Introduction

High-quality astronomical images delivered by modern ground-based and space observatories demand adequate, reliable software for their analysis and accurate extraction of sources, filaments, and other structures, containing massive amounts of detailed information about the complex physical processes in space. The multi-wavelength observations with highly variable angular resolutions across wavebands require extraction tools that preserve and use the invaluable high-resolution information. Complex fluctuating backgrounds and filamentary structures appear differently on various scales, calling for multi-scale approaches for complete and reliable detection of sources and filaments. Availability of uncalibrated extraction tools with widely varying qualities highlights the need to use standard benchmarks for choosing the most reliable and accurate method for the astrophysical research.

These pages present getsf, a new method for extracting sources and filaments in astronomical images using separation of their structural components, designed to handle multi-wavelength sets of images and very complex filamentary backgrounds. The method spatially decomposes the original images and separates the structural components of sources and filaments from each other and from their backgrounds, flattening their resulting images. It spatially decomposes the flattened components, combines them over wavelengths, and detects the positions of sources and skeletons of filaments. Finally, getsf measures the detected sources and filaments and creates the output catalogs and images. This almost universal and fully automated method has a single user-definable parameter, the maximum size of the structures to extract, that users have to determine from the input images on the basis of their research interests.

These pages describe a realistic multi-wavelength set of benchmark images for testing and comparing source and filament extraction methods to enable the selection of the most reliable and accurate method. The images include a complex fluctuating background cloud, a long dense filament, and many starless and protostellar cores. The benchmark enables conclusive comparisons according to the extraction completeness, reliability, and goodness, as well as the detection and measurement accuracies and the overall quality. Multi-wavelength source and filament extractions with getsf using three variants of the new benchmark with increasing complexity levels show that the new method is superior to older methods.

An improved algorithm for the derivation of high-resolution surface density images from the multi-wavelength far-infrared and submillimeter continuum imaging is shown to create the densities as sharp as the shortest-wavelength image of a sufficient quality, with the angular resolution reaching 5.6" when using the Herschel 70 μm image. If the image is excessively noisy, contaminated by the PAH emission, or unusable for other reasons, the highest resolution is defined by the 100 or 160 μm images (6.811.3"). Such high resolution is useful for detailed studies of the enormous structural diversity in space, hence, for deeper understanding of the physical processes within the sub-structured filaments and their relation to the formation of stars.

Star-Forming Cloud DR21 (Cyg X)

Illustration of the new algorithm for the derivation of high-resolution surface densities from multi-wavelength observations. The density was derived from 5 images (at 70500 μm) obtained with the Herschel Space Observatory (Motte et al. 2010; Hennemann et al. 2012; Bontemps et al., in prep.).

High-res surface density (36.3, 24.9, 18.2, 11.7, 5.9")

Supernova Remnant RXJ1713.7-3946

RXJ1713.7-3946 was observed with XMM-Newton (EPIC camera) in the X-ray waveband (0.66 keV) centered at 0.0024 μm. The image below is a mosaic of multiple observations, first presented in Acero et al. (2017). With an average angular resolution of 7", it reveals the south-east segment of the supernova remnant shell, possibly created by the explosion of the historical supernova SN393, whose center of explosion is located beyond the upper-right image corner. For this source and filament extraction with getsf, a maximum size of 15" of the sources and 25" of the filaments of interest was adopted.

XMM-Newton (EPIC) image at 0.0024 micron

Background of sources

Background of filaments

Component of sources

Component of filaments

Extracted sources (footprints)

Extracted filaments (on scales 725")

Extracted filaments (on scales ~28")

Star-Forming Galaxy NGC6744

NGC6744 was observed with GALEX in a far-ultraviolet (FUV) waveband (13501750 Å) centered at 0.15 μm (Lee et al. 2011). The image below with an angular resolution of 4" shows the spiral galaxy, considered similar to our own Galaxy. Despite noisiness of the FUV image, it displays the spiral arms with many hundreds of unresolved emission sources – the regions of ongoing star formation, heated by the embedded young massive stars. For this source and filament extraction with getsf, a maximum size of 20" of the sources and filaments of interest was adopted.

GALEX (FUV) image at 0.15 micron

Background of sources

Background of filaments

Component of sources

Component of filaments

Extracted sources

Supernova Remnant NGC6960

NGC6960 was observed with Hubble in five UVIS wavebands (0.50.8 nm) centered at 0.6 μm, within the frame of the Hubble Heritage project (PI: Z. Levay; Mack et al. 2015). The small image below with an angular resolution of 0.2" represents a small fragment of the Veil Nebula, which is a segment of the Cygnus Loop, the large expanding shell of a supernova remnant (Fesen et al. 2018). For this source and filament extraction with getsf, a maximum size of 0.5" of the sources and 2" of the filaments of interest was adopted.

Hubble (WFC3) image at 0.6 micron

Background of sources

Background of filaments

Component of sources

Component of filaments

Extracted sources

Star-Forming Cloud L1688

L1688 was observed with Spitzer in the IRAC 8 μm waveband (Evans et al 2009). The image below with an angular resolution of 6" shows a complex intensity distribution in this well-known star-forming region, with the background varying by almost two orders of magnitude and many sources situated in both faint and bright background areas. For this source and filament extraction with getsf, a maximum size of 30" of the sources and filaments of interest was adopted.

Spitzer (IRAC) image at 8 micron

Background of sources

Background of filaments

Component of sources

Component of filaments

Extracted sources

Embedded Starless Core L1689B

L1689B, one of the nearest well-resolved starless cores (a distance of 140 pc) embedded in a resolved filament, was observed with Herschel in five PACS and SPIRE wavebands (Ladjelate et al. 2020). The 160500 μm images were used to compute a high-resolution (13.5") surface density image to illustrate the new extraction method on a single image. Besides the reduction of the number of images, the use of surface densities allows getsf to catalog physical parameters of the core and filament. For this source and filament extraction with getsf, a maximum size of 90" of the sources and 180" of the filaments of interest was adopted.

High-res (13.5") surface density

Background of sources

Background of filaments

Component of sources

Component of filaments

Extracted sources (footprints)

Extracted filaments (on scales 14200")

Extracted filaments (on scales ~200")

Star-Forming Cloud NGC6334

NGC6334 was observed with APEX at 350 μm, equipped with the ArTéMiS camera (André et al. 2016). The image below with an angular resolution of 8" represents an improvement by a factor of 3 with respect to the Herschel images at 350 μm. Subtraction of the correlated sky noise resulted in the image that has no signals on spatial scales above 120" (André et al. 2016). Therefore, the large-scale background and the zero level of the image are not known and the structures in the image are smaller than the largest scale. Fortunately, such observational problems are totally unimportant for getsf. For this source and filament extraction with getsf, a maximum size of 30" of the sources and filaments of interest was adopted.

APEX (ARTEMIS) image at 350 micron

Background of sources

Background of filaments

Component of sources

Component of filaments

Extracted sources (footprints)

Extracted filaments (on scales 830")

Extracted filaments (on scales ~30")

Star-Forming Cloud Orion A

Orion A was observed with JCMT at 450 and 850 μm with the SCUBA-2 camera (Lane et al. 2016) with angular resolutions of 9.8" and 14.6", respectively. The 850 μm image below displays the northern part of the integral-shaped filament (ISF). Like with other ground-based submillimeter observations that must subtract sky background, large-scale emission in the images has been filtered out (Kirk et al. 2018). For this two-wavelength getsf extraction in both 450 and 850 μm images, a maximum size of {20, 30}" of the sources and {30, 45}" of the filaments of interest was adopted.

JCMT (SCUBA-2) image (at 850 micron)

Background of sources (at 850 micron)

Background of filaments (at 850 micron)

Component of sources (at 850 micron)

Component of filaments (at 850 micron)

Extracted sources (at 850 micron, footprints)

Star-Forming Cloud W43-MM1

W43-MM1 was observed with the 12-m array of the ALMA interferometer (baselines 13–1045 m) in the 233 GHz band centered at 1300 μm (Motte et al. 2018; Nony et al. 2020). The small image below with an angular resolution of 0.44" contains spatial scales of up to 12", beyond which the interferometer was insensitive to the emission. For this source and filament extraction with getsf, a maximum size of 0.8" of the sources and 1.3" of the filaments of interest was adopted.

ALMA (12-m array) image at 1.3 mm

Background of sources

Background of filaments

Component of sources

Component of filaments

Extracted sources (footprints)

Extracted filaments (on scales 0.81.3")

Extracted filaments (on scales ~2")

Sasha

This man photographed himself in July 2019 with a smartphone Samsung Galaxy S9 Edge from a distance of ~60 cm. The JPEG image was then converted to the FITS format using ImageMagick and the resulting 661 × 661-pixels image was arbitrarily assigned a 2" pixel size and an angular resolution of 6". The pixel values were assumed to have the units of MJy/sr. For this source and filament extraction with getsf, a maximum size of 6" of the sources and 15" of the filaments of interest was adopted.

Original Image

Background of sources

Background of filaments

Component of sources

Component of filaments

Extracted sources (footprints)

Extracted filaments (on scales 615")

Extracted filaments (on scales ~15")

< Support Method >

Extraction Method

Flexible multi-wavelength design enables getsf to handle from one up to 99 images, not necessarily all of them observed in different wavebands. Any subset of the input images, deemed beneficial for the detection purposes, can be used to identify the sources and filaments, whereas measurements of the detected sources and filaments are provided for all input images. In a non-standard application, the method can also be employed for the position-velocity cubes, if the latter are split into separate images along the velocity axis. The method is almost universally applicable – it has been tested on a very wide range of various astronomical observations, numerical simulations, and even digital photographs.

The method is designed and expected to work for all "normal" images, that are not very sparse: most pixels must contain detectable signals (measurable data). Examples of the images, for which getsf might not produce reliable results, are some extremely faint X-ray or UV low-count images that have isolated spiking pixels, surrounded by large areas of pixels that were not assigned any detectable signal. On the other extreme, the images suitable for getsf must not be extremely smooth: they must have some variations on spatial scales of the order of angular resolution. For both types of non-standard images, getsf would still work and complete extractions, but its results might not be reliable. This is because the method relies on the standard deviations of the background or noise fluctuations outside structures at each of its processing steps and the values in such unusual images may not correctly represent the statistics of the observed data.

Main processing steps of getsf:

  1. Preparation of a complete set of images on the same grid of pixels and optional derivation of high-resolution surface densities and dust temperatures.
  2. Separation of the structural components of sources and filaments from each other and from their backgrounds using spatially decomposed images.
  3. Flattening of the residual noise and background fluctuations in the images of the separated components of sources and filaments.
  4. Combination of the flattened components of sources and filaments over several (or all) wavebands in their spatially decomposed images.
  5. Detection of sources (positions) and filaments (skeletons) in the combined images of the components in their spatially decomposed images.
  6. Measurements of the properties of detected sources and filaments, creation of the multi-wavelength output catalogs and images.

In the getsf flowchart, the colored blocks highlight the steps of preparation, background subtraction, image flattening, and extraction of sources and filaments:

The method has a single user-definable parameter (per image): the maximum size of the structures of interest to extract. This parameter is determined by users from the images on the basis of their research interests.

Before starting any extraction, it is necessary to formulate the aim of the study – what structures of interest must be extracted. The method knows and is able to separate three types of structures: sources, filaments, and backgrounds. To separate the structural components with getsf, the maximum size X of the sources and Y of the filaments of interest need to be manually (visually) estimated from the prepared images, independently for each waveband. This can be accomplished by opening an image in ds9, finding the largest structure of interest, and placing a circular region, fully covering its width.

The maximum sizes X and Y are defined as the footprint radii (arcsec) of the largest source and the widest filament, respectively, to be extracted. A footprint size has the meaning of a full width at zero (background) level: the largest two-sided extent from a source peak or filament crest, where that structure is still visible in the image against its background. For a Gaussian intensity distribution, the footprint radius is slightly larger than the half-maximum width H. For a power-law intensity profile, the footprint radius may become much larger than H. If the widest filaments of interest are blended (overlapping each other with their footprints), the Y values must be increased accordingly, to approximate the full extent of the blend. In contrast, it is not necessary to adjust X for blended sources, because their final backgrounds are determined from their footprints at the measurement step.

It is not necessary (also not possible) to evaluate the maximum size parameter X and Y very precisely, a 50% accuracy is quite sufficient. Its purpose is to set a reasonable limit to the spatial scales, when separating the structural components, and to the size of the structures to be measured and cataloged. Indeed, getsf works with spatially-decomposed images and it needs to know the maximum scale. It makes no sense to perform the decomposition up to a very large scale, if the extraction is aimed at much smaller sources or narrower filaments. The method has no limitations with respect to the sizes (widths) of the structures to extract. However, it is important to avoid detecting, measuring, and cataloging the peaks that are unnecessarily too wide, because they would likely overlap with other sources of interest, which potentially would make their measurements less accurate.

Notable Improvements or New Features

 Revision v210907 (new feature). Starting with this version, getsf adds the global, scale-independent skeletons to its output. Several users experienced difficulties analyzing the complex nature of observed filaments with the default set of scale-dependent skeletons produced by getsf. Responding to their needs, the updated getsf produces also images of the global skeletons tracing the filamentary structures on all scales, independently of the filament widths. They are created from the scale-dependent skeletons within the range of scales of interest for the user, that is up to the maximum width Y of the filaments specified in the getsf configuration file.

 Revision v210915 (new feature). Starting with this version, getsf provides also the measurements of filaments for the global skeletons the same way the measurements are done for the set of scale-dependent skeletons.

Preparation of images

High-resolution surface density

Separation of components

Spatial decomposition (41400", selected scales)

Component of sources

Background of sources

Component of filaments

Background of filaments

Taurus, Aquila, and IC 5146

The surface densities of the filaments in three well-studied star-forming regions in Taurus, Aquila, and IC 5146 were derived from their images (at 160500 μm) obtained with the Herschel Space Observatory (HGBS key project, Ph. André). They demonstrate that the observed filaments are highly sub-structured and their shapes, traced by their skeletons, are very different on various spatial scales. Therefore, when studying filaments, it is necessary to detect their skeletons on those scales that correspond to the widths of the structures being studied.

Taurus filaments on scales 18576"

Aquila filaments on scales 18576"

IC 5146 filaments on scales 18576"

< Intro Code >

Numerical Code

The method has been developed as a Bash script getsf that executes a suite of utilities doing most of the numerical computations. Linux or macOS with ifort or gfortran can be used to install the code. To read and write FITS images, it uses cfitsio (Pence 1999); for resampling and reprojecting images, it executes swarp (Bertin et al. 2002); for determining source coordinates (α, δ), it employs xy2sky from wcstools (Mink 2002); colored screen output is produced by highlight (André Simon).

The latest version can be downloaded below. When downloading getsf for the first time, users are requested to register; the registered users will benefit from notifications on improved versions.

  • GETSF 210920
  • GETSF Guide

The initial reference version of the code and results of a validation extraction can also be downloaded below. They are made available for those who feel the need to have the same version of the code that was used in the published paper and for those who want to validate their getsf installation and configuration file by comparing their results with a reference extraction of sources and filaments in a very small simulated image.

Note that getsf may become improved in the future and that only the most recent version is supported.

GETSF Utilities and Scripts

The following list of the getsf utilities and scripts explains their purpose and functions. They are quite useful for command-line image manipulations, even if there is no need in a source or filament extraction. Their usage information is displayed when a utility is run without any parameter; they are sorted in a decreasing sequence of their general usability outside getsf.

modfits modify an image or its header in various ways: math transformations; profiling an image along a line; image segmentation; filament skeletonization; removal of connected clusters of pixels; addition or removal of border areas; correction of saturated or bad pixel areas; conversion of intensity units; changes of the header keywords; etc.
operate operate on two input images: addition, subtraction, multiplication, division; relative differencing; minimization or maximization; extension or expansion of masks; copying of an image header; computation of surface densities, temperatures, or intensities; etc.
imgstat display and/or save image statistical quantities; produce mode-, mean-, or median-filtered images; compute images of standard deviations, skewness, kurtosis; etc.
fftconv fast Fourier transform or convolve image with few predefined kernels or an external kernel image
fitfluxes fit spectral shapes of source fluxes or image pixel intensities to derive masses or surface densities
convolve convolve an image to a desired lower resolution
resample resample and re-project an image with optional rotation
hires derive high-resolution densities and temperatures
prepobs convert all observed images to the same grid of pixels
installg install getsf on a computer system (macOS, Linux)
iospeed test I/O speed of a hard drive for a specific image
readhead display an image header or save selected keywords
cleanbg interpolate background under footprints of sources
ellipses overlay an image with ellipses of extracted sources
sfinder detect sources in combined clean single-scale images
smeasure measure and catalog properties of detected sources
fmeasure measure and catalog properties of detected filaments
finalcat produce the final multi-wavelength catalogs of sources
expanda expand masked areas of an image to its edges
extractx extract all FITS extensions into separate images
splitcube split a FITS data cube into separate images

< Method Bench >

Benchmarks

The source and filament extraction methods are growing in numbers: in the area of star formation, a new method was published every year or two within the seven-year period after the launch of Herschel.

The various extraction tools often have completely different approaches and quality of their results can be expected to be very different. The new developments are driven by new ideas and realization that existing tools, developed for older or simpler images, do not work well, when applied to more complex images, obtained with the improved technologies of new telescopes. Source and filament extraction methods are the critically important tools that must be calibrated and validated using a standard set of benchmark images with fully known properties of all components, before their astrophysical applications.

It seems very unlikely that methods, based on very different approaches, would give consistent results in terms of detection completeness, number of false positive detections, and measurement accuracy. In practice, various methods do perform very differently, as can be shown quantitatively on simulated benchmarks with known properties of all components. This highlights the need of systematic comparisons of different methods in order to understand their qualities, inaccuracies, and biases. There is a real danger that application of different, uncalibrated methods for the same type of star formation studies would give inconsistent, contradictory results and wrong conclusions, creating serious, lasting problems for the science.

It would be desirable to use the same extraction tool to exclude any biases or dissimilarities, caused by different methods. If a new extraction method is to be used, it must be tested on standard benchmarks to ensure that its detection and measurement qualities are consistent or better. This approach is usually practiced within research consortia, but this does not solve the global problem that the results, obtained from the same data by different consortia or research groups using different tools may still be affected by the uncalibrated (or suboptimal) tools used.

The new multi-wavelength benchmark, presented below, contains simulated Herschel images of a dense background cloud with strong nonuniform fluctuations, a wide dense filament with a power-law intensity profile, and hundreds of radiative transfer models of starless and protostellar cores with wide ranges of sizes, masses, and profiles. The simulated benchmark, in which parameters of all components are precisely known, allows quantitative analyses of extraction results and conclusive comparisons of different methods and their extraction completeness, reliability, and goodness, along with the detection and measurement accuracies.

The benchmark can serve as a standard benchmark problem for source and filament extraction methods, allowing researchers perform their own tests of available methods to choose the most reliable and accurate one for their studies. The benchmark images, together with the truth catalogs and the extraction results, are made available to anyone who wants to get their own comparisons and conclusions. Future methods can also be calibrated using the benchmark and compared with the quality of the older methods.

Benchmark A

Benchmark A was created by Men'shchikov et al. (2012) to develop getsources and used in Men'shchikov (2017) to illustrate getimages, a method for background subtraction and image flattening. This old benchmark contains three simulated components: background, 459 sources, and instrumental noise, and is referred to as A3. Although the background in A3 is not as complex and realistic as in Benchmark B, its sources are allowed to overlap. This makes it suitable for testing, how well a source extraction method is able to handle blended sources. Its simpler variant is A2 (no background).

The compressed archives below contains the simulated images at 75, 110, 170, 250, 350, and 500 μm (resolutions of 5, 7, 11, 17, 24, and 35"), the surface density images (resolution of 11"), and the truth catalogs with parameters of all model sources. The archives were produced with rar, because it creates smaller archives with best compression; the two archives have sizes of 90 and 70 MB, respectively.

Background

Sources

Background+Sources

Benchmark B

The new Benchmark B, created by Men'shchikov (2021) to develop getsf, features a dense and highly variable background cloud, a dense spiral filament with its markedly anisotropic properties, 919 sources with a wide range of fluxes, sizes, and intensity profiles, and uniform noise. In contrast to Benchmark A, the sources do not overlap. Benchmark B with 4 components (background, filament, sources, and noise) is referred to as B4. Simpler variants include B3 (no filament) and B2 (no filament, no background).

The simplest Benchmark B2 must not present any problems to those extraction methods that do not set any upper limit on sizes of extractable sources and do not assume Gaussian shapes of their intensity profiles. Only the faintest sources are expected to be lost within the noise fluctuations in B2 and measurements of sources are expected to be accurate, although for the faintest sources inaccuracies must somewhat increase due to the noise fluctuations.

The more complex Benchmark B3 could present serious problems to those extraction methods that are not explicitly designed to handle complex backgrounds. The background fluctuations are strongly nonuniform and they progressively increase in the denser background area, resembling the observed clouds. In comparison with B2, this means that more of the sources are expected to remain undetected in B3 and possibly more spurious sources to become cataloged.

The most complex Benchmark B4 with the dense filamentary background better resembles the extreme complexity of the interstellar clouds revealed by observations, further complicating the source extraction problem. It can be expected that a larger number of sources would vanish in the background in B4 and that measurements of the sources would become less accurate than in the simpler B3 and B2.

The compressed archives below contain the simulated images at 70, 100, 160, 250, 350, and 500 μm (resolutions of 8.4, 9.4, 13.5, 18.2, 24.9, and 36.3"), the surface density images (resolution of 13.5"), and the truth catalogs with parameters of all model sources. The archives were produced with rar, because it creates smaller archives with best compression; the three archives have sizes of 170, 120, and 120 MB, respectively.

Background

Filament

Background+Filament

Sources

Background+Filament+Sources

< Code Support >

Support

The author supports getsf users and helps them get started, with the usual response times ranging from minutes to hours. Please note that the code may become updated in the future and that only the latest version of getsf is supported. It is strongly recommended that users read the User's Guide containing essential information that would help them install and use the tool correctly.

If apparently abnormal results are found or the code aborts for unknown reasons, the user is asked to install the latest version of the code and restart the problematic run from the nearest (previous) processing step to verify that the problem persists. If the user needs assistance in finding reasons of the problems, the author expects receiving a clear description of the problem, a copy or screenshot of the contents of the terminal window, and the log files of the runs. In rare cases, it may also be necessary to re-run the problematic extraction with an updated version.

  • GETSF Guide

GETSF In Publications

  1. Nony T., Robitaille J.-F., Motte F., et al. (2021) A&A, 645, A94
  2. Men'shchikov A. (2021) A&A, 649, A89  (getsf method)
  3. Schuller F., André Ph., Shimajiri Y., et al. (2021) A&A, 651, A36
  4. Louvet F., Hennebelle P., Men'shchikov A., et al. (2021) A&A, in press
  5. Men'shchikov A. (2021) A&A, in press  (benchmarking)
  6. Kumar M. S. N., Arzoumanian D., Men'shchikov A., et al. (2021) A&A, submitted
  7. Motte F., Bontemps S., Csengeri T., et al. (2021) A&A, submitted
  8. Louvet F., et al. (2022) A&A, in prep.
  9. Puteau Y., Motte F., et al. (2022) A&A, in prep.
  10. Cunningham N., et al. (2022) A&A, in prep.

< Bench Intro >

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