301.7. Seagull Nebula

301.7. Seagull Nebula¶

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: 2026-01-30
Repository: github.com/lsst/tutorial-notebooks

Learning objective: An overview of the LSSTComCam observations of the Seagull Nebula.

LSST data products: deep_coadd, CcdVisit, Visit, Object

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¶

The Seagull Nebula, so named for its resemblanced to a bird in flight, is a dusty emission and reflection nebula spanning 2.5 degrees of sky at the border of the Canis Major and Monoceros constellations, in the plane of the Milky Way. A popular astrophotography target, beautiful images of the Seagull Nebula appeared, for example, on the Astronomy Picture of the Day (APOD) on January 19 2023 and March 13 2024.

The LSSTComCam observations of the Seagull Nebula were centered on the "head" of the Seagull, IC 2177 (CDS Portal page for IC 2177). The region covered spans a diameter of about 1 degree.

• RA, Dec: 106.300, -10.510
• RA, Dec: 07h 05m 12s, -10d 30m 36s

The extended nebulosity across the entire LSSTComCam field for the Seagull Nebula pose a unique challenge for the data reduction pipelines, and especially the generation of the HiPS maps.

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), and standardized multiband plotting (lsst.utils.plotting) for LSST data analysis and visualization.

In [1]:

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
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.

In [2]:

service = get_tap_service("tap")
assert service is not None

Create an instance of the butler, and assert that it exists.

In [3]:

butler = Butler("dp1", collections="LSSTComCam/DP1")
assert butler is not None

Define the approximate central coordinates of the Seagull field, and the region to use when querying for overlapping images.

In [4]:

ra_cen = 106.23
dec_cen = -10.51
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.

In [5]:

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, $ugrizy$.

In [6]:

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.

In [7]:

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.

In [8]:

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"])

In [9]:

print('There are %s r-band deep_coadd images.' % len(coadd_datasetrefs))

There are 76 r-band deep_coadd images.

Retrieve the deep_coadd image associated with the second of the dataset references returned by the butler query.

In [10]:

coadd = butler.get(coadd_datasetrefs[1])

Display the image, and set the mask plane to be fully transparent.

In [11]:

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 $r$-band exposure time as hspmap_rexptime.

In [12]:

hspmap_rmaglim = butler.get('deepCoadd_psf_maglim_consolidated_map_weighted_mean',
                            skymap='lsst_cells_v1', band='r')
hspmap_rexptime = butler.get('deepCoadd_exposure_time_consolidated_map_sum',
                             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.

In [13]:

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)

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.

In [14]:

print('r-band magnitude limit mean and median: %5.2f %5.2f' %
      (np.nanmean(values_rmaglim), np.nanmedian(values_rmaglim)))
print('r-band magnitude min and max: %5.2f %5.2f' %
      (np.nanmin(values_rmaglim), np.nanmax(values_rmaglim)))
print('r-band exposure time (s) mean and median: %5.1f %5.1f' %
      (np.nanmean(values_rexptime), np.nanmedian(values_rexptime)))
print('r-band exposure time min and max (s): %5.1f %5.1f' %
      (np.nanmin(values_rexptime), np.nanmax(values_rexptime)))

r-band magnitude limit mean and median: 24.94 25.18
r-band magnitude min and max: 22.23 25.87
r-band exposure time (s) mean and median: 482.8 390.0
r-band exposure time min and max (s):  30.0 1290.0

2.3. Coverage map¶

Define unique colors and linestyles for plotting each of the tracts in the dataset.

In [15]:

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)}
print('unique tracts: ', unique_tracts)

unique tracts:  [7610 7611 7849 7850]

Use TAP to find the vertices of each patch for each of the unique tracts covering the field in the dp1.coaddPatches table.

In [16]:

list_unique_tracts = "(" + ", ".join(str(tract) for tract in unique_tracts) + ")"
query = "SELECT lsst_patch, lsst_tract, s_ra, s_dec, s_region " \
        "FROM dp1.CoaddPatches " \
        "WHERE lsst_tract IN {}".format(list_unique_tracts)
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

Fetch the results as an astropy table.

In [17]:

assert job.phase == 'COMPLETED'
coadd_patches = job.fetch_result().to_table()

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.

In [18]:

fig, ax = plt.subplots(figsize=(6, 5))

mesh = ax.pcolormesh(x, y, values_rmaglim, cmap='Greys_r',
                     vmin=23.5, shading='auto')
fig.colorbar(mesh, ax=ax, label="r-band limiting magnitude (mag)")

tracts = set(coadd_patches['lsst_tract'])

for tract in tracts:
    s_regions = coadd_patches[coadd_patches['lsst_tract'] == tract]['s_region']

    for s_region in s_regions:
        coordinates = np.array(s_region.split()[2:], dtype=float)
        ra = coordinates[0::2]
        ra = (ra + 180) % 360 - 180
        dec = coordinates[1::2]
        vertices_deg = np.vstack([ra, dec]).T

        polygon = Polygon(vertices_deg, closed=True, facecolor='None',
                          edgecolor=style_dict[tract]['color'],
                          linestyle=style_dict[tract]['linestyle'],
                          linewidth=2)
        ax.add_patch(polygon)

ax.set_xlim(105.5, 107)
ax.set_ylim(-11.25, 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()
ax.invert_xaxis()

plt.tight_layout()
plt.show()

Figure 1: For the region of the Seagull nebula, the $r$-band magnitude limit survey property map in greyscale, overplotted with the patch boundaries colored by their tract number (as in the legend).

Clean up.

In [19]:

del coadd_datasetrefs
del hspmap_rmaglim, hspmap_rexptime
del x, y, values_rmaglim, values_rexptime
del style_dict
del tracts, s_regions, coordinates, ra, dec

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.

In [20]:

query = """
        SELECT visit, band, expMidptMJD, expMidpt
        FROM dp1.Visit
        WHERE CONTAINS(POINT('ICRS', ra, dec), CIRCLE('ICRS', {}, {}, {}))=1
        """.format(ra_cen, dec_cen, radius)

In [21]:

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

In [22]:

assert job.phase == 'COMPLETED'
visits = job.fetch_result().to_table()
print(f"Total number of visits: {len(visits)}")

Total number of visits: 100

Option to display the table of results.

In [23]:

# visits

3.1. Filter distribution¶

Count and list the number of visits per filter.

In [24]:

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 37
r 43
u 10
z 10

There are no i- or y-band observations for this field.

Create a list of only the filters observed for this field, to use below.

In [25]:

field_filters = ['u', 'g', 'r', 'z']

In [26]:

del all_bands, unique_bands, counts

3.2. Visit dates¶

Plot two characteristics of the visit dates:

1. Plot the cumulative distribution of visit dates for each of the four bands ($griy$).
2. Plot the distribution of the time separation between visits for each band and for all bands combined.

In [27]:

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.

In [28]:

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.

In [29]:

query = """
        SELECT visitId, ra, dec, band, magLim, seeing
        FROM dp1.CcdVisit
        WHERE CONTAINS(POINT('ICRS', ra, dec),CIRCLE('ICRS', {}, {}, {}))=1
        """.format(ra_cen, dec_cen, radius)

In [30]:

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

In [31]:

assert job.phase == '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.

In [32]:

seeing_bins = np.arange(0.5, 2.0, 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()
del seeing_bins

Median in u-band:      1.51
Median in g-band:      1.34
Median in r-band:      1.19
Median in z-band:      1.20

Figure 3: Histogram of delivered image quality of detector images, per filter.

Plot the distribution of the magnitude limit, magLim.

In [33]:

min_mag, max_mag = np.min(ccd_visits['magLim']), 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()
del maglim_bins

Median in u-band:              23.38
Median in g-band:              24.28
Median in r-band:              24.11
Median in z-band:              23.19

Figure 4: Histogram of limiting magnitude at signal-to-noise=5 on detector images, by filter.

4. Objects (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.

In [34]:

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
        """.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)
print(query)

SELECT objectId, coord_ra, coord_dec, ebv,
        u_psfMag, u_cModelMag, g_psfMag, g_cModelMag,
        r_psfMag, r_cModelMag, z_psfMag, z_cModelMag,
        refExtendedness
        FROM dp1.Object
        WHERE CONTAINS(POINT('ICRS', coord_ra, coord_dec),
              CIRCLE('ICRS', 106.23, -10.51, 1.0)) = 1

In [35]:

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

In [36]:

assert job.phase == 'COMPLETED'
objtab = job.fetch_result().to_table()

4.1. Magnitude distribution¶

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.

In [37]:

ptsource = (objtab['refExtendedness'] == 0)

In [38]:

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.legend(loc='upper left', ncols=2)
ax2.minorticks_on()

plt.show()

Number of objects in u-band: 66163
Number of objects in g-band: 83039
Number of objects in r-band: 85306
Number of objects in z-band: 88192

Figure 5: Histogram of object magnitudes separated by morphological type. The left panel shows likely stars (refExtendedness == 0) using PSF magnitudes, while the right panel shows likely galaxies using cModel magnitudes.

5. Analysis¶

A short demonstration of some stanard scientific analyses.

5.1. Color-magnitude diagrams¶

Plot $r$ vs. $(g - r)$ color–magnitude diagrams (CMDs) and $(r - i)$ vs. $(g - r)$ color–color diagrams (CCDs). Create separate plots for stars and galaxies using PSF magnitudes for stars and cModel magnitudes for galaxies.

In [39]:

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]['z_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'$(r-z)$')
ax1.set_xticklabels([])
ax1.minorticks_on()

ax2.hexbin(objtab[~ptsource]['g_cModelMag']-objtab[~ptsource]['r_cModelMag'],
           objtab[~ptsource]['r_cModelMag']-objtab[~ptsource]['z_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.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: Color-magnitude (bottom) and color-color (top) diagrams for objects that were not flagged as extended (left) and those that were (right).

5.2. Milky Way extinction¶

The dustiness of the Seagull Nebula make it one of the Milky Way's many objects that contribute to line-of-sight Galactic extinction and reddening for background stars and the extragalactic objects beyond.

In Section 4, the Galactic extinction E(B-V) value was returned for every object.

Create scatter plots showing where the objects are in the field, and the approximate contours of E(B-V) values.

In [40]:

fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(10, 4))

ax1.plot(objtab['coord_ra'], objtab['coord_dec'], 'o',
         ms=2, mew=0, alpha=0.05, color='grey')
ax1.invert_xaxis()
ax1.set_title('all objects')

e1 = 0
for e, ebv in enumerate([1, 1.5, 2, 3, 4]):
    tx = np.where((objtab['ebv'] >= e1) & (objtab['ebv'] < ebv))[0]
    ax2.plot(objtab['coord_ra'][tx], objtab['coord_dec'][tx], 'o', ms=2, mew=0,
             alpha=0.05, color=colors[e])
    e1 = ebv
tx = np.where(objtab['ebv'] > ebv)[0]
ax2.plot(objtab['coord_ra'][tx], objtab['coord_dec'][tx], 'o', ms=2, mew=0,
         alpha=0.05, color=colors[5])
ax2.invert_xaxis()
ax2.set_title('objects colored by E(B-V)')

plt.show()
del tx

Figure 7: At left, a scatter plot showing the locations of objects in the field. At right, the same objects are colored by their E(B-V) value to show the contours of the dust map.

As another example of visualizing the same data, plot the 2-dimensional histograms of the number of objects, and the mean value of E(B-V), over the region.

In [41]:

counts, xbins, ybins = np.histogram2d(objtab['coord_dec'], objtab['coord_ra'],
                                      bins=(30, 30))
sums, _, _ = np.histogram2d(objtab['coord_dec'], objtab['coord_ra'],
                            weights=objtab['ebv'], bins=(xbins, ybins))

fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(10, 4))
m1 = ax1.pcolormesh(ybins, xbins, counts, cmap='viridis')
plt.colorbar(m1, ax=ax1)
ax1.set_title('number of objects')
with np.errstate(divide='ignore', invalid='ignore'):
    m2 = ax2.pcolormesh(ybins, xbins, sums / counts, cmap='coolwarm')
plt.colorbar(m2, ax=ax2)

ax2.set_title('mean value of E(B-V)')
ax1.set_xlabel('RA')
ax1.set_ylabel('Dec')
ax1.invert_xaxis()
ax2.set_xlabel('RA')
ax2.set_ylabel('Dec')
ax2.invert_xaxis()
plt.tight_layout()
plt.show()

Figure 8: At left, a 2d histogram of the number of objects, and at right, a 2d heatmap of the mean value of E(B-V) of the objects in the bin. This plot demonstrates the (known and expected) inverse correlation between dust extinction and object detectability: more objects are detected where the dust extinction is lower.

In [ ]: