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For public ultraviolet (UV) sky-survey data, start with NASA’s Mikulski Archive for Space Telescopes (MAST), the public archive for the Galaxy Evolution Explorer (GALEX). Use the MAST Portal for a target or coordinate search, GALEX tile forms to browse survey fields and download files, CasJobs for SQL catalog work, and Astroquery for repeatable Python searches of observations and products. GALEX’s GalexView interface is retired, but its data remain available through these alternatives.
What public ultraviolet survey data are available?
GALEX was a space telescope designed for wide-field ultraviolet imaging. Its archive contains direct images in two bands—far-ultraviolet (FUV) and near-ultraviolet (NUV)—plus low-resolution grism spectroscopy. The mission’s observing operations have ended, but processed observations and catalogs remain publicly distributed through MAST’s GALEX GR6/GR7 Public Archive.
That distinction matters: an archive search can find data from a completed mission, but it cannot create new observations. A target may have no record because it was never observed, because only one UV band covered it, because a mask removed the relevant area, or because a catalog threshold excluded a faint source.
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Before searching, decide whether you need an image, a catalog measurement, a spectrum, or information about separate visits. GALEX products are not interchangeable.
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| Survey or mode | What it provides | When it is useful |
|---|---|---|
| Direct FUV imaging | Images and source measurements in the far-ultraviolet band | Very hot stars, recent star formation, and sources with strong short-wavelength UV emission |
| Direct NUV imaging | Images and source measurements in the near-ultraviolet band | Broader source detection and ultraviolet-to-optical comparisons |
| Grism spectroscopy | Low-resolution dispersed spectra | Studying spectral features when imaging alone is insufficient |
| AIS | All-sky-imaging-style survey coverage with relatively short exposures | Large-area source searches and bright or moderately faint objects |
| MIS | Medium-depth imaging over smaller areas | Deeper photometry than AIS in selected fields |
| DIS | Deep imaging fields | Fainter-source studies in targeted regions |
| NGS | Nearby-galaxy survey observations | Ultraviolet studies of selected nearby galaxies |
The survey names describe observing programs, not a guarantee that every position has the same exposure time or depth. A single sky location can have multiple visits, different band coverage, or overlapping programs.
GALEX’s FUV detector stopped operating in 2009, while NUV observations continued for the remainder of the mission. Consequently, a later NUV observation does not imply that a contemporaneous FUV exposure exists. The MAST Getting Started with GALEX guide describes the bands, survey fields, and tile-retrieval process.
Which interface should you use?
| Route | Best for | Important limitation or check |
|---|---|---|
| MAST Portal | Searching one or a few objects by resolvable name or right ascension/declination; adding cross-mission context | Filter explicitly to GALEX. It is not designed for submitting thousands of catalog queries at once. |
| GALEX tile forms | Browsing survey tiles, viewing previews, and selecting image or catalog downloads | Confirm whether the selected file is a coadd, a single visit, a catalog, or a spectrum before downloading. |
| CasJobs | SQL searches across formal GALEX source catalogs | Use the GALEX_GR6Plus7 context and select the table appropriate to coadd or visit-level work. |
| Astroquery MAST | Repeatable Python searches for observations and associated products | The observation workflow does not directly query the GALEX source catalogs; retrieve tile catalog FITS files as products when you need catalog data. |
These routes answer different questions. An observation search asks, “Was this position covered, and which files exist?” A catalog query asks, “Which detected sources and measurements meet my criteria?” Do not treat an empty result from one route as proof that the other contains nothing.
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1. Define the target and the required evidence
Write down the object name or precise coordinates, the search radius, and the data product you need. Decide whether your analysis requires FUV, NUV, or both; imaging or grism spectroscopy; a combined measurement or individual visits; and photometry only or the pixel data themselves.
For a moving object, a crowded field, or an extended galaxy, record coordinates from a trusted reference and plan to inspect the footprint visually. A name resolver can return a position that is adequate for a broad search but not for a crowded UV field.
2. Search the MAST Portal
- Open the MAST Portal and enter a resolvable object name or right ascension and declination.
- Set a search radius large enough to include the target and possible tile edges, but small enough to avoid an unmanageable list.
- Filter the results to the GALEX mission.
- Review observation dates, detector bands, survey names, exposure information, and footprint geometry.
- Open the associated products and select the file level that matches your question.
A portal hit establishes that an observation record exists; it does not by itself establish that the target appears in a source catalog or that both bands are usable.
3. Browse tiles when you need images or field context
Use the GALEX tile forms described by MAST to search by coordinates, inspect tile previews, and choose downloads. Tile browsing is particularly useful when a target lies near a field boundary, when you need to see masked regions, or when you want to compare overlapping observations.
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- Enter the target coordinates in the tile search form.
- List nearby tiles and note their survey program, band coverage, and observation identifiers.
- Inspect the preview or footprint for the target’s position and nearby artifacts.
- Select the required image, catalog, or grism product.
- Record the tile and product identifiers with your downloaded files so the selection can be reproduced.
4. Search catalogs with CasJobs
For a formal source query, open CasJobs and select the GALEX_GR6Plus7 context. MAST’s end-of-life guidance identifies photoobjall for coadd-level source catalog data and visitphotoobjall for visit-level source data.
A typical query pattern is to select coordinates and photometry for a bounded sky region, then apply quality and magnitude criteria appropriate to your analysis. Keep the coordinate system, angular units, and band columns explicit in your SQL. Save the query text and context name along with the result, because catalog schemas and table meanings differ between coadd and visit products.
5. Retrieve observations with Python
Astroquery can make an observation search and product download repeatable. The MAST documentation is at Astroquery’s MAST observation-query guide.
from astroquery.mast import Observations
# Replace these coordinates and radius with your target values.
obs = Observations.query_region("12:34:56.7 -45:12:34", radius="0.05 deg")
galex_obs = obs[obs["obs_collection"] == "GALEX"]
products = Observations.get_product_list(galex_obs)
# Inspect products before downloading; filter by product type or filename.
# manifest = Observations.download_products(selected_products)The example demonstrates the observation workflow, not a direct catalog query. Inspect returned rows and product metadata before downloading, and use the archive’s product descriptions and FITS headers to determine the actual file type.
Understand coadds, visits, catalogs, and spectra
Coadd products
A coadd combines compatible visits into a deeper or more complete representation of a field. Coadd intensity maps and merged source catalogs are convenient for a combined measurement, especially when your science question is not time-dependent. A coadd can also hide visit-to-visit changes and may combine observations with different conditions.
Visit-level products
Visit-level intensity maps and source catalogs retain the individual observations. They are the appropriate starting point for checking variability, exposure differences, detector changes, or a suspicious measurement in a coadd. The visit tables are also useful when you need to model each exposure separately.
Grism spectra
Grism observations disperse light across the detector and provide low-resolution spectra rather than ordinary images. Crowding and overlapping spectra can affect interpretation, so inspect the product documentation and two-dimensional data when available rather than treating every extracted spectrum as an isolated point-source measurement.
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Inspect metadata instead of guessing from filenames
File naming conventions and units vary by product. Check the archive description and FITS header for band, exposure, calibration state, coordinate information, units, observation date, and processing level. Keep those metadata with your analysis output. A filename that contains a familiar survey acronym is not sufficient evidence that the file is a calibrated coadd, a visit image, or a source table.
Build a reproducible download record
A useful archive record is more than a downloaded FITS file. Save the target coordinates in the form used for the query, the search radius, the selected mission filter, and the date of the archive lookup. For each result, retain the observation or tile identifier, survey name, band, observation date, and product type. This makes it possible to distinguish a later catalog update from a genuinely different observation.
When using the Portal or tile forms, capture the filters and product-selection choices. For CasJobs, preserve the complete SQL, the GALEX_GR6Plus7 context, and whether the query used photoobjall or visitphotoobjall. For Astroquery, keep the code, package version, returned observation identifiers, and the product-filtering logic. These details are especially important when a broad coordinate search returns multiple visits or overlapping survey programs.
Do not overwrite files from different product levels under the same name. Put coadds, visit maps, catalogs, and grism files in separate directories or add an explicit product label. Keep the FITS headers with derived tables, and document any masking, quality filtering, or custom scan-mode reduction applied after download.
How deep and complete is GALEX coverage?
Coverage is uneven in both area and sensitivity. The MAST mission summary says GALEX covered 77% of the sky at varying depths in at least one band; this broad figure is not the same as a uniformly sampled, two-band all-sky map. Historical avoidance zones included parts of the Galactic plane, the Magellanic Clouds, and regions around very bright stars. Later releases added some low-latitude, Magellanic Cloud, and other coverage.
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| Sample described by Bianchi et al. (2014) | Published figure | Scope and qualification |
|---|---|---|
| AIS two-band fields | 28,707 fields covering 22,080 square degrees; typical depth about 20 AB mag in FUV and 21 AB mag in NUV | Selected two-band fields with a central one-degree-field restriction in the paper |
| MIS fields | 3,008 fields covering 2,251 square degrees; depth about 22.7 AB mag | Survey sample described in the paper, not every MIS-related product |
| AIS source catalog | About 71 million sources | Catalog population reported by that paper |
| MIS source catalog | About 16.6 million sources | Catalog population reported by that paper |
Read the full overview at The Ultraviolet Sky: An Overview from the GALEX Surveys. Its depths are AB magnitudes and are representative values for the stated samples, not a promise that every tile reaches the same limit.
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Why a target may be missing or look undetected
- No observation: The coordinates may fall outside GALEX’s footprint or inside a historical avoidance zone.
- Band-specific coverage: NUV may exist without FUV, especially after the FUV detector ceased operations in 2009.
- Edge or mask: The target may lie near a tile boundary, a detector artifact, a bright-star mask, or another excluded region.
- Catalog threshold: A source can be present in an image but absent from a catalog because its measured significance or quality flags fail the catalog’s selection rules.
- Resolution and blending: Nearby sources or extended emission can produce a catalog result that does not correspond cleanly to the optical object you searched for.
- Product mismatch: You may have searched a coadd table while the relevant detection appears only in a visit-level table, or vice versa.
When a lookup is blank, first verify the observation footprint and band, then inspect the image or tile metadata, and only afterward interpret the absence as a physical non-detection. Keep the archive status, mask information, and catalog-selection criteria in your notes.
Scan-mode observations require extra care
MAST warns that the standard calibration pipeline was not optimized for scan-mode observations. Such files may require additional analysis or a user-written reduction before quantitative science use. Do not assume that every archive product has the same readiness for photometry or spectral analysis.
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If you encounter scan-mode data, identify the observing mode in the metadata, read the archive guidance for that product, and document any custom calibration or masking steps. Treat a pipeline value as an input to validation, not as an automatic guarantee of a science-ready measurement.
Is GalexView still available?
No. GalexView depended on Adobe Flash, which browsers disabled in 2021. MAST documents the retirement and alternatives on its GalexView End of Life page. Do not rely on old instructions that tell you to open the former interface. Use the MAST Portal, GALEX tile forms, CasJobs, or Astroquery instead.
A practical decision guide
- You have one object and want to know whether it was observed: Start with the MAST Portal, filter to GALEX, and inspect the footprint and bands.
- You need a downloadable image and visual context: Use the GALEX tile forms, compare nearby tiles, and select the appropriate coadd or visit product.
- You need thousands of catalog sources or a reproducible selection: Use CasJobs with
GALEX_GR6Plus7; choosephotoobjallfor coadd-level work orvisitphotoobjallfor visit-level work. - You need an automated observation-and-download pipeline: Use Astroquery’s MAST observation interface, then filter and inspect the associated products in code.
- You need time-resolved behavior: Prefer visit-level products and preserve observation dates rather than relying only on a coadd.
- You need spectroscopy: Search specifically for grism products and account for low resolution, overlap, and extraction quality.
Common workflow mistakes to avoid
- Calling AIS all-sky: AIS is broad-area coverage, but exclusions, later additions, and band differences mean that “all-sky” is an unsafe shorthand for any individual target.
- Assuming a survey label fixes the depth: AIS, MIS, DIS, and NGS describe programs; depth still varies with field, band, visits, and processing.
- Using a catalog count as a sky-coverage count: The Bianchi et al. figures apply to the defined samples in that paper and should retain their sample qualifications.
- Downloading the first matching file: A search may return a coadd, visit map, catalog, and grism product for related observations. Confirm the product type and units first.
- Equating “no row” with “no flux”: A source can be masked, blended, below a catalog threshold, or absent from that band while still having useful image data.
- Using Astroquery as a catalog SQL client: Astroquery’s MAST interface handles observation discovery and associated products; direct GALEX catalog work belongs in CasJobs or in catalog FITS files retrieved as products.
Checklist before you publish a result
- Record the target coordinates, search radius, and archive query date.
- Identify the survey, band, observation date, and product level.
- State whether a measurement comes from a coadd or an individual visit.
- Check masks, edges, artifacts, and scan-mode status.
- Use AB-magnitude depths only with their stated survey and sample scope.
- Distinguish “no catalog row” from “no observation” and from “no detectable flux.”
- Preserve query text, product identifiers, FITS headers, and any custom reduction code.
Official archive references
- MAST GALEX GR6/GR7 Public Archive
- Getting Started with GALEX
- MAST GALEX mission page
- GalexView End of Life and alternatives
- Astroquery MAST observation queries
- Bianchi et al. (2014), The Ultraviolet Sky: An Overview from the GALEX Surveys
Frequently Asked Questions
How do I find GALEX data for an object?
Search the object name or coordinates in the MAST Portal, filter to GALEX, and inspect the observation footprint, bands, dates, and associated products. Use the tile forms when you need previews or field boundaries.
Where can I download GALEX images?
Use MAST’s GALEX tile forms to locate a field and select an image product. Check whether the download is a coadd or visit-level map and verify its band and units in the product description and FITS header.
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Use CasJobs with the GALEX_GR6Plus7 context. Query photoobjall for coadd-level sources or visitphotoobjall for visit-level sources, and save the SQL with the result.
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Is GalexView still available?
No. GalexView relied on Adobe Flash, which browsers disabled in 2021. MAST directs users to the Portal, GALEX tile forms, CasJobs, and Astroquery instead.
What is the difference between AIS and MIS?
AIS is the broad-area imaging program with relatively short exposures; MIS covers smaller selected fields more deeply. Depth and band coverage still vary by field, so use the observation metadata rather than the survey name alone.
How do I access GALEX data with Python?
Astroquery’s MAST Observations interface can search GALEX observations and retrieve associated products. It is not a direct GALEX catalog-query service; obtain catalog FITS files as products or use CasJobs for SQL catalog searches.
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Why is there NUV data but no FUV data?
The FUV detector ceased operating in 2009, while NUV observations continued. A later NUV observation therefore does not guarantee a matching FUV exposure.
Should I use a coadd or a visit-level product?
Use a coadd for a combined field measurement when time information is not central. Use visit-level images or catalogs to study variability, compare exposure conditions, or investigate an unexpected coadd result.
What does a blank catalog result mean?
It may indicate no observation, band-specific coverage, a mask or tile edge, a catalog threshold, blending, or a mismatch between coadd and visit tables. Check the footprint and image metadata before calling it a physical non-detection.
Can I treat every GALEX file as science-ready?
No. MAST cautions that calibration was not optimized for scan-mode observations. Identify the observing mode and perform any additional analysis or custom reduction required by the archive guidance.
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The Bottom Line
Start with MAST, choose the interface and product level that match your question, and verify coverage, masks, bands, and calibration status before interpreting a UV measurement. GALEX data are public and powerful, but archive presence, catalog detection, and scientific non-detection are three different claims.
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