IRAC: Performing Photometry on IRAC Images

A. Point source photometry on a mosaic
B. Point source photometry on individual BCDs
C. Extended source photometry

A. Point source photometry on a mosaic

1. If you are only interested in photometry down to about 10% accuracy and have bright point sources, you can usually perform photometry on the pipeline mosaic. Set the aperture size to 10 pixels and the sky annulus to between 12 and 20 pixels. The IRAC calibration is based on an aperture of this size, so for this aperture no aperture correction is necessary. For fainter stars, it is better to use a smaller aperture and then apply an aperture correction (Table 5.7 in the IRAC Data Handbook; multiply your measured flux densities by the aperture corrections). Remember that the units of the images are in MJy/sr, so you need to convert your measured values into flux density units in (micro-)Jy, by accounting for the pixel size in steradians. Conversion into magnitudes is mag = 2.5*log10(f/f(0)), where f is your measured flux density and f(0) is the zero magnitude flux density. If using software such as "phot" or "qphot" in IRAF/DAOPHOT which requires a magnitude zeropoint, the "zmag" keyword in photpars should be set to 17.30 (ch1), 16.82 (ch2), 16.33 (ch3) and 15.69 (ch4) if using the default mosaic pixel scale of 1.2 arcsec/pixel. Note that if you require photometry to a higher accuracy than 10%-20%, you should follow the steps listed below.
2. Examine your data (BCDs) and identify artifacts that could affect your photometry and that need to be corrected.
3. First perform artifact mitigation on the pipeline-produced BCDs. Pipeline version S16 does a decent job of correcting muxbleed; however, the first several muxbleed-affected pixels are not corrected well. Column pulldown and banding corrections will be implemented in S17 (winter 2007-2008). There is contributed software to help you perform these corrections as well. The pipeline and contributed software have difficulty recognizing very saturated pixels that produce artifacts. As a result, they will not usually correct artifacts from very saturated sources. Saturated sources can be estimated using data from 2MASS and MSX when available. These sources can be rectified using the Iracworks contributed software (click here for a Solaris version) and then the associated artifacts should be flagged and/or mitigated. Data at 5.8 and 8.0 um exhibiting the bandwidth effect should be masked as there is no current ability to mitigate this artifact.
4. Make a mosaic of artifact-corrected images, for example with the SSC's MOPEX package. When creating the mosaic, the overlap correction option should be used in MOPEX, most importantly in channels 3 and 4, to match the backgrounds. Inspect the mosaic to confirm that outlier rejection is acceptable. If not, then remosaic with more appropriate MOPEX parameters. Comparing mosaics of adjacent channels on a per-pixel basis will readily identify if outliers remain in a mosaic. The mosaic coverage maps should be inspected to verify that the outlier rejection has not preferentially removed data from actual sources. If the coverage map systematically shows lower weights on actual sources, then the rejection is too aggressive and should be redone.
5. If you are interested in blue point sources (sources with spectral energy distributions, SEDs, that decline toward the longer wavelength IRAC passbands) you should create an array-location-dependent photometric correction image mosaic. If you are interested in only red sources (with SEDs that rise toward the longer wavelength IRAC passbands), you do not need to apply the photometric correction images and make a mosaic out of them. We recommend making a correction mosaic, instead of multiplying the correction images with the BCDs and then mosaicking these BCDs together, since you may need to iterate this a few times and/or you may have both red and blue sources in the field, and thus the correction only applies to a subset of sources. This location-dependent effect is as large as 10%. It is the dominant source of uncertainty in the photometry of IRAC images. For observations that well sample the array for each sky position the effect will average out. To make a mosaic of photometric correction images, first copy the FITS header keywords CTYPE1, CTYPE2, CRPIX1, CRPIX2, CRVAL1, CRVAL2, CD1_1, CD1_2, CD2_1, CD2_2 from the headers of the BCDs to the headers of the photometric correction images in each channel using your favorite FITS manipulation software. Thus, you make the same number of photometric correction images (otherwise identical except for the keyword information) as there are BCDs in each channel. The correction images must be divided by the pixel solid angle correction images before mosaicking them together, because the pixel solid angle effect is essentially corrected for already in the photometric correction images and thus needs to be "canceled out" before running the images through MOPEX (which corrects for this effect). Then, copy the namelist you used to make the BCD images to some other name, and edit the namelist to disable all the outlier rejection modules. Do not run the fiducial image frame module but instead point MOPEX to the existing "FIF.tbl" file used for generating the corresponding BCD mosaic. Next, specify the RMASK_LIST file (generate a file listing the rmasks and their path, as created by the mosaicker run for the corresponding BCDs). Finally, make the correction image mosaic with MOPEX.
6. Perform photometry with your favorite software. Currently, aperture photometry is strongly preferred over PSF-fitting photometry due to the undersampled nature of the data. To properly estimate the uncertainties in your photometry, the uncertainty images provided with the BCDs can be used and mosaicked into an uncertainty mosaic. The BCD uncertainties are slightly conservative as they take into account the uncertainties in each pipeline calibration step. For packages that estimate noise directly from the data assuming Poisson noise, you can convert the mosaic into electron units, so as to calculate the uncertainty due to source shot noise and background correctly. The conversion from MJy/sr is *GAIN * EXPTIME / FLUXCONV where GAIN, EXPTIME and FLUXCONV are the keywords from the BCD header. In determining the noise, the coverage of the observation at the position of your target should also be taken into account (e.g., by entering the correct number of frames in DAOPHOT or by dividing the noise by the sqrt[coverage] from the coverage mosaic at the position of each target). Your aperture photometry software should of course subtract the appropriate background (usually in an annulus around the source).
7. Apply aperture correction, found in Chapter 5 of the IRAC Data Handbook. if you perform aperture photometry in an aperture different from the 10 pixel radius aperture used for IRAC calibration or determine the background by other means than an annulus. Observers can determine their own aperture corrections by downloading IRAC calibration star observations with Leopard and comparing the photometry to that published in the IRAC Calibration Paper.
8. Observers should apply the array-location-dependent photometric correction for blue sources and the appropriate color correction for all sources (based on the spectral energy distribution of the source). Determine the array-location-dependent photometric correction (for blue compact sources) from the correction mosaic, constructed in step 5 above, by looking at the values of the pixels at the positions of the peaks of your point sources. Apply a color correction from Chapter 5 of the IRAC Data Handbook using the tabulated values, if appropriate, or calculate the color correction for a source spectral energy distribution as done in that chapter. To calculate a color correction, you will need the IRAC spectral response curves. Color corrections are typically a few percent for stellar and blackbody sources, but can be more significant for sources with ISM-like source functions (50%-250% depending on spectrum and passband). Measured flux density is the flux density at the effective wavelength of the array: 3.550, 4.493, 5.731 and 7.872 microns, for channels 1-4, respectively.
9. A pixel phase correction to the measured channel 1 flux densities should then be considered. More information on the pixel phase correction can be found in Chapter 5 of the IRAC Data Handbook. This effect is as large as 4% peak-to-peak at 3.6 microns and <1% at 4.5 microns. To apply a correction for mosaicked data is difficult as the pixel phase correction depends on the placement of the source centroid on each BCD. For well-sampled data the pixel phase should average out for the mosaic. For precise photometry in low coverage data, the source centroids on the BCDs should be measured and the phase corrections averaged together and applied to the final source photometry.

B. Point source photometry on individual BCDs

Although most of the time it is a good idea to use the mosaic for performing photometry, performing photometry on the BCD stack is important for variability studies and can be useful for faint sources as one can measure N out of M statistics (how many times you found the source). When performing source profile fitting, the stack is theoretically better as the phase information of the PRF is preserved.

1. Examine your data (BCDs) and identify artifacts that could affect your photometry and that need to be corrected.
2. First perform artifact mitigation on the pipeline-produced BCDs. Pipeline version S16 does a decent job of correcting muxbleed; however, the first several muxbleed-affected pixels are not corrected well. Column pulldown and banding corrections will be implemented in S17. There is contributed software to help you perform these corrections as well. The pipeline and contributed software have difficultly recognizing very saturated pixels that produce artifacts. As a result they will not usually correct artifacts from very saturated sources. Saturated sources can be estimated using data from 2MASS and MSX when available. These sources can be rectified using the Iracworks contributed software (click here for a Solaris version) and then the associated artifacts should be flagged and mitigated. Data at 5.8 and 8.0 um exhibiting the bandwidth effect should be masked as there is no current ability to mitigate this artifact. If performing aperture photometry on the BCDs, a particular BCD should not be used for a source when there are masked (bad) data in the source aperture.
3. Make a mosaic of artifact-corrected images, for example with the SSC's MOPEX package. This needs to be done to create the proper rmask files to be applied to the BCDs when performing the photometry on them, and also to get a nice comparison of BCD-revealed and mosaic-revealed image features. When creating the mosaic, the overlap correction option should be used in MOPEX, most importantly in channels 3 and 4, to match the backgrounds. Inspect the mosaic to confirm that outlier rejection is acceptable, if not, then remosaic with more appropriate parameters. Comparing mosaics of adjacent channels on a per-pixel basis will readily identify if outliers remain in a mosaic. The mosaic coverage maps should be inspected to verify that the outlier rejection has not preferentially removed data from actual sources. If the coverage map systematically shows lower weights on actual sources, then the rejection is too aggressive and should be redone. One result of making the mosaic is the production of rmask files which identify bad pixels in the BCDs. One should apply the rmasks when performing the photometry in the next step so that bad pixels are not included within the apertures.
4. Perform photometry with your favorite software. Currently, aperture photometry is strongly preferred over PSF-fitting photometry due to the undersampled nature of the data. The BCD uncertainties are slightly conservative as they take into account the uncertainties in each pipeline calibration step. For packages that estimate noise directly from the data assuming Poisson noise, you can convert the BCDs into electron units, so as to calculate the uncertainty due to source shot noise and background correctly. The conversion from MJy/sr is *GAIN * EXPTIME / FLUXCONV where GAIN, EXPTIME and FLUXCONV are the keywords from the BCD header. Your aperture photometry software should of course subtract the appropriate background (usually in an annulus around the source).
5. Apply aperture correction, found in Chapter 5 of the IRAC Data Handbook, if you perform aperture photometry in an aperture different from the 10 pixel radius aperture used for IRAC calibration. Observers can determine their own aperture corrections by downloading IRAC calibration star observations with Leopard and comparing the photometry to that published in the IRAC Calibration Paper.
6. Observers should apply the array-location-dependent photometric correction for blue sources and the appropriate color correction for all sources (based on the spectral energy distribution of the source). The photometric array-location-dependent correction images can be found here. Apply a color correction from Chapter 5 of the IRAC Data Handbook, using the tabulated values, if appropriate, or calculate the color correction for a source spectral energy distribution as done in that chapter. To calculate a color correction, you will need the IRAC spectral response curves. Color corrections are typically a few percent for stellar and blackbody sources, but can be more significant for sources with ISM-like source functions (50%-250% depending on spectrum and passband). The measured flux density is the flux density at the effective wavelength of the array: 3.550, 4.493, 5.731 and 7.872 microns, for channels 1-4, respectively.
7. A pixel phase correction to channel 1 sources in individual BCDs should then be considered. More information on the pixel phase correction can be found in Chapter 5 of the IRAC Data Handbook. This effect is as large as 4% peak-to-peak at 3.6 microns and <1% at 4.5 microns.
8. Combine photometry from BCDs, taking into account uncertainties, to generate a robust, weighted mean value. Verify that the dispersion in these measurements is comparable to the uncertainty of the individual measurements (if not, use the dispersion until you track down the source of extra error, e.g., bad pixels/cosmic rays in source).

C. Extended source photometry