Resolving names
List of arguments
The list of arguments for resolving names can be found at https://fink-portal.org/api/v1/resolver
Naming objects is a complex endeavor, often resulting in multiple names or designations for the same object. In the era of big data, this challenge becomes even more pronounced, as the need to quickly assign names to millions of objects can lead to non-intuitive designation processes.
Instead of proposing a new naming scheme, we aim to provide a service that allows users to explore existing names for a given object. Currently, you can resolve ZTF object names within the SIMBAD database, the Transient Name Server, and Solar System databases recognized by the Quaero service from SSODNET. Additionally, this service allows you to determine if a corresponding ZTF object exists for any object found in these three databases.
We are committed to expanding this service and will continue to add new sources of information.
TNS to ZTF
I have a TNS identifier, are there ZTF objects corresponding?
import io
import requests
import pandas as pd
r = requests.post(
'https://fink-portal.org/api/v1/resolver',
json={
'resolver': 'tns',
'name': 'SN 2023vwy'
}
)
# Format output in a DataFrame
pdf = pd.read_json(io.BytesIO(r.content)) # (1)!
- Output:
Downloading the full TNS table
You can also download the full TNS table by specifying an empty name:
import io
import requests
import pandas as pd
r = requests.post(
'https://fink-portal.org/api/v1/resolver',
json={
'resolver': 'tns',
'name': '',
'nmax': 1000000
}
)
pdf = pd.read_json(io.BytesIO(r.content)) # (1)!
- Output:
d:declination d:fullname d:internalname d:ra d:redshift d:type key:time 0 -55.409677 AT 1976S GSC2.3 SATX032616 286.245594 NaN nan 1704445383818 1 -25.480611 AT 1978M GSC2.3 S64P036598 122.320327 NaN nan 1704445383818 2 47.530987 AT 1991bm GSC2.3 N0ZY037003 273.810015 NaN nan 1704445388697 3 14.414089 AT 1992bw GSC2.3 NB6I007107 1.510637 NaN nan 1704445388697 4 25.566352 AT 1993an GSC2.3 NBIP012777 10.505964 NaN nan 1704445383818 ... ... ... ... ... ... ... ... 174736 40.9886174 SN 2023zzk ATLAS23xnr 54.7199172 0.020127 SN II 1704445385595 174737 40.9886174 SN 2023zzk ZTF23abtycgb 54.7199172 0.020127 SN II 1704445385595 174738 -42.6586722222 SN 2023zzn ATLAS23xjb 46.8617011111 0.094 SLSN-I 1704445385595 174739 -24.7868458611 SN 2024fa ATLAS24adw 36.255712664 0.01 SN II 1704445385595 174740 -24.7868458611 SN 2024fa PS24ca 36.255712664 0.01 SN II 1704445385595 [174741 rows x 7 columns]
ZTF to TNS
I have a ZTF object name, are there counterparts in TNS?
import io
import requests
import pandas as pd
r = requests.post(
'https://fink-portal.org/api/v1/resolver',
json={
'resolver': 'tns',
'reverse': True,
'name': 'ZTF23abmwsrw'
}
)
# Format output in a DataFrame
pdf = pd.read_json(io.BytesIO(r.content)) # (1)!
- Output:
SIMBAD to ZTF
I have an astronomical object name referenced in SIMBAD, are there counterparts in ZTF? As these objects can be extended, we typically provide coordinates, and then you need to run a conesearch:
import io
import requests
import pandas as pd
r = requests.post(
'https://fink-portal.org/api/v1/resolver',
json={
'resolver': 'simbad',
'name': 'Markarian 2'
}
)
if r.json() != []:
print('Object found!')
print(r.json())
print()
r = requests.post(
'https://fink-portal.org/api/v1/explorer',
json={
'ra': r.json()[0]['jradeg'],
'dec': r.json()[0]['jdedeg'],
'radius': 60
}
)
# Format output in a DataFrame
pdf = pd.read_json(io.BytesIO(r.content))
print('Object(s) in ZTF: {}'.format(pdf['i:objectId'].to_numpy()))
else:
print('No objects found')
Leading in this example to:
Object found!
[
{
'name': 'Si=Simbad, all IDs (via url)',
'oid': 1579005,
'oname': 'Mrk 2',
'otype': 'GiG',
'jpos': '01:54:53.80 +36:55:04.6',
'jradeg': 28.7241958,
'jdedeg': 36.9179556,
'refPos': '2006AJ....131.1163S',
'z': None,
'MType': 'SBa',
'nrefs': 138
}
]
Object(s) in ZTF: ['ZTF18aabfjoi']
ZTF to SIMBAD
I have a ZTF object name, are there counterparts in SIMBAD?
import io
import requests
import pandas as pd
r = requests.post(
'https://fink-portal.org/api/v1/resolver',
json={
'resolver': 'simbad',
'reverse': True,
'name': 'ZTF18aabfjoi'
}
)
pdf = pd.read_json(io.BytesIO(r.content)) # (1)!
- Output:
d:cdsxmatch i:candid i:dec i:jd i:objectId i:ra 0 GinGroup 1045306711115010048 36.917909 2.458800e+06 ZTF18aabfjoi 28.724092 1 GinGroup 1400219061115010048 36.917912 2.459155e+06 ZTF18aabfjoi 28.724130 2 GinGroup 1626461821115010048 36.917924 2.459381e+06 ZTF18aabfjoi 28.724110 3 GinGroup 1977476361115010048 36.917913 2.459732e+06 ZTF18aabfjoi 28.724100 4 GinGroup 2119325081115010048 36.917892 2.459874e+06 ZTF18aabfjoi 28.724094
5 different alerts from the same object (ZTF18aabfjoi).
SSO to ZTF
I have a SSO name or number, are there ZTF objects corresponding?
You first need to resolve the corresponding ZTF ssnamenr:
import io
import requests
import pandas as pd
r = requests.post(
'https://fink-portal.org/api/v1/resolver',
json={
'resolver': 'ssodnet',
'name': '624188'
}
)
pdf = pd.read_json(io.BytesIO(r.content)) # (1)!
- Output:
and then search for corresponding alerts for all ssnamenr:
import io
import requests
import pandas as pd
r = requests.post(
'https://fink-portal.org/api/v1/sso',
json={
'n_or_d': ','.join(pdf['i:ssnamenr'].to_numpy()),
'columns': 'i:objectId,i:ra,i:dec,i:magpsf,i:sigmapsf,i:ssnamenr'
}
)
sso = pd.read_json(io.BytesIO(r.content)) # (1)!
- Output:
i:dec i:magpsf i:objectId i:ra i:sigmapsf i:ssnamenr 0 38.761579 19.651100 ZTF19acrplsa 84.760898 0.201959 2002MA06 1 35.097359 19.678823 ZTF19adagirr 74.131671 0.154512 2002MA06 2 32.709072 20.320038 ZTF23aazubcu 38.761726 0.181345 2002MA6 3 36.039245 20.226397 ZTF23abcakzn 42.951283 0.208451 2002MA6 4 37.399041 20.620817 ZTF23abecthp 44.314170 0.181139 2002MA6 5 37.412476 19.931238 ZTF23abeeoen 44.324915 0.122155 2002MA6 6 38.092562 19.839323 ZTF23abfvtyr 44.875277 0.110749 2002MA6 7 38.102591 20.627150 ZTF23abfxptv 44.881451 0.182423 2002MA6 8 39.028132 19.811459 ZTF23abhahgo 45.412434 0.116035 2002MA6 9 40.087669 19.518000 ZTF23abidfxt 45.472807 0.134570 624188 10 40.253863 20.037786 ZTF23abilvaz 45.374426 0.172761 624188 11 40.263377 19.517801 ZTF23abiotst 45.365776 0.116226 624188 12 40.717564 19.452196 ZTF23abkempy 44.508594 0.087599 624188 13 40.718352 19.871597 ZTF23abkfquu 44.504111 0.144978 624188 14 40.760788 19.485823 ZTF23abkwjee 44.211183 0.093203 624188 15 40.777337 19.365993 ZTF23ablsjlg 43.858879 0.078582 624188 16 40.777324 19.830032 ZTF23ablthyz 43.850151 0.109873 624188 17 40.046433 19.359879 ZTF23abogmeh 40.809196 0.127990 624188 18 39.832539 19.788800 ZTF23aboqpks 40.349063 0.139298 624188 19 39.153497 19.270357 ZTF23abpqqiz 39.155381 0.140963 624188 20 39.134613 19.803095 ZTF23abpsedk 39.124796 0.165054 624188 21 38.846470 19.866776 ZTF23abqbtrv 38.709943 0.139531 624188 22 38.826889 19.179682 ZTF23abqdzil 38.680996 0.095241 624188 23 38.508815 19.166033 ZTF23abqphse 38.268334 0.072787 624188 24 33.059517 19.892330 ZTF23absmjqg 35.439591 0.186579 624188 25 32.477462 19.840815 ZTF23abtpaxv 35.544006 0.173815 624188 26 32.294998 19.863436 ZTF23abtxgba 35.595829 0.105220 624188 27 32.292988 19.976093 ZTF23abtxjux 35.596339 0.081324 624188 28 32.094647 20.584602 ZTF23abuivpe 35.662240 0.160992 624188 29 31.925860 20.086443 ZTF23abuppio 35.728953 0.138072 624188 30 31.916201 20.444067 ZTF23abuqdev 35.732460 0.172873 624188
Exact/inexact searches
By default, the SSO resolver will perform an inexact search, meaning it will return the exact search plus closest matches. The number of closest matches is controlled by the parameter nmax, with default nmax=10:
import io
import requests
import pandas as pd
r = requests.post(
'https://fink-portal.org/api/v1/resolver',
json={
'resolver': 'ssodnet',
'name': '33'
}
)
pdf = pd.read_json(io.BytesIO(r.content)) # (1)!
- Output:
Note that there are duplicates, as data is indexed by name, number, and ssnamenr.
If you want to perform an exact search, just put nmax=1:
import io
import requests
import pandas as pd
r = requests.post(
'https://fink-portal.org/api/v1/resolver',
json={
'resolver': 'ssodnet',
'name': '33',
'nmax': 1
}
)
pdf = pd.read_json(io.BytesIO(r.content)) # (1)!
- Output:
ZTF to SSO
I have a ZTF object name, is there a counterpart in the SsODNet quaero database, and what are all the known aliases to Fink?
import io
import requests
import pandas as pd
r = requests.post(
'https://fink-portal.org/api/v1/resolver',
json={
'resolver': 'ssodnet',
'reverse': True,
'name': 'ZTF23abidfxt'
}
)
if r.json() != []:
name = r.json()[0]['i:ssnamenr']
print('Asteroid counterpart found with designation {}'.format(name))
print()
r2 = requests.post(
'https://fink-portal.org/api/v1/resolver',
json={
'resolver': 'ssodnet',
'name': name,
'nmax': 1
}
)
pdf = pd.read_json(io.BytesIO(r2.content))
print('In the Fink database, {} also corresponds to: '.format(name))
print(pdf)
Leading to: