Tutorial#
We start importing the CaliStar class from the calistar package.
[1]:
from calistar import CaliStar
Next, we create an instance of the CaliStar class by providing Gaia source ID of the target star and the (optional) Gaia data release that should be used. In this example, we will use the star HD 206893. The Gaia DR3 source ID of the star is easily found on Simbad.
[2]:
cal_star = CaliStar(gaia_source=6843672087120107264, gaia_release="DR3")
===============
calistar v0.0.4
===============
[3]:
target_prop = cal_star.target_star(write_json=True, get_gaiaxp=False)
-> Querying GAIA DR3...
INFO: Query finished. [astroquery.utils.tap.core]
GAIA DR3 source ID = 6843672087120107264
Reference epoch = 2016.0
Parallax = 24.53 +/- 0.04 mas
RA = 326.341701 deg +/- 0.0268 mas
Dec = -12.783352 deg +/- 0.0226 mas
Coordinates = +21h45m22.01s -12d47m00.07s
Proper motion RA = 94.11 +/- 0.04 mas/yr
Proper motion Dec = -0.46 +/- 0.03 mas/yr
Radial velocity = -11.80 +/- 0.14 km/s
G mag = 6.585952 +/- 0.002761
BP mag = 6.798026 +/- 0.002837
RP mag = 6.213689 +/- 0.003815
GRVS mag = 6.067284 +/- 0.004563
Effective temperature = 6448 K
Surface gravity = 4.13
Metallicity = -0.82
G-band extinction = 0.00
Astrometric excess noise = 0.17
RUWE = 1.12
Non single star = 0
Single star probability from DSC-Combmod = 0.99
XP continuous = True
XP sampled = True
RVS spectrum = False
-> Querying Simbad...
Simbad ID = HD 206893
Spectral type = F5V
-> Querying VizieR...
2MASS source ID = 21452190-1246599
Separation between Gaia and 2MASS source = 1.4 mas
2MASS J mag = 5.869 +/- 0.023
2MASS H mag = 5.687 +/- 0.034
2MASS Ks mag = 5.593 +/- 0.021
ALLWISE source ID = J214521.98-124659.9
Separation between Gaia and WISE source = 18.9 mas
WISE W1 mag = 5.573 +/- 0.176
WISE W2 mag = 5.452 +/- 0.052
WISE W3 mag = 5.629 +/- 0.015
WISE W4 mag = 5.481 +/- 0.043
-> Querying Washington Double Star catalog...
Target not found in WDS catalog
Storing JSON output: target_dr3_6843672087120107264.json
[4]:
print(target_prop)
{'Gaia ID': 6843672087120107264, 'Gaia release': 'DR3', 'Gaia epoch': 2016.0, 'Gaia RA': (326.3417007918157, 6.28893832779593e-06), 'Gaia Dec': (-12.783352283336647, 6.28893832779593e-06), 'Gaia pm RA': (94.11170797027064, 0.03547043353319168), 'Gaia pm Dec': (-0.4633855782241898, 0.025660403072834015), 'Gaia parallax': (24.527534260182925, 0.03544711321592331), 'GAIA/GAIA3.G': (6.585951805114746, 0.002761077445367911), 'GAIA/GAIA3.Gbp': (6.798026084899902, 0.0028371437114444007), 'GAIA/GAIA3.Grp': (6.21368932723999, 0.0038149464919949023), 'GAIA/GAIA3.Grvs': (6.067283630371094, 0.004563245456665754), 'Simbad ID': 'HD 206893', 'SpT': 'F5V', '2MASS/2MASS.J': (5.86899995803833, 0.023000000044703484), '2MASS/2MASS.H': (5.686999797821045, 0.03400000184774399), '2MASS/2MASS.Ks': (5.5929999351501465, 0.020999999716877937), 'WISE/WISE.W1': (5.572999954223633, 0.17599999904632568), 'WISE/WISE.W2': (5.452000141143799, 0.052000001072883606), 'WISE/WISE.W3': (5.629000186920166, 0.014999999664723873), 'WISE/WISE.W4': (5.480999946594238, 0.0430000014603138)}
[5]:
df = cal_star.find_calib(search_radius=3., g_mag_range=(-1.0, 2.0))
-> Finding calibration stars...
Radius of search cone = 3.0 deg
G mag search range = (5.59, 8.59)
INFO: Query finished. [astroquery.utils.tap.core]
Number of found sources: 53
Storing output: calib_find_dr3_6843672087120107264.csv
[6]:
print(df.head())
Simbad ID Gaia ID SpT Separation \
0 HD 206893 6843672087120107264 F5V 0.0
1 HD 207006 6843703766798867072 G8III 0.293244
2 HD 206942 6843448783180406272 K2/3III 0.566973
3 HD 206878 6843329348729972224 K3III 0.892679
4 HD 207272 6844094574462996608 K5III 0.918348
Astrometric excess noise RUWE Non single star Single star probability \
0 0.174123 NaN NaN NaN
1 0.115425 NaN NaN NaN
2 0.126161 NaN NaN NaN
3 0.127814 NaN NaN NaN
4 0.134155 NaN NaN NaN
GAIA/GAIA3.G 2MASS/2MASS.J ... WISE/WISE.W4 WDS ID WDS epoch 1 \
0 6.585952 5.869 ... 5.481 NaN NaN
1 7.264789 5.799 ... 5.052 NaN NaN
2 8.236968 6.228 ... 5.151 NaN NaN
3 8.125083 6.217 ... 5.239 WDS J21453-1341A 1905
4 8.021875 5.864 ... 4.698 NaN NaN
WDS epoch 2 WDS sep 1 WDS sep 2 WDS PA 1 WDS PA 2 \
0 NaN NaN NaN NaN NaN
1 NaN NaN NaN NaN NaN
2 NaN NaN NaN NaN NaN
3 2015 26.799999237060547 27.299999237060547 226 223
4 NaN NaN NaN NaN NaN
WDS mag 1 WDS mag 2
0 NaN NaN
1 NaN NaN
2 NaN NaN
3 8.739999771118164 11.550000190734863
4 NaN NaN
[5 rows x 25 columns]
[7]:
df = cal_star.select_calib(filter_names=["2MASS/2MASS.Ks", "WISE/WISE.W1"],
mag_diff={"2MASS/2MASS.Ks": 0.3, "WISE/WISE.W1": 0.5})
-> Selecting calibration stars...
Number of selected sources: 7
Storing output: calib_select_dr3_6843672087120107264.csv
[8]:
print(df.head())
Simbad ID Gaia ID SpT Separation \
0 HD 206893 6843672087120107264 F5V 0.000000
3 HD 206878 6843329348729972224 K3III 0.892679
9 HD 207503 6843863127265189760 A1/2III 1.054286
20 HD 205827 6844675872517129728 K1III 1.882230
23 * 45 Cap 6838704699744176768 A7IV/V 1.993162
Astrometric excess noise RUWE Non single star Single star probability \
0 0.174123 NaN NaN NaN
3 0.127814 NaN NaN NaN
9 0.366081 NaN NaN NaN
20 0.136001 NaN NaN NaN
23 0.232292 NaN NaN NaN
GAIA/GAIA3.G 2MASS/2MASS.J ... WISE/WISE.W4 WDS ID \
0 6.585952 5.869 ... 5.481 NaN
3 8.125083 6.217 ... 5.239 WDS J21453-1341A
9 6.279203 5.894 ... 5.798 NaN
20 7.779989 6.160 ... 5.338 NaN
23 5.913185 5.565 ... 5.330 WDS J21440-1445AB
WDS epoch 1 WDS epoch 2 WDS sep 1 WDS sep 2 WDS PA 1 WDS PA 2 \
0 NaN NaN NaN NaN NaN NaN
3 1905.0 2015.0 26.799999 27.299999 226.0 223.0
9 NaN NaN NaN NaN NaN NaN
20 NaN NaN NaN NaN NaN NaN
23 2008.0 2016.0 4.300000 4.200000 229.0 229.0
WDS mag 1 WDS mag 2
0 NaN NaN
3 8.74 11.55
9 NaN NaN
20 NaN NaN
23 5.41 9.50
[5 rows x 25 columns]