301.1. 47 Tuc globular cluster#
301.1. 47 Tuc Field¶
For the Rubin Science Platform at data.lsst.cloud.
Data Release: Data Preview 1
Container Size: large
LSST Science Pipelines version: r29.2.0
Last verified to run: 2025-09-02
Repository: github.com/lsst/tutorial-notebooks
Learning objective: An overview of the LSSTComCam data in the "47 Tuc Globular Cluster" 47_Tuc field.
LSST data products: deepCoadd, Visit, Object, CcdVisit
Packages: lsst.rsp, lsst.daf.butler, lsst.afw.display
Credit: Originally developed by the Rubin Community Science team. Please consider acknowledging them if this notebook is used for the preparation of journal articles, software releases, or other notebooks.
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.
1. Introduction¶
This notebook examines the DP1 data products in the "47 Tuc" field: assess sky coverage and depth, visit distribution over time, image quality, and the distributions of stars and galaxies in color-magnitude and color-color diagrams (CMD and CCD).
47 Tucanae ("47 Tuc") is a bright, dense star cluster in the constellation Tucana. The ComCam field targeting this cluster is centered at (RA, Dec) = (6.128, –72.090) degrees. It is representative of the crowded fields that LSST will encounter in the Milky Way. 47 Tuc overlaps with the Small Magellanic Cloud (SMC) on the sky, but these are distinct objects: 47 Tuc is a globular cluster of the Milky Way's halo, while the SMC is a dwarf satellite galaxy orbiting around the Milky Way.
Related tutorials: The 100-level tutorials demonstrate how to use the butler, the TAP service, and the Firefly image display. The 200-level tutorials introduce the types of image and catalog data.
1.1. Import packages¶
Import numpy, a fundamental package for scientific computing with arrays in Python (numpy.org), and matplotlib, a comprehensive library for data visualization (matplotlib.org; matplotlib gallery), including custom shapes (Polygon) and lines (mlines). itertools supports efficient iteration and combinatorics.
From the lsst package, import modules for accessing the Table Access Protocol (TAP) service, for retrieving datasets from the butler, and for image displaying from the LSST Science Pipelines (pipelines.lsst.io). Additional modules support spherical geometry (sphgeom), 2D geometry (geom), and standardized multiband plotting (lsst.utils.plotting) for LSST data analysis and visualization.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Polygon
import matplotlib.lines as mlines
import itertools
from lsst.rsp import get_tap_service
from lsst.daf.butler import Butler
import lsst.afw.display as afw_display
import lsst.sphgeom as sphgeom
import lsst.geom as geom
from lsst.utils.plotting import (
get_multiband_plot_colors,
get_multiband_plot_symbols,
get_multiband_plot_linestyles,
)
1.2. Define parameters and functions¶
Create an instance of the TAP service, and assert that it exists.
service = get_tap_service("tap")
assert service is not None
Create an instance of the butler, and assert that it exists.
butler = Butler('dp1', collections="LSSTComCam/DP1")
assert butler is not None
Define the approximate central coordinates of the 47 Tuc field, and the region within 1 degree of this center.
ra_cen = 6.128
dec_cen = -72.090
radius = 1.0
region = sphgeom.Region.from_ivoa_pos(f"CIRCLE {ra_cen} {dec_cen} {radius}")
Define parameters to use colorblind-friendly colors with matplotlib.
plt.style.use('seaborn-v0_8-colorblind')
prop_cycle = plt.rcParams['axes.prop_cycle']
colors = prop_cycle.by_key()['color']
Define colors, symbols, and linestyles to represent the six LSST filters, $ugrizy$.
filter_colors = get_multiband_plot_colors()
filter_symbols = get_multiband_plot_symbols()
filter_linestyles = get_multiband_plot_linestyles()
Set afwDisplay to use Firefly to display images, and open Firefly frame 1.
afw_display.setDefaultBackend('firefly')
display = afw_display.Display(frame=1)
2. Coadd sky coverage and depth¶
The individual LSSTComCam images have been combined (stacked) into deep_coadd images, and sky maps of the accumulated exposure time and image depth have been created.
2.1. Display a deep coadd¶
Query the butler for all $r$-band deep_coadd images that overlap the defined region.
query = "patch.region OVERLAPS region AND band='r'"
coadd_datasetrefs = butler.query_datasets("deep_coadd",
where=query,
bind={"region": region},
with_dimension_records=True,
order_by=["patch.tract"])
print('There are %s r-band deep_coadd images.' % len(coadd_datasetrefs))
There are 64 r-band deep_coadd images.
Retrieve the deep_coadd image associated with the first of the dataset references returned by the butler query.
coadd = butler.get(coadd_datasetrefs[0])
Display the image, and set the mask plane to be fully transparent.
display.mtv(coadd)
display.setMaskTransparency(100)
2.2. Exposure time and depth¶
Retrieve the entire survey property map of the $r$-band magnitude limit as hspmap_rmaglim,
and of the total $r$-band exposure time as hspmap_rexptime.
hspmap_rexptime = butler.get('deepCoadd_exposure_time_consolidated_map_sum',
skymap='lsst_cells_v1', band='r')
hspmap_rmaglim = butler.get('deepCoadd_psf_maglim_consolidated_map_weighted_mean',
skymap='lsst_cells_v1', band='r')
Extract the healsparse map values within the radius of the field center and divide the region into a 250×250 grid. Evaluate each survey property map at these grid points. Replace all negative values (no-data placeholders) with NaN to enable proper map display.
dec_size = radius
ra_size = dec_size / np.cos(np.radians(dec_cen))
ra_min, ra_max = ra_cen - ra_size, ra_cen + ra_size
dec_min, dec_max = dec_cen - dec_size, dec_cen + dec_size
ra = np.linspace(ra_min, ra_max, 250)
dec = np.linspace(dec_min, dec_max, 250)
x, y = np.meshgrid(ra, dec)
values_rmaglim = hspmap_rmaglim.get_values_pos(x, y)
values_rmaglim = np.where(values_rmaglim < 0.0, np.nan, values_rmaglim)
values_rexptime = hspmap_rexptime.get_values_pos(x, y)
values_rexptime = np.where(values_rexptime < 0.0, np.nan, values_rexptime)
Clean up.
del ra, dec
Print the mean, median, and min/max values for the $r$-band magnitude limit and exposure time. Exposure times are accumulated over multiple visits, not single exposures.
print('r-band magnitude limit mean/median: %5.2f %5.2f' %
(np.nanmean(values_rmaglim), np.nanmedian(values_rmaglim)))
print('r-band magnitude min/max: %5.2f %5.2f' %
(np.nanmin(values_rmaglim), np.nanmax(values_rmaglim)))
print('r-band exposure time (s) mean/median: %5.1f %5.1f' %
(np.nanmean(values_rexptime), np.nanmedian(values_rexptime)))
print('r-band exposure time min/max (s): %5.1f %5.1f' %
(np.nanmin(values_rexptime), np.nanmax(values_rexptime)))
r-band magnitude limit mean/median: 24.97 25.08 r-band magnitude min/max: 23.57 27.20 r-band exposure time (s) mean/median: 256.6 210.0 r-band exposure time min/max (s): 30.0 750.0
2.3. Coverage map¶
Define unique colors and linestyles for plotting each of the tracts in the dataset.
alltracts = [rec.dataId['tract'] for rec in coadd_datasetrefs]
unique_tracts = np.unique(alltracts)
tract_colors = plt.cm.tab10(np.linspace(0, 1, len(unique_tracts)))
linestyles = ['-', '--', '-.', ':'] * ((len(unique_tracts) // 4) + 1)
style_dict = {
tract: {'color': tract_colors[i], 'linestyle': linestyles[i]}
for i, tract in enumerate(unique_tracts)
}
The coadd_datasetrefs returned from the butler include region information for each dataset, stored as a ConvexPolygon3D with four vertices defining the sky footprint of each overlapping tract and patch.
Convert these vertices to 2D sky coordinates (RA, Dec) using geom.SpherePoint, and use matplotlib.patches.Polygon along with ax.add_patch() to draw each patch outline. Each tract is plotted with a distinct color and linestyle for visual clarity.
fig, ax = plt.subplots(figsize=(6, 5))
mesh = ax.pcolormesh(x, y, values_rmaglim, cmap='Greys_r', shading='auto')
fig.colorbar(mesh, ax=ax, label="r-band limiting magnitude (mag)")
for rec in coadd_datasetrefs:
vertices = rec.dataId.patch.region.getVertices()
vertices_deg = []
for vertex in vertices:
vertices_deg.append([geom.SpherePoint(vertex).getRa().asDegrees(),
geom.SpherePoint(vertex).getDec().asDegrees()])
polygon = Polygon(vertices_deg, closed=True, facecolor='None',
edgecolor=style_dict[rec.dataId['tract']]['color'],
linestyle=style_dict[rec.dataId['tract']]['linestyle'],
linewidth=2)
ax.add_patch(polygon)
ax.set_xlim(ra_max, ra_min)
ax.set_ylim(dec_min, dec_max)
ax.set_xlabel('RA (deg)')
ax.set_ylabel('Dec (deg)')
handles = [
mlines.Line2D(
[], [],
color=style['color'],
linestyle=style['linestyle'],
label=f"Tract {tract}"
)
for tract, style in style_dict.items()
]
ax.legend(handles=handles, loc='upper left', ncol=2)
ax.minorticks_on()
plt.tight_layout()
plt.show()
Figure 1: $r$-band limiting magnitude map for the 47 Tuc field, shown in greyscale, overplotted with the patch boundaries colored by their tract number (as in the legend). The core of 47 Tuc is saturated in the LSSTComCam commissioning images, resulting in missing coadds in that region.
Clean up.
del coadd_datasetrefs
del hspmap_rmaglim, hspmap_rexptime
del x, y, values_rmaglim, values_rexptime
del style_dict
3. Visits (observations)¶
Retrieve all visits (individual observations of the sky) that fall within a circular region centered at (RA, Dec) = (ra_cen, dec_cen) with a radius of 1 degree. Return the visit ID, band, observation midpoint time in both MJD and calendar date.
query = """
SELECT visit, band, expMidptMJD, expMidpt
FROM dp1.Visit
WHERE CONTAINS(POINT('ICRS', ra, dec),CIRCLE('ICRS', {}, {}, {}))=1
ORDER BY expMidptMJD
""".format(ra_cen, dec_cen, radius)
job = service.submit_job(query)
job.run()
job.wait(phases=['COMPLETED', 'ERROR'])
print('Job phase is', job.phase)
if job.phase == 'ERROR':
job.raise_if_error()
Job phase is COMPLETED
visits = job.fetch_result().to_table()
print(f"Total number of visits: {len(visits)}")
Total number of visits: 66
Option to display the table of results.
# visits
3.1. Filter distribution¶
Count and list the number of visits per filter.
all_bands = np.array([visit['band'] for visit in visits], dtype=str)
unique_bands, counts = np.unique(all_bands, return_counts=True)
for band, count in zip(unique_bands, counts):
print(band, count)
g 10 i 19 r 32 y 5
Note: no $u$-band observations for 47 Tuc pass the DP1 quality cuts.
Create a list of only the filters observed for this field to use below.
field_filters = ['g', 'r', 'i', 'y']
Clean up.
del all_bands, unique_bands, counts
3.2. Visit dates¶
Plot two characteristics of the visit dates:
- Plot the cumulative distribution of visit dates for each of the four bands ($griy$).
- Plot the distribution of the time separation between visits for each band and for all bands combined.
t_start = visits[0]['expMidptMJD'] - 1.0
t_end = visits[-1]['expMidptMJD'] + 1.0
time_bins = np.arange(t_end - t_start, dtype='int') + t_start
diff_bins = np.arange(0, np.max(time_bins) - np.min(time_bins) + 3, 1)
all_time_diffs = []
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(11, 4))
for band in field_filters:
mjds = sorted([
visit['expMidptMJD']
for visit in visits
if visit['band'] == band
])
mjds = np.array(mjds, dtype=float)
label = f"{band} ({len(mjds)})"
counts, _, patches = ax1.hist(
mjds, bins=time_bins, cumulative=True, histtype='step',
linewidth=2, color=filter_colors[band], label=label
)
for patch in patches:
patch.set_linestyle(filter_linestyles[band])
tdiffs = []
for comb in itertools.combinations(mjds, 2):
tdiffs.append(comb[1] - comb[0])
time_diffs = np.array(tdiffs)
all_time_diffs.append(time_diffs)
counts_diff, _, patches_diff = ax2.hist(
time_diffs, bins=diff_bins, histtype='step', linewidth=2,
color=filter_colors[band], label=band
)
for patch in patches_diff:
patch.set_linestyle(filter_linestyles[band])
all_time_diffs = np.concatenate(all_time_diffs)
ax2.hist(
all_time_diffs, bins=diff_bins, histtype='step', linewidth=2,
color='black', label='all filters'
)
ax1.set_xlabel('Modified Julian Date')
ax1.set_ylabel('Cumulative number of visits')
ax1.minorticks_on()
ax2.set_xlabel('Separation between observations (d)')
ax2.set_ylabel('Number of observations')
ax2.set_yscale('log')
ax2.legend(loc='upper right', ncol=2)
ax2.minorticks_on()
plt.show()
Figure 2: The left panel shows the cumulative number of visits per filter as a function of time. The right panel shows histograms of visit-to-visit time separations for each filter individually (colors), and all together (black).
Clean up.
del time_bins, all_time_diffs
3.3. Visit image quality¶
Retrieve statistics about the image quality of the individual visit_images from the CcdVisit table.
query = """
SELECT visitId, ra, dec, band, magLim, seeing
FROM dp1.CcdVisit
WHERE CONTAINS(POINT('ICRS', ra, dec),CIRCLE('ICRS', {}, {}, {}))=1
ORDER BY visitId
""".format(ra_cen, dec_cen, radius)
job = service.submit_job(query)
job.run()
job.wait(phases=['COMPLETED', 'ERROR'])
print('Job phase is', job.phase)
if job.phase == 'ERROR':
job.raise_if_error()
Job phase is COMPLETED
ccd_visits = job.fetch_result().to_table()
Plot the distribution of seeing, which is the mean full width at half maximum of the PSF, for all visits and detector combinations.
min_seeing = np.min(ccd_visits['seeing'])
max_seeing = np.max(ccd_visits['seeing'])
seeing_bins = np.arange(min_seeing, max_seeing, 0.05)
fig = plt.figure(figsize=(7, 5))
for band in field_filters:
print(f"Median in {band}-band: \
{np.ma.median(ccd_visits[ccd_visits['band'] == band]['seeing']): .2f}")
n, bins, patches = plt.hist(ccd_visits[ccd_visits['band'] == band]['seeing'],
seeing_bins, histtype='step',
linewidth=2,
color=filter_colors[band],
label=band)
for patch in patches:
patch.set_linestyle(filter_linestyles[band])
plt.legend(loc='upper right')
plt.xlabel('PSF FWHM (arcsec)')
plt.ylabel('Number of visits')
plt.minorticks_on()
plt.show()
Median in g-band: 1.27 Median in r-band: 1.25 Median in i-band: 1.11 Median in y-band: 1.33
Figure 3: Histogram of delivered image quality of detector images, per filter.
Clean up.
del seeing_bins
Plot the distribution of the magnitude limit, magLim.
min_mag = np.min(ccd_visits['magLim'])
max_mag = np.max(ccd_visits['magLim'])
maglim_bins = np.arange(min_mag, max_mag, 0.1)
fig = plt.figure(figsize=(7, 5))
for band in field_filters:
print(f"Median in {band}-band: \
{np.ma.median(ccd_visits[ccd_visits['band'] == band]['magLim']): .2f}")
n, bins, patches = plt.hist(ccd_visits[ccd_visits['band'] == band]['magLim'],
maglim_bins, histtype='step',
linewidth=2,
color=filter_colors[band],
label=band)
for patch in patches:
patch.set_linestyle(filter_linestyles[band])
plt.legend(loc='upper left')
plt.xlabel('limiting magnitude')
plt.ylabel('Number of visits')
plt.minorticks_on()
plt.show()
Median in g-band: 24.14 Median in r-band: 24.14 Median in i-band: 23.77 Median in y-band: 21.66
Figure 4: Histogram of limiting magnitude at signal-to-noise=5 on detector images, per filter.
Clean up.
del maglim_bins
4. Object (detections)¶
The Object table contains forced measurements on the deep coadded images at the locations of all objects detected with signal-to-noise ratio > 5 in a deep_coadd of any filter.
Query the Object table for objects in the field, and return their coordinates, the value of E(B-V) at their location, and their PSF and cModel AB magnitudes.
query = """SELECT objectId, coord_ra, coord_dec, ebv,
{}_psfMag, {}_cModelMag, {}_psfMag, {}_cModelMag,
{}_psfMag, {}_cModelMag, {}_psfMag, {}_cModelMag,
refExtendedness
FROM dp1.Object
WHERE CONTAINS(POINT('ICRS', coord_ra, coord_dec),
CIRCLE('ICRS', {}, {}, {})) = 1
ORDER BY objectId ASC
""".format(field_filters[0], field_filters[0],
field_filters[1], field_filters[1],
field_filters[2], field_filters[2],
field_filters[3], field_filters[3],
ra_cen, dec_cen, radius)
job = service.submit_job(query)
job.run()
job.wait(phases=['COMPLETED', 'ERROR'])
print('Job phase is', job.phase)
if job.phase == 'ERROR':
job.raise_if_error()
Job phase is COMPLETED
objtab = job.fetch_result().to_table()
4.1. Magnitude distributions¶
Plot histograms of object magnitudes, separating the samples into stars and galaxies.
Use the refExtendedness flag to distinguish them: treat objects with refExtendedness == 1 as likely galaxies (extended) and those with refExtendedness == 0 as likely stars (point sources).
Use cModelMag mags for galaxies and psfMag for stars.
ptsource = (objtab['refExtendedness'] == 0)
mag_bins = np.arange(15.4, 28.6, 0.2)
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(9, 4), sharey=True)
plt.subplots_adjust(hspace=0, wspace=0)
for band in field_filters:
mag_psf = objtab[f"{band}_psfMag"]
mag_cmodel = objtab[f"{band}_cModelMag"]
valid_mags = (15 < mag_psf) & (mag_psf < 30) & (15 < mag_cmodel) & (mag_cmodel < 30)
print(f"Number of objects in {band}-band: {np.sum(valid_mags)}")
n_psf, bins_psf, patches_psf = ax1.hist(
mag_psf[valid_mags & ptsource],
bins=mag_bins,
histtype='step',
linewidth=2,
color=filter_colors[band],
label=band,
)
for patch in patches_psf:
patch.set_linestyle(filter_linestyles[band])
n_cmodel, bins_cmodel, patches_cmodel = ax2.hist(
mag_cmodel[valid_mags & ~ptsource],
bins=mag_bins,
histtype='step',
linewidth=2,
color=filter_colors[band],
label=band,
)
for patch in patches_cmodel:
patch.set_linestyle(filter_linestyles[band])
ax1.set_xlim(mag_bins.min(), mag_bins.max())
ax1.set_yscale('log')
ax1.set_xlabel('Magnitude')
ax1.set_ylabel('Number of objects')
ax1.set_title('Point sources (PSF mags)')
ax1.legend(loc='upper left', ncols=2)
ax1.minorticks_on()
ax2.set_xlim(mag_bins.min(), mag_bins.max())
ax2.set_xlabel('Magnitude')
ax2.set_title('Extended sources (cModel mags)')
ax2.minorticks_on()
plt.show()
Number of objects in g-band: 79169 Number of objects in r-band: 73840 Number of objects in i-band: 101962 Number of objects in y-band: 47349
Figure 5: Histogram of object magnitudes separated by morphological type. The left panel shows likely stars (
refExtendedness= 0) usingPSFmagnitudes, while the right panel shows likely galaxiescModelmagnitudes. The bump near 19 mag in the stellar distribution corresponds to red clump stars in the SMC.
Clean up.
del mag_bins, mag_psf, mag_cmodel, valid_mags, n_psf, bins_psf
del patches_psf, n_cmodel, bins_cmodel, patches_cmodel
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(7, 6),
height_ratios=[1, 2.5])
plt.subplots_adjust(hspace=0, wspace=0)
grmin, grmax = -0.9, 2.3
rimin, rimax = -1.3, 2.8
magmin, magmax = 15.8, 26.8
ax1.hexbin(objtab[ptsource]['g_psfMag']-objtab[ptsource]['r_psfMag'],
objtab[ptsource]['r_psfMag']-objtab[ptsource]['i_psfMag'],
gridsize=(150, 150),
extent=(grmin, grmax, rimin, rimax), bins='log', cmap='Grays')
ax1.set_title('Point sources (PSF mags)')
ax1.set_xlim(grmin, grmax)
ax1.set_ylim(rimin, rimax)
ax1.set_ylabel(r'$(r-i)$')
ax1.set_xticklabels([])
ax1.minorticks_on()
ax2.hexbin(objtab[~ptsource]['g_cModelMag']-objtab[~ptsource]['r_cModelMag'],
objtab[~ptsource]['r_cModelMag']-objtab[~ptsource]['i_cModelMag'],
gridsize=(150, 150),
extent=(grmin, grmax, rimin, rimax), bins='log', cmap='Grays')
ax2.set_title('Extended sources (cModel mags)')
ax2.set_xlim(grmin, grmax)
ax2.set_ylim(rimin, rimax)
ax2.set_xticklabels([])
ax2.set_yticklabels([])
ax2.minorticks_on()
ax3.hexbin(objtab[ptsource]['g_psfMag']-objtab[ptsource]['r_psfMag'],
objtab[ptsource]['r_psfMag'], gridsize=(150, 200),
extent=(grmin, grmax, magmin, magmax), bins='log', cmap='Grays')
ax3.plot([-0.2, -0.2, 0.6, 0.6, -0.2], [25, 19.5, 19.5, 25, 25], color='Red')
ax3.set_xlim(grmin, grmax)
ax3.set_ylim(magmax, magmin)
ax3.set_xlabel(r'$(g-r)$')
ax3.set_ylabel(r'$r$ magnitude')
ax3.minorticks_on()
ax4.hexbin(objtab[~ptsource]['g_cModelMag']-objtab[~ptsource]['r_cModelMag'],
objtab[~ptsource]['r_cModelMag'], gridsize=(150, 200),
extent=(grmin, grmax, magmin, magmax), bins='log', cmap='Grays')
ax4.set_xlim(grmin, grmax)
ax4.set_ylim(magmax, magmin)
ax4.set_xlabel(r'$(g-r)$')
ax4.set_yticklabels([])
ax4.minorticks_on()
plt.show()
Figure 6: CMDs and CCDs for objects classified by morphology. The left column shows likely stars (
refExtendedness= 0) usingPSFmagnitudes, and the right column shows likely galaxies usingcModelmagnitudes. Top panels display $(r - i)$ vs. $(g - r)$ CCDs; bottom panels show $r$ vs. $(g - r)$ CMDs.
5.2. SMC contamination¶
The red box in Figure 6 represents stellar populations in the outer SMC. 47 Tuc lies more than 2 degrees from the main body of the SMC, but a diffuse stellar population from the SMC extends into the 47 Tuc field, contributing to the CMD and CCD of 47 Tuc.
The figure below, adapted from Nidever et al. 2021, is based on DECam images acquired through the Survey of the MAgellanic Stellar History (SMASH) program, depicting the sky positions of the two objects.
