In [1]:
# Loading configuration
# Don't forget that mac has this annoying configuration that leads
# to limited number of figures/files
# ulimit -n 4096 <---- osx limits to 256
import warnings
warnings.catch_warnings()
warnings.simplefilter("ignore")
%pylab inline
%config InlineBackend.figure_format='retina'
import pylab as plt
import numpy as np
import figrc, setup_mpl
setup_mpl.theme()
setup_mpl.solarized_colors()
Gaia Archive website: https://gea.esac.esa.int/archive/
The entry point is a TAP (Table Access Protocol) server.
TAP provides two operation modes, Synchronous and Asynchronous.
Synchronous: the response to the request will be generated as soon as the request received by the server.
Asynchronous: the server will start a job that will execute the request. The first response to the request is the required information (a link) to obtain the job status. Once the job is finished, the results can be retrieved.
Gaia Archive TAP server provides two access modes: public and authenticated
Public: this is the standard TAP access. A user can execute ADQL queries and upload tables to be used in a query 'on-the-fly' (these tables will be removed once the query is executed). The results are available to any other user and they will remain in the server for a limited space of time.
Authenticated: some functionalities are restricted to authenticated users only. The results are saved in a private user space and they will remain in the server for ever (they can be removed by the user). ADQL queries and results are saved in a user private area.
Cross-match operations: a catalogue cross-match operation can be executed. Cross-match operations results are saved in a user private area.
ADQL = Astronomical Data Query Language
ADQL has been developed based on SQL92 and supports a subset of the SQL grammar with extensions to support generic and astronomy specific operations.
In other words, ADQL is a SQL-like searching language improved with geometrical functions.
for more information see the IVOA documentation http://www.ivoa.net/documents/latest/ADQL.html
SELECT *
FROM "gaiadr1.tgas_source"
SELECT top 1000 ra, dec, phot_g_mean_mag AS mag
FROM "gaiadr1.gaia_source"
ORDER BY mag
TOP limits the number of records to displayORDER BY sorts records in ascending (ASC, default) or descending (DESC)WHERE filters records according to logical expressionsIN, NOT IN operator that can determine whether a value is (not) within a given setBETWEEN x AND y operator can determine whether a value is within a given intervalLIKE operator allows for a partial comparison, It uses wild cards % and _ ('percent' and 'underscore'). The wild card % replaces any string of characters, including the empty string. The underscore replaces exactly one character.GROUP BY groups records by identical values (or set of values)= or > or < or >= or <= or <>, different operators of logical comparisons+, -, *, /, compute columns using mathematical operationsPOWER(column_name, n) returns values raised to the power n. n must be a integer positive or negative.SQRT(column_name) returns the square root of values.CEILING(column_name) rounds up to the nearest integer value.FLOOR(column name) rounds down to the next least integer value.ABS(column_name) returns the absolute value.AVG (column_name) this function returns the average value in a column for a group of data linesCOUNT (column_name) this function returns a count of rows from a reference column values if it is not NULL.SUM(column_name) this function returns the sum of values in a column for a group of data lines. MAX, MIN, return the largest or smallest value of a column for a group of data lines.COS, SIN, TAN, ACOS, ASIN, ATAN of an angle in radian compute the trigonometric transformationADQL provides a set of 2D-functions and geometries or regions
A region is always attached to a coordinate System: FK4, FK5, ICRS, GALACTIC.
The coordinates expressed in degree, can be constant or the result of a mathematical expression.
POINT('coordinate system', right ascension, declination) expresses a point source on the sky
CIRCLE('coordinate system',right ascension center, declination center, radius in degrees) expresses a circular region on the sky (a cone in space)
BOX('coordinate system', right ascension center, declination center, width, height) defines a centered box
POLYGON('coordinate system', coordinate point 1, coordinate point 2, coordinate point 3...) expresses a region on the sky with sides denoted by great circles passing through specified list of POINT objects.
DISTANCE(point1, point2) computes distance between two points.
CONTAINS(region1, region2) returns a boolean value : true if region2 contains region1, false otherwise.
INTERSECTS(region1, region2) returns a boolean value : true if region2 intersect region1, false otherwise.
Some common code to send ADQL Queries to TAP services and notebook polishing
GaiaArchive is a shortcut from TAP_service to the interface with the Gaia ArchiveTAPVizieR is a shortcut from TAP_service to the interface with TAP service of VizieR (CDS).GAVO is a shortcut from TAP_service to interface with GAVO service.resolve interfaces CDS/Sesame name resolver to get positions of known objects by their names.QueryStr is a polished string that parses an SQL syntax to make it look nicer (for notebook and console)timeit a context manager/decorator that reports execution time (for notebook and console)
In [2]:
try:
from tap import (GaiaArchive, TAPVizieR, resolve, QueryStr, timeit)
except ImportError:
# Running from the repository
import sys
sys.path.append('..')
from tap import (GaiaArchive, TAPVizieR, resolve, QueryStr, timeit)
In [3]:
gaia = GaiaArchive()
select a small number of rows in the gaia DR1 data table.
Note: we use QueryStr only for giving an easier reading. A string would work as well.
In [4]:
adql = QueryStr("""
select top 5 * from gaiadr1.gaia_source
""")
Now we run the query.
Note: we use timeit in this notebook to indicate how long the operation gaia.query took.
In [5]:
timeit(gaia.query)(adql)
Out[5]:
Asynchronous mode
From the same service, use the query_async method. The job will be submitted and accessible later.
Why this mode would be prefered? Some services (incl. the Gaia Archive) limit strongly the queries using the synchronous mode. For example the Gaia Archive limits to 1 minute jobs, mostly to avoid comminucation issues. Read the documentation of the service you want to use to decide.
Below I use the async mode to redo the exact same query as before.
In [6]:
q = gaia.query_async(adql, silent=True)
q # pretty print display
Out[6]:
One can interogate the service to know if the task is complete.
In [7]:
q.status
Out[7]:
Finally we can download the result when available.
(The provided python interface has an option to wait or not for completion; keyword wait=True, default behavior)
In [8]:
q.get()
Out[8]:
Luckily, we obtain the same result as before.
Authenticate with your account
The current python package also allows you to authenticate. This is mostly relevant when using async queries, or user tables.
gaia.login("my_user_name")
The above will prompt for a password.
Note that the password can also be provided as argument if you need to script some queries. It will not be stored, only the relevant cookie will be conserved until the end of the session.
In [9]:
gaia.login('mfouesne')
In [10]:
q = gaia.query_async(adql, silent=True)
q # pretty print display
Out[10]:
In [13]:
q.get()
Out[13]:
Callback previous jobs
In some cases (mostly when authenticated) you might want to download a previous job. This python package allows you to do so using recall_query, which returns an asynchronous result.
In [14]:
qprime = gaia.recall_query(q.jobid)
qprime.get()
Out[14]:
In this example, we want to make the luminosity function of TGAS stars. Of course we could download all stars and do it on our computer but one can do it on the server side and only download the computed histogram.
hint: Building a luminosity function means that we want to select all stars in TGAS (with magnitude) and count them after grouping them per bin of magnitudes. Additionally one will want to sort the bins for plotting them in order.
In [10]:
adql = QueryStr("""
select
count(*) as n,
round(phot_g_mean_mag, 1) as val
from
gaiadr1.tgas_source
group by val
order by val
""")
Run the query
In [11]:
data_tgas = timeit(gaia.query)(adql)
Note: Sometimes the Gaia Archive crashes... BUG! This is needed feedback to the service.
You can bypass the issue by using async queries or re-run until you give up...
In [12]:
data_tgas = gaia.query_async(adql).get()
Plot the histogram data.
In [13]:
plt.step(data_tgas['val'], data_tgas['n'],
lw=2, where='pre', label='TGAS')
plt.yscale('log')
plt.xlabel('G magnitude')
plt.ylabel('counts / mag')
figrc.hide_axis('top right'.split())
Note: Creating the luminosity function for the full DR1 required more than 1 min and thefore an async query, which is not handled by this simple query interface.
In [14]:
adql = QueryStr("""
select
count(*) as n,
round(l, 0) as x,
round(b, 0) as y
from
gaiadr1.tgas_source
group by x, y
order by x, y
""")
Run the query
In [15]:
data = timeit(gaia.query)(adql)
Below you can see that the table is flat (i.e, no 2d matrix) and it contains a number $n$ for each pair $(x,y)$. Note also that empty bins are not included.
In [16]:
data
Out[16]:
Skipping polishing the projection
In [17]:
from matplotlib.colors import LogNorm
l = np.arange(0, 360, 1)
b = np.arange(-90, 90, 1)
n = np.zeros((len(l), len(b)))
ix = np.digitize(data['x'], l)
iy = np.digitize(data['y'], b)
n[ix - 1, iy - 1] = data['n']
plt.pcolormesh(l, b, n.T,
cmap=plt.cm.viridis,
norm=LogNorm())
figrc.hide_axis('top right'.split())
plt.xlim(l.min(), l.max())
plt.ylim(b.min(), b.max());
Using the region objects, one can query around a point. Below is an example of cone-search, i.e., selecting stars within 2 degrees of an object:
select * from gaiadr1.gaia_sources
where contains(point('ICRS', ra, dec), circle('ICRS',10.6847083,41.26875,2) ) = 1
The where condition selects points from the data that are contained into the circle of given center and size.
Below we make the density map of stars around the same object. We can therefore combine the 2D-histogram above and the cone-search technique.
Any idea of what object is the center of this selection?
In [18]:
adql = QueryStr("""
select
count(*) as n,
round(l, 2) as latitude,
round(b, 2) as longitude
from
gaiadr1.gaia_source
where
contains(point('ICRS',gaiadr1.gaia_source.ra,gaiadr1.gaia_source.dec),
circle('ICRS',10.6847083,41.26875,2) )=1
group by latitude, longitude
order by latitude, longitude
""")
Run the query
In [19]:
data = timeit(gaia.query)(adql)
Finally we plot the map
In [20]:
plt.figure(figsize=(6,6))
plt.subplot(111, aspect=1)
plt.scatter(data['latitude'], data['longitude'], c=data['n'],
edgecolor='None', s=6, rasterized=True, norm=LogNorm(),
cmap=plt.cm.magma, marker='s'
)
plt.xlim(data['latitude'].min(), data['latitude'].max())
plt.ylim(data['longitude'].min(), data['longitude'].max())
plt.axis('off');
Let's proceed to query around another object for example M33
ADQL does not provide a name resolver function. So you'll have to figure that for M33 you need
circle('ICRS',23.4621,30.6599417,0.5)
Alternatively, this python package provides a name resolver function based on the CDS/Sesame service.
from tap import resolve
ra, dec = resolve('m33')
Below I show the latter option.
In [21]:
ra, dec = resolve('m33')
adql = QueryStr("""
select
count(*) as n,
round(l, 2) as latitude,
round(b, 2) as longitude
from
gaiadr1.gaia_source
where
contains(point('ICRS',gaiadr1.gaia_source.ra,gaiadr1.gaia_source.dec),
circle('ICRS',{ra:f}, {dec:f},0.5) )=1
group by latitude, longitude
order by latitude, longitude
""".format(ra=ra, dec=dec))
data = timeit(gaia.query)(adql)
plt.figure(figsize=(6,6))
plt.subplot(111, aspect=1)
plt.scatter(data['latitude'], data['longitude'], c=data['n'],
edgecolor='None', s=9, rasterized=True, norm=LogNorm(),
cmap=plt.cm.magma, marker='s'
)
plt.xlim(data['latitude'].min(), data['latitude'].max())
plt.ylim(data['longitude'].min(), data['longitude'].max())
plt.axis('off');
One of the first results published by the Gaia Consortium was the color-magnitude diagram (CMD) of the TGAS data, showing how good the parallaxes are.
Let's reproduce the figure.
Let's make a binned CMD with bins of $0.01$ mag and $0.05$ mag in color and magnitude, respectively. Additionally, we will need to use the parallax as distance measurements. As Bailer-Jones 2015 showed one must be careful, thus let's only consider stars with $\varpi/\sigma_\varpi > 5$. One could also filter on photometric signal-to-noise (in flux).
In [22]:
adql = QueryStr("""
select
count(*) as n,
floor((hip.bt_mag - hip.vt_mag) / 0.01) * 0.01 as color,
floor((gaia.phot_g_mean_mag + 5*log10(gaia.parallax)-10) / 0.05) * 0.05 as mag
from
gaiadr1.tgas_source as gaia
inner join
public.tycho2 as hip
on gaia.hip = hip.hip
where
gaia.parallax / gaia.parallax_error >= 5
and (2.5/log(10)) * (gaia.phot_g_mean_flux_error / gaia.phot_g_mean_flux) <= 0.05
group by color, mag
""")
data = timeit(gaia.query)(adql)
Plotting the CMD is the only local operation.
In [23]:
plt.scatter(data['color'], data['mag'], c=data['n'],
edgecolor='None', s=1, rasterized=True, norm=LogNorm(),
cmap=plt.cm.magma, marker='o'
)
plt.xlim(data['color'].min(), data['color'].max())
plt.ylim(data['mag'].max(), data['mag'].min())
plt.xlabel('B-V (Hipparcos)')
plt.ylabel(r'G + 5 log($\varpi$) - 10')
figrc.hide_axis('top right'.split())
Many surveys are already crossmatched by Gaia DPAC (Data Processing and Analysis Consortium). However the access may not be as trivial for many. It requires to join tables by some id values.
Getting TGAS and Tycho2 missing Ids
Note the use of left outer join, which adds to the left table (here gaia) the missing columns when and fills it in whenever possible.
In [24]:
adql = QueryStr("""
select top 10
gaia.hip, gaia.tycho2_id, gaia.source_id,
tycho2.bt_mag, tycho2.vt_mag, tycho2.e_bt_mag, tycho2.e_vt_mag
from
gaiadr1.tgas_source as gaia
left outer join
public.tycho2 as tycho2
on gaia.tycho2_id = tycho2.id
""")
timeit(gaia.query)(adql)
Out[24]:
What happens if you do inner join instead?
In [25]:
adql = QueryStr("""
select top 10
gaia.hip, gaia.tycho2_id, gaia.source_id,
gaia.ra, gaia.ra_error, gaia.dec, gaia.dec_error,
gaia.parallax, gaia.parallax_error, gaia.pmra, gaia.pmra_error,
gaia.pmdec, gaia.pmdec_error, gaia.ra_dec_corr, gaia.ra_parallax_corr,
gaia.phot_g_n_obs, gaia.phot_g_mean_flux, gaia.phot_g_mean_flux_error,
gaia.phot_g_mean_mag, gaia.phot_variable_flag, gaia.l, gaia.b,
gaia.ecl_lon, gaia.ecl_lat, tycho2.bt_mag, tycho2.vt_mag,
tycho2.e_bt_mag, tycho2.e_vt_mag,
allwise.allwise_oid, allwise.w1mpro,
allwise.w1mpro_error, allwise.w2mpro, allwise.w2mpro_error,
allwise.w3mpro, allwise.w3mpro_error, allwise.w4mpro,
allwise.w4mpro_error, allwise.var_flag, allwise.w1mjd_mean,
allwise.w2mjd_mean, allwise.w3mjd_mean, allwise.w4mjd_mean,
allwise.w1gmag, allwise.w1gmag_error, allwise.w2gmag,
allwise.w2gmag_error, allwise.w3gmag, allwise.w3gmag_error,
allwise.w4gmag, allwise.w4gmag_error,
tmass.tmass_oid, tmass.j_m, tmass.j_msigcom, tmass.h_m, tmass.h_msigcom,
tmass.ks_m, tmass.ks_msigcom
from
gaiadr1.tgas_source as gaia
left outer join
public.tycho2 as tycho2
on gaia.tycho2_id = tycho2.id
left outer join
gaiadr1.allwise_best_neighbour as allwisexmatch
on gaia.source_id = allwisexmatch.source_id
left outer join
gaiadr1.allwise_original_valid as allwise
on allwisexmatch.allwise_oid = allwise.allwise_oid
left outer join
gaiadr1.tmass_best_neighbour as tmassxmatch
on gaia.source_id = tmassxmatch.source_id
left outer join
gaiadr1.tmass_original_valid as tmass
on tmassxmatch.tmass_oid = tmass.tmass_oid
""")
data = timeit(gaia.query)(adql)
This example is from C. A. L. Bailer-Jones
TGAS apparently has the nasty property that it does not report both Hipparcos and Tycho-2 Id at the same time but only one of the 2. However, when you need/want to have both, this starts to get a little bit tricky.
Additionally CBJ wanted to do some selections on the parallax and proper motion for updating his stellar encounter study.
Below we select the various id values but fill the Tycho-2 ids for Hipparcos stars when available in the Tycho-2 data. Moreover, CBJ wants to filter stars based on their motion:
where $4.74047$ is the equivalent of $1$ AU/yr in km/s and a radial velocity $R_V=500$ km/s.
In [30]:
adql = QueryStr("""
select
tgas.tycho2_id, tycho2.id, tgas.source_id, tgas.phot_g_mean_mag,
tgas.ra, tgas.dec, tgas.parallax, tgas.pmra, tgas.pmdec, tgas.ra_error,
tgas.dec_error, tgas.parallax_error, tgas.pmra_error, tgas.pmdec_error,
tgas.ra_dec_corr, tgas.ra_parallax_corr, tgas.ra_pmra_corr,
tgas.ra_pmdec_corr, tgas.dec_parallax_corr, tgas.dec_pmra_corr,
tgas.dec_pmdec_corr, tgas.parallax_pmra_corr, tgas.parallax_pmdec_corr,
tgas.pmra_pmdec_corr
from
gaiadr1.tgas_source as tgas
left outer join
public.tycho2 as tycho2
on tgas.hip = tycho2.hip
where ( (
1000 * 4.74047 * sqrt(power(tgas.pmra, 2)
+ power(tgas.pmdec, 2)) / power(tgas.parallax, 2)
) /
( sqrt(
(power(tgas.pmra, 2)
+ power(tgas.pmdec, 2)) * power(4.74047 / tgas.parallax, 2)
+ power(500, 2) ) )
) < 10
""")
data = timeit(gaia.query)(adql)
In [31]:
data
Out[31]:
In [32]:
adql = QueryStr("""
select
tgas.tycho2_id, tycho2.id, tgas.source_id, tgas.phot_g_mean_mag,
tgas.ra, tgas.dec, tgas.parallax, tgas.pmra, tgas.pmdec, tgas.ra_error,
tgas.dec_error, tgas.parallax_error, tgas.pmra_error, tgas.pmdec_error,
tgas.ra_dec_corr, tgas.ra_parallax_corr, tgas.ra_pmra_corr,
tgas.ra_pmdec_corr, tgas.dec_parallax_corr, tgas.dec_pmra_corr,
tgas.dec_pmdec_corr, tgas.parallax_pmra_corr, tgas.parallax_pmdec_corr,
tgas.pmra_pmdec_corr
from
gaiadr1.tgas_source as tgas
left outer join
public.tycho2 as tycho2
on tgas.hip = tycho2.hip
where ( (
1000 * 4.74047 * sqrt(power(tgas.pmra, 2)
+ power(tgas.pmdec, 2)) / power(tgas.parallax, 2)
) /
( sqrt(
(power(tgas.pmra, 2)
+ power(tgas.pmdec, 2)) * power(4.74047 / tgas.parallax, 2)
+ power(500, 2) ) )
) < 10
""")
gaia.query_async(adql).get()
Out[32]:
As you can see this does not return the same number of entries but many more than the sync mode.
This can be for many reasons (esp. a bug of the service) but it could be that one of the sync mode limits was reached. Most likely the 1min time limit, but the reported time is still under that...
The VizieR ADQL service (http://tapvizier.u-strasbg.fr/) a service is hosted by the CDS - Strasbourg allows access to any VizieR table and catalog. The same operations are provided through the ADQL protocols.
Below I reproduce all examples using the TAPVizieR Service.
Important note: column and table names in Vizier are not identical to the Gaia Archive ones
Testing interface
In [33]:
adql = QueryStr("""
select top 100
gaia.source_id, gaia.hip,
gaia.phot_g_mean_mag+5*log10(gaia.parallax)-10 as g_mag_abs_gaia,
gaia.phot_g_mean_mag+5*log10(hip.plx)-10 as g_mag_abs_hip,
hip."B-V"
from
"I/337/tgas" as gaia
inner join
"I/311/hip2" as hip
on gaia.hip = hip.HIP
where
gaia.parallax/gaia.parallax_error >= 5
and hip.Plx/hip.e_Plx >= 5
and hip."e_B-V" > 0.0 and hip."e_B-V" <= 0.05
and (2.5/log(10))*(gaia.phot_g_mean_flux_error/gaia.phot_g_mean_flux) <= 0.05
""")
vizier = TAPVizieR()
result = timeit(vizier.query)(adql)
result
Out[33]:
TGAS Luminosity function
In [34]:
adql = QueryStr("""
select
count(*) as n,
round(gaia.phot_g_mean_mag, 1) as val
from
"I/337/tgas" as gaia
group by val
order by val
""")
data_tgas = timeit(vizier.query)(adql)
# Apparently some parsing issues in TAPVizieR...
n = [int(k) for k in data_tgas['n']]
vals = [float(k) for k in data_tgas['val']]
#
plt.step(vals, n, lw=2, where='pre', label='TGAS')
plt.yscale('log')
plt.xlabel('G magnitude')
plt.ylabel('counts / mag')
figrc.hide_axis('top right'.split())
TGAS density map in l,b coordinates
In [35]:
adql = QueryStr("""
select
count(*) as n,
round(glon, 0) as x,
round(glat, 0) as y
from
"I/337/tgas"
group by x, y
order by x, y
""")
data = timeit(vizier.query)(adql)
from matplotlib.colors import LogNorm
dx = [float(k) for k in data['x']]
dy = [float(k) for k in data['y']]
dn = [int(k) for k in data['n']]
l = np.arange(0, 360, 1)
b = np.arange(-90, 90, 1)
n = np.zeros((len(l), len(b)))
ix = np.digitize(dx, l)
iy = np.digitize(dy, b)
n[ix - 1, iy - 1] = dn
plt.pcolormesh(l, b, n.T,
cmap=plt.cm.viridis,
norm=LogNorm())
figrc.hide_axis('top right'.split())
plt.xlim(l.min(), l.max())
plt.ylim(b.min(), b.max());
M31 cone-search
In [36]:
adql = QueryStr("""
select
count(*) as n,
round(ra, 2) as latitude,
round(dec, 2) as longitude
from
"I/337/gaia"
where
contains(point('ICRS', ra, dec),
circle('ICRS',10.6847083,41.26875,2) )=1
group by latitude, longitude
order by latitude, longitude
""")
data = timeit(vizier.query)(adql)
lat = [float(k) for k in data['latitude']]
lon = [float(k) for k in data['longitude']]
n = [int(k) for k in data['n']]
plt.figure(figsize=(6,6))
plt.subplot(111, aspect=1)
plt.scatter(lat, lon, c=n,
edgecolor='None', s=6, rasterized=True, norm=LogNorm(),
cmap=plt.cm.magma, marker='s'
)
plt.xlim(min(lat), max(lat))
plt.ylim(min(lon), max(lon))
plt.axis('off');
M33 cone search
In [37]:
adql = QueryStr("""
select
count(*) as n,
round(ra, 2) as latitude,
round(dec, 2) as longitude
from
gaiadr1.gaia_source
where
contains(point('ICRS', ra, dec),
circle('ICRS',23.4621,30.6599417,0.5) )=1
group by latitude, longitude
order by latitude, longitude
""")
data = timeit(gaia.query)(adql)
lat = [float(k) for k in data['latitude']]
lon = [float(k) for k in data['longitude']]
n = [int(k) for k in data['n']]
plt.figure(figsize=(6,6))
plt.subplot(111, aspect=1)
plt.scatter(lat, lon, c=n,
edgecolor='None', s=9, rasterized=True, norm=LogNorm(),
cmap=plt.cm.magma, marker='s'
)
plt.xlim(min(lat), max(lat))
plt.ylim(min(lon), max(lon))
plt.axis('off');
TGAS CMD
In [38]:
adql = QueryStr("""
select
count(*) as n,
floor((hip."B-V") / 0.01) * 0.01 as color,
floor((gaia.phot_g_mean_mag + 5*log10(gaia.parallax)-10) / 0.05) * 0.05 as mag
from
"I/337/tgas" as gaia
inner join
"I/311/hip2" as hip
on gaia.hip = hip.HIP
where
gaia.parallax/gaia.parallax_error >= 5
and hip."e_B-V" > 0.0 and hip."e_B-V" <= 0.05
and hip.Plx/hip.e_Plx >= 5
and (2.5/log(10))*(gaia.phot_g_mean_flux_error/gaia.phot_g_mean_flux) <= 0.05
group by color, mag
""")
data = timeit(vizier.query)(adql)
color = [float(k) for k in data['color']]
mag = [float(k) for k in data['mag']]
n = [int(k) for k in data['n']]
plt.scatter(color, mag, c=n,
edgecolor='None', s=2, rasterized=True, norm=LogNorm(),
cmap=plt.cm.magma, marker='s'
)
plt.xlim(min(color), max(color))
plt.ylim(max(mag), min(mag))
plt.xlabel('B-V (Hipparcos)')
plt.ylabel(r'G + 5 log($\varpi$) - 10')
figrc.hide_axis('top right'.split())
Based on Watkins et al 2016.
Paper Abstract They perform a systematic search for Galactic globular cluster (GC) stars in the Tycho-Gaia Astrometric Solution (TGAS) catalog that formed part of Gaia Data Release 1 (DR1), and identify 5 members of NGC 104 (47 Tucanae), 1 member of NGC 5272 (M 3), 5 members of NGC 6121 (M 4), 7 members of NGC 6397, and 2 members of NGC 6656 (M 22).
By taking a weighted average of the member stars, fully accounting for the correlations between parameter estimates, they estimate the parallax (and, hence, distance) and proper motion (PM) of the GCs. This provides a homogeneous PM study of multiple GCs based on an astrometric catalogue with small and well-controlled systematic errors, and yields random PM errors that are similar to existing measurements. Detailed comparison to the available Hubble Space Telescope (HST) measurements generally shows excellent agreement, validating the astrometric quality of both TGAS and HST.
By contrast, comparison to ground-based measurements shows that some of those must have systematic errors exceeding the random errors. Our parallax estimates have uncertainties an order of magnitude larger than previous studies, but nevertheless imply distances consistent with previous estimates. By combining our PM measurements with literature positions, distances and radial velocities, we measure Galactocentric space motions for the clusters and find that these are also in good agreement with previous orbital analyses.
Our results highlight the future promise of Gaia for the determining accurate distances and PMs of Galactic GCs, which will provide crucial constraints on the near end of the cosmic distance ladder, and provide accurate GC orbital histories.
method For each cluster, they calculate the likelihood of each nearby stars of being a cluster member by decomposing this likelihood into a positional part $L_{\alpha\delta,i}$ and a motion part $L_{\varpi\mu,i}$.
On the one hand, stars close to the cluster centre of the cluster are more likely to be members than stars near to the $2\,R_{tidal}$ boundary, so we also calculate the likelihood of a star $i$ with coordinates $(\alpha_i, \delta_i)$ being a member of the GC with centre $(\alpha_{GC}, \delta_{GC})$ as,
\begin{eqnarray} L_{\alpha\delta,i} &=& p(\alpha_i, \delta_i | \alpha_{GC}, \delta_{GC}, \sigma) &=& \exp\left[-\frac{1}{2\sigma^2} \left((\alpha_i - \alpha_{GC})^2 + (\delta_i - \delta_{GC})^2\right) \right]. \end{eqnarray}where they use $\sigma = \frac{1}{2} R_{tidal}$ to account for the approximate extent of the cluster. The uncertainties on the cluster centre coordinates and on the positions of the stars are negligible compared to the extent of the cluster, so we neglect the measurement errors in this calculation.
On the other hand, for a star $i$ with parallax and PM measurements $m_i = (\varpi, \mu_\alpha, \mu_\delta)$, and covariance $C_i$, they ask what is the likelihood $L_{\varpi\mu,i}$ that this star is a member of a GC with measurements $m_{GC}$ and covariance $C_{GC}$,
\begin{eqnarray} L_{\varpi\mu,i} &=& p(m_i | C_i, m_{GC}, C_{GC})\\ &=& \frac{1}{ \left[(2\pi)^3 |\det(C_{i} + C_{GC})|\right]^{1/2}}\,\exp\left[-\frac{1}{2} (m_i - m_{GC})^T \cdot (C_{i} + C_{GC})^{-1} \cdot (m_i - m_{GC}) \right]. \end{eqnarray}The above is a standard 3-dimensional Gaussian.
To construct the GC covariance matrix $C_{GC}$, they assume that the errors are uncorrelated, so the diagonal terms are the squared uncertainties on the parallax and PM measurements and the off-diagonal elements are zero. They further add the GC velocity dispersion (H96) in quadrature to the PM terms to account for the expected spread in velocities. \begin{eqnarray} C_{GC} = \left[\begin{matrix} \sigma^2_\varpi & 0 & 0\\ 0 & \mu^2_\alpha + \sigma^2_v & 0 \\ 0 & 0 & \mu^2_\delta + \sigma^2_v \end{matrix}\right] \end{eqnarray}
additionally, (they do not explain but worth mentioning) the covariance matrix for the stars $C_i$ is given by \begin{eqnarray} C_{i} = \left[\begin{matrix} \sigma^2_\varpi & \rho_{\varpi,\mu\alpha}\sigma_\varpi\sigma_{\mu\alpha} & \rho_{\varpi,\mu\delta}\sigma_\varpi\sigma_{\mu\delta}\\ \rho_{\varpi,\mu\alpha}\sigma_\varpi\sigma_{\mu\alpha} & \sigma^2_{\mu,\alpha} & \rho_{\mu\alpha,\mu\delta}\sigma_{\mu,\alpha}\sigma_{\mu,\delta} \\ \rho_{\varpi,\mu\delta}\sigma_\varpi\sigma_{\mu\delta} & \rho_{\mu\alpha,\mu\delta}\sigma_{\mu,\alpha}\sigma_{\mu,\delta} & \sigma^2_{\mu,\delta} \end{matrix}\right] \end{eqnarray}
Finally, they compute the full likelihood as the product of the two pieces, $L_i = L_{\varpi\mu,i} \cdot L_{\alpha\delta,i}$ and they keep all stars with $\ln L_i > −11$ as possible cluster members.
| Cluster | $\alpha$ | $\delta$ | $c$ | $R_{\rm core}$ | $R_{\rm tidal}$ | $D$ | $\mu_\alpha$ | $\mu_\delta$ | $E$(B-V) | [Fe/H] | $v_{\rm r}$ | $\sigma_{v}$ |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| (deg) | (deg) | (arcmin) | (arcmin) | (kpc) | (mas/yr) | (mas/yr) | (dex) | (km/s) | (km/s) | |||
| NGC 104 | 6.02 | -72.08 | 2.07 | 0.360 | 42.296 | 4.5 | 5.63 $\pm$ 0.21 | -2.73 $\pm$ 0.29 | 0.04 | -0.72 | -18.0 $\pm$ 0.1 | 11.0 $\pm$ 0.3 |
| NGC 288 | 13.19 | -26.58 | 0.99 | 1.350 | 13.193 | 8.9 | 4.67 $\pm$ 0.22 | -5.60 $\pm$ 0.35 | 0.03 | -1.32 | -45.4 $\pm$ 0.2 | 2.9 $\pm$ 0.3 |
| NGC 3201 | 154.40 | -46.41 | 1.29 | 1.300 | 25.348 | 4.9 | 5.28 $\pm$ 0.32 | -0.98 $\pm$ 0.33 | 0.24 | -1.59 | 494.0 $\pm$ 0.2 | 5.0 $\pm$ 0.2 |
| NGC 4372 | 186.44 | -72.66 | 1.30 | 1.750 | 34.917 | 5.8 | -6.49 $\pm$ 0.33 | 3.71 $\pm$ 0.32 | 0.39 | -2.17 | 72.3 $\pm$ 1.2 | $\dots$ |
| NGC 4590 | 189.87 | -26.74 | 1.41 | 0.580 | 14.908 | 10.3 | -3.76 $\pm$ 0.66 | 1.79 $\pm$ 0.62 | 0.05 | -2.23 | -94.7 $\pm$ 0.2 | 2.5 $\pm$ 0.4 |
| NGC 4833 | 194.89 | -70.88 | 1.25 | 1.000 | 17.783 | 6.6 | -8.11 $\pm$ 0.35 | -0.96 $\pm$ 0.34 | 0.32 | -1.85 | 200.2 $\pm$ 1.2 | $\dots$ |
A bit of code
First, one can surely code the position likelihood in ADQL, which will also allow us to filter already stars that are too far to be member candidates.
Second, one can potentially implement the other likelihood, but it requires to code the dot-product and matrix inversion manually. This is feasible as we only manipulate 3x3 matrices. But let just not complicate our task, we will still download some data afterall. We can however compute as much as possible on the server side.
The query below shows how to implement for instance the computation of the covariance matrices and its determinant on top of the rest.
In [27]:
from tap import (GaiaArchive, QueryStr, timeit)
def get_tgas_stars(center_ra, center_dec, Rtidal, parallax, parallaxerr,
mualpha, mualphaerr, mudelta, mudeltaerr, s_v):
""" (sync)Query the database for a particular position and cluster properties.
Parameters
----------
center_ra: float
RA of the cluster center
center_dec: float
Dec of the cluster center
Rtidal: float
tidal radius of the cluster (in degrees)
parallax: float
mean parallax of the cluster in mas (1 mas <-> 1kpc)
parallaxerr: float
uncertainty on the cluster parallax
mualpha: float
mean proper motion of the cluster along RA (in mas/yr)
mualphaerr: float
mean proper motion uncertainty of the cluster along RA (in mas/yr)
mudelta: float
mean proper motion of the cluster along Dec (in mas/yr)
mudeltaerr: float
mean proper motion uncertainty of the cluster along Dec (in mas/yr)
s_v: float
internal velocity dispersion
Returns
-------
data: Table
entries from the query
"""
adql = QueryStr("""
select *,
q.a * (q.d * q.f - q.e * q.e) - q.b * (q.b * q.f - q.c * q.e) + q.c * q.e * (q.b - q.c) as det_pm_cov
from (
select
gaia.source_id, gaia.ra, gaia.dec, gaia.parallax, gaia.pmra, gaia.pmdec,
gaia.phot_g_mean_mag as G_mag, tycho2.bt_mag, tycho2.vt_mag, parallax_error,
pmra_error, pmdec_error, pmra_pmdec_corr, parallax_pmra_corr, parallax_pmdec_corr,
(-0.5 / power({Rtidal:f},2) * (power({center_ra:+f} - gaia.ra , 2) + power({center_dec:+f}-gaia.dec, 2))) as lnl_alpha_delta,
(-0.5 / power({Rtidal:f},2) * distance(point('icrs', gaia.ra, gaia.dec), point('icrs', {center_ra:+f}, {center_dec:+f}))) as lnl_alpha_delta2,
power({s_gcparallax:f}, 2) + power(gaia.parallax_error, 2) as a,
gaia.parallax_pmra_corr * gaia.parallax_error * gaia.pmra_error as b,
gaia.parallax_pmdec_corr * gaia.parallax_error * gaia.pmdec_error as c,
power({s_gcmualpha:f},2) + power({s_gcv:f}, 2) + power(gaia.pmra_error, 2) as d,
gaia.pmra_pmdec_corr * gaia.pmra_error * gaia.pmdec_error as e,
power({s_gcmudelta:f},2) + power({s_gcv:f}, 2) + power(gaia.pmdec_error, 2) as f,
(gaia.parallax - {gc_parallax:f}) as delta_parallax,
(gaia.pmra + (-1) * {gc_pmra:f}) as delta_pmalpha,
(gaia.pmdec + (-1) * {gc_pmdec:f}) as delta_pmdec
from
gaiadr1.tgas_source as gaia
inner join
public.tycho2 as tycho2
on gaia.tycho2_id = tycho2.id
where
contains(point('ICRS', gaia.ra, gaia.dec),
circle('ICRS',{center_ra:f}, {center_dec:f}, {size:f}) )=1
) as q
""".format(center_ra=center_ra, center_dec=center_dec, Rtidal=Rtidal, size=3 * Rtidal,
gc_parallax=parallax, gc_pmra=mualpha, gc_pmdec=mudelta,
s_gcparallax=parallaxerr, s_gcmualpha=mualphaerr, s_gcmudelta=mudeltaerr, s_gcv=s_v
))
gaia = GaiaArchive()
return timeit(gaia.query)(adql)
def get_gaia_density(center_ra, center_dec, size):
""" Query the database and produce a density map from the full DR1 catalog """
adql = QueryStr("""
select
count(*) as n,
round(ra, 2) as latitude,
round(dec, 2) as longitude
from
gaiadr1.gaia_source as gaia
where
contains(point('ICRS', gaia.ra, gaia.dec),
circle('ICRS',{0:f}, {1:f}, {2:f}) )=1
group by latitude, longitude
order by latitude, longitude
""".format(center_ra, center_dec, size))
gaia = GaiaArchive()
return timeit(gaia.query)(adql)
Below I code the second likelihood in python for convenience.
In [28]:
def add_lnl_mu(recs):
""" Adding the motion likelihood and final likelihood values """
lnl_mu = np.zeros(len(recs), dtype=float)
for k, data in enumerate(recs):
a = data['a']
b = data['b']
c = data['c']
d = data['d']
e = data['e']
f = data['f']
cov = np.array(((a, b, c),
(b, d, e),
(c, e, f)))
invcov = np.linalg.inv(cov)
det_cov = np.linalg.det(2 * np.pi * invcov)
m = np.array((data['delta_parallax'], data['delta_pmalpha'], data['delta_pmdec']))
lnl_mu[k] = - 0.5 * np.log(abs(det_cov)) - 0.5 * m.T @ (invcov @ m)
from astropy.table import Column
if 'lnl_mu' in ngc104.keys():
recs.remove_column('lnl_mu')
recs.remove_column('lnl')
recs.remove_column('lnl2')
recs.add_column(Column(lnl_mu, name='lnl_mu'))
recs.add_column(Column(recs['lnl_alpha_delta'] + lnl_mu, name='lnl'))
recs.add_column(Column(recs['lnl_alpha_delta2'] + lnl_mu, name='lnl2'))
return recs[np.argsort(recs['lnl'][::-1])]
We make also some figures
In [29]:
def fig_plot(data, ngc104, **kwargs):
""" Plot some figures """
from matplotlib.colors import LogNorm
lat = [float(k) for k in data['latitude']]
lon = [float(k) for k in data['longitude']]
n = [int(k) for k in data['n']]
members = kwargs.pop("members", None)
plt.figure(figsize=(10,4))
plt.subplot(121)
plt.scatter(lat, lon, c=n,
edgecolor='None', s=12, rasterized=True, norm=LogNorm(),
cmap=plt.cm.magma, marker='s', alpha=0.4
)
plt.plot(ngc104['ra'], ngc104['dec'], 'o', mfc='b', mec='0.8', mew=2)
if members is not None:
plt.plot(members['ra'], members['dec'], 'o', mfc='r', mec='0.8', mew=2)
plt.xlim(min(lat), max(lat))
plt.ylim(min(lon), max(lon))
plt.axis('off');
plt.subplot(122)
plt.plot(ngc104['bt_mag']-ngc104['vt_mag'], ngc104['vt_mag'], 'o')
if members is not None:
plt.plot(members['bt_mag']-members['vt_mag'], members['vt_mag'], 'ro')
plt.ylim(plt.ylim()[::-1])
plt.xlabel('B$_T$ - V$_T$')
plt.ylabel('V$_T$')
figrc.hide_axis('top right'.split())
plt.tight_layout()
plt.figure(figsize=(10, 5))
ax = plt.subplot(221)
plt.plot(ngc104['pmra'], ngc104['parallax'], 'o', **kwargs)
if members is not None:
plt.plot(members['pmra'], members['parallax'], 'ro', **kwargs)
plt.ylabel(r'$\varpi$ [mas]')
figrc.hide_axis('top right'.split())
ax = plt.subplot(223, sharex=ax)
plt.plot(ngc104['pmra'], ngc104['pmdec'], 'o', **kwargs)
if members is not None:
plt.plot(members['pmra'], members['pmdec'], 'ro', **kwargs)
plt.xlabel(r'$\mu_\alpha$ [mas/yr]')
plt.ylabel(r'$\mu_\delta$ [mas/yr]')
figrc.hide_axis('top right'.split())
ax = plt.subplot(224, sharey=ax)
plt.plot(ngc104['parallax'], ngc104['pmdec'], 'o', **kwargs)
if members is not None:
plt.plot(members['parallax'], members['pmdec'], 'ro', **kwargs)
plt.xlabel(r'$\varpi$ [mas]')
figrc.hide_axis('top right'.split())
plt.tight_layout(h_pad=0, w_pad=0)
Let's look at NGC 104, a.k.a. 47 Tuc
We can use the values from the referenced paper to define our cluster properties and run our query.
In [30]:
# 1 milliarcsec = 1kpc
ra, dec, Rtidal, parallax, parallaxerr, mualpha, mualphaerr, mudelta, mudeltaerr, s_v = (
6.02, -72.08, 0.360, 1. / 4.5, 0, 5.63, 0.21, -2.73, 0.29, 0.0
)
data = get_gaia_density(ra, dec, 3 * Rtidal)
ngc104 = get_tgas_stars(ra, dec, Rtidal, parallax, parallaxerr, mualpha, mualphaerr, mudelta, mudeltaerr, s_v)
We also add the second likelihood and final one. Finally we filter stars to keep those with lnl > 11.
In [31]:
result = add_lnl_mu(ngc104)
fields = ['source_id', 'ra', 'dec', 'parallax', 'lnl_alpha_delta', 'lnl_alpha_delta2', 'lnl_mu', 'lnl', 'lnl2']
members = result[result['lnl'] > -11]
members.sort('lnl')
members[fields][::-1]
Out[31]:
Interestingly, Watkins et al. claim only 5 candidates for this cluster, and we obtain 8. It would be interesting to understand the differences...
They find also stars with source_id:
but do not find
The following figure shows the DR1 density with on top the TGAS stars in blue, and member candidates in red.
In [32]:
fig_plot(data, ngc104, members=members, ms=4, alpha=0.5)
Correcting the distance-based likelihood
In [33]:
members = result[result['lnl2'] > -11]
members.sort('lnl2')
members[fields][::-1]
Out[33]:
In [34]:
fig_plot(data, ngc104, members=members, ms=4, alpha=0.5)
The likelihood values based on spherical distance are significantly different from their definition. However, for this particular cluster we do not change the result by correcting their likelihood.
A little bit of crazy: make it all in ADQL query
Below I added the calculations of the second likelihood directly in ADQL. Why? Actually just for the fun of it...
As you can see, this is (i) tedious for a simple 3x3 matrix only, (ii) not very readable.
Of course it gives the same results directly with lnl and therefore one can filter directly.
But let's be honest, it's faster to download a bit more and finish the calculations locally.
This example is mostly to show that one can implement complex variables and if necessary embed one query into another for more complex calculations.
In [45]:
def get_tgas_stars_full(center_ra, center_dec, Rtidal, parallax, parallaxerr,
mualpha, mualphaerr, mudelta, mudeltaerr, s_v):
""" (sync)Query the database for a particular position and cluster properties.
Parameters
----------
center_ra: float
RA of the cluster center
center_dec: float
Dec of the cluster center
Rtidal: float
tidal radius of the cluster (in degrees)
parallax: float
mean parallax of the cluster in mas (1 mas <-> 1kpc)
parallaxerr: float
uncertainty on the cluster parallax
mualpha: float
mean proper motion of the cluster along RA (in mas/yr)
mualphaerr: float
mean proper motion uncertainty of the cluster along RA (in mas/yr)
mudelta: float
mean proper motion of the cluster along Dec (in mas/yr)
mudeltaerr: float
mean proper motion uncertainty of the cluster along Dec (in mas/yr)
s_v: float
internal velocity dispersion
Returns
-------
data: Table
entries from the query
"""
adql = QueryStr("""
select
r.source_id, r.ra, r.dec, r.parallax, r.pmra, r.pmdec,
r.G_mag, r.bt_mag, r.vt_mag, r.parallax_error,
r.pmra_error, r.pmdec_error, r.pmra_pmdec_corr,
r.parallax_pmra_corr, r.parallax_pmdec_corr,
r.lnl_alpha_delta, r.lnl_alpha_delta2, r.lnl_mu,
(- 0.5 * log(abs(r.det_pm_cov)) + r.lnl_mu + r.lnl_alpha_delta) as lnl,
(- 0.5 * log(abs(r.det_pm_cov)) + r.lnl_mu + r.lnl_alpha_delta2) as lnl2
from (
select *,
q.a * (q.d * q.f - q.e * q.e) - q.b * (q.b * q.f - q.c * q.e) + q.c * q.e * (q.b - q.c) as det_pm_cov,
-0.5 * (delta_parallax*(delta_parallax*(-(q.a*q.d - power(q.b, 2))*(-q.b*(q.e - q.b*q.c/q.a)/(q.a*(q.d - power(q.b, 2)/q.a)) + q.c/q.a)*(q.b*(q.e - q.b*q.c/q.a)/(q.a*(q.d - power(q.b, 2)/q.a)) - q.c/q.a)/(q.a*q.d*q.f - q.a*power(q.e, 2) - power(q.b, 2)*q.f + 2*q.b*q.c*q.e - power(q.c, 2)*q.d) + 1.0/q.a + power(q.b, 2)/(power(q.a, 2)*(q.d - power(q.b, 2)/q.a))) + delta_pmalpha*((q.e - q.b*q.c/q.a)*(q.a*q.d - power(q.b, 2))*(-q.b*(q.e - q.b*q.c/q.a)/(q.a*(q.d - power(q.b, 2)/q.a)) + q.c/q.a)/((q.d - power(q.b, 2)/q.a)*(q.a*q.d*q.f - q.a*power(q.e, 2) - power(q.b, 2)*q.f + 2*q.b*q.c*q.e - power(q.c, 2)*q.d)) - q.b/(q.a*(q.d - power(q.b, 2)/q.a))) - delta_pmdec*(q.a*q.d - power(q.b, 2))*(-q.b*(q.e - q.b*q.c/q.a)/(q.a*(q.d - power(q.b, 2)/q.a)) + q.c/q.a)/(q.a*q.d*q.f - q.a*power(q.e, 2) - power(q.b, 2)*q.f + 2*q.b*q.c*q.e - power(q.c, 2)*q.d)) + delta_pmalpha*(delta_parallax*(-(q.e - q.b*q.c/q.a)*(q.a*q.d - power(q.b, 2))*(q.b*(q.e - q.b*q.c/q.a)/(q.a*(q.d - power(q.b, 2)/q.a)) - q.c/q.a)/((q.d - power(q.b, 2)/q.a)*(q.a*q.d*q.f - q.a*power(q.e, 2) - power(q.b, 2)*q.f + 2*q.b*q.c*q.e - power(q.c, 2)*q.d)) - q.b/(q.a*(q.d - power(q.b, 2)/q.a))) + delta_pmalpha*(1.0/(q.d - power(q.b, 2)/q.a) + power(q.e - q.b*q.c/q.a, 2)*(q.a*q.d - power(q.b, 2))/(power(q.d - power(q.b, 2)/q.a, 2)*(q.a*q.d*q.f - q.a*power(q.e, 2) - power(q.b, 2)*q.f + 2*q.b*q.c*q.e - power(q.c, 2)*q.d))) - delta_pmdec*(q.e - q.b*q.c/q.a)*(q.a*q.d - power(q.b, 2))/((q.d - power(q.b, 2)/q.a)*(q.a*q.d*q.f - q.a*power(q.e, 2) - power(q.b, 2)*q.f + 2*q.b*q.c*q.e - power(q.c, 2)*q.d))) + delta_pmdec*(delta_parallax*(q.a*q.d - power(q.b, 2))*(q.b*(q.e - q.b*q.c/q.a)/(q.a*(q.d - power(q.b, 2)/q.a)) - q.c/q.a)/(q.a*q.d*q.f - q.a*power(q.e, 2) - power(q.b, 2)*q.f + 2*q.b*q.c*q.e - power(q.c, 2)*q.d) - delta_pmalpha*(q.e - q.b*q.c/q.a)*(q.a*q.d - power(q.b, 2))/((q.d - power(q.b, 2)/q.a)*(q.a*q.d*q.f - q.a*power(q.e, 2) - power(q.b, 2)*q.f + 2*q.b*q.c*q.e - power(q.c, 2)*q.d)) + delta_pmdec*(q.a*q.d - power(q.b, 2))/(q.a*q.d*q.f - q.a*power(q.e, 2) - power(q.b, 2)*q.f + 2*q.b*q.c*q.e - power(q.c, 2)*q.d))) as lnl_mu
from (
select
gaia.source_id, gaia.ra, gaia.dec, gaia.parallax, gaia.pmra, gaia.pmdec,
gaia.phot_g_mean_mag as G_mag, tycho2.bt_mag, tycho2.vt_mag, parallax_error,
pmra_error, pmdec_error, pmra_pmdec_corr, parallax_pmra_corr, parallax_pmdec_corr,
(-0.5 / power({Rtidal:f},2) * (power({center_ra:+f} - gaia.ra , 2) + power({center_dec:+f}-gaia.dec, 2))) as lnl_alpha_delta,
(-0.5 / power({Rtidal:f},2) * distance(point('icrs', gaia.ra, gaia.dec), point('icrs', {center_ra:+f}, {center_dec:+f}))) as lnl_alpha_delta2,
power({s_gcparallax:f}, 2) + power(gaia.parallax_error, 2) as a,
gaia.parallax_pmra_corr * gaia.parallax_error * gaia.pmra_error as b,
gaia.parallax_pmdec_corr * gaia.parallax_error * gaia.pmdec_error as c,
power({s_gcmualpha:f},2) + power({s_gcv:f}, 2) + power(gaia.pmra_error, 2) as d,
gaia.pmra_pmdec_corr * gaia.pmra_error * gaia.pmdec_error as e,
power({s_gcmudelta:f},2) + power({s_gcv:f}, 2) + power(gaia.pmdec_error, 2) as f,
(gaia.parallax - {gc_parallax:f}) as delta_parallax,
(gaia.pmra + (-1) * {gc_pmra:f}) as delta_pmalpha,
(gaia.pmdec + (-1) * {gc_pmdec:f}) as delta_pmdec
from
gaiadr1.tgas_source as gaia
left outer join
public.tycho2 as tycho2
on gaia.tycho2_id = tycho2.id
where
contains(point('ICRS', gaia.ra, gaia.dec),
circle('ICRS',{center_ra:f}, {center_dec:f}, {size:f}) )=1
) as q
) as r
""".format(center_ra=center_ra, center_dec=center_dec, Rtidal=Rtidal, size=3 * Rtidal,
gc_parallax=parallax, gc_pmra=mualpha, gc_pmdec=mudelta,
s_gcparallax=parallaxerr, s_gcmualpha=mualphaerr, s_gcmudelta=mudeltaerr, s_gcv=s_v
))
gaia = GaiaArchive()
return timeit(gaia.query)(adql)
In [46]:
# 1 milliarcsec = 1kpc
ra, dec, Rtidal, parallax, parallaxerr, mualpha, mualphaerr, mudelta, mudeltaerr, s_v = (
6.02, -72.08, 0.360, 1. / 4.5, 0, 5.63, 0.21, -2.73, 0.29, 0.0
)
ngc104 = get_tgas_stars_full(ra, dec, Rtidal, parallax, parallaxerr,
mualpha, mualphaerr, mudelta, mudeltaerr,
s_v)
In [47]:
fields = ['source_id', 'ra', 'dec', 'parallax', 'lnl_alpha_delta', 'lnl_alpha_delta2', 'lnl_mu', 'lnl', 'lnl2']
members = result[result['lnl'] > -11]
members.sort('lnl')
members[fields][::-1]
Out[47]:
In [48]:
fig_plot(data, ngc104, members=members, ms=4, alpha=0.5)
Some comment for the authors of Watkins et al.
In this example, I show how to get a quick extinction map by applying the RJCE (Rayley Jeans Color Excess) equation to the DR1 data.
In [11]:
q = gaia.query_async("""
SELECT
gaia.source_id,
gaia.ra, gaia.dec, gaia.l, gaia.b,
0.918 * (tmass.h_m - allwise.w2mpro - 0.08) as Ak_RJCE
FROM
gaiadr1.tgas_source as gaia
LEFT OUTER JOIN
gaiadr1.allwise_best_neighbour AS allwisexmatch
ON gaia.source_id = allwisexmatch.source_id
LEFT OUTER JOIN
gaiadr1.allwise_original_valid AS allwise
ON allwisexmatch.allwise_oid = allwise.allwise_oid
LEFT OUTER JOIN
gaiadr1.tmass_best_neighbour AS tmassxmatch
ON gaia.source_id = tmassxmatch.source_id
LEFT OUTER JOIN
gaiadr1.tmass_original_valid AS tmass
ON tmassxmatch.tmass_oid = tmass.tmass_oid
""")
q
Out[11]:
In [ ]:
q.get()
In [ ]: