Catalog Generation

The Hubble Advanced Products (HAP) project generates, by default, two types of catalogs for each detection image (i.e., all the images in the visit from the same detector drizzled together), and each filter-product image (i.e., all the images in the visit acquired with the same detector and filter drizzled together). The two types of catalogs are colloquially referred to as the Point and Segmentation catalogs. Both catalogs are generated using utilities from Photutils with the Point catalog created based upon functionality similar to DAOPhot-style photometry, and the Segmentation catalog created with Source Extractor segmentation capabilities and output. The HAP catalogs are intended to replicate the Hubble Source Catalog (HSC) by Whitmore et al. 2016 AJ, 151, 134W.

The HAP catalogs provide aperture photometry in the ABmag system in two small apertures, but they are calibrated using the photometric zeropoints corresponding to an ‘infinite’ aperture. To convert to total magnitudes, aperture corrections must be applied to account for flux falling outside of the selected aperture. For details, see Section 2.2.3 “Aperture Corrections”.

For WFC3, see Section 9.1.3 “Aperture and Encircled Energy Corrections” in the WFC3 Data Handbook (Pagul & Rivera et. al. 2024). The latest WFC3/UVIS EE tables and WFC3/IR EE tables are also available for download.

For ACS, see Section 5.1.2 “Aperture and Color Corrections” in the ACS Data Handbook (Lucas & Ryan et al. 2022). ACS EE tables are available for download from the website.

For a discussion of HAP drizzled data products, see the DrizzlePac Handbook (Anand, Mack et al. 2025).

1: Support Infrastructure for Catalog Generation

1.1: Important Clarifications

As previously discussed in Single-visit Mosaic Processing, AstroDrizzle creates a single multi-filter, detector-level drizzle-combined image for source identification and one or more detector/filter-level drizzle-combined images (depending on which filters were used in the dataset) for photometry measurements. The same set of sources identified in the multi-filter detection image is used to measure photometry for each filter. We use this method to maximize the signal across all available wavelengths at the source detection stage, thus providing photometry with the best quality source list across all available input filters.

It should also be stressed here that the point and segment photometry source list generation algorithms identify source catalogs independently of each other and DO NOT use a shared common source catalog for photometry.

While the detection image for a specific catalog type is used to identify potential sources, unless the Flags entry for a source in all of the corresponding filter catalogs is less than the value of the flag_trim_value (i.e., an 8-bit value where each bit represents a condition), the source will be removed from the associated total detection catalog. Finally, if somehow all measured values for a source in all the filter catalogs are marked as invalid (value of -9999.0), then the source will be removed from the total detection catalog. A discussion of the flag values is found in Section 2.4.2.

Note

A catalog file will always be written out for each type of catalog whether or not there are any identified sources in the exposure. If there are no identified viable sources, the file will only contain header information and no source rows.

1.2: Generation of Pixel Masks

Every drizzle-combined image is associated with a boolean footprint mask which defines the illuminated (True) and non-illuminated (False) portions of the image based upon its constituent exposures and the corresponding WCS solution. The boundary of the illuminated portion is iteratively eroded or contracted to minimize the impact of regions where signal quality is known to be degraded, and thereby, could affect source identification and subsequent photometric measurements. The erosion depth is approximately ten pixels and is done by using the ndimage.binary_erosion scipy utility. For computational convenience, an inverse footprint mask is also available for functions which utilize masks to indicate pixels which should be ignored during processing of the input data. These pixel masks are used through-out the processing to define clearly portions of the drizzled images which should be used for the background determination and source identification.

1.3: Detection Image Background Determination

For consistency, the same background and background RMS images are used by both the point and segment algorithms. To ensure optimal source detection, the multi-filter detection image must be background-subtracted. In order to accommodate the different types of detectors, disparate signal levels, and highly varying astronomical image content, three background computations are used as appropriate. The background determination algorithms are: Zero, Constant, and Conformal.

1.3.1: Special Case - Zero Background Algorithm

The first category of background definition is a special case situation only used for ACS SBC images. It has been observed that some background regions of ACS SBC drizzle-combined detection images are measured to have values identically equal to zero. If the number of identically zero pixels in the footprint portion of the detection image exceeds a configurable percentage threshold value (default is 25%), then a two-dimensional background image is constructed and set to the value of zero, hence the moniker of Zero Background Algorithm. Its companion constructed RMS image set to the RMS value computed for the non-zero pixels which reside within the footprint portion of the image.

1.3.2: Constant Background Algorithm (aka sigma-clipped statistics)

When the Zero Background Algorithm is not applicable, then sigma-clipped statistics are computed for the detection image using the astropy.stats.sigma_clipped_stats Astropy utility. This algorithm, given the moniker Constant Background Algorithm, uses the detection image and its inverse footprint mask, as well as a specification for the number of standard deviations and the maximum number of iterations to compute the mean, median, and RMS of the sigma-clipped data. The specification for the number of standard deviations and the maximum number of iterations are configurable values which are set to 3.0 and 3 by default, respectively, in catalog configuration files discussed in Section 1.4.1. If the median value is negative, the the input image data is adjusted by a combination of the negative median and the RMS so the entire image is more positive. This adjustment should correct for any gross over-subtraction of the background from the image, and the sigma-clipped statistics are recomputed.

At this point in the Constant Background Algorithm, the Pearson’s second coefficient of skewness is computed.

\[skewness = 3.0 * (mean - median) / rms\]

The skewness compares the sample distribution with a normal distribution where the larger the absolute value of the skewness, the more the sample distribution differs from a normal distribution. The skewness is computed in this context to aid in determining whether it is worth computing the background by other means (i.e., our third option of the Conformal Background Algorithm). For example, a high positive skew value can be indicative of there being a significant number of sources in the image which need to be taken into account with a more complex background.

Since the median and RMS values will be used to generate full two-dimensional background and RMS images to be used for the remainder of the processing, the values need to be deemed reasonable. The sigma-clipped statistics are computed yet again, but this time the input image data consists of only non-negative values, and the outlier values greater than the previously computed median are excluded. At this point the background median, mean, and RMS have been computed for the massaged input data using the sigma-clipped algorithm, but we are not done! Two additional techniques are used to compute alternate RMS values.

The first alternate RMS is a minimum RMS based upon the number of exposures in the combined drizzled image, the readnoise, and the total exposure time, where the SBC is handled in a special manner as MAMA detectors do not suffer from readnoise. For SBC, in place of the readnoise portion of the computation, the background median is scaled by the exposure time with a forced minimum value of 1.0.

The second alternate RMS computation uses the Median Absolute Deviation technique by Stoehr et al. 2008 ASPC, 505, 505S. Values of zero and NaN are masked for the input combined image and the computation is

\[rms = (k1 / sqrt(k2)) * median(abs(2 * image(i) - image(i-2) - image(i+2)))\]

where k1 and k2 are constants, and the median calculations are done over all pixels i. Please see the referenced paper for details.

Finally, the RMS values computed using the three different techniques are compared, and the largest of the RMS values is adopted as the final background RMS value. Using the sigma-clipped background median and the chosen background RMS values, a two-dimensional image matching the dimensions of the detection image are constructed for each value.

Note

Through a configuration setting, a user can specify the sigma-clipped statistics algorithm be the chosen method used to compute the background and RMS images, though the special case of identically zero background data will always be evaluated and will supersede the user request when applicable.

1.3.3: Conformal Background Algorithm (aka Background2D)

For the final background determination algorithm, Conformal Background Algorithm, the photutils.background.Background2d Astropy utility is invoked, but only if the Zero Background Algorithm has not been applied, the user has not requested that only the Constant Background Algorithm computed, and the skewness value derived using the sigma-clipped statistics is less than a pre-defined and configurable threshold (default value 0.5).

The Conformal Background Algorithm uses sigma-clipped statistics to determine background and RMS values across the image, but in a localized fashion in contrast to Constant Background Algorithm. An initial low-resolution estimate of the background is performed by computing sigma-clipped median values in 27x27 pixel boxes across the image. This low-resolution background image is then median-filtered using a 3x3 pixel sample window to correct for local small-scale overestimates and/or underestimates. Both the 27 and 3 pixel settings are configurable variables for the user.

Once a background and RMS image are determined using this final technique, a preliminary background-subtracted image is computed so it can be evaluated for the percentage of negative values in the illuminated portion of the image. If the percentage of negative values exceeds a configurable and defined threshold (default value 15%), the computation of the background and RMS images from this algorithm are discarded. In addition, if the RMS computed for this technique is larger than the RMS ultimately chosen for the Constant Background Algorithm, the background and RMS images from this technique are discarded. Instead the background and RMS images computed using Constant Background Algorithm, with the associated updates, are chosen as the images to use for further computation.

Attention

It cannot be emphasized enough that a well-determined background measurement, leading to a good threshold definition, is very crucial for proper and successful source identification.

1.4: Catalog Configuration Variables

Through-out the previous section variables have been mentioned which can be configured by the user. The values used for these variables for generating the default catalogs are deemed to be the best for the general situation, but users can tune these values to optimize for their own data. The easiest way for users to tune the catalog configuration settings is to copy the appropriate detector configuration parameter settings file (e.g., acs_wfc_catalog_generation_all.json) used by default during the catalog processing done by Drizzlepac to the local working directory and modify values appropriately. Please see Section 1.4.1 which discusses the variables in the catalog configuration file. The default files are located in **rootpath**/drizzlepac/pars/hap_pars/svm_parameters/<instrument>/<detector>, where rootpath in this instance could be the top level of the users’s cloned working directory of Drizzlepac from the repository (e.g., /home/yourName/repository_code/drizzlepac), or the Conda root in your home directory (e.g., /home/yourName/miniconda3/envs/currentCondaEnv/lib/python3.11/site-packages/drizzlepac).

To run the single-visit mosaic pipeline using the custom parameter file, users simply need to specify the name of the file with the ‘-c’ optional command-line argument when using runsinglehap or set the ‘input_custom_pars_file’ optional input argument when executing the run_hap_processing function in hapsequencer from Python or from another Python script.

1.4.1: Variables in the Catalog JSON Files

Default values for the numeric configuration parameters are detector-dependent, though many of the values may be the same from detector to detector. In the actual configuration files the parameters are split into three sections: General (which is unlabeled), DAO (for the point algorithm), and SOURCEX (for the segmentation algorithm). The variables listed in the General section apply to both the point and segmentation algorithms.

GENERAL

DAO

bigsig_sf:
DEPRECATED
kernel_sd_aspect_ratio:
DEPRECATED
nsigma: float
The “sigma” in threshold=(sigma * background_rms). Threshold is an image greater than the background which defines, on a pixel-by-pixel basis, the low signal limit above which sources are detected.
starfinder_algorithm: string (default = “psf”)
Algorithm to use for source detection: “dao” (DAOStarFinder), “iraf” (IRAFStarFinder), and “psf” (UserStarFinder).
region_size: int
Size of the box used to recognize a point source. Also, the kernel size for the maximum filter window when computing weight masks. In the latter case of “kernel size”, the variable applies to both algorithms.

SOURCEX

1.5: Image Kernel

By default, the software uses a two-dimensional Gaussian smoothing kernel on the multi-filter detection image in an effort to identify sources. The kernel is based upon the FWHM information represented as the TWEAK_FWHMPSF variable in the detector-dependent catalog configuration files (e.g., acs_wfc_catalog_generation_all.json), and the astropy.convolution.Gaussian2DKernel Astropy utility. In extreme cases, a large number of candidate sources may be blended together and can be mistakenly identified as a single source covering a large percentage of the image. To address this situation, an alternative kernel is derived using the astropy.convolution.RickerWavelet2DKernel Astropy utility.

2: Point (Aperture) Photometric Catalog Generation

2.1: Source Identification Options

A number of options have been implemented within the catalog generation code in order to best match the contents of the exposure, including presence of saturated sources and cosmic-rays. The available options include:

dao : The photutils DAOStarFinder class that provides an implementation of the DAOFind algorithm.

iraf : The photutils IRAFStarFinder class that implements IRAF’s starfind algorithm.

psf [DEFAULT] : This option is a modification of DAOStarFinder which relies on a library of TinyTim (model) PSFs to locate each source then uses DAOStarFinder to measure the final position and photometry of each identified source.

These options are selected through the “starfinder_algorithm” parameter in the DAO section of the catalog_generation configuration files in the drizzlepac/pars/hap_pars/svm_parameters directory.

2.1.1: Source Identification using DAOStarFinder

We use the photutils.detection.DAOStarFinder Astropy utility to identify sources in the background-subtracted multi-filter detection image. Here, the background computed using one of the algorithms discussed in Section 1.3 is applied to the science data to initialize point-source detection processing. This algorithm works by identifying local brightness maxima with roughly gaussian distributions whose peak values are above a predefined minimum threshold. This minimum threshold value is computed as the background noise times a detector-dependant scale factor, nsigma (listed below in Table 0). Full details of the process are described in Stetson 1987; PASP 99, 191. The exact set of input parameters fed into DAOStarFinder is detector-dependent. The parameters can be found in the <instrument>_<detector>_catalog_generation_all.json files mentioned in the previous section.

Table 0: Background scale factor values used to compute minimum detection thresholds
Instrument/Detector	Scale Factor
ACS/HRC	5.0
ACS/SBC	6.0
ACS/WFC	5.0
WFC3/IR	5.0
WFC3/UVIS	5.0

2.1.2: Source Identification using PSFs

This option, introduced in Drizzlepac v3.3.0, drizzles model PSFs created using TinyTim to match the orientation and plate scale of the observation to look for sources in the image. Where DAOFind convolves the image with a perfect Gaussian whose FWHM has been specified by the user, this option convolves the image with the model PSF to identify all sources which most closely match the PSF used. Those positions are then turned into a list that is fed to photutils DAOStarFinder code to measure them using the Gaussian models with a FWHM measured from the model PSF.

One benefit of this method is that features in the core of saturated or high S/N sources in the image that would normally be erroneously identified as a separate point-source by DAOFind will be recognized as part of the full PSF as far out as the model PSF extends. For exposures which are comprised of images taken in different filters, the model PSF used is the drizzle combination of the model PSFs for each filter that comprised the image. This allows the code to best match the PSF found in the image of the total detection image. The model PSFs definitely do not exactly match the PSFs from the images due to focus changes and other telescope effects. However, they are close enough to allow for reasonably complete identification of actual point-sources in the images. Should the images suffer from extreme variations in the PSF, though, this algorithm will end up not identifying valid sources from the image. The user can provide their own library of PSFs to use in place of the model PSFs included with this package in order to match more reliably and measure the sources from their data. The user-provided PSFs can be used to directly replace the PSFs installed with this package as long as they maintain the same naming convention. All model PSFs installed with the code can be found in the pars/psfs directory, with all PSFs organized by instrument and detector. Each PSF file has a filename of <instrument>_<detector>_<filter_name>.fits. The model PSFs all extend at least 3.0” in radius in order to recognize the features of the diffraction spikes out as far as possible to mitigate false detections for saturated sources.

2.2: Aperture Photometry Measurements

2.2.1: Flux Determination

Aperture photometry is computed for the identified sources using a pair of small, concentric apertures listed in Table 1 for each instrument/detector. The radii for the two aperture measurements (Aper1 and Aper2) are 1 and 3 pixels for ACS/WFC, 1.25 and 3.75 pixels for WFC3/UVIS, and 1.2 and 3.5 pixels for WFC3/IR. See Table 1 for the corresponding sizes in arcsec. Both the Point and Segmentation source catalogs contain aperture photometry in both of these two small apertures. Users must manually apply aperture corrections in order to correct HAP magnitude values to infinite aperture.

Table 1: For each HST Instrument/Detector, the scale of the HAP drizzled (drc/drz) image is given in column 2.
Instrument/ Detector	Drizzled Scale (“/pix)	Aper1 (“)	Aper2 (“)	Aper1 (pix)	Aper2 (pix)
WFC3/IR	0.128	0.15	0.45	1.2	3.5
WFC3/UVIS	0.040	0.05	0.15	1.25	3.75
ACS/WFC	0.050	0.05	0.15	1.0	3.0
ACS/HRC	0.025	0.03	0.125	1.2	5.0
ACS/SBC	0.025	0.07	0.125	2.8	5.0

Raw (non-background-subtracted) flux values are computed by summing up the enclosed flux within the two specified apertures using the photutils.aperture.aperture_photometry function. Input values are detector-dependent and are computed in real-time, read from the FITS keywords in the input image headers, or read from the <instrument>_<detector>_catalog_generation_all.json files described in section 1.3.

Local background values are computed based on the 3-sigma-clipped mode of pixel values present in a circular annulus with an inner radius of 0.25 arcseconds and an outer radius of 0.50 arcseconds surrounding each identified source, based upon values skyannulus_arcsec and dskyannulus_arcsec in the configuration file. This local background value is then subtracted from the raw inner and outer aperture flux values to compute the background-subtracted inner and outer aperture flux values found in the output ECSV catalog file by the formula

\[f_{bgs} = f_{raw} - f_{bg} \cdot a\]

where

\(f_{bgs}\) is the background-subtracted flux, in electrons second^-1
\(f_{raw}\) is the raw, non-background-subtracted flux, in electrons second^-1
\(f_{bg}\) is the per-pixel background flux, in electrons second ^-1 pixel^-1
\(a\) is the area of the photometric aperture, in pixels

The overall standard deviation and mode values of pixels in the background annulus are also reported for each identified source in the output ECSV catalog file in the “STDEV” and “MSKY” columns respectively (see Section 3 for more details).

2.2.2: Computation of ABmag

The conversion of the flux to ABmag is a two-step process. The computations involve photflam and photplam which are FITS keywords stored in the science extension header of the input drizzled image. References for these equations are: Whitmore et al. 2016 (https://iopscience.iop.org/article/10.3847/0004-6256/151/6/134/pdf), and Sirianni et al. 2005 (https://iopscience.iop.org/article/10.1086/444553/pdf).

First, convert flux according to the formula:

\[f_{lambda} = f \cdot photflam\]

where

\({f_{lambda}}\) is the mean flux density, in ergs cm^-2 A ^-1 second^-1
\({f}\) is the flux, in electrons second^-1
\({photflam}\) is the inverse sensitivity, in ergs cm^-2 A ^-1 electrons^-1

Now convert the \({f}_{lambda}\) to STmag:

\[STmag = -2.5 \cdot log({f}_{lambda}) - 21.10\]

where

\(-2.5\) is the ratio of brightness between two stars differing by one magnitude (Pogson’s ratio)
\(21.10\) is the STmag permanently set zeropoint stored in the FITS photzpt keyword in the science extension header

Finally, convert STmag to ABmag:

\[ABmag = STmag - 5.0 \cdot log(photplam) + 18.6921\]

where

\({photplam}\) is the bandpass pivot wavelength, in Angstroms

Some additional citations for the magnitude systems are the following: ACS Data Handbook, analysis of the relationship between photflam, photzpt, and photplam to the STmag and ABmag zeropoints (Bohlin et al. 2011), discussion of STmag (Koornneef, J. et al. 1986), and a discussion of ABmag (Oke, J.B. 1964).

2.2.3: Aperture Corrections

HAP (and HSC) photometry is measured in small apertures in order to reduce errors due to source crowding or background variations. The photometric header keywords, on the other hand, correspond to an ‘infinite’ aperture enclosing all of the light from a source. Aperture corrections are not applied to the point and segmentation catalogs and must be applied by the user to determine the total magnitude of the source. Blind application of aperture corrections using the EE tables should be avoided, since the measured photometry (and the EE fraction) in small apertures is strongly dependent on the telescope focus and orbital breathing effects.

To convert aperture magnitudes to total magnitudes, a two-step process is recommended. First small aperture photometry is corrected to a larger ‘standard’ aperture for each instrument, beyond which the fraction of enclosed light is insensitive to changes in telescope focus, orbital breathing effects, or spatial variations in the PSF (see Mack et al. 2022). This correction may be measured from isolated stars in the drizzled science frames, when possible. Alternatively, the MAST PSF search tool can be used to download PSFs extracted from archival data at a similar focus level and detector position, and the appropriate aperture corrections can be calculated using these. For example, WFC3 Observed PSFs can be accessed on the MAST Portal interface by choosing the ‘Select a collection’ to ‘WFC3 PSF’. For details, see WFC3 ISR 2021-12.

Next, the ‘standard’ aperture is corrected to ‘infinite’ aperture using the encircled energy (EE) tables provided by the HST instrument teams. These tables are derived from high signal-to-noise ratio observations of isolated stars out to large radii, where the EE fraction is converted to magnitude units. ACS EE Tables and interactive plots are available on the ACS website. The latest solutions are described in Bohlin (2016 AJ….152) for the WFC and HRC detectors and in ACS ISR 2016-05 for the SBC detector. WFC3/UVIS EE tables are available the WFC3 website and described in WFC3 ISR 2021-04, and the WFC3/IR EE tables are described in WFC3 ISR 2009-37.

2.2.4: Hubble Source Catalog (HSC)

The legacy HSC FAQ page links to an older set of Aperture Corrections Tables recommended by Whitmore et al. 2016 for each HST detector. While these represented the best solutions at the time (e.g. Sirianni et al. 2005 for ACS; Hartig 2009 for WFC3), the updated encircled energy solutions from the instrument webpages should be used instead. See Section 2.2.3.

2.3: Calculation of Photometric Errors

2.3.1: Calculation of Flux Uncertainties

For every identified source, the photutils.aperture_photometry function calculates standard deviation values for each aperture based on a 2-dimensional RMS array computed using the photutils.background.Background2d function that we previously utilized to compute the 2-dimensional background array in order to background-subtract the detection image for source identification. We then compute the final flux errors as seen in the output ECSV catalog file using the following formula:

\[\Delta f = \sqrt{\frac{\sigma^2 }{g}+(a\cdot\sigma_{bg}^{2})\cdot (1+\frac{a}{n_{sky}})}\]

where

\({\Delta} f\) is the flux uncertainty, in electrons second^-1
\({\sigma}\) is the standard deviation of photometric aperture signal, in counts second^-1
\({g}\) is effective gain in electrons count^-1
\({a}\) is the photometric aperture area, in pixels
\({\sigma_{bg}}\) is standard deviation of the background
\({n_{sky}}\) is the sky annulus area, in pixels

2.3.2: Calculation of ABmag Uncertainties

Magnitude error calculation comes from computing \({\frac{d(ABmag)}{d(flux)}}\). We use the following formula:

\[\Delta {ABmag} = 1.0857 \cdot \frac{\Delta f}{f}\]

where

\({\Delta {ABmag}}\) is the uncertainty in ABmag
\({\Delta f}\) is the flux uncertainty, in electrons second^-1
\({f}\) is the flux, in electrons second^-1

2.4: Calculation of Concentration Index (CI) Values and Flag Values

2.4.1: Calculation of Concentration Index (CI) Values

The Concentration index is a measure of the “sharpness” of a given source’s PSF and is computed with the following formula:

\[CI = m_{inner} - m_{outer}\]

where

\({CI}\) is the concentration index, in ABmag
\({m_{inner}}\) is the inner aperture ABmag
\({m_{outer}}\) is the outer aperture ABmag

We use the concentration index to classify automatically each identified photometric source as either a point source (e.g. stars), an extended source (e.g. galaxies, nebulosity, etc.), or as an “anomalous” source (e.g. saturation, hot pixels, cosmic-ray hits, etc.). This designation is described by the value in the “flags” column.

2.4.2: Determination of Flag Values

The flag value associated with each source provides users with a means to distinguish between legitimate point sources, legitimate extended sources, and scientifically dubious sources (those likely impacted by low signal-to-noise ratio, detector artifacts, saturation, cosmic-rays, etc.). The values in the “flags” column of the catalog are a sum of one or more of these values. Specific flag values are defined below in Table 2:

Table 2: Flag definitions
Flag value	Meaning
0	Point source \({(CI_{lower} < CI < CI_{upper})}\)
1	Extended source \({(CI > CI_{upper})}\)
2	DEPRECATED Single-pixel Saturation
4	Questionable Photometry (Multi-pixel Saturation)
8	Faint Detection Limit
16	Hot pixels \({(CI < CI_{lower})}\)
32	False Detection: Swarm Around Saturated Source
64	False detection due proximity of source to image edge or other region with a low number of input images
128	DEPRECATED Bleeding and Cosmic-Rays

Attention

The final output filter-specific sourcelists do not contain all detected sources. Sources that are considered scientifically dubious are filtered out and not written to the final source catalogs. For all detectors, sources with a flag value greater than 5 are filtered out. Users can adjust this value using a custom input parameter file and changing the “flag_trim_value” parameter. For more details on how to create a custom parameter file, please refer to the generate_custom_svm_mvm_param_file documentation page.

2.4.2.1: Assignment of Flag Values 0 (Point Source), 1 (Extended Source), and 16 (Hot Pixels)

Assignment of flag values 0 (point source), 1 (extended source), and 16 (hot pixels) are determined purely based on the concentration index (CI) value. The majority of commonly used filters for all ACS and WFC3 detectors have filter-specific CI threshold values that are automatically set at run-time. However, if filter-specific CI threshold values cannot be found, default instrument/detector-specific CI limits are used instead. Instrument/detector/filter combinations that do not have filter-specific CI threshold values are listed below in Table 3 and the default CI values are listed below in Table 4. The CI threshold values are contained in a configuration support file, ci_ap_cor_table_ap_20_2016.txt, located in drizzlepac/pars/hap_pars/svm_parameters/any directory.

Table 3: Instrument/detector/filter combinations that **do not** have filter-specific CI threshold values
Instrument/Detector	Filters without specifically defined CI limits
ACS/HRC	F344N
ACS/SBC	All ACS/SBC filters
ACS/WFC	F892N
WFC3/IR	None
WFC3/UVIS	None

Note

As photometry is not performed on observations that utilized grisms, prisms, polarizers, ramp filters, or quad filters, these elements were omitted from the above list.

Table 4: Default concentration index threshold values
Instrument/Detector	\({CI_{lower}}\)	\({CI_{upper}}\)
ACS/HRC	0.9	1.6
ACS/SBC	0.15	0.45
ACS/WFC	0.9	1.23
WFC3/IR	0.25	0.55
WFC3/UVIS	0.75	1.0

2.4.2.2: Assignment of Flag Value 4 (Saturated Source)

A flag value of 4 is assigned to sources that are saturated. The process of identifying saturated sources starts by first transforming the input image XY coordinates of all pixels flagged as saturated in the data quality arrays, pixels having a value of 256 assigned during calibration processing, of each input flc/flt.fits image (the images drizzled together to produce the drizzle-combined filter image being used to measure photometry) from non-rectified, non-distortion-corrected coordinates to the rectified, distortion-corrected frame of reference of the filter-combined image. We then identify impacted sources by cross-matching this list of saturated pixel coordinates against the positions of sources in the newly created source catalog and assign flag values where necessary.

2.4.2.3: Assignment of Flag Value 8 (Faint Detection Limit)

A flag value of 8 is assigned to sources whose signal-to-noise ratio is below a predefined value. We define sources as being above the faint object limit if the following is true:

\[f_{outer} >= snr \cdot \Delta f_{outer}\]

where

\(f_{outer}\) is the flux measured for the outer aperture, in electrons second^-1
\({\Delta} f_{outer}\) is the flux uncertainty for the outer aperture, in electrons second^-1
\({snr}\) is the signal-to-noise ratio which is 5.0 for detectors.

2.4.2.4: Assignment of Flag Value 32 (False Detection: Swarm Around Saturated Source)

The source identification routine has been shown to identify false sources in regions near bright or saturated sources, and in image artifacts associated with bright or saturated sources, such as diffraction spikes, and in the pixels surrounding saturated PSF where the brightness level “plateaus” at saturation. We identify impacted sources by locating all sources within a predefined radius of a given source and checking if the brightness of each of these surrounding sources is less than a radially-dependent minimum brightness value defined by a predefined stepped encircled energy curve. The parameters used to determine assignment of this flag are instrument-dependent, can be found in the “swarm filter” section of the *_quality_control_all.json files in the path described above in section 1.3.

2.4.2.5: Assignment of Flag Value 64 (False Detection Due Proximity of Source to Image Edge or Other Region with a Low Number of Input Images)

Sources flagged with a value of 64 are flagged as “bad” because they are inside of or in close proximity to regions characterized by low or null input image contribution. These are areas where for some reason or another, very few or no input images contributed to the pixel value(s) in the drizzle-combined image. We identify sources impacted with this effect by creating a two-dimensional weight image that maps the number of contributing exposures for every pixel. We then check each source against this map and flag appropriately.

2.4.2.6: Assignment of Flag Value 2 (Single Pixel Saturation) and 128 (Bleeding or Cosmic-Rays)

These flags are deprecated.

3: The Output Point Catalog File

Both the Point and Segmentation output catalogs are Enhanced Character-Separated Values (ECSV) format files which contain metadata followed by tablular information regarding measurements for each detected source.

3.1: Filename Format

This section applies to both the Point and Segmentation Catalogs.

The naming convention for the total detection and filter catalogs is nearly the same except for use of the literal string total for the total detection catalog and the actual filter name (e.g., f606w) for the sources and measurements done on a single-filter drizzled image: <TELESCOPE>_<PROPOSAL ID>_<OBSERVATION SET ID>_<INSTRUMENT>_<DETECTOR>_<total | FILTER>_<DATASET NAME>_<CATALOG TYPE>.ecsv where CATALOG TYPE is point-cat or segment-cat.

For example, for the following information:

TELESCOPE = HST
PROPOSAL ID = 98765
OBSERVATION SET ID = 43
INSTRUMENT = acs
DETECTOR = wfc
FILTER = f606w, For the total detection catalog, the Filter name is “total”.
DATASET NAME = j65c43
CATALOG TYPE = point-cat

The resulting auto-generated point total detection catalog filename will be:

hst_98765_43_acs_wfc_total_j65c43_point-cat.ecsv

and a filter catalog filename will be:

hst_98765_43_acs_wfc_f606w_j65c43_point-cat.ecsv

3.2 Catalog Metadata

This section applies to both the Point and Segmentation Catalogs.

The total detection and all individual single filter-level catalogs contain a significant amount of metadata at the beginning of the file which is also predominantly the same between the catalogs, where the differences arise with respect to the specific columns present in the catalog. The lines essentially beginning with the literal name contain the names of the columns in the catalog with associated units, datatype, units, formatting, and description for each table column. The next section is prefaced with header line numbers (e.g., hnn) and contains information explaining STScI’s use policy for HAP data in refereed publications. The third section contains relevant image-specific metadata which includes the following items:

WCS (world coordinate system) name

WCS (world coordinate system) type

Proposal ID

Image filename

Target name

Observation date

Observation time

Instrument

Detector

Target right ascension in degrees

Target declination in degrees

Orientation in degrees

Aperture right ascension in degrees

Aperture declination in degrees

Aperture position angle in degrees

Aperture 1 inner aperture radius in arcseconds

Aperture 2 outer aperture radius in arcseconds

Threshold (sigma) (threshold = sigma * background_rms)

Exposure start in MJD

Total exposure duration in seconds

CCD Gain

Filter 1 name

Filter 2 name (if applicable)

WCSAXES number of axes

CRPIX1 reference pixel for first dimension

CRPIX2 reference pixel for second dimension

CRUNIT1 axis units for first dimension

CRUNIT2 axis units for second dimension

CTYPE1 axis type for first dimension

CTYPE2 axis type for second dimension

CRVAL1 reference value for first dimension

CRVAL2 reference value for second dimension

CRDER1 random error for first dimension

CRDER2 random error for second dimension

MJDREF time zero point

RADESYS reference frame

CD1_1 WCS transformation matrix

CD1_2 WCS transformation matrix

CD2_1 WCS transformation matrix

CD2_2 WCS transformation matrix

Total Number of sources in catalog

The last header section continues the use of the header line numbers, hnn, and contains explanatory notes regarding the columns in the table. In particular, it contains the Concentration index (CI) formulaic definition and the Flag value definitions.

3.3: Point Total Detection Catalog

This section applies to both the Point and Segmentation Catalogs.

The multi-filter detection level (aka total) catalog contains the fundamental position measurements of the detected sources: ID, X-Center, Y-Center, RA, and DEC, supplemented by some of the aperture photometry measurements from each of the filter catalogs (ABmag of the outer aperture, Concentration Index, and Flags). The column names for the aperture measurements are constructed based upon the contributing Point Filter Catalogs and have the following names: MagAP2_<filter>, CI_<filter>, and Flags_<filter>. Effectively, the output Total Detection Catalog is a distilled version of all of the Filter Catalogs.

3.4: Point Filter Catalog Content

Finally, the last section contains the catalog of source locations and photometry values. It should be noted that the specific columns and their ordering were deliberately chosen to facilitate a 1:1 exact mapping to the_daophot.txt catalogs produced by Hubble Legacy Archive. As this code was designed to be the HLA’s replacement, we sought to minimize any issues caused by the transition. The column names are as follows (note that this is the same left-to-right ordering in the ECSV file as well):

X-Center: 0-indexed X-coordinate position

Y-Center: 0-indexed Y-coordinate position

RA: Right ascension (sky coordinates), in degrees

DEC: Declination (sky coordinates), in degrees

ID: Object catalog index number

MagAp1: Inner aperture brightness, in ABmag

MagErrAp1: Inner aperture brightness uncertainty, in ABmag

FluxAp1: Inner aperture flux, in electrons/sec

FluxApErr1: Inner aperture flux error, in electrons/sec

MagAp2: Outer aperture brightness, in ABmag

MagErrAp2: Outer aperture brightness uncertainty, in ABmag

MSkyAp2: Outer aperture background brightness, in ABmag

StdevAp2: Standard deviation of the outer aperture background brightness, in ABmag

FluxAp2: Outer aperture flux, in electrons/sec

FluxApErr2: Outer aperture flux error, in electrons/sec

CI: Concentration index (MagAp1 – MagAp2), in ABmag

Flags: See Section 2.4.2 for flag value definitions

3.5 Rejection of Cosmic-Ray Dominated Catalogs

This section applies to both the Point and Segmentation Catalogs.

Not all sets of observations contain multiple overlapping exposures in the same filter. This makes it impossible to ignore all cosmic-rays that have impacted those single exposures. The contributions of cosmic-rays often overwhelm any catalog generated from those single exposures making recognizing astronomical sources almost impossible among the noise of all the cosmic-rays. As a result, those catalogs can not be trusted. In an effort to publish only catalogs which provide the highest science value, criteria developed by the Hubble Legacy Archive (HLA) has been implemented to recognize catalogs dominated by cosmic-rays such that the output catalogs have an empty source table.

Note

This rejection criteria is NOT applied to WFC3/IR or ACS/SBC data since they are not affected by cosmic-rays in the same way as the other detectors.

3.5.1 Single-image Cosmic-Ray Rejection Algorithm

An algorithm has been implemented to identify and ignore cosmic-rays in single exposures. This algorithm has been used for ignoring cosmic-rays during the image alignment code used to determine the a posteriori alignment to GAIA.

This algorithm starts by evaluating the central moments of all sources from the segmentation catalog. Any source where the maximum central moment (as determined by photutils.segmentation.SourceCatalog) is 0 for both X and Y moments is identified as a cosmic-ray. The implication is the source has a concentration of flux greater than a point-source and most probably represents a ‘head-on cosmic-ray’.

In addition to these ‘head-on cosmic-rays’, ‘glancing cosmic-rays’ produce streaks across the detector. Those are identified by identifying sources with a minimum width (semiminor_axis) less than the FWHM of a point source and an elongation > 2. The width and elongation are also properties defined by photutils.segmentation.SourceCatalog).

The combination of these criteria allows for the identification of a vast majority of cosmic-rays. The DQ array of the single exposure then gets updated to flag those pixels identified as cosmic-rays based on these criteria. These DQ flags are then ONLY applied when creating the TotalProduct to limit the contribution of cosmic-rays in the total detection image. These flags are NOT used to generate any other product in order to avoid affecting the photometry or astrometry of any source from the total detection image more than necessary.

3.5.2 Rejection Criteria

The rejection criteria sets a minimum threshold for the number of “sources” detected. If either the point source or the segmentation catalog fails, then both catalog files will be generated, but the table in the files will be empty.

In its simplest form the criteria for rejection is:

n_cat < thresh

where

thresh = crfactor * (n1_residual * n1_exposure_time)**2 / texptime

and

n_cat : Number of good point and extended sources in the catalog (flag < 2)
crfactor : Number of expected cosmic-rays per second across the entire detector
n1_exposure_time : Amount of exposure time for all single filter exposures
texptime : Total exposure time of the combined drizzle product
n1_residual : Remaining fraction of cosmic-rays after applying single-image CR removal

The value of crfactor is based upon the physical area of the detector and is the number of expected cosmic-rays per hour over the area. The value should be adjusted for sub-arrays to account for the smaller area being read out, but that logic has not yet been implemented. The values used in the processing of single-visit mosaics are:

Table 5: Detector area, pixel size, and crfactor
Instrument/Detector	Area (pixel)	Pixel Size (um)	Point Crfactor	Segment Crfactor
ACS/HRC	1024²	21²	37	18.5
ACS/WFC	4096²	15²	300	150
WFC3/UVIS	4096²	15²	300	150
WFPC2/PC	1600²	15²	46	23

These numbers are deliberately set high to be conservative about which catalogs to keep. The cosmic-ray rate varies with position in the orbit, and these are set high enough that it is rare for approved catalogs to be dominated by cosmic-rays (even though they can obviously have some cosmic-rays included.)

Finally, the n1_residual term gets set as a configuration parameter with a default value of 5% (0.05). This indicates that the single-image cosmic-ray identification process was expected to leave 5% of the cosmic-rays unflagged. This process can be affected by numerous factors, and having this as a user settable parameter allows the user to account for these effects when reprocessing the data manually. Pipeline processing, though, may still be subject to situations where this process does not do as well which can result in a catalog with a higher than expected contribution of cosmic-rays. Should this number of sources trigger the rejection criteria, the Point and Segmentation Filter catalog ECSV files will still be generated, but the output table in each file will be empty.

Also note that we reject both the point and segmentation catalogs if either one fails this test. The reasoning behind that is that since the catalogs are based on the same image, it is unlikely that one catalog will be good and the other contaminated.

4: Segmentation Catalog Generation

4.1: Source Identification with PhotUtils

For the segmentation algorithm the photutils.segmentation subpackage is used to identify sources in the background-subtracted multi-filter detection image. As is the case for the point-source detection algorithm, this is the juncture where the common background computed in Section 1.3, relevant for both the point and segment algorithms, is applied to the science data to begin the source detection process. To identify a signal as a source, the signal must have a minimum number of connected pixels, source_box, each of which is greater than its two-dimensional threshold image counterpart. Connectivity refers to how pixels are literally touching along their edges and corners, and the threshold image is the background RMS image (Section 1.3) multiplied by a configurable n-sigma value, segm_nsigma, and modulated by a weighting scheme based upon the WHT extension of the detection image. Before applying the threshold, the detection image is filtered by the image kernel (Section 1.5) to smooth the data and enhance the ability to identify signal which is similar in shape to the kernel. This process generates a two-dimensional segmentation image or map where a segment is defined to be a number of connected pixels which are all identified by a numeric label and are considered part of the same source.

The derived segmentation map is then evaluated in three ways. Both the fraction of sources which are larger than a user-specified fraction of the image (“large” segments), rw2d_biggest_source, as well as the total fraction of the image covered by segments, rw2d_biggest_fraction, are computed. Additionally, the size in pixels of the largest segment is checked to determine whether or not the size exceeds a user-specified limit, rw2d_biggest_pixels. If any of these scenarios are true, this is a strong indication the detection image is a crowded astronomical field. In such a crowded field, the Gaussian kernel (astropy.convolution.Gaussian2DKernel, discussed in Section 1.5) can blend objects in close proximity together, making it difficult to differentiate between the independent objects. In extreme cases, a large number of astronomical objects are blended together and are mistakenly identified as a single segment covering a large percent of the image. To address this situation an alternative kernel is derived using the astropy.convolution.RickerWavelet2DKernel Astropy utility. The RickerWavelet2DKernel is approximately a Gaussian surrounded by a negative halo, and it is useful for peak or multi-scale detection. This new kernel is then used for the generation of an improved segmentation map from the multi-filter detection image.

The new segmentation map is then evaluated using the same three evaluation techniques as the original segmentation map. If the new map also fails the evaluation criteria, the algorithm tries a different tactic for a Round 2 evaluation by changing either the way the background was generated, or by increasing the threshold for source detection. If a Constant Background Algorithm (aka sigma-clipped) were used initially, a Conformal Background Algorithm (aka Background2D) is now employed. If the Conformal Background Algorithm were used initially, the n-sigma value is doubled and a higher threshold for source detection is computed, still using the Conformal Background Algorithm. The processing then iterates again, using the Gaussian smoothing kernel and then the RickerWavelet kernel, in order to generate an acceptable segmentation map. However, for this iteration larger limits are utilized for the “large” segments, biggest_source_deblend_limit, as well as the total fraction of the image covered by segments, source_fraction_deblend_limit.

If the evaluation of the Round 2 segmentation maps still fails to be acceptable, there is a last ditch effort to salvage a viable solution. A ratio is computed for the large island values calculated using the Gaussian and RickerWavelet kernels. This ratio is believed to be indicative of overlapping PSFs versus large areas of nebulosity. If this ratio is greater than the value of ratio_bigsource_limit (default = 2), then it is deemed the deblending process can be successful, and processing proceeds using the segmentation image generated with the RickerWavelet kernel smoothing.

Note

If no segmentation map is found to be acceptable for further processing, only empty table (i.e., no detected sources) source catalogs will be produced for the segmentation algorithm.

If the final segmentation map contains large segments which did not exceed any threshold, but are still generous in size, a deblending process is applied to the map. Because different sources in close proximity can be mis-identified as a single source, it is necessary to apply a deblending procedure to the segmentation map. The deblending is a combination of multi-thresholding, as is done by Source Extractor and the watershed technique.

Caution

The deblending can be problematic if the background determination has not been well-determined, resulting in segments which are a large percentage of the map footprint. In this case, the deblending can take unreasonable amounts of time (e.g., literally days) to conclude. This led to the implementation of logic to limit the use of deblending to only those segments which are larger than the PSF kernel. This will result in some faint close sources being identified as a single source in the final catalog.

The segmentation map/image is the same shape as the input image, source regions (aka segments) are labeled with positive integer values, and the background (non-segment pixels) has a value of zero. The segmentation map, deblended as necessary, derived from and used with the multi-filter detection image for measuring source properties is used to determine the centroids of sources.

The final step for source identification is to convert the segmentation map into a table in order to identify readily bad rows which are characterized as centroids which have values of NaN or infinity. These rows are removed from the table, and the corresponding segments are removed from the segmentation map.

Note

Many of the Photutils utilities use the segmentation map directly, in contrast to using its information in a tabular format, for processing sources. The tabular format is for user convenience.

Note

The catalog generation code began its development circa 2019 and was based on an older version of Photutils. Since that time the Photutils package has matured significantly and provides much more functionality which could supersede functionality used by the catalog generation algorithms.

4.2: Isophotal Photometry Measurements

In contrast to the Point algorithm which uses a coordinate list of source locations to perform measurements on the source candidates, the actual isophotal photometry measurements are made on the single-filter drizzled images in conjunction with the cleaned segmentation map. As was the case for the multi-filter detection image, the single-filter drizzled image is used in the determination of appropriate background and RMS images (Section 1.3). In preparation for the photometry measurements, the background-subtracted image, as well as the RMS image, are used to compute a total error array by combining a background-only error array with the Poisson noise of sources.

The isophotal photometry and morphological measurements are then performed on the background-subtracted single-filter drizzled image using the segmentation map derived from the multi-filter detection image, the background and total error images, the image kernel, and the known WCS with the photutils.segmentation.SourceCatalog utility. The measurements made using this utility and retained for the output segmentation catalog are denoted in Table 6.

Table 6: Isophotal Measurements - Subset of Segmentation Catalog Measurements and Descriptions
PhotUtils Variable	Catalog Column	Description
area	Area	Total unmasked area of the source segment (pixels^2)
background_at_centroid	Bck	Background measured at the centroid position
bbox_xmin	Xmin	Min X pixel in the minimal bounding box segment
bbox_ymin	Ymin	Min Y pixel in the minimal bounding box segment
bbox_xmax	Xmax	Max X pixel in the minimal bounding box segment
bbox_ymax	Ymax	Max Y pixel in the minimal bounding box segment
covariance_xx	X2	Variance of position along X (pixels^2)
covariance_xy	XY	Covariance of position between X and Y (pixels^2)
covariance_yy	Y2	Variance of position along Y (pixels^2)
ellipse_cxx	CXX	SExtractor’s CXX ellipse parameter (pixel^-2)
ellipse_cxy	CXY	SExtractor’s CXY ellipse parameter (pixel^-2)
ellipse_cyy	CYY	SExtractor’s CYY ellipse parameter (pixel^-2)
elongation	Elongation	Ratio of the semi-major to the semi-minor length
ellipticity	Ellipticity	1 minus the Elongation
id	ID	Numeric label of the segment/Catalog ID number
orientation	Theta	Angle between the semi-major and NAXIS1 axes
sky_centroid_icrs	RA and DEC	Equatorial coordinates in degrees
source_sum	FluxIso	Sum of the unmasked data within the source segment
source_sum_err	FluxIsoErr	Uncertainty of FluxIso, propagated from input array
x_centroid	X-Centroid	X-coordinate of the centroid in the source segment
y_centroid	Y-Centroid	Y-coordinate of the centroid in the source segment

Note

These column names were updated with photutils version 3.0.0, and differ slightly from the previous versions. These former column names are still valid when using prior versions of phoutils.

4.3: Aperture Photometry Measurements

The aperture photometry measurements included with the segmentation algorithm use the same configuration variable values and literally follow the same steps as what is done for the point algorithm as documented in Sections 2.2 - 2.4. The fundamental difference between the point and segment computations is the source position list used for the measurements.

5: The Output Segmentation Catalog Files

The discussion in Sections 3.1 and 3.2 applies not only the Point catalogs, but also to the Segmentation catalogs. Differences arise with respect to the specific columns actually present in the different catalog types. Using the same example from Section 3.1, the resulting auto-generated segmentation total detection catalog filename will be:

hst_98765_43_acs_wfc_total_j65c43_segment-cat.ecsv

and a filter catalog filename will be:

hst_98765_43_acs_wfc_f606w_j65c43_segment-cat.ecsv

The metadata contained in the catalogs represents the same observational parameters though any measured values will probably be different.

5.1: Segmentation Total Detection Catalog

Similar to the Point Total Detection Catalog, the Segmentation Total Detection Catalog contains the fundamental position measurements of the detected sources: ID, X-Centroid, Y-Centroid, RA, and DEC, supplemented by some of the aperture photometry measurements from each of the filter catalogs (ABmag of the outer aperture, Concentration Index, and Flags). The column names for the aperture measurements are constructed based upon the contributing Segmentation Filter Catalogs and have the following names: MagAP2_<filter>, CI_<filter>, and Flags_<filter>. The fundamental difference between the Point and Segmentation Total Detection Catalogs is the initial detection source list/coordinates as they are determined by different algorithms.

5.2: Segmentation Filter Catalog Content

Section 3.2 discusses the general metadata found at the top of all the output catalogs, where the latter portion of this section is specific to the point catalogs. The general commentary is still relevant for the segmentation catalogs, except for the specific columns. In the case of the segmentation filter catalogs, the specific columns and the order of the columns were designed to be similar to the Source Extractor catalogs produced by the Hubble Legacy Archive (HLA) project.

Having said this, the PhotUtils/Segmentation utility is not as mature as Source Extractor, and it was not clear that all of the output columns in the HLA product were relevant for most users. As a result, some measurements in the HLA Source Extractor catalog may be missing from the output segmentation catalog at this time. The current Segment column measurements are in Table 7 with the same left-to-right ordering as found in the ECSV file.

The rejection of cosmic-ray dominated catalogs as discussed in Section 3.5 also applies to the Segmentation catalogs.

Table 7: Segmentation Filter Catalog Measurements and Descriptions
Segment Column	SExtactor Column	Description	Units
X-Centroid	X_IMAGE	0-indexed Coordinate position	pixel
Y-Centroid	Y_IMAGE	0-indexed Coordinate position	pixel
RA	RA	Sky coordinate at epoch of observation	degrees
DEC	DEC	Sky coordinate at epoch of observation	degrees
ID		Catalog Object Identification Number
CI	CI	Concentration Index
Flags	FLAGS	See Section 2.4.2 for flag value definitions
MagAp1	MAG_APER1	ABmag of source, inner (smaller) aperture	ABmag
MagErrAp1	MAGERR_APER1	Error of MagAp1	ABmag
FluxAp1	FLUX_APER1	Flux of source, inner (smaller) aperture	electrons/s
FluxErrAp1	FLUXERR_APER1	Error of FluxAp1	electrons/s
MagAp2	MAG_APER2	ABmag of source, outer (larger) aperture	ABmag
MagErrAp2	MAGERR_APER2	Error of MagAp2	ABmag
FluxAp2	FLUX_APER2	Flux of source, outer (larger) aperture	electrons/s
FluxErrAp2	FLUXERR_APER2	Error of FluxAp2	electrons/s
MSkyAp2		ABmag of sky, outer (larger) aperture	ABmag
Bck	BACKGROUND	Background, position of source centroid	electrons/s
Area		Total unmasked area of the source segment	pixels^2
FWHM		FWHM of 2D Gaussian with same second-order moments as the source	pixels
MagSegment	MAG_ISO	Magnitude corresponding to FluxSegment	ABmag
FluxSegment	FLUX_ISO	Sum of unmasked data values in segment	electrons/s
FluxSegmentErr	FLUXERR_ISO	Uncertainty of FluxSegment	electrons/s
KronRadius		Unscaled first-moment Kron radius	pixels
Xmin	XMIN_IMAGE	Min X pixel in minimal bounding box segment	pixels
Ymin	YMIN_IMAGE	Min Y pixel in minimal bounding box segment	pixels
Xmax	XMAX_IMAGE	Max X pixel in minimal bounding box segment	pixels
Ymax	YMAX_IMAGE	Max Y pixel in minimal bounding box segment	pixels
X2	X2_IMAGE	Variance along X	pixel^2
Y2	Y2_IMAGE	Variance along Y	pixel^2
XY	XY_IMAGE	Covariance of position between X and Y	pixel^2
CXX	CXX_IMAGE	SExtractor’s ellipse parameter	pixel^2
CYY	CYY_IMAGE	SExtractor’s ellipse parameter	pixel^2
CXY	CXY_IMAGE	SExtractor’s ellipse parameter	pixel^2
Elongation	ELONGATION	Ratio of semi-major to semi-minor length
Ellipticity	ELLIPTICITY	The value of 1 minus the elongation
Theta	THETA_IMAGE	Angle between semi-major and NAXIS1 axes	radians

6: Reading The Point and Segmentation Output Catalog Files

All of the Point and Segmentation catalogs, filter and total, are Enhanced Character-Separated Values (ECSV) files which are human-readable ASCII tables. As such, it is straight-foward to access the astronomical source data contained in the rows of the files in a programmatic way via Astropy or Pandas.

An Astropy example with a Segmentation filter catalog will generate the following Astropy table (abridged view):

>>> from astropy.table import Table
>>> astro_tab=Table.read("hst_15064_11_acs_wfc_f814w_jdjb11_segment-cat.ecsv", format="ascii.ecsv")
>>> astro_tab
<Table length=375>
X-Centroid Y-Centroid       RA           DEC         ID      CI   ...    CYY       CXY    Elongation Ellipticity  Theta
   pix        pix          deg           deg              mag(AB) ...  1 / pix2  1 / pix2                          rad
 float64    float64      float64       float64     int64  float64 ...  float64   float64   float64     float64   float64
---------- ---------- ------------- ------------- ------- ------- ... --------- --------- ---------- ----------- --------
  3774.045     87.935   313.5799763    -0.1839533       1   2.144 ...   0.25651  -0.13623       1.30        0.23   52.349
  3630.189    101.246   313.5819743    -0.1837685       2   1.642 ...   0.12165  -0.00195       1.03        0.03   82.412

The “comment” parameter in this Pandas example is necessary so that the reader will skip over the header lines which it cannot parse. The first line which is actually read is the “line 0” (header=0) which consists of the ascii column names. The result is a Pandas dataframe for this example of the Point filter catalog:

>>> import pandas
>>> df=pandas.read_csv("hst_15064_11_acs_wfc_f814w_jdjb11_point-cat.ecsv", sep=" ", header=0, comment="#")
>>> df
        X-Center     Y-Center          RA       DEC   ID  ...   MSkyAp2  StdevAp2      FluxAp2        CI  Flags
0    3774.738972    89.759486  313.579967 -0.183928    1  ...  0.165745  0.009700     4.732320  1.561092      1
1    3630.522602   102.347181  313.581970 -0.183753    2  ...  0.151377  0.227345   834.948972  1.189462      4