Public Data Release 1
- Summary of the ZTF Public (MSIP) Survey
- Scope of this Release
- Summary of Data Products
- File-based Product Statistics
- Source and Lightcurve Statistics
- DR1 Sky Coverage
- Lightcurve products: timespans and epoch distributions
- File-based Product Listings in Archive
- Epoch-based Data Quality Flags and Filtering
- Things you should know about the ZTF Release Products
- Extended (detailed) Advisories and Cautionary Notes
- How to Access ZTF Data Products
- Help Desks
- Further Documentation & Relevant Publications
An overview of ZTF and the science objectives is given in Bellm et al. 2019 and Graham et al. 2019. The ZTF Science Data System (ZSDS) is housed at IPAC/Caltech and is described in Masci et al. 2019. This page provides a summary and quick guide to the ZTF Public DR1 products. Please familiarize yourself with all advisories highlighted in red before accessing and using the products.
ZTF observing time is split three ways: (i) The Public Survey (40%); (ii) Private Collaboration and Partnership Surveys (40%); and (iii) Programs granted by the Caltech Time Allocation Committee (20%). Funding is provided by both the ZTF collaboration and the U.S. National Science Foundation through the Mid-Scale Innovations Program (MSIP). The Public Survey enables a wide range of community science and constitutes the following:
- A Northern-equatorial sky survey: a three-night cadence of the Northern sky visible from Palomar Observatory (≈ 2.5 to 3π steradians), comparable to the LSST wide-fast-deep cadence, irrespective of Galactic latitude. This component surveys ≈ 15,000 to 20,000 square degrees every three nights.
- A Galactic plane-targeted survey: a one-night cadence survey of the Galactic plane at |b| ≤ 7° and Dec. >∼ -25°, whenever a survey-field falls in this footprint. The area covering this footprint is seasonal and varies from ∼ 1000 to 2000 square degrees.
- For each of the above surveys, there is one g-filter and one r-filter exposure of the same pointing separated by at least 40 minutes.
For publications that use ZTF Science Data Products, please include the following text in your acknowledgments:
Based on observations obtained with the Samuel Oschin 48-inch Telescope at the Palomar Observatory as part of the Zwicky Transient Facility project. ZTF is supported by the National Science Foundation under Grant No. AST-1440341 and a collaboration including Caltech, IPAC, the Weizmann Institute for Science, the Oskar Klein Center at Stockholm University, the University of Maryland, the University of Washington, Deutsches Elektronen-Synchrotron and Humboldt University, Los Alamos National Laboratories, the TANGO Consortium of Taiwan, the University of Wisconsin at Milwaukee, and Lawrence Berkeley National Laboratories. Operations are conducted by COO, IPAC, and UW.
Furthermore, we appreciate you citing the following publication:
Masci, F. J., Laher, R. R., Rusholme, B., et al. 2018, The Zwicky Transient Facility: Data Processing, Products, and Archive, PASP, 131, 995.
The data selection criteria for DR1 are as follows.
- Single Exposure Products associated with individual science observation epochs (including lightcurve epochs) satisfying:
- Program ID = 1: implying only products from the public (MSIP) survey, AND
- Observation MJD ≥ 58194.0 (= 2018-03-17 00hrs UTC), AND
- Observation MJD ≤ 58483.0 (= 2018-12-31 00hrs UTC), AND
- Only exposures acquired in g-filter or r-filter.
- All Reference (co-add) Image Products created before May 8, 2019 (the DR1 publication date) that contain any exposures falling within the observation period defined in (1). Only good quality science exposures were co-added.
NOTE: The public distribution of transient alerts extracted from difference images constructed using products (1) and (2) commenced on June 4, 2018. These alerts continue to be generated and distributed using new (unreleased) image data from the Public Survey.
In accord with the criteria for data-inclusion in Section 2, the following products are available in DR1:
- Raw CCD-based image data files and metadata in FITS format.
- CCD-quadrant-based calibration image data and metadata in FITS format.
- Stacked bias images.
- Relative pixel-responsivity maps (flatfield corrections).
- Bad-pixel masks derived from these calibrations.
- Instrumentally calibrated CCD-quadrant-based, single-exposure science images, source-catalogs, and metadata; specifically:
- Science images with astrometric and photometric calibration solutions, and QA information.
- Corresponding bit-mask images.
- PSF templates (image and look-up-table) for each science image.
- Source-catalog FITS-table files containing exclusively PSF-fit photometry measurements.
- Source-catalog FITS-table files containing exclusively concentric aperture photometry & source-shape metrics.
- Difference images constructed in the sense: "science (single-exposure) image minus reference image". For DR1, these are only available for science images that had a preexisting reference image when first observed.
- Effective PSF template for difference image.
- Tar file of already publicly-released alert packet files (in Avro format) corresponding to the CCD-quadrant.
- Processing log files.
- Reference Images (co-added/stacked single-exposures) per CCD-quadrant, source-catalogs, and metadata; specifically:
- Co-add images with astrometric and photometric calibration solutions, and QA information.
- Corresponding pixel-uncertainty images.
- Corresponding pixel depth-of-coverage images (recording effective number of input exposures per pixel).
- Source-catalog FITS-table files containing exclusively PSF-fit photometry measurements.
- Source-catalog FITS-table files containing exclusively concentric aperture photometry & source-shape metrics.
- Processing log files.
- Objects (database) Table storing sources from all Reference Image PSF-fit-based catalog files.
- Includes "collaped-lightcurve" metrics (from prior position-matching of detected sources across all overlapping, CCD-quadrant-based exposures in the public survey).
- This table facilitates initial object-position searches and filtering on any of the lightcurve-collapsed metrics for subsequent extraction of lightcurve data.
- A datastore of lightcurves containing additional metrics at the single-epoch (exposure) level. This is searchable by interfacing with queries on the Objects Table. Lightcurve data can be visualized and downloaded via a GUI or API (Section 12).
For details on data and file formats, associated ancillary products, product construction and usage, see the ZSDS Explanatory Supplement. See Section 10 below for a list of MUST READ ADVISORIES about these products. See Section 11 below for a more detailed list of advisories and known caveats.
The ZTF camera consists of 16 CCDs, each of which is partitioned into 4 readout quadrants. Therefore, 64 CCD-quadrant images are generated per ZTF exposure. A CCD-quadrant is the basic image-unit for pipeline processing and from which all science data products are derived. A CCD-quadrant covers ≈ 0.854° x 0.854° on the sky. Table 1 summarizes the CCD-quadrant-based (single exposure) image counts. The second column shows the number of full camera exposures from which these were generated. The CCD-quadrant image counts are separated into "bad" (probably unusable due to non-photometric conditions) and "good" images. The criteria used to identify "bad" vs "good" images are described in Section 2.4 of the ZSDS Advisories & Cautionary Notes. The relative fractions of bad and good images in this table provides an indicator of the overall fraction of "bad" (likely suspect) single-epoch photometry measurements within individual lightcurves.
|Filter(s)||#Exposures||#Good (usable)||#Bad (suspect)||Total number||Bad / Total [%]|
|g + r||56,567||3,144,611||264,828||3,409,439||7.77|
The number of CCD-quadrant-based reference images (co-adds) per filter in DR1 are shown in Table 2. Note: unless brought to our attention, all of these are expected to have good photometric and astrometric quality in general. This is because they were constructed from good-quality single-exposure images satisfying a range of criteria, which are more stringent than those used to tag the "good" images in Table 1. The criteria used to select inputs for generating reference images are described in Section 6.7 of the ZSDS Explanatory Supplement.
The existence of a reference image for a specific survey field, CCD-quadrant, and filter is important since it means that for this region of sky: (i) image differencing and alerts can be triggered, and (ii) objects can be extracted and then used as seeds to generate lightcurves. The third column in Table 2 shows the percentage of ZTF survey fields/CCD-quadrants with at least 15 single-exposure images (regardless of quality) that have a reference image. 15 good-quality images is the minimum number to generate a reference image. These percentages are < 100 since a number of fields/CCD-quadrants do not yet have ≥ 15 good-quality science images. These fields are not visited as often during the survey.
|Filter(s)||#Reference Images||Visited-sky coverage [%]|
|g + r||102,024||92.35|
Table 3 reports the number of sources in all PSF-fit-based and aperture-based catalog file products (summarized in Section 3), separately for single-exposure (sci) images and reference (ref or co-add) images. The PSF-fit and aperture catalogs are treated independently (with no merging or cross-linking of records) in the Data System. This means a large fraction of the same sources are present in both flavors of catalog. The PSF-fit-based catalogs contain more sources since first, PSF-fitting generally yields more accurate photometry at faint fluxes and hence is more sensitive, and second, it includes deblending of closely-separated sources. Deblending is more important in high source-density regions such as the Galactic plane.
Only sources from the PSF-fit-based sci catalogs are positionally-matched across observation epochs to construct lightcurves (see Table 4 for numbers). As mentioned above, sources in the deeper PSF-fit-based ref catalogs are initially used as seeds to trigger source-matching and generate lightcurves.
|Filter(s)||#PSFcat-sci sources||#Aperturecat-sci sources||#PSFcat-ref sources||#Aperturecat-ref sources|
|g + r||59,360,774,178||37,863,027,997||4,208,075,184||1,407,266,831|
Table 4 reports the number of lightcurves from positional-matching of sources in the single-exposure PSF-fit-based science catalogs with length (number of independent epochs) greater than some limit. The distribution in the number of observation epochs and corresponding timespans is shown in Figure 3. All observation epochs, regardless of data quality are included in the counts in Table 4. For an estimate of the average fraction of "bad" epochs per lightcurve (with likely suspect and unusable photometry), see Table 1. The criteria used to identify "bad" vs "good" epochal data are described in Section 2.4 of the ZSDS Advisories & Cautionary Notes.
|Filter(s)||#lightcurves with Nobs ≥ 1||#lightcurves with Nobs ≥ 2||#lightcurves with Nobs ≥ 5||#lightcurves with Nobs ≥ 10||#lightcurves with Nobs ≥ 20|
|g + r||1,690,194,001||1,442,871,122||1,222,406,466||1,048,052,897||806,204,364|
Figure 1 shows the approximate spatial distribution in the number of single-exposure epochs in each filter included in DR1, as Aitoff-Hammer projections in Galactic coordinates. These are approximate since the spatial resolution (bin size) of each map is 1° while a CCD-quadrant subtends ≈ 0.854° x 0.854° at the center of a map. The small-scale dark regions are not holes in coverage, but due to aliasing from the warped reprojection and resampling of CCD-quadrant centers onto larger scales. The presence of ~ 3 arcmin gaps between the camera's CCDs also contributes to these holes.
FITS-formatted image representations of Figure 1 with Word Coordinate System metadata in their headers can be downloaded for each filter:
- g-filter single-exposure coverage map: sciimage_galcovmap_dr1_g.fits
- r-filter single-exposure coverage map: sciimage_galcovmap_dr1_r.fits
These maps can be visualized using a FITS-image viewer to determine if a specific sky-position is covered by a survey-field/CCD-quadrant. If so, the approximate number of overlapping epochs on/near this position (to within ≈ 1°) can be determined from examining the pixel values.
Figure 2 shows the approximate spatial distribution in CCD-quadrants per filter from which lightcurves were generated for inclusion in DR1. These maps closely track the Reference Image coverage since (PSF-catalog-based) sources extracted therefrom were used as seeds to trigger the lightcurve generation. As in Figure 1, these maps are approximate due to their finite spatial resolution (bin size) of 1° compared to the remapped CCD-quadrants that subtend ≈ 0.854° x 0.854° at the center of a map. The small-scale dark regions are not holes in coverage, but due to aliasing from the warped reprojection and resampling of CCD-quadrant centers onto larger scales. The presence of ~ 3 arcmin gaps between the camera's CCDs also contributes to these holes.
FITS-formatted image representations of Figure 2 with Word Coordinate System metadata in their headers can be downloaded for each filter:
- g-filter lightcurve (and ≈ Reference Image) coverage map: matchfile_galcovmap_dr1_g.fits
- r-filter lightcurve (and ≈ Reference-Image) coverage map: matchfile_galcovmap_dr1_r.fits
These maps can be visualized using a FITS-image viewer to determine if a specific position is covered by a survey field/CCD-quadrant. If so, one can determine if lightcurves (and/or Reference Images) exist on/near this position, to within ≈ 1°.
To complement the epoch-coverage maps in Figure 1 and lightcurve numbers in Table 4, Figure 3a shows the distribution in the number of observation epochs spanning all CCD-quadrant footprints. Figure 3b maps these into actual timespans per survey-field (latest minus earliest exposure) and therefore depicts the distribution of lightcurve timespans and their cadence in DR1. The clustering of points at top-left in Figure 3b is a consequence of the observation span for inclusion of data in DR1: Mar 17 - Dec 31, 2018, and survey-fields that are only observable during the Winter months (~ start and end of 2018).
The following is a gzipped text file listing some metadata and the archive path/filenames of all single-exposure science image files in DR1:
DR1_sci_data.txt.gz (462 MB uncompressed).
The columns in this file are: internal productID; Observation JD [days]; Center R.A. [decimal degrees]; Center Dec. [decimal degrees]; archive path/filename of primary science image. Some of these columns are also present in the archive metadata database to support more generic spatial queries (see Section 12).
Any archive path/filename in the above file can be prepended with the following root URL to access and download the specific sciimg.fits file:
For example, the first listed science image has URL:
Alternatively, the sciimg.fits suffix can be replaced with any of the following ancillary file suffixes, with products summarized in Section 3:
mskimg.fits - bit-mask image corresponding to sciimg.fits psfcat.fits - PSF-fit source catalog for sciimg.fits sexcat.fits - aperture-based (SExtractor) photometry catalog for sciimg.fits sciimgdao.psf - PSF for sciimg.fits in DOAPhot look-up-table format sciimgdaopsfcent.fits - PSF stamp at center of sciimg.fits scimrefdiffimg.fits.fz - difference image in fpack'd FITS format diffimgpsf.fits - effective PSF stamp for difference-image alerts.tar.gz - gzipped tar-directory of all alert packets sciimlog.txt - processing log for sciimg.fits (instrumental calibration) diffimlog.txt - processing log for difference image and event extraction log.txt - overall processing log for realtime pipeline run
The following is a gzipped text file listing some metadata and the archive path/filenames of all Reference Image files in DR1:
DR1_ref_data.txt.gz (16 MB uncompressed).
The columns in this file are: internal referenceID; number of science images co-added; 5σ limiting magnitude; observation JD of earliest input science image; observation JD of latest input science image; Center R.A. [decimal degrees]; Center Dec. [decimal degrees]; archive path/filename of primary reference image. Some of these columns are also present in the archive metadata database to support more generic spatial queries (see Section 12).
Any archive path/filename in the above file can be prepended with the following root URL to access and download the specific refimg.fits file:
For example, the first listed reference image has URL:
Alternatively, the refimg.fits suffix can be replaced with any of the following ancillary file suffixes, with products summarized in Section 3:
refcov.fits - pixel depth-of-coverage image refunc.fits - pixel (1σ) uncertainty image refpsfcat.fits - PSF-fit source catalog for refimg.fits refsexcat.fits - aperture-based (SExtractor) photometry catalog for refimg.fits refimlog.txt - processing log for sciimg.fits (instrumental calibration) log.txt - overall processing log for realtime pipeline run
Other relevant root URLs to access file-based products in DR1 are as follows.
The root URL to access raw CCD-based image files is: https://irsa.ipac.caltech.edu/ibe/data/ztf/products/raw/2018/
The root URL to access calibration (cal) related files is: https://irsa.ipac.caltech.edu/ibe/data/ztf/products/cal/2018/
For a description of all path and filename identifiers, see Section 7 of the ZSDS Explanatory Supplement.
The ZTF Observing System is a fully robotic system. One limitation is that it cannot determine in advance when conditions are non-photometric, for example, when intermittent clouds partially (or fully) cover the field-of-view during an exposure. Scattered moonlight can also wreak havoc. This severely affects the accuracy of the derived photometric calibration solutions. There is one estimate of the photometric zeropoint (magzp) with accompanying color term (clrcoeff) per CCD-quadrant, where each of the 64 quadrants is calibrated independently in processing. Spatial variations in transparency at the intra-quadrant level will have led to unusable science products. The lightcurve photometry extracted from the single-exposure image products will also be suspect.
There are two flavors of science products that can be queried from the archive for which data quality flags are available for filtering: (a) single-exposure CCD-quadrant-based files; and (b) Lightcurves. The quality filters for each are as follows:
Bad-quality or generally unusable CCD-quadrant-based images from individual epochs (including accompanying source catalog files) can be omitted when querying the archive by thresholding the INFOBITS value in the archive metadata. If INFOBITS for an image has value < 33554432 (i.e., does not contain bit 25), the image and catalog data are probably usable.
Examples of image-based queries that include filtering on INFOBITS are given in Section 12b.i below.
Analogous to the flagging of “bad-quality” images (Section 9a), bad or generally unusable observation epochs in lightcurves can be omitted by thresholding the catflags column in the lightcurve metadata. If catflags for an image has value < 32768 (i.e., does not contain bit 15), the photometry at that epoch is probably usable.
This flagging removes epochs based on their overall image/calibration quality. catflags also encodes possible issues at the source level, for example, contamination by bad pixels. These bits are defined in Section 10.6 of the ZSDS Explanatory Supplement. If you demand perfectly clean extractions at every epoch, we advise specifying catflags = 0 when querying lightcurve epochs.
Examples of lightcurve queries that include filtering on catflags are given in Section 12b.iii below.
The following are high-level advisories. See Section 11 for a more detailed list. Click on the links to learn more.
- DR1 contains only products generated from the ZTF Public Survey and derived from individual exposures acquired during the period: 2018-03-17:00hr to 2018-12-31:00hr UTC (Section 2). Only data acquired in the ZTF-g and ZTF-r filters are included. All exposures correspond to an integration time of 30 seconds.
- The archive contains products with basic automated data-quality assurance applied during processing. The primary criterion for archiving a science image (along with its derived products) is that it must have passed astrometric and photometric calibration using thresholds on metrics tuned to yield potentially usable science products. "Bad" (unusable) data in the archive is still possible. This is data affected by clouds, low atmospheric transparency and spatial variations therein, moon contamination, bright source scattering artifacts, unmasked aircraft and artificial satellites, transient detector behavior including cross-talk, bad seeing or focus, and/or inaccurate telescope tracking. The archive metadata contains flags at both the image-level and epoch-level within individual lightcurves to identify and omit likely unusable data from queries (see Section 9).
- The calibrated ZTF photometry can show biases of ~ 0.01 and up to 0.02 mag for predominately bright sources with g, r magnitudes <~ 15.5 relative to source photometry in the Pan-STARRS1 (PS1) MeanObject DR1 Catalog. This is present in both the PSF-fit-derived and aperture-derived catalogs. These biases are field, CCD-quadrant, and filter dependent. Work is in progress to better characterize and correct these biases in future.
- There is no separate searchable database storing source measurements from the single-exposure catalog files. These measurements can only be accessed by downloading the catalog files, which are in FITS-binary table format. Each table file corresponds to a single CCD-quadrant.
- Single-exposure source photometry can be accessed at the source-level indirectly by retrieving entire lightcurves: first by querying the Objects Table to select and filter objects of interest, then retrieving pre-generated lightcurves from an internal datastore.
- The GUI-driven lightcurve service is still undergoing performance tuning. Large spatial queries may take longer than expected depending on user-load. An alternative for retrieving lightcurves is to submit source positions to the lightcurve API. We recommend first querying the Objects Table to ensure your positions return source data (however see note 9 below).
- Lightcurves were constructed using exclusively measurements from the calibrated single-exposure PSF-fit-derived catalogs, not the aperture-based photometry catalogs.
- The photometry catalogs, including lightcurve products, contain measurements of both point-like and extended objects. The PSF-fit photometry measurements are optimal for point-sources only. Concentric aperture photometry measurements are more optimal for extended objects.
- Lightcurves were constructed using seed detections from the deeper (co-add) Reference Image PSF-fit catalogs. These "seed sources" were loaded into the searchable Objects Table where their photometry is ~ 2 to 2.5 magnitudes deeper than the single-exposure catalogs. Therefore, a majority of faint sources in the Objects Table (fainter than the single-exposure sensitivity limits) will not have lightcurves. Futhermore, objects with photometry hovering near the single-exposure detection limits will have incomplete temporal-coverage and therefore more sparsely sampled lightcurves.
- Reference Images and their catalogs (loaded into the Objects Table) were generated by stacking single-exposures acquired from all science programs (public, private, and Caltech-time observations). These programs covered two overlapping tiling grids on the sky, a primary grid and a secondary grid, to fill-in the gaps between CCDs on the focal plane. The public single-exposures however (from which the DR1 lightcurves were constructed), only covered the primary observing grid. Therefore, expect to see missing lightcurves for many sources in the Object Table. These would have been extracted from Reference Images constructed from exposures acquired only under private and/or Caltech-time and possibly only on the secondary observing grid.
- Related to the previous note, given the DR1 lightcurves include only public-survey epochs that were observed only on the primary sky-grid, gaps between the CCDs on the ZTF camera will also result in missing lightcurves. For example, sources that were detected on a secondary grid Reference Image but fell in a CCD-gap on the primary grid.
- When querying the Objects Table to retrieve lightcurves, the nobs (and ngoodobs) metrics report the total (and total unflagged) number of observation epochs on which a measurement was made. This includes data from all science programs (public, private, and Caltech-time observations). You will only see epochs from the public survey when downloading actual lightcurves. The number of lightcurve epochs returned may be different from nobs (or ngoodobs). Furthermore, all the lightcurve-collapsed metrics in the Objects Table are computed using epochal data from all science programs.
- The lightcurve photometry measurements (mag values) already have their epoch-dependent photometric zeropoints applied (magzp column in returned metadata). All photometry is in the native ZTF photometric system. No color corrections are applied (or rather, zero color in the AB system is assumed). To transform photometry onto the AB (PS1) system, you will require knowledge of the source color in that system. The exact color used during initial photometric calibration against PS1 is defined by column pcolor and the color coefficient to transform to the PS1 system is given by column clrcoeff. The procedure for applying color corrections is described in Section 10.1.1 of the ZSDS Explanatory Supplement.
- In DR1, Reference Images (co-adds) cover ~ 92% of the sky surveyed in each filter (Table 2 above). This implies lightcurves were extracted for a similar fraction of the sky (see coverage maps in Figure 2).
- In DR1, a difference image (file suffix *scimrefdiffimg.fits.fz; see Section 8a) will only accompany a single-exposure science image in the archive if a Reference Image corresponding to the same field/CCD-quadrant/filter existed on/before a single-exposure image was processed. Reference Image generation continued throughout 2018 and image differencing could only be triggered if a Reference Image was present.
- The units of the pixel values in all image products are in Digital Numbers (DN). Methods on how to apply the photometric calibration solutions to your own measurements using these products are outlined in Section 10 of the ZSDS Explanatory Supplement.
A more detailed compilation of advisories and known caveats specific to each data product are given in:
ZSDS Advisories & Cautionary Notes
These notes are progressively updated as issues are resolved, new ones become known, or as we learn of new tips or software that could be of use to the community.
Access to all ZTF data products is through the online (GUI-based) web-tools and API services of the NASA/IPAC Infrared Science Archive (IRSA) linked from: https://irsa.ipac.caltech.edu/Missions/ztf.html.
Below we provide links to specific GUIs and documentation describing how to access the archive products. Examples using the API services are also given.
- To visualize, analyze, and retrieve any of the file-based products (single-exposure science or reference images, and/or their catalogs or other files), use the following GUI: https://irsa.ipac.caltech.edu/applications/ztf/. You can supply a sky position, a list of positions, or names of objects that can be resolved, including Solar System objects or an orbital ephemeris. The query returns the CCD-quadrant images touching these positions and metadata for additional filtering. An important metadata column you will want to filter is the INFOBITS value, primarily for "bad" quality data (see Section 9a above). The GUI allows selected-image previews and interactive analysis. Entire file products or cutouts on specific regions can be packaged and downloaded.
- Lightcurves can be retrieved, visualized, and analyzed using the GUI services through three steps. The third step below uses the Time Series Tool to visualize and analyze an individual lightcurve.
- First query the DR1 Objects Table containing sources extracted from Reference Images (co-added single-exposures): ZTF DR1 Objects. You can perform a spatial cone-search, box or polygon search, supply a list of search positions, as well as include filtering on any of the pre-computed lightcurve-collapsed metrics shown in the form. This query returns a list of the objects for which lightcurves potentially exist (subject to advisories 9, 10, 11 in Section 10). This list can be saved to a file and fed to the lightcuve API, or the results can be sent directly to the Time Series Tool (see next step).
- On the results page following the query in step 1, click on "To Time Series Tool" to retrieve the lightcurve(s) of your object(s). At this stage, you can click "Download Light Curve Table" to save the lightcurves for all objects to a single ASCII table, concatenated and distinguished by object ID (oid). Metadata is also written, which includes the catflags column for identifying and filtering bad observation epochs (see Section 9b above).
- Alternatively, given the list of object IDs that have lightcurve data in step 2, you can click on an individual object and send its lightcurve to the Time Series Tool. This tool allows interactive filtering on the lightcurve metadata (for example, omitting bad observation epochs by filtering the catflags value). This tool provides a visualization of the single-exposure images for neighboring lightcurve epochs, allows you to compute a periodogram and a period therefrom, and then plot a phase-folded lightcurve. There is also an option to supply your own period for the phase-folding.
- A custom tool for recovering and characterizing known Solar System Objects (SSOs) in ZTF data is the Moving Object Search Tool (MOST): https://irsa.ipac.caltech.edu/applications/MOST/. This is a precovery tool where you can specify a known SSO name, NAIF ID, ephemeris, or orbital elements, and the observation time range. The service returns the single-exposure science image filenames containing the SSO, associated archive metadata, orbit plots, refined ephemeris, and more orbital parameters. Images or cutouts (and/or associated photometry catalogs) can then be retrieved using the image-download API. For more information, see the full usage instructions for the MOST GUI. An API for the MOST tool is also available.
|Figure 4 - Schematic of the workflow for retrieving and analyzing lightcurves using the GUI services (see Section 12a). Click to enlarge.|
Application Programming Interfaces (APIs) accompany most of the GUI data-retrieval services. These APIs can be executed from within your own software to enable repetitive and/or bulk data downloads.
Examples of using APIs to retrieve file-based products and lightcurves are given below.
To retrieve file-based single-exposure science-image products that touch a fixed R.A.,Dec position, fall within an observation JD range, and are likely to be of "good" (usable) quality with INFOBITS < 33554432 (see Section 9a), first query their metadata using the wget utility, e.g.,
wget "https://irsa.ipac.caltech.edu/ibe/search/ztf/products/sci? POS=255.9302,11.8654&WHERE=obsjd>2458219.9678+AND+obsjd<2458228.8155+ AND+infobits<33554432" -O out.tbl
where all inputs reside on one line. Then use the output metadata table (out.tbl) contents to construct the image URL-paths/filenames. Using the metadata column names, a science-image will have the generic URL-path/filename:
https://irsa.ipac.caltech.edu/ibe/data/ztf/products/sci/ YYYY/MMDD/fracday/ztf_filefracday_000field_filtercode_ cccdid_imgtypecode_qqid_sciimg.fits
where identifiers in bold green are the values of actual columns in the out.tbl table, and YYYY (year), MM (month), DD (day), and fracday (fractional time of day) can be extracted from filefracday. These images can then be retrieved using the wget utility. For the example above, the first metadata record in out.tbl would be retrieved using:
wget https://irsa.ipac.caltech.edu/ibe/data/ztf/products/sci/ 2018/0411/467847/ztf_20180411467847_000535_zr_c11_o_q3_sciimg.fits
Any of the accompanying products can be retrieved by replacing sciimg.fits with another filename suffix (see Section 8a). Instead of retrieving the entire image, you can download a square cutout centered at a specific R.A.,Dec and specific size, and save the output to a file (optionally gzipped). For the example above:
wget "https://irsa.ipac.caltech.edu/ibe/data/ztf/products/sci/ 2018/0411/467847/ztf_20180411467847_000535_zr_c11_o_q3_sciimg.fits? center=255.8535,12.0503&size=60arcsec&gzip=false" -O cutout.fits
If the API fails to return a file (or its metadata), you can try navigating the archive tree directly to search for the specific product: https://irsa.ipac.caltech.edu/ibe/data/ztf/products/sci/. For guidance, listings of the DR1 single-exposure Science products are given in Section 8a. A description of the path and filename identifiers is given in Section 7 of the ZSDS Explanatory Supplement.
To retrieve file-based Reference Image products that touch a fixed R.A.,Dec position in only the r-filter (fid=2), first query their metadata using the wget utility, e.g.,
wget "https://irsa.ipac.caltech.edu/ibe/search/ztf/products/ref? POS=358,25.6&WHERE=fid=2" -O out.tbl
where all inputs reside on one line. Then use the output metadata table (out.tbl) contents to construct the image URL-paths/filenames. Using the metadata column names, a Reference Image will have the generic URL-path/filename:
https://irsa.ipac.caltech.edu/ibe/data/ztf/products/ref/ prefield/fieldfield/filtercode/ccdccdid/qqid/ ztf_field_filtercode_cccdid_qqid_refimg.fits
where identifiers in bold green are the values of actual columns in the out.tbl table, and prefield is the first three (left-zero-padded) digits of the six-digit field value. These images can then be retrieved using the wget utility. For the example above, the first metadata record in out.tbl would be retrieved using:
wget https://irsa.ipac.caltech.edu/ibe/data/ztf/products/ref/ 001/field001596/zr/ccd16/q2/ztf_001596_zr_c16_q2_refimg.fits
Any of the accompanying products can be retrieved by replacing refimg.fits with another filename suffix (see Section 8b). Instead of retrieving the entire image, you can download a square cutout centered at a specific R.A.,Dec and specific size, and save the output to a file (optionally gzipped). For the example above:
wget "https://irsa.ipac.caltech.edu/ibe/data/ztf/products/ref/ 001/field001596/zr/ccd16/q2/ztf_001596_zr_c16_q2_refimg.fits? center=357.46871,26.00549&size=100arcsec&gzip=false" -O cutout.fits
If the API fails to return a file (or its metadata), you can try navigating the archive tree directly to search for the specific product: https://irsa.ipac.caltech.edu/ibe/data/ztf/products/ref/. For guidance, listings of the DR1 Reference Image products are given in Section 8b. A description of the path and filename identifiers is given in Section 7 of the ZSDS Explanatory Supplement.
The API functionality corresponding to the GUI-driven recipe for retrieving lightcurves is described on the Lightcurve Programming Interface Page. Numerous examples are given on this page. Below we provide a more generic example that includes most parameters of interest. You will always want to exclude observation epochs associated with bad/unusable data by masking catflags values equal to 32768, -- the "cloud-affected and/or moon-contamination" flag (decimal bit 15); see Section 9b. Alternatively, you can exclude epochs with any non-zero bit by using the catflags mask value 65535 in the query below.
The following query returns a concatenated list of lightcurves for objects falling within a 10 arcsec (~0.0028°) radius from position R.A.,Dec = 255.9302°, 11.8654° in the r-filter, where each has ≥ 3 epochs that fall within the MJD range 58194.0...58483.0 and all consist of only "good" (likely usable) measurements. Lightcurves are stored in the output ASCII table: out.tbl. The concatenated lightcurves are distinguished by their ObjectIDs therein (column oid).
wget "https://irsa.ipac.caltech.edu/cgi-bin/ZTF/nph_light_curves? POS=CIRCLE+255.9302+11.8654+0.0028&BANDNAME=r&NOBS_MIN=3& TIME=58194.0+58483.0&BAD_CATFLAGS_MASK=32768&FORMAT=ipac_table" -O out.tbl
All inputs reside on one line. A lightcurve (with common oid values) is represented by columns mag ± magerr versus mjd or hjd. The out.tbl also contains metadata to reconstruct the single-exposure science image path/file names that you can later download using the wget utility, or alternatively, cutouts on your position of interest (see Section 12b.i).
You can also upload the out.tbl file from above to the Time Series Tool to enable visualization, analysis, and period finding, analogous to the GUI-driven method described in Section 12a. Ensure you select "ztf" from the menu before uploading. Following your upload, ensure only one ObjectID (oid) is selected for visualizing, using the filter ( ) icon in the Time Series Tool.
The API equivalent of the Moving Object Search Tool (GUI) is described on the MOST Programming Interface Page. For example, to retrieve all single-exposure science images containing Asteroid 438973 Masci, first query their metadata using the wget utility:
wget "https://irsa.ipac.caltech.edu/cgi-bin/MOST/nph-most? catalog=ztf&input_type=name_input&obj_name=Masci& obs_begin=2018+03+17&obs_end=2018+12+31&output_mode=Brief" -O out.tbl
where all inputs reside on one line. Then use the output metadata table (out.tbl) contents to construct the science image URL-paths/filenames (see Section 12b.i above). These images can then be downloaded using the wget utility. Alternatively, cutouts centered on the object's R.A.,Dec in each image, as listed in out.tbl, can be downloaded.
The lightcurves included in this release have been packaged into gzipped tar files. A selection of basic (most necessary) metadata accompany each lightcurve, primarily IDs to enable the retrieval of associated metadata from the archive, for example, images or Object Table metrics using the access methods above.
The following URL contains a listing of the tar-gzipped filenames and their (compressed) sizes:
When uncompressed, their total volume is ∼ 2.2 TB. You can click on individual files to download them, or use the wget utility to automate the downloads. If your operating system does not automatically unzip these, you can use gunzip file.tar.gz followed by tar -xvf file.tar to unpack. A list of corresponding MD5 checksums is also provided for those who want to verify file contents following a download. On a Unix/Linux OS, the files can be verified using md5sum -c checksums.md5.
The files are labelled by survey-Field ID where each Field spans ≈ 7° x 7°. Each filename also includes the minimum to maximum RA and Dec values spanned by all the objects contained therein. These RA, Dec ranges can be used to identify the sky region(s) of interest in case you are interested in downloading a subset. The DR1 lightcurve data are spread across 613 fields.
Each file unpacks into an ASCII table with the following structure:
# ObjectID (oid), nDR1epochs, FilterID, FieldID, RcID, ObjRA, ObjDec HMJD, mag, magerr, clrcoeff, catflags ... Click for example (truncated)
The lightcurves are delimited by lines: "# Object / lightcurve metadata ...". Following this line is the actual lightcurve for that object. The columns are as follows:
ObjectID: Unique Object Identifier. This is the oid that is returned by queries on the Objects Database Table. This ID can be used to retrieve additional metadata for an Object or lightcurve, subject to Cautionary Note #12 in Section 10. nDR1epochs: The number of observation epochs in the lightcurve for this object, included in this data release only. FilterID: The filter corresponding to the lightcurve photometry: 1 => g; 2 => r. FieldID: The survey Field Identifier. Lightcurves in DR1 are spread across 613 Fields. This identifier can also be used to retrieve additional archive metadata. RcID: The readout-channel Identifier (or CCD-Quadrant index) that contains the object. The RcID runs from 0 to 63 over the ZTF focal plane. In combination with FieldID, this can be used to retrieve additional archive metadata. ObjRA: Object Right Ascension from the input Reference Image (seed) catalog corresponding to the FieldID, RcID, and FilterID [decimal degrees]. ObjDec: Object Declination from the input Reference Image (seed) catalog corresponding to the FieldID, RcID, and FilterID [decimal degrees]. ----------- HMJD: Heliocentric-based Modified Julian Date [days] corresponding to the start of each exposure and approximate position of the object. Note: the integration time for all exposures in this release is 30 seconds. The HMJD is derived from "HJD - 2400000.5" where HJD is the Heliocentric Julian Date. mag: Calibrated magnitude for a source with color g - r = 0 in the AB photometric system. See Cautionary Note #13 in Section 10. magerr: Corresponding 1-σ uncertainty in mag estimate; excludes the (usually small) uncertainty associated with the calibration zeropoint and possible systematics relative to external photometry catalogs (for example PS1). clrcoeff: Linear color coefficient term from photometric calibration that can be applied to the mag estimate if you know the AB g - r color of your source. See Cautionary Note #13 in Section 10. catflags: Photometric/image quality flags encoded as bits (Section 9b). In particular, you will always want to exclude observation epochs affected by clouds and/or the moon. These epochs have catflags = 32768 (decimal bit 15).
For any archive or data-access related issues, which includes anomalies with any of the GUI tools or APIs for data retrieval and analysis, please email:
For questions related to data-quality, format, content, processing algorithms, or product usage, including documentation, please email:
A detailed description of the processing pipelines, data products (including alert packets), instructions for product retrieval, filename identifiers, data formats, and product usage can be found in:
Two relevant publications from the special ZTF Focus Issue of PASP:
- An overview of the ZTF Science Data System:
The Zwicky Transient Facility: Data Processing, Products, and Archive, 2019, vol 131 (issue 995), 18003
- An overview of the technical specifications of the camera and observing system, its performance, and ZTF survey design:
The Zwicky Transient Facility: System Overview, Performance, and First Results, 2019, PASP, vol 131 (issue 995), 18002
Last update: 2019 July 12