301.6. Low Galactic Latitude Field

For the Portal Aspect of the Rubin Science Platform at data.lsst.cloud.

Data release: DP1

Last verified to run: 2025-06-27

Learning objective: Understand the observations and data available for the Low Galactic Latitude field RubinSV_95_-25.

LSST data products: deep_coadd images, Visit, CcdVisit, and Object tables

Credit: Originally developed by the Rubin Community Science team. Please consider acknowledging them if this tutorial is used for the preparation of journal articles, software releases, or other tutorials.

Get support: Everyone is encouraged to ask questions or raise issues in the Support Category of the Rubin Community Forum. Rubin staff will respond to all questions posted there.

Related tutorials: The 100-level Portal tutorials demonstrate how to use the Portal interface. The 200-level tutorials explore the individual catalogs and image types.

1. Introduction

This tutorial explores the LSSTComCam observations for the “RubinSV_95_-25” field, including magnitude limits, visit distribution with time, data quality, and the distributions of stars and galaxies in color-magnitude and color-color diagrams.

The field denoted “RubinSV_95_-25” is sometimes referred to as the “low-latitude field” because it was at the lowest Galactic latitude of any of the fields observed during LSSTComCam on-sky commissioning. RubinSV_95_-25 is centered at (RA, Dec) = (95.0, -25.0) degrees, corresponding to Galactic longitude/latitude of (l, b) = (232.5, -17.6) degrees. It is relatively far from the Galactic plane, and not representative of the crowded fields that LSST will see in the Milky Way disk. Nonetheless, it contains the highest source density of any of the LSSTComCam fields in DP1. The region covered spans a diameter of about 1 degree.

Central coordinates: (RA, Dec) = 95.0, -25.0 degrees

1.1. Log in to the Portal Aspect of the RSP. In a web browser, navigate to data.lsst.cloud and select the “Portal” panel.

1.2. Browse the gri color HiPS map. Go to the gri color HiPS map for the RubinSV_95_-25 field.

2. Examine a deep coadd image

2.1. Select the “DP1 Images” tab. If it does not appear across the top of the user interface, click the “menu” icon at upper left to open the left-hand menu.

2.2. Switch to the “Edit ADQL” interface. Click on the “Edit ADQL” button at upper right.

2.3. Execute the ADQL query for deep coadd images. Copy-paste the following ADQL query into the query box and click “Search” at lower left. This query will retrieve all images of subtype deep_coadd that contain the central coordinates of the RubinSV_95_-25 field.

SELECT dataproduct_type, dataproduct_subtype, calib_level, lsst_band,
       em_min, em_max, lsst_tract, lsst_patch,
       s_ra, s_dec, s_fov, s_region, s_xel1, s_xel2, obs_id, obs_collection, o_ucd,
       facility_name, instrument_name, obs_title, access_url,
       access_format, obs_publisher_did
FROM ivoa.ObsCore
WHERE obs_collection = 'LSST.DP1' AND calib_level = 3 AND dataproduct_type = 'image'
      AND instrument_name = 'LSSTComCam' AND dataproduct_subtype = 'lsst.deep_coadd'
      AND CONTAINS(POINT('ICRS', 95, -25), s_region)=1

2.4. View the results. The query will return 6 lsst.deep\_coadd results – one for each of the ugrizy bands. The results interface will appear similar to Figure 1.

The image results

Figure 1: The results of the deep_coadd image search.

2.5. Explore the images. Use the image display interface to zoom, pan, rescale, and generally explore the deep images of the RubinSV_95_-25 field.

3. Create a patch coverage map

3.1. Return to the query interface. Navigate back to the “DP1 Images” tab, and switch to the “Edit ADQL” interface. Delete the previous query from the box.

3.2. Execute a new image query. Copy-paste the following ADQL query into the query box and click “Search” at lower left. This query will retrieve all images of subtype deep_coadd that were obtained with the r-band filter and overlap the ~1 degree RubinSV_95_-25 field (not just its central coordinates).

SELECT dataproduct_type, dataproduct_subtype, calib_level, lsst_band,
       em_min, em_max, lsst_tract, lsst_patch,
       s_ra, s_dec, s_fov, s_region, s_xel1, s_xel2, obs_id, obs_collection, o_ucd,
       facility_name, instrument_name, obs_title, access_url,
       access_format, obs_publisher_did
FROM ivoa.ObsCore
WHERE obs_collection = 'LSST.DP1' AND calib_level = 3 AND dataproduct_type = 'image'
      AND instrument_name = 'LSSTComCam' AND dataproduct_subtype = 'lsst.deep_coadd'
      AND CONTAINS(POINT('ICRS', s_ra, s_dec),CIRCLE('ICRS', 95, -25, 1))=1
      AND ( 622e-9 BETWEEN em_min AND em_max )

3.3. Switch to the Coverage map. In the results interface, switch from image display to Coverage map. The boundaries of the 79 patches are overlaid onto a HiPS coverage map, as in Figure 2.

The image results

Figure 2: The search results showing the coadd footprints (“patches”) on the HiPS coverage map.

3.4. Explore the coverage map. In the coverage map, click any patch and its corresponding image will be highlighted in the table and plot.

4. Make visit summary plots

4.1. Go to the catalog query interface. Click on the “DP1 Catalogs” tab and then on the “Edit ADQL” button.

4.2. Execute a query on the Visit table. This query will retrieve the coordinates, band, and MJD for all visits from the Visit table with central coordinates within the RubinSV_95_-25 field.

SELECT ra, dec, band, expMidptMJD
FROM dp1.Visit
WHERE CONTAINS(POINT('ICRS', ra, dec), CIRCLE('ICRS', 95, -25, 1))=1
ORDER BY expMidptMJD ASC

4.3. View the query results. In the results interface, the central coordinates of the 292 visits are automatically marked on the Coverage map, illustrating how the field was dithered.

4.4. Obtain the filter distribution. Use the filter function in the table to select each of the ugrizy values from the “band” column in turn, and note how many observations there were in each filter. There should be 33 u, 82 g, 84 r, 23 i, 60 z, and 10 y-band visits.

Visit dates cumulative histogram

The ADQL query for visits included an “ORDER BY” statement to return a table that is sorted by expMidptMJD in ascending order. Use this to plot a cumulative histogram of exposure acquisition dates.

4.5. Add a new column. Add a new column to the table by clicking the column+ icon. Click “Use preset function”, and select “Number rows in current sort order”. Give the new column a name (e.g., “cumulative_expnum”) and click “Add Column”.

4.6. Create the histogram. In the “Active Chart” panel, click the icon of the plus sign in a circle to open the “Add New Chart” popup. Choose “Plot Type: Scatter”, then plot column “expMidptMJD” on the x-axis, and “cumulative_expnum” on the y-axis. Set the “Trace Style” to “connected points”, and click “OK”.

4.7. View the plot. The resulting plot should look like Figure 3, showing the growing number of exposures with MJD.

A cumulative histogram of number of exposures as a function of expMidptMJD. Values steadily increase with time over a span of 17 days.

Figure 3: The figure showing the cumulative number of exposures obtained with time.

Visit image quality plots

Derived quantities that characterize the quality of images and their properties are found in the CcdVisit table.

4.8. Return to the catalog query interface. Click on the “DP1 Catalogs” tab and then on the “Edit ADQL” button. Delete the last query statement.

4.9. Execute a query on the CcdVisit table. This query retrieves a table of all CcdVists (visit and detector combinations) that were observed of the RubinSV_95_-25 field.

SELECT visitId, ra, dec, band, seeing, magLim
FROM dp1.CcdVisit
WHERE CONTAINS(POINT('ICRS', ra, dec),CIRCLE('ICRS', 95.0, -25.0, 1.0))=1
ORDER BY visitId

4.10. View the results. The query returns 2628 results, with the central locations of each detector for each CcdVisit overplotted on the coverage map.

4.11. Create histograms of seeing and magnitude limit. In the “Active Chart” panel, create two new plots that show a histogram of the seeing column and a histogram of the magLim column (the 5-sigma limiting magnitude of each detector image). It will look like Figure 4.

A plot showing two histograms. On the left is the distribution of seeing in arcsec, and on the right a histogram of magLim in mag.

Figure 4: The two histograms showing the distribution of seeing and limiting magnitude over all LSSTComCam detectors and visits, in all bands, in DP1.

5. Analyze object photometry

The Object table, which contains detections and measurements from the deep_coadd images.

5.1. Return to the catalog query interface. Delete the last ADQL statement.

5.2. Execute a query on the Object table. This query will retrieve the PSF and cModel magnitudes in g, r, and i bands, as well as the refExtendedness parameter, for objects with SNR>5 measurements in g and r bands in the RubinSV_95_-25 field.

SELECT coord_ra, coord_dec,
       g_psfMag, i_psfMag, r_psfMag,
       g_cModelMag, i_cModelMag, r_cModelMag,
       g_psfFlux, g_psfFLuxErr,
       r_psfFlux, r_psfFLuxErr,
       i_psfFlux, i_psfFLuxErr,
       refExtendedness
FROM dp1.Object
WHERE CONTAINS(POINT('ICRS', coord_ra, coord_dec), CIRCLE('ICRS', 95, -25, 1))=1
      AND g_psfFlux/g_psfFluxErr > 5
      AND r_psfFlux/r_psfFluxErr > 5
      AND i_psfFlux/i_psfFluxErr > 5

5.3. Select point-like objects. Filter the table for only point-like objects (“stars”) by filtering the refExtendedness column to be equal 0.

5.4. Create a color-magnitude diagram. Add a chart and select the “Heatmap” plot type. Use color (g_psfMag-r_psfMag) on the x-axis and magnitude (r_psfMag) on the y-axis. Select 300 bins in X and 200 bins in Y. Set the X Min, X Max values to -1, 2, and the Y Min, Y Max values to 16, 26. Select “reverse” under “Options” for the y-axis to display brighter magnitudes (i.e., lower numbers) toward the top of the plot.

5.5. Create a color-color diagram. Open a new plot window by clicking the “Add a chart” button. Make a color-color diagram by plotting r_psfMag-i_psfMag vs. g_psfMag-r_psfMag. Place the two figures side-by-side, as in Figure 5.

A plot showing color-color and color-magnitude diagrams as heatmaps.

Figure 5: A color-color and color-magnitude diagram of stars in the RubinSV_95_-25 field.

6. Exercises for the learner

Try plotting the color-color and color-magnitude diagrams for galaxies (refExtendedness = 1) instead. Recall that cModel magnitudes are better suited for extended sources.