frommert 2006). It serves as a go o d candidate for the purpose of a photometry training exercise as well as creating a color magnitude diagram (CMD) due to the large number of stars it contains. The goal of this training exercise is to become familiar with photometry using the Hubble Space Telescope (HST ) and to create a CMD of 47 Tuc. This photometry pro ject involves several steps in order to pro duce the final CMD. These steps include retrieving HST Advanced Camera for Surveys (ACS) data from the archive, identifying stars on the image to use for photometry, computing aperture photometry, applying aperture corrections, computing photometric transformations and plotting the CMD. Each of these steps are discussed in more detail in the following sections.

Copyright c 2008 The Association of Universities for Research in Astronomy, Inc. All Rights Reserved.


Data & Observations
The dataset of 47 Tuc used for this pro ject was taken in May 2002 with HST WFC/ACS (Proposal ID 9018, PI Guido De Marchi ). The first step is to retrieve the raw data pro duct and the calibrated data pro ducts of datasets j8c0a1011, j8c0c1011 and j8c0d1011 from MAST1 (Multimission Archive at STScI). Knowing the dataset numbers is all that is needed to retrieve this data and this can be entered in the "Dataset" entry on the HST search form in MAST. The raw data files were re-calibrated with the IRAF task CALACS using the reference files on centralized storage. In order to run CALACS the PHOTCORR switch needs to be changed from PERFORM to OMIT using the IRAF task HEDIT. CALACS needs to be run on each asn file separately in order to create the flt's and crj's. Using the IRAF task GSTAT on the calibrated raw data and on the calibrated data pro duct from the archive pro duces identical results. Table 1 shows the results from GSTAT. Image NPIX MEAN 9.7843 37.139 42.561 STDDEV 526.904 1544.33 1482.98 MIN MAX

j8c0a1011 crj.fits 8388608 j8c0c1011 crj.fits 8388608 j8c0d1011 crj.fits 8388608 Table 1: GSTAT results.

-3876.86 135092. -2691.99 174208. -3626.64 133633.

Various keywords in the image header can be queried in IRAF by using the HSELECT task. Table 2 shows the results of querying selected keywords in the image header such as the filter, exposure time, PHOTFLAM and the ST magnitude zero point. In order to make calibration corrections it is important to know some information about the units of the data. Raw ACS images are in units of Data Numbers (DN), calibrated images are in units of electrons and drizzled images are in units of electrons/second. The pipeline calibration tasks do not alter the units of the pixels in the image. Instead they calculate and write the inverse sensitivity conversion factor (PHOTFLAM) and the ST magnitude scale zero point (PHOTZPT) into header keywords in the calibrated data. The PHOTFLAM is defined as the mean flux density Flam in units of erg cm-2 s-1 °-1 that pro duces 1 count per A second in the HST observing mo de (HST Data Handbook for ACS 2006). The PHOTFLAM value can be found either in the image header of the dr z image or it can be derived using the bandpar task in SYNPHOT. Table 3 shows the results of comparing the PHOTFLAM values

1

http://archive.stsci.edu/

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Image j8c0a1011 j8c0a1abq j8c0a1adq j8c0c1011 j8c0c1vbq j8c0c1vdq j8c0d1011 j8c0d1buq j8c0d1bwq drz.fits drz.fits drz.fits drz.fits drz.fits drz.fits drz.fits drz.fits drz.fits

Filter CLEAR CLEAR CLEAR F 606W F 606W F 606W CLEAR CLEAR CLEAR 1L 1L 1L CL CL CL 1L 1L 1L F 435W F 435W F 435W EAR2L EAR2L EAR2L F 814W F 814W F 814W

EXPTIME 6 3 3 4 2 2 6 3 3 0 0 0 5 2 2 0 0 0 .0 .0 .0 .0 .5 .5 .0 .0 .0

PHOTFLAM 3 3 3 7 7 7 6 6 6 .1 .1 .1 .7 .7 .7 .9 .9 .9 0 0 0 6 6 6 2 2 2 9 9 9 6 6 6 5 5 5 6 6 6 4 4 4 5 5 5 3 3 3 0 0 0 5 5 5 4 4 4 4 4 4 7 7 7 3 3 3 6 6 6 4 4 4 EEEEEEEEE1 1 1 2 2 2 2 2 2 9 9 9 0 0 0 0 0 0

PHOTZPT 2 2 2 2 2 2 2 2 2 .1 .1 .1 .1 .1 .1 .1 .1 .1 1 1 1 1 1 1 1 1 1 E+ E+ E+ E+ E+ E+ E+ E+ E+ 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1

Table 2: HSELECT results. f ro m differ differ cm-2 the header with those derived using the bandpar task in SYNPHOT. The smallest ence is seen in F435W with a difference of 0.074 erg cm-2 s-1 °-1 , for F606W the A -2 -1 °-1 ence is 0.096 erg cm s A and the largest difference between the values is 0.11 erg A s-1 °-1 for F814W. Image j8c0a1abq j8c0a1adq j8c0c1vbq j8c0c1vdq j8c0d1buq j8c0d1bwq flt.fits flt.fits flt.fits flt.fits flt.fits flt.fits Header PHOTFLAM 3 3 7 7 6 6 .1 .1 .7 .7 .9 .9 0 0 6 6 2 2 9 9 6 6 5 5 6 6 4 4 5 5 3 3 0 0 5 5 4 4 4 4 7 7 3 3 6 6 4 4 EEEEEE1 1 2 2 2 2 9 9 0 0 0 0 SYNPHOT PHOTFLAM 3 3 7 7 7 7 .1 .1 .8 .8 .0 .0 8 8 6 6 3 3 4 4 2 2 3 3 0 0 5 5 2 2 EEEEEE1 1 2 2 2 2 9 9 0 0 0 0

Table 3: PHOTFLAM values Using the PHOTFLAM value from the header the zeropt is computed. For example, for F435W drz.fits the zero point is: z pt = -2.5 log (3.1096343E For F606W drz.fits the zero point is: z pt = -2.5 log (7.7664046E 3
-20 -19

) - 21.1 = 25.168

(1 )

) - 21.1 = 26.674

(2 )


For F814W drz.fits the zero point is: z pt = -2.5 log (6.9255574E
-20

) - 21.1 = 26.799

(3 )

Using the IRAF task IMHEADER on the drz images allows one to see that not each image is identical due to filter-specific distortions. Table 4 shows the IMHEADER results which show a slight change in the image size due to filter-specific distortions. Image j8c0a1011 drz.fits[1] j8c0c1011 drz.fits[1] j8c0d1011 drz.fits[1] Table 4: IMEADER results. Each image then needs to be multiplied by the exposure time so that the IRAF task PHOT will compute the photometric errors correctly. At the same time, I want to make the images the same size. This can be done by using the IRAF task IMCALC. imcalc imcalc imcalc j8c0a1011$\_$drz.fits[1][1:4218, 1:4232] f435w$\_$drz.fits ``im1*60." j8c0c1011$\_$drz.fits[1][1:4218, 1:4232] f435w$\_$drz.fits ``im1*45." j8c0d1011$\_$drz.fits[1][1:4218, 1:4232] f435w$\_$drz.fits ``im1*60." Size [4219,4233][real]: [4219,4233][real]: [4218,4232][real]:

I displayed the cropped images and measured the position of the stars to be sure they are roughly equal from one filer to the next. I determined that the stars are indeed roughly equal from each filter.

Ap erture Photometry
A python script was created to run the photometry of 47 Tuc. Appendix A includes a python photometry script I created for this assignment. In order to perform photometry the stars in the field need to be identified. The next step involves creating a combined color image in order to find stars in the field. This simplifies matters because I only have one source list and won't need to cross correlate my lists for each filter after doing photometery. By comparing the values of PHOTFLAM I can determine how to weight the images when coadding (Step 6A in my python co de). The images can be added by using IMCALC for example, 4


imcalc f435w drz.fits, f606w drz.fits, f814w drz.fits color drz.fits "(4*im1+im2+im3)/6)" The stars in the color drz.fits image were detected with the IRAF task DAOFIND (Step 6B in my python co de). The DAOFIND parameters used are shown in Table 5. Using these parameters DAOFIND found 4,460 ob jects however, it should be noted that some of these "ob jects" include saturated points. Because DAOFIND was having trouble with excluding saturated points and it was finding multiple points within a single star it was suggested to me to try to use SExTractor 2 to determine the stars on the image. This proved to be very worthwhile because SExTractor did a much better job at finding stars and excluding saturated points than DAOFIND. My script includes using DAOFIND as an example of how this can be done within a script however, in the end I did not use the DAOFIND results and instead I used the SExTractor results. Figure 6 shows the parameters used in the default.sex file used for SExTractor. Using these parameters SExTractor found 9,253 stars. Using this list pro duced a much deeper and cleaner CMD than the DAOFIND results. Parameter findpar.thresh findpar.sharplo findpar.sharphi findpar.roundlo findpar.soundhi datapar.sigma datapar.fwhmpsf Table 5: DAOFIND Parameters. The next step is to compute aperture photometry on a list of stars using the PHOT task in IRAF (Step 7 in my python co de). The parameters used for F435W are shown in Table 7. Using the task TDUMP I can extract the following columns from the APPHOT output including x,y,sky,flux3,mag3,err3,flux10,mag10 and err10 (Step 8 in python co de). Next I merge the photometry table for the 3 filters using the task TMERGE and change the column names to something meaningful using the task TCHOL (Step 9 in my python co de). Changing the column names is very useful for keeping track of columns as well as for needing to call those columns for any sort of calculation which we will need to do later in this example. Value 50 0.2 0.8 -1.0 1.0 3.6 2.0

2

http://sextractor.sourceforge.net/

5


Parameter DETECT MINAREA DETECT THRESH ANALYSIS THRESH DEBLEND NTHRESH DEBLEND MINCONT CLEAN PARAM

Value 5.0 5.0 5.0 32 0.01 1.0

Table 6: SExTractor Parameters for extraction in default.sex Parameter datapar.exposure datapar.gain datapar.sigma centerpars.calgorithm centerpars.cbox centerpars.maxshift fitskypars.salgorithm fitskypars.annulus fitskypars.dannulus fitskypars.skyvalue photpars.apertures photpars.zmag phot.image phot.co ords. Table 7: PHOT Parameters. Aperture Correction Aperture corrections need to be applied to my photometry. Accurate aperture corrections are a function of time and lo cation on the chip and should be derived for each frame. First we need to compute the apcor for each filter which is simply mag3 - mag10 (Step 10 in my python co de). Next we find the ac05 value (eventually needed to calculate STmag) which can be found "by eye" by lo oking at a graph of the apcor of each filter versus the mag3 of that filter and estimating the center of the graph. This can be done in IRAF with 6 Value exptime ccdgain 3.6 centroid 10 6 mo de 10 5 0 3,10 25.1682 f435 drz.fits color drz.fits


Fig. 1.-- 435 apcor vs. mag3. the task SGRAPH. For example, sgraph `phot_all.tab 435_apcor mag3_435' wl=-0.5 wr=1 wt=15 wb=23 pointmode=yes Figures 1, 2 and 3 show the results of using SGRAPH. Table 8 compares the apcor values estimated from the graphs to what is predicted from the WFC encircled energy from Sirianni (2 0 0 5 ). Filter F 415W F 606W F 814W Siriani (2005) Apcor Value My Apcor Value 0.131 0.124 0.156 0.15 0.14 0.2

Table 8: Comparison of aperture correction measured from 3 pixels to 0.5" (ac05)

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Fig. 2.-- 606 apcor vs. mag3. In order to go from aperture photometry to absolute magnitude the STmag needs to be calculated (Step 12 of my python co de). The STmag is defined as the magnitude system based on constant flux per unit wavelength. STmag can be calculated using the following equation: S T M AG = z pt - 2.5log (f lux/exptime) - 2.5log (photf lam) - ac05 - AC 05 - C T E (4) where flux is the counts in aperture, exptime is the exposure time, photflam is the inverse sensitivity (erg sec-1 cm-2 °-1 per DN), ac05 is the apcorr from r = 3 to 10 (0.5") (average A value measured for each image), AC05 is apcorr from r=10 (0.5") to infinity (filter specific value taken from ACS table) and CTE is the charge transfer efficiency loss. The next step is to apply the aperture correction measured from 3 pixels to 0.5" (ac05) as well as the aperture correction from 0.5" to infinity (AC05) using Table 5 from Sirianni (2005). These values are listed in Table 9 of this do cument. I have also included the CTE correction in this photometry excercise. The CTE per pixel is defined as the fraction of charge transfered from one pixel to the next during readout. In an ideal CCD, the CTE would be exactly 1.0, with no charge being lost. However, due to imperfections on a CCD which are caused by either the manufacturing pro cess or the 8


Fig. 3.-- 814 apcor vs. mag3. Filter F 415W F 606W F 814W Table 9: AC05 Values from Sirianni (2005) space radiation environment can act as charge traps. The amount of charge lost per pixel is typically a very small fraction of the total charge. The total CTE becomes increasingly significant as larger CCD arrays are manufactured and flown in space (Chiaberge 2005). The CTE is calculated using the following equation: mag = 10A S K Y where A=-0.15 (±0.04), measured near the star, the y-transfers where Y that MultiDrizzle (which
B

AC05 from Siriani (2005) 0.107 0.088 0.087

F LU X C Y /2000 (M J D - 52333)/365

(5 )

B=-0.25 (±0.01),C=-0.44 (±0.02). SKY is the sky in electrons FLUX is the flux of the star in a 3 pixel aperture radius, Y is = y for WFC2 and Y = 4096 - y for WFC1. It should be noted was run through the pipeline) has removed the sky in the drizzled 9


CTE vs. Y position on CCD of WFC/ACS

0.01

F435W F606W F814W

CTE 0.005 0 0 1000 2000 Y position on CCD 3000 4000

Fig. 4.-- CTE vs. Y position on CCD of WFC/ACS. pro duct but not in the flt's or crj pro ducts. For this excercise we use the flt's and crj sky values by plotting an image histogram on the flt's which gives a value of approx 5.5 electrons for SKY (this is the exactly the case for F814W). Applying the CTE, however, only caused a very small difference to the overall photometry. This is shown in Figure 4 which plots the CTE vs. Y position on the CCD. The biggest change o ccurs in the center of the CCD chip just slightly under 0.01 CTE. Thus, the overall effect of the CTE is very small, however, the effect is probably much greater now as the camera has been degraded by the high-radiation space environment longer.

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The next step is to compute the transformation to VEGAmag, using the spectrum of an F5V star. VEGAmag is the standard magnitude system for which Vega has magnitude 0 at all wavelengths. This can be done by executing this command for both the STmag and VEGAmag: calphot "band(acs,wfc1,f435w)" /Volumes/grp/hst/cdbs/grid/bz77/bz$\_$20.fits" The correction for each filter is thus the VEGAmag - STmag using the spectrum of an F5V star as seen below: F 435W = -12.5389 + 13.163 = +0.624 F 606W = -13.096 + 12.8409 = -0.255 F 814W = -13.5356 + 12.2734 = +1.262 This correction is then applied to all of represents my calibrated magnitudes. These (the last part of Step 12 - 14 in my python can be read into a plotting program and my (6 ) (7 ) (8 )

my STmags and the new VEGAmag value now magnitudes can now be used to create my CMD script writes out files for plotting). These files CMD is now complete.

Results
Figure 5 and Figure 6 shows the CMD of 47 Tuc in V - I vs. I and the B - V vs. V filters respectively. There are a number of basic features we can see from the CMD including the main sequence (MS), main sequence turn-off (MSTO), blue stragglers (BS), the red giant branch (RGB) and even some white dwarfs (WD). One of the key features of globular clusters is the well-defined main sequence extending from the turn-off to fainter magnitudes and redder colors. The sharp main-sequence turn-off is typical of globular cluster color-magnitude diagrams, indicating that the stars within an individual globular cluster all formed at roughly the same time. The turnoff of the cluster is at 17 in V and 0.5 in V - I which agrees with the CMD from Piotto et al. (2004). The narrowness of the CMD also indicates that all the stars in the globular cluster have very similar chemical composition. The RGB extends from the subgiants which connect it to the main sequence to brighter magnitudes and redder colors until the tip of the RGB is reached. One o ddity of the CMD of globular clusters is the presence of `blue stragglers' in which several are seen in my CMD of 47 Tuc. The blue stragglers give the appearance of being an extension of the main sequence beyond the turn-off point. Since BS lie along an extension of the MS in a region which is 11


brighter and bluer than the MSTO, they may undergo abnormal stellar evolution. The cause of this is not yet clearly known, but one of the leading hypothesis is that they are current or former binary stars that are in the pro cess of merging or have already done so (Ferraro et al. 2003). Also, in the fainter magnitude and bluer colors of the CMD is the presence of WD's. From the CMD we can determine the cluster's approximate age and evolutionary status and get a relative idea of the age of the cluster. Of key importance is the turn off point on the diagram where the cluster turns-off the main sequence. The further down the main sequence the turnoff point is, the older the cluster. Since the turnoff is 0.5 V - I we know that the cluster is relatively old. In a paper by Kaluzny et al. (2007) they used spectroscopic observations of the eclipsing binary V228 to derive an upper limit to the age of 47 Tuc as 14 Gyr. This can be compared with another recent age estimate of the cluster by Gratton et al (2003) with an estimated age of 10.8 Gyr found using standard iso chrones and incorporating microscopic diffusion in their mo dels. The CMD allows one to visually get a rough idea whether or not the cluster is young or old and in this case we can see on the CMD that 47 Tuc is an old cluster which is also verified in literature. Of course, it not surprising that 47 Tuc is old given that globular clusters are among the oldest ob jects in our Galaxy and have been used to provide lower limits to the age of the Universe.

Color Image
I have created a color image of 47 Tuc using Adobe Photoshop and FITS Liberator. FITS Liberator is an Adobe Photoshop plugin which helps streamline the pro cess of creating a color image. Another advantage of using FITS Liberator allows one to import a .fits file into Photoshop directly. FITS Liberator allows you to select a function from the stretch function menu in order to scale the gray brightness levels in the image. The image that I created uses an ArcSin(x) stretch function. The basic idea is to combine our 3 images using Photoshop's "layers" which permits multiple images to be combined in a single do cument. In this case we will "colorize" the image by applying a different hue to each layer. This is accomplished by "pro jecting" each layer onto the virtual screen of a displayed image using Photoshop's "Screen layer blend" mo de. You can assign a color and name to each layer using the "Layer properites". In this case for the F434W image we apply a blue hue, F606W a green hue and F814W a red hue. The resulting color composite typically appears dull and a number of "Adjustment Layers" can be used to help improve the brightness, contrast and color. Either you can adjust the 12


NGC 104 (47 Tuc), WFC/ACS
14

16

18
F606W

m

20

22

24 0 0.5 1 - mF814W 1.5 2

m

F606W

Fig. 5.-- CMD V - I vs I composite as a whole inserting adjustment layers or add adjustment layers to each layer individually. In general the colors will not be "real" as in what we would see visually. But the colors do represent actual physical properties of the sub ject. Figure 7 shows the color image I have made of the WFC1 and Figure 8 shows the color image of WFC2. Since this image is huge I decided to crop the image and separate each chip. It should be noted that some of the resolution of the color image is lost in this LaTex do cument. The cropped portions of the images are also huge so Figure 9 shows a zo omed in portion of the WFC2 color image. The image lo oks much better when zo oming in and you can see much more detail. This cropped 13


NGC 104 (47 Tuc), WFC/ACS

16

18

m 22 24 26 0

F435W

20

0.5

1

m

F435w

1.5 - mF606W

2

2.5

3

Fig. 6.-- CMD B - V vs. V image allows one to see the color a bit more. It always helpful to know some information about the image before creating the color image and in this case we know 47 Tuc is old so we expect to see more stars with a reddish hue. Taking this into consideration allowed me to get a feel of how to make adjustments and how to make my stars lo ok more realistic. It should be kept in mind that some of this work can be sub jective and because Photoshop allows you to make many adjustments one can play with these adjustment parameters for a very long time until the creator of the color image deems it "pretty".

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Fig. 7.-- Color Image of WFC1.

Fig. 8.-- Color Image of WFC2.

Acknowledgments
Thanks to Jennifer Mack for providing this photometry exercise which has been insightful as well as a great experience to learn HST photometry. I would like to thank Brain York 15


Fig. 9.-- Zo omed in piece of color image of ACS WFC2. for sharing his python wisdom. I would also like to thank Lisa Frattare for teaching me about FITS Liberator and working with me on the creation of my color image.

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References
Chiaberge, M., et al. 2005, HST Calibration Workshop, 36-40 Ferraro, F.R., et al. 2003, APJ 588, 464-477 Frommert, H., Kronberg, C., 2006, http://seds.org/MESSIER/xtra/ngc/n0104.html Gratton, R., et al. 2002, AAP Vol. 408, 529-543 HST Data Handbo ok for ACS, Version 5.0 (Baltimore: STScI) Kaluzny, J., et al. 2007, AJ Vol. 134, 541 - 546 Mack, J., HST Photometry RIA Training Presentation, STScI Piotto, G., et al. 2004, Mem. S.A.It Suppl. Vol. 5, 71 Sirianni, M., et al. 2005, PASP Vol. 117, 1049-1112

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App endix
A. Title ################################################################################ # Photometry Script # Steps 6 - 14 of Photometry Training Excercise # Goal: Create a CMD of HST ACS data as observed by filters F435W,F606W,and F814W. # # Tiffany Borders- borders@stsci.edu # Space Telescope Science Institute # # Last Updated 10/13/08 ################################################################################ #Load the packages import pyraf,os from numarray import * from from from from from from from from pyraf iraf iraf iraf iraf iraf iraf iraf import import import import import import import import iraf digiphot,apphot,daofind imgtools,imcalc noao,digiphot,apphot,phot ttools,tdump,tmerge ttools,tchcol ttools,tcalc imutil, imheader

################################################################################ # Multiply the images by the exposure time so that PHOT will compute the # photometric errors correctly. At the same time this makes all the images the # same size. # Step 6A iraf.imgtools.imcalc(\ input='j8c0a1011_drz.fits[1][1:4218,1:4232]',\ output='f435w_drz.fits',\ equals='im1*60')

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iraf.imgtools.imcalc(\ input='j8c0c1011_drz.fits[1][1:4218,1:4232]',\ output='f606w_drz.fits',\ equals='im1*45') iraf.imgtools.imcalc(\ input='j8c0d1011_drz.fits[1][1:4218,1:4232]',\ output='f814w_drz.fits',\ equals='im1*60') #Coadding images so that we can run DAOFIND on 1 image iraf.imgtools.imcalc(\ input='f435w_drz.fits,f606w_drz.fits,f814w_drz.fits',\ output='color_drz.fits',\ equals='(4*im1+im2+im3)/6') ################################################################################ # Run DAOFIND on the color image. # Step 6B iraf.unlearn(daofind) iraf.apphot.daofind.findpar(\ thresh='50',\ sharplo='0.2',\ sharphi='0.8',\ roundlo='-1.0',\ roundhi='1.0') iraf.apphot.daofind.datapar(\ exposure='exptime',\ gain='ccdgain',\ sigma='3.6',\ fwhmpsf='2.0') iraf.apphot.daofind(\ image='color_drz.fits[1]',\ output='color_drz.cdt',\ inter='no',\ 19


verify='no') ################################################################################ # Compute aperature photometry # Step 7 phot.datapars.sigma = 3.6 phot.datapars.exposure = "EXPTIME" phot.datapars.gain = "ccdgain" phot.centerpars.calgorithm="centroid" phot.centerpars.cbox=10.0 phot.centerpars.maxshift=6.0 phot.fitskypars.salgorith phot.fitskypars.annulus=1 phot.fitskypars.dannulus= phot.fitskypars.skyvalue= m="mode" 0 5 0.0

phot.photpars.apertures="3,10" phot.photpars.zmag=25.1682267 phot(image="f435w_drz.fits[1]", coords="color_drz.cdt", interac="no") phot.photpars.zmag=26.67445 phot(image="f606w_drz.fits[1]", coords="color_drz.cdt", interac="no") phot.photpars.zmag=26.7988632 phot(image="f814w_drz.fits[1]", coords="color_drz.cdt", interac="no") #################################### # Using the task 'tdump' to extract # apphot output: # x,y,sky,flux3,mag3,err3,flux10,mag # Step 8 iraf.ttools.tdump(\ table='f435w_drz.fits1.mag.1',\ columns='c7,8,15,29,30,31,38,39,40', datafile='f435w.phota') ############################################ the following columns from the 10,err10

\

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iraf.ttools.tdump(\ table='f606w_drz.fits1.mag.1',\ columns='c7,8,15,29,30,31,38,39,40',\ datafile='f606w.phota') iraf.ttools.tdump(\ table='f814w_drz.fits1.mag.1',\ columns='c7,8,15,29,30,31,38,39,40',\ datafile='f814w.phota') ################################################################################ # Merges the photometry table for the 3 filters # Step 9 iraf.ttools.tmerge(\ intable='f435w.phota,f606w.phota,f814w.phota',\ outtable='phot_all.tab',\ option='merge')

# Changes the column names to something meaningful. # For F435 iraf.ttools,tchcol(\ table='phot_all.tab',\ oldname='c1',\ newname='x_435',\ newfmt='%8.6g',\ newunits="") iraf.ttools,tchcol(\ table='phot_all.tab',\ oldname='c2',\ newname='y_435',\ newfmt='%8.6g',\ newunits="") iraf.ttools,tchcol(\ table='phot_all.tab',\ 21


oldname='c3',\ newname='sky_435',\ newfmt='%6.5g',\ newunits="") iraf.ttools,tchcol(\ table='phot_all.tab',\ oldname='c4',\ newname='flux3_435',\ newfmt='%11.9g',\ newunits="") iraf.ttools,tchcol(\ table='phot_all.tab',\ oldname='c5',\ newname='mag3_435',\ newfmt='%7.6g',\ newunits="") iraf.ttools,tchcol(\ table='phot_all.tab',\ oldname='c6',\ newname='err3_435',\ newfmt='%6.5g',\ newunits="") iraf.ttools,tchcol(\ table='phot_all.tab',\ oldname='c7',\ newname='flux10_435',\ newfmt='%11.9g',\ newunits="") iraf.ttools,tchcol(\ table='phot_all.tab',\ oldname='c8',\ newname='mag10_435',\ newfmt='%7.6g',\ 22


newunits="") iraf.ttools,tchcol(\ table='phot_all.tab',\ oldname='c9',\ newname='err10_435',\ newfmt='%6.5g',\ newunits="") # # For F606 # iraf.ttools,tchcol(\ table='phot_all.tab',\ oldname='c10',\ newname='x_606',\ newfmt='%8.6g',\ newunits="") iraf.ttools,tchcol(\ table='phot_all.tab',\ oldname='c11',\ newname='y_606',\ newfmt='%8.6g',\ newunits="") iraf.ttools,tchcol(\ table='phot_all.tab',\ oldname='c12',\ newname='sky_606',\ newfmt='%6.5g',\ newunits="") iraf.ttools,tchcol(\ table='phot_all.tab',\ oldname='c13',\ newname='flux3_606',\ newfmt='%11.9g',\ newunits="") 23


iraf.ttools,tchcol(\ table='phot_all.tab',\ oldname='c14',\ newname='mag3_606',\ newfmt='%7.6g',\ newunits="") iraf.ttools,tchcol(\ table='phot_all.tab',\ oldname='c15',\ newname='err3_606',\ newfmt='%6.5g',\ newunits="") iraf.ttools,tchcol(\ table='phot_all.tab',\ oldname='c16',\ newname='flux10_606',\ newfmt='%11.9g',\ newunits="") iraf.ttools,tchcol(\ table='phot_all.tab',\ oldname='c17',\ newname='mag10_606',\ newfmt='%7.6g',\ newunits="") iraf.ttools,tchcol(\ table='phot_all.tab',\ oldname='c18',\ newname='err10_606',\ newfmt='%6.5g',\ newunits="") # # For F814 24


# iraf.ttools,tchcol(\ table='phot_all.tab',\ oldname='c19',\ newname='x_814',\ newfmt='%8.6g',\ newunits="") iraf.ttools,tchcol(\ table='phot_all.tab',\ oldname='c20',\ newname='y_814',\ newfmt='%8.6g',\ newunits="") iraf.ttools,tchcol(\ table='phot_all.tab',\ oldname='c21',\ newname='sky_814',\ newfmt='%6.5g',\ newunits="") iraf.ttools,tchcol(\ table='phot_all.tab',\ oldname='c22',\ newname='flux3_814',\ newfmt='%11.9g',\ newunits="") iraf.ttools,tchcol(\ table='phot_all.tab',\ oldname='c23',\ newname='mag3_814',\ newfmt='%7.6g',\ newunits="") iraf.ttools,tchcol(\ table='phot_all.tab',\ 25


oldname='c24',\ newname='err3_814',\ newfmt='%6.5g',\ newunits="") iraf.ttools,tchcol(\ table='phot_all.tab',\ oldname='c25',\ newname='flux10_814',\ newfmt='%11.9g',\ newunits="") iraf.ttools,tchcol(\ table='phot_all.tab',\ oldname='c26',\ newname='mag10_814',\ newfmt='%7.6g',\ newunits="") iraf.ttools,tchcol(\ table='phot_all.tab',\ oldname='c27',\ newname='err10_814',\ newfmt='%6.5g',\ newunits="") ################################################################################ # Adds a column to phot_all.tab calculating apcor # Step 10 iraf.ttools.tcalc(\ table='phot_all.tab',\ outcol='435_apcor',\ equals='mag3_435-mag10_435',\ colfmt='%6.5g') iraf.ttools.tcalc(\ table='phot_all.tab',\ outcol='606_apcor',\ 26


equals='mag3_606-mag10_606',\ colfmt='%6.5g') iraf.ttools.tcalc(\ table='phot_all.tab',\ outcol='814_apcor',\ equals='mag3_814-mag10_814',\ colfmt='%6.5g') ######################################################################### # Calculating CTE, STMAG, VEGAMAG and writing out files for plotting # Extract the MJD from the ACS header # Step 12 - 14 mjd_435= [] mjd_606= [] mjd_814= [] my_tmp1 = imheader(images="f435w_drz.fits[0]", longheader="yes", Stdout=1) for item in my_tmp1: if item[0:8] == "EXPSTART": mjd_435 = int(math.floor(float(item.strip().split()[1]))) my_tmp2 = imheader(images="f606w_drz.fits[0]", longheader="yes", Stdout=1) for item in my_tmp2: if item[0:8] == "EXPSTART": mjd_606 = int(math.floor(float(item.strip().split()[1]))) my_tmp3 = imheader(images="f814w_drz.fits[0]", longheader="yes", Stdout=1) for item in my_tmp3: if item[0:8] == "EXPSTART": mjd_814 = int(math.floor(float(item.strip().split()[1])))

phot_file=open('phot_all.tab','r') my_data=phot_file.readlines() phot_file.close() #y_435 is at column 2 27


tmp_y_435 = [] #y_606 is at column 11 tmp_y_606 = [] #y_814 is at column 20 tmp_y_814= [] # y_435 all tmp_y_435_all = [] # y_606 all tmp_y_606_all = [] # y_814 all tmp_y_814_all = [] #flux_435 is at column 4 tmp_flux_435 = [] #flux_606 is at column 13 tmp_flux_606 = [] #flux_814 is at column 22 tmp_flux_814= [] #mag3_435 is at column 5 tmp_mag3_435 = [] #mag3_606 is at column 14 tmp_mag3_606 = [] #mag3_814 is at column 23 tmp_mag3_814 = [] for line in my_data: if line[0] != "#" and line.find("INDEF") == -1: # skip comments as "INDEF's" 28


items = line.strip().split() tmp_y_435.append(float(items[1])) #testing value of last element in array #don't need to know how many elements in array if tmp_y_435[-1] > 2048: tmp_y_435[-1] = 4096 - tmp_y_435[-1] #append needs things to exist tmp_y_606.append(float(items[10])) if tmp_y_606[-1] > 2048: tmp_y_606[-1] = 4096 - tmp_y_606[-1] tmp_y_814.append(float(items[19])) if tmp_y_814[-1] > 2048: tmp_y_814[-1] = 4096 - tmp_y_814[-1] tmp_flux_435.append(float(items[3])) tmp_flux_606.append(float(items[12])) tmp_flux_814.append(float(items[21])) tmp_mag3_435.append(float(items[4])) tmp_mag3_606.append(float(items[13])) tmp_mag3_814.append(float(items[22])) tmp_y_435_all.append(float(items[1])) tmp_y_606_all.append(float(items[10])) tmp_y_814_all.append(float(items[19])) y_435 = asarray(tmp_y_435, 'Float64') y_606 = asarray(tmp_y_606, 'Float64') y_814 = asarray(tmp_y_814, 'Float64') flux_435 = asarray(tmp_flux_435, 'Float64') flux_606 = asarray(tmp_flux_606, 'Float64') flux_814 = asarray(tmp_flux_814, 'Float64') mag3_435 = asarray(tmp_mag3_435, 'Float64') mag3_606 = asarray(tmp_mag3_606, 'Float64') mag3_814 = asarray(tmp_mag3_814, 'Float64') y_435_all = asarray(tmp_y_435_all, 'Float64') y_606_all = asarray(tmp_y_435_all, 'Float64') y_814_all = asarray(tmp_y_435_all, 'Float64') 29


A=-0.15 B=-0.25 C=-0.44 SKY=5.5 delmag_435 = 10**A * SKY**B * (flux_435/2)**C * y_435/2000 * (mjd_435 - 52333)/365 delmag_606 = 10**A * SKY**B * (flux_606/2)**C * y_606/2000 * (mjd_606 - 52333)/365 delmag_814 = 10**A * SKY**B * (flux_814/2)**C * y_814/2000 * (mjd_814 - 52333)/365 #print len(delmag_814) print delmag_814 # need to stmag_435 stmag_606 stmag_814 get mag3's = mag3_435 - 0.15 - 0.107 - delmag_435 = mag3_606 - 0.14 - 0.088 - delmag_606 = mag3_814 - 0.20 - 0.087 - delmag_814

vegamag_435 = stmag_435 + 0.624 vegamag_606 = stmag_606 - 0.256 vegamag_814 = stmag_814 - 1.262 b = vegamag_435 b_v = vegamag_435 - vegamag_606 # want to plot b_v, b v = vegamag_606 v_i = vegamag_606 - vegamag_814 # want to plot v_i, v # open vi_v = bv_b = cte_435 cte_606 cte_814 files for plotting open("vi_v.dat",'w') open("bv_b.dat",'w') = open("cte435.dat",'w') = open("cte606.dat", 'w') = open("cte814.dat", 'w')

# write out files for plotting CMD 30


for j in range(len(b)): vi_v.write("%5.5g %5.5g\n" % (v_i[j], v[j])) # vi_v.write(str(v_i[j])+' '+str(v[j])+'\n') # bv_b.write(str(b_v[j])+' '+str(b[j])+'\n') bv_b.write("%5.5g %5.5g\n" % (b_v[j], b[j])) cte_435.write("%5.5g %5.5g\n" % (y_435_all[j], delmag_435[j])) cte_606.write("%5.5g %5.5g\n" % (y_606_all[j], delmag_606[j])) cte_814.write("%5.5g %5.5g\n" % (y_814_all[j], delmag_814[j])) vi_v.close() bv_b.close() cte_435.close() cte_606.close() cte_814.close() # The END

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