100
The Fifth Catalogue of Nearby Stars: Continuously
Updated Version (CNS5-updated)
The Fifth Catalogue of Nearby Stars (CNS5) aims to provide the most
volume-complete sample of stars in the solar neighbourhood. This is
a continuously-updated version of the published CNS5
(:bibcode:`2023A&A...670A..19G`).
The CNS5 is compiled based on trigonometric parallaxes from Gaia DR3 and
Hipparcos, and supplemented with astrometric data from Spitzer and
ground-based surveys carried out in the infrared. The CNS5 catalogue is
statistically complete down to 19.7 mag in G-band and 11.8 mag in W1-band
absolute magnitudes, corresponding to a spectral type of L8.
Continuous updates of observational data for nearby stars from all sources
were collected and evaluated. For all known stars in the 25 pc sphere
around the Sun, the best values of positions in space, velocities, and
magnitudes in different filters are presented.
\internallink{cns5/q/cone/form}
RV data has been updated with RVs from Gaia DR3.
Please note that 138 stars with Hipparcos astrometry are missing
photometric data from Gaia DR3, 2MASS and WISE. These data will be added in
the near future.
The primary_flag, gj_system_primary information has been corrected for 28
objects. cns5_id of the affected objects:
1450, 1451, 1456, 1457, 2807, 2808, 2946, 3129, 3130, 4218, 4222, 4244, 4247,
4270, 4271, 4272, 4332, 4333, 5044, 5110, 5111, 5281, 5282, 5459, 5506, 5655,
5656, 5658.
Position ICRS SPHER3 "ra" "dec" "parallax"
Velocity "pmra" "pmdec" "rv"
for k,v in vars.items():
if v!=v:
vars[k] = None
elif isinstance(v, str) and v.strip()=="":
vars[k] = None
CNS5 Update Upload Facility
Use this service to upload new versions
of CNS5 as FITS binary tables. Since this will also trigger an
import of the data, please have a bit of patience. This will
only store one file per day CET, which means that you can overwrite
your uploads as long as you re-upload on the same day.
destPath = rd.getAbsPath(
datetime.date.today().strftime("data/cns-%Y-%m-%d.fits"))
with open(destPath, "wb") as f:
f.write(inputTable.args["upload"][1].read())
latest = rd.getAbsPath("data/cns-latest.fits")
os.unlink(latest)
os.symlink(destPath, latest)
with base.getWritableAdminConn() as conn:
rsc.makeData(rd.getById("import"), connection=conn)
limits.updateForRD(rd, conn)
with base.getTableConn() as conn:
res = conn.queryToDicts("SELECT column_name, min_value, median,"
" max_value, fill_factor from dc.simple_col_stats"
" where tablename='cns5update.main'")
return rsc.TableForDef(self.outputTable, rows=list(res))
CNS5 update
res/response.template
res/response.template
312.27882
37.4712279
0.001
cone/scs.xml
row = self.getFirstVOTableRow()
self.assertEqual(row['coordinates_bibcode'], '2020yCat.1350....0G')
self.assertEqual(row['w2_mag_error'], None)
self.assertAlmostEqual(row['g_mag'], 13.035794258117676)
/tap/sync
row = self.getFirstVOTableRow()
self.assertEqual(row["n_components"], 2)
self.assertEqual(row['nexa_id'], '')
self.assertEqual(row['component_id'], 'A')
self.assertEqual(row['coordinates_bibcode'], '2020yCat.1350....0G')