There are several astronomy databases that store various information about space objects such as stars and planets. Some (most?) of those databases expose their data via TAP interface, so it can be queried using ADQL.

TAP ADQL sandbox

Here I’ll be telling about how to get data from such sources and what sort of issues/problems one can encounter.

How to query data

Sending requests

Very simplified description for how this works is that you send SQL-like queries as a part of query string to a TAP-endpoint URL of a database/datasource via HTTP POST request (or HTTP GET, if you are fine with cached results).

An example of one such request being sent with cURL:

$ curl -X "POST" "http://voparis-tap-planeto.obspm.fr/tap/sync?REQUEST=doQuery&LANG=ADQL&FORMAT=JSON&QUERY=SELECT%20granule_uid,%20mass,%20radius%20FROM%20exoplanet.epn_core%20WHERE%20star_name%20%3D%20%27Kepler-107%27%20ORDER%20BY%20granule_uid"

Here’s the same request in a more readable form:

POST http://voparis-tap-planeto.obspm.fr/tap/sync
?REQUEST=doQuery
&LANG=ADQL
&FORMAT=JSON
&QUERY=SELECT granule_uid, mass, radius FROM exoplanet.epn_core WHERE star_name = 'Kepler-107' ORDER BY granule_uid

As you can see, the QUERY parameter is indeed a pretty much regular SQL query (but here’s some more information about ADQL specifics):

SELECT granule_uid, mass, radius
FROM exoplanet.epn_core
WHERE star_name = 'Kepler-107'
ORDER BY granule_uid

And as the FORMAT parameter is set to JSON, the response to this request will look like this:

HTTP/1.1 200 OK
Transfer-Encoding: chunked
Date: Sun, 20 Feb 2022 17:31:52 GMT
Content-Type: application/json
Server: TwistedWeb/18.9.0

{
    "data":
    [
        [
            "Kepler-107 b",
            0.01104,
            0.137
        ],
        [
            "Kepler-107 c",
            0.02954,
            0.142
        ],
        [
            "Kepler-107 d",
            0.01196,
            0.077
        ],
        [
            "Kepler-107 e",
            0.02706,
            0.259
        ]
    ],
    "contains": "table",
    "params":
    {
        "contains": "params"
    },
    "columns":
    [
        {
            "dbtype": "text",
            "xtype": null,
            "utype": null,
            "description": "Internal table row index Unique ID in data service, also in v2. Can be alphanumeric.",
            "name": "granule_uid",
            "datatype": "char",
            "ucd": "meta.id",
            "arraysize": "*",
            "ref": null,
            "id": "granule_uid",
            "unit": ""
        },
        {
            "dbtype": "double precision",
            "xtype": null,
            "utype": null,
            "description": "Mass of the planet",
            "name": "mass",
            "datatype": "double",
            "ucd": "phys.mass",
            "arraysize": null,
            "ref": null,
            "id": "mass",
            "unit": "'jupiterMass'"
        },
        {
            "dbtype": "double precision",
            "xtype": null,
            "utype": null,
            "description": "Radius of the planet",
            "name": "radius",
            "datatype": "double",
            "ucd": "phys.size.radius",
            "arraysize": null,
            "ref": null,
            "id": "radius",
            "unit": "'jupiterRad'"
        }
    ]
}

But be aware that not all the services support JSON output. Other possible formats are CSV, XML and VOTable - a semi-binary format (meta-information in XML and Base64-encoded data), which is actually the only format that is required to be supported by every TAP service (all other formats are optional).

Sure enough, I needed an programmatic way to work with these queries, and as I wasn’t too eager to implement sending requests and processing results myself, I started looking for a library that would do that for me, and preferably that would be a Python library, as I’m doing my data analysis in Jupyter notebooks.

PyVO

Almost immediately I discovered The Extrasolar Planets Encyclopaedia API tutorial, which is referring to PyVO library.

It is rather easy to use. First, install it:

$ pip install pyvo

and then you can use it in your Python code:

import pyvo
import pandas
from tabulate import tabulate

service = pyvo.dal.TAPService("http://voparis-tap-planeto.obspm.fr/tap")

tableName = "exoplanet.epn_core"

fields = [
    "star_name",
    "star_teff",
    "granule_uid",
    "mass",
    #"mass_error_min",
    #"mass_error_max",
    "radius"
    #"radius_error_min",
    #"radius_error_max",
    #"semi_major_axis"
]

starName = "Kepler-106"

queryPlanets = " ".join((
    f"SELECT {', '.join(fields)}",
    f"FROM {tableName}",
    f"WHERE star_name = '{starName}'",
    "ORDER BY granule_uid"
))
# check the final query string
#print("Query to execute: ", queryPlanets)

# execute the query
results = service.search(queryPlanets)

print("Total planets found:", len(results))

# fields datatypes can be extracted too
#print(resultPlanets.fielddescs)

The PyVO results can be then printed using tabulate:

# print results as a table
print(
    tabulate(
        resultPlanets.to_table(),
        headers=resultPlanets.fieldnames,
        tablefmt="psql"
    )
)

# +-------------+-------------+---------------+---------+----------+
# | star_name   |   star_teff | granule_uid   |    mass |   radius |
# |-------------+-------------+---------------+---------+----------|
# | Kepler-106  |        5858 | Kepler-106 b  | 0.01668 |    0.073 |
# | Kepler-106  |        5858 | Kepler-106 c  | 0.033   |    0.223 |
# | Kepler-106  |        5858 | Kepler-106 d  | 0.02549 |    0.085 |
# | Kepler-106  |        5858 | Kepler-106 e  | 0.035   |    0.228 |
# +-------------+-------------+---------------+---------+----------+

or, better yet, it can be turned into a Pandas object:

resultPandas = results.to_table().to_pandas("granule_uid")

which can be displayed in a nicer manner, if you are using a Jupyter notebook client that supports display():

with pandas.option_context('display.max_rows', None, 'display.max_columns', None):
    display(resultPandas)
DataSpell, displaying Pandas

This screenshot is taken from DataSpell, but VS Code supports display() too (even though it might be underlined there as unknown).

Finally, results can be saved to a file:

# as a NumPy object
#import numpy
#numpy.save("./results.npy", resultPlanets.to_table())
#tbl = numpy.load("./results.npy")

# or as a pickle (it's a pickle! pickle fiiiiiile!)
resultPandas.to_pickle("./results.pkl")
#tbl = pandas.read_pickle("./results.pkl")

If you haven’t used Pandas before, you absolutely definitely should, as it makes working with table data very convenient.

A sandbox application

Soon enough I discovered that I need a faster way to query data, for when I’d like to quickly check some values.

Opening a Jupyter notebook in Java-based DataSpell or in Electron-based VS Code is too damn slow. All the waiting for the environment to load and for Jupyter server to start, modifying queries in cells and scrolling between results and code, urgh.

Running bare Python scripts from terminal is faster, but then viewing the results isn’t that nice, especially if there are a lot of columns in the resulting table, so it gets wrapped in the terminal window. And again, constantly switching between code editor and terminal window isn’t very convenient.

Having been annoyed enough by the inconveniences of both options, I decided to write a small GUI application for the sole purpose of executing ADQL queries and browsing results in a scrollable table. There surely must be at least several already existing applications for that, but I’ve managed to find only TOPCAT, which in my opinion is quite ugly, clumsy (no offense, that’s just the way Java-based applications usually are) and too overloaded with functionality (but that’s more of a compliment, really). So yeah, I decided to make one of my own.

At first I wanted to create a native Mac OS application using SwiftUI, which I’ve been wanting to try out for a long time. But even though Mac OS is my main environment, I often work on Windows too, and sometimes on GNU/Linux too, so a cross-platform framework would be a better choice.

The next candidate was Qt, but then I remembered that I read about Python binding for Dear ImGui - Dear PyGui - which was a perfect variant, because most of the work would be then taken care of by PyVO, and I’d only need to handle the GUI part. It’s true that I could’ve taken PySide or PyQt and still benefit from relying on PyVO, but I reckoned that ImGui-based application will be more lightweight and responsive, plus I wanted to try something new.

And so here’s what I’ve come up with:

If video doesn’t play in your browser, you can download it here

Very nice indeed! Cross-platform application (powered by GLFW), with almost native performance (but not quite, as I notice Python ears here and there) and not too horrible GUI. Some might even say, beautifully ascetic.

While we are here, I can say that Dear PyGui turned out to be even better than I expected. It is quite an easy and comfortable tooling for creating GUI application. It took me just 3 days to get from the very scratch to the working application that was executing queries and displaying results in a table widget. And most of that time I was working on styling and theme customization, rather than on the actual functionality.

You can install the current application version from PyPI:

$ pip install tap-adql-sandbox
$ tap-adql-sandbox --help

And here’s the project source code.

TAP services with astronomical data

There are several astronomical databases that expose their data via TAP interface. So far I’ve been working with these two:

All of the TAP-services (should) support a certain set of standard functions. For instance, you can query the service availability:

$ curl http://voparis-tap-planeto.obspm.fr/tap/availability

<avl:availability xmlns:avl="http://www.ivoa.net/xml/VOSIAvailability/v1.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.ivoa.net/xml/VOSIAvailability/v1.0 http://vo.ari.uni-heidelberg.de/docs/schemata/VOSIAvailability-v1.0.xsd">
    <avl:available>true</avl:available>
    <avl:upSince>2022-02-05T12:44:11Z</avl:upSince>
</avl:availability>

And get a list of available tables:

$ curl http://voparis-tap-planeto.obspm.fr/tap/tables

which can also be queried with ADQL:

SELECT table_name, description
FROM tap_schema.tables
ORDER BY table_name

as well as columns from a particular table:

SELECT column_name, datatype, description
FROM tap_schema.columns
WHERE table_name = 'exoplanet.epn_core'
ORDER BY column_name

About access and (not) abusing the service

I haven’t yet encountered a service that would limit access to its data, and I believe that this is the way things should be - that kind of data must remain open for the scientific community.

In turn, you too need to be a responsible user - do not execute crazy queries that fetch a milliard of columns with bazillion of rows. If your query doesn’t have narrow enough WHERE clause, do add a TOP limiter to your SELECT, for example:

SELECT TOP 11
    star_name, granule_uid, mass, radius
FROM exoplanet.epn_core

Problems and challenges

Working with the data, I did stumble upon various differences and peculiarities among TAP services.

NASA Exoplanet Archive

Almost all the problems I had were with NASA Exoplanet Archive database. At the same time, the data from NASA seems to be the most updated and trustworthy, so I have mixed feelings about this source.

PS vs PSCompPars

There are two main tables in the database that you are likely to use: PS and PSCompPars. The difference between them is described in the Choosing the Right Table For Your Needs section. In short, the PS table is organized in a way that there is a row for every publication and the set of parameters described in that publication, so there can be several rows per planet (and star?); while PSCompPars has exactly one row per planet, and every parameter should have a value from the latest publication, and the publication is provided in the PARAMETER-NAME_reflink field/column.

So it’s obvious that one should use PSCompPars, right? Well, for me it was not so obvious, and I actually went with PS.

Below I described specific details.

No simple mass values

Unlike ps table, pscomppars doesn’t have “simple” mass (and probably other) values, for instance pl_massj and pl_msinij. Instead, the mass-related parameters are stored like this:

SELECT column_name, datatype, description
FROM tap_schema.columns
WHERE table_name='pscomppars' AND column_name LIKE 'pl_bmass%'
ORDER BY column_name
Results found: 13
+-------------------+------------+------------------------------------------------------+
| column_name       | datatype   | description                                          |
|-------------------+------------+------------------------------------------------------|
| pl_bmasse         | double     | Planet Mass or Mass*sin(i) [Earth Mass]              |
| pl_bmasse_reflink | char       | Planet Mass or Mass*sin(i) [Earth Mass] Reference    |
| pl_bmasseerr1     | double     | Planet Mass or Mass*sin(i) [Earth Mass] Upper Unc.   |
| pl_bmasseerr2     | double     | Planet Mass or Mass*sin(i) [Earth Mass] Lower Unc.   |
| pl_bmasselim      | int        | Planet Mass or Mass*sin(i) [Earth Mass] Limit Flag   |
| pl_bmassestr      | char       | Planet Mass or Mass*sin(i) [Earth Mass]              |
| pl_bmassj         | double     | Planet Mass or Mass*sin(i) [Jupiter Mass]            |
| pl_bmassj_reflink | char       | Planet Mass or Mass*sin(i) [Jupiter Mass] Reference  |
| pl_bmassjerr1     | double     | Planet Mass or Mass*sin(i) [Jupiter Mass] Upper Unc. |
| pl_bmassjerr2     | double     | Planet Mass or Mass*sin(i) [Jupiter Mass] Lower Unc. |
| pl_bmassjlim      | int        | Planet Mass or Mass*sin(i) [Jupiter Mass] Limit Flag |
| pl_bmassjstr      | char       | Planet Mass or Mass*sin(i) [Jupiter Mass]            |
| pl_bmassprov      | char       | Planet Mass or Mass*sin(i) Provenance                |
+-------------------+------------+------------------------------------------------------+

So instead of pl_massj one should use pl_bmassj, right, but then what does one suppose to use instead of pl_msinij? Turns out, it’s the same pl_bmassj! And the type of what is stored in pl_bmassj is provided in pl_bmassprov (does it also correspond for pl_bmasse?). Here all the possible values of pl_bmassprov:

SELECT COUNT(*) AS "How many", pl_bmassprov
FROM pscomppars
GROUP BY pl_bmassprov
+------------+------------------+
|   how many | pl_bmassprov     |
|------------+------------------|
|        872 | Msini            |
|       1335 | Mass             |
|         21 | Msin(i)/sin(i)   |
|       2786 | M-R relationship |
+------------+------------------+

So okay, apparently you’d need to add some more complexity to your queries and additionally check whether the value in pl_bmassj is of type Mass (for pl_massj in ps) or Msini (for pl_msinij in ps)? And what are the Msin(i)/sin(i) and M-R relationship types? By the way, there is another issue related to that.

Finally, as you can see, the pl_bmassprov field/column is not a dictionary value but a mere string! Needless to say how that adds to unreliability of querying the data.

Calculated values

If we query pl_radj for HD 95872 b planet from ps table, then we’ll see that out of all publications there is no known value for it:

SELECT hostname, pl_name, pl_radj, pl_pubdate
FROM ps
WHERE pl_name = 'HD 95872 b'
ORDER BY pl_pubdate DESC
Results found: 2
+------------+------------+-----------+--------------+
| hostname   | pl_name    |   pl_radj | pl_pubdate   |
|------------+------------+-----------+--------------|
| HD 95872   | HD 95872 b |       nan | 2017-03      |
| HD 95872   | HD 95872 b |       nan | 2016-02      |
+------------+------------+-----------+--------------+

However, if we query the same from the pscomppars table, then suddenly there is a value:

SELECT hostname, pl_name, pl_radj, pl_radj_reflink
FROM pscomppars
WHERE pl_name = 'HD 95872 b'
Results found: 1
+------------+------------+-----------+----------------------------------------------------------------------------------------------+
| hostname   | pl_name    |   pl_radj | pl_radj_reflink                                                                              |
|------------+------------+-----------+----------------------------------------------------------------------------------------------|
| HD 95872   | HD 95872 b |      1.16 | <a refstr=CALCULATED_VALUE href=/docs/composite_calc.html target=_blank>Calculated Value</a> |
+------------+------------+-----------+----------------------------------------------------------------------------------------------+

which, according to reflink, is a “calculated value”, so it is questionable whether one can rely on it or not. And if you’d like to exclude “calculated values” from your results, then apparently you’d need to add a (reliable enough) condition for *_reflink fields, which are strings, of course. Oh joy.

Missing values

If we query pl_msinij for HD 10180 d planet from ps table, then we’ll get values from two publications:

SELECT hostname, pl_name, pl_massj, pl_msinij, pl_pubdate
FROM ps
WHERE pl_name = 'HD 10180 d'
ORDER BY pl_pubdate DESC
Results found: 2
+------------+------------+------------+-------------+--------------+
| hostname   | pl_name    |   pl_massj |   pl_msinij | pl_pubdate   |
|------------+------------+------------+-------------+--------------|
| HD 10180   | HD 10180 d |        nan |     0.0378  | 2014-09      |
| HD 10180   | HD 10180 d |        nan |     0.03697 | 2011-04      |
+------------+------------+------------+-------------+--------------+

So the latest value is 0.0378 from 2014-09 publication. But if we query this from pscomppars, then we’ll only get 0.03697 value, which is from 2011-04 publication, so it is not the latest one available:

SELECT hostname, pl_name, pl_bmassprov, pl_bmassj, pl_bmassj_reflink
FROM pscomppars
WHERE pl_name = 'HD 10180 d'
Results found: 1
+------------+------------+----------------+-------------+--------------------------------------------------------------------------------------------------------------------------------------+
| hostname   | pl_name    | pl_bmassprov   |   pl_bmassj | pl_bmassj_reflink                                                                                                                    |
|------------+------------+----------------+-------------+--------------------------------------------------------------------------------------------------------------------------------------|
| HD 10180   | HD 10180 d | Msin(i)/sin(i) |     0.03697 | <a refstr=LOVIS_ET_AL__2011 href=https://ui.adsabs.harvard.edu/abs/2011A%26A...528A.112L/abstract target=ref> Lovis et al. 2011 </a> |
+------------+------------+----------------+-------------+--------------------------------------------------------------------------------------------------------------------------------------+

On top of that, the pl_bmassprov says that pl_bmassj is of Msin(i)/sin(i) type, and so if we were looking for the Msini type, then we’d miss it. And by the way, if according to pscomppars that is Msin(i)/sin(i) type, then how can one know that this value in ps table is of that type and not of pl_msinij/Msini? Or are Msini and Msin(i)/sin(i) actually the same type? Why then it’s two different types in pscomppars table? Argh…

Okay, now back to general issues with the NASA database.

Planetary data is organized by publications

Say you’d like to get mass and radius of the Kepler-107 b planet. With PADC service the query will be this:

SELECT star_name, granule_uid, mass, radius
FROM exoplanet.epn_core
WHERE granule_uid = 'Kepler-107 b'
Results found: 1
+-------------+---------------+---------+----------+
| star_name   | granule_uid   |    mass |   radius |
|-------------+---------------+---------+----------|
| Kepler-107  | Kepler-107 b  | 0.01104 |    0.137 |
+-------------+---------------+---------+----------+

If you’ll try to execute a similar query with NASA service, then all of a sudden you’ll get more than one result:

SELECT hostname, pl_name, pl_massj, pl_radj
FROM ps
WHERE pl_name = 'Kepler-107 b'
Results found: 13
+------------+--------------+------------+-----------+
| hostname   | pl_name      |   pl_massj |   pl_radj |
|------------+--------------+------------+-----------|
| Kepler-107 | Kepler-107 b |  nan       |   nan     |
| Kepler-107 | Kepler-107 b |  nan       |   nan     |
| Kepler-107 | Kepler-107 b |  nan       |   nan     |
| Kepler-107 | Kepler-107 b |  nan       |   nan     |
| Kepler-107 | Kepler-107 b |  nan       |     0.138 |
| Kepler-107 | Kepler-107 b |  nan       |     0.142 |
| Kepler-107 | Kepler-107 b |  nan       |     0.139 |
| Kepler-107 | Kepler-107 b |  nan       |   nan     |
| Kepler-107 | Kepler-107 b |  nan       |     0.141 |
| Kepler-107 | Kepler-107 b |  nan       |   nan     |
| Kepler-107 | Kepler-107 b |    0.01104 |     0.137 |
| Kepler-107 | Kepler-107 b |  nan       |   nan     |
| Kepler-107 | Kepler-107 b |  nan       |   nan     |
+------------+--------------+------------+-----------+

What the hell, right? 13 results instead of just one. Turns out, the data is stored by publications, which can be determined by the pl_pubdate column. Let’s add it to the query:

SELECT hostname, pl_name, pl_massj, pl_radj, pl_pubdate
FROM ps
WHERE pl_name = 'Kepler-107 b'
ORDER BY pl_pubdate DESC
Results found: 13
+------------+--------------+------------+-----------+------------------+
| hostname   | pl_name      |   pl_massj |   pl_radj | pl_pubdate       |
|------------+--------------+------------+-----------+------------------|
| Kepler-107 | Kepler-107 b |  nan       |   nan     | 2019-03          |
| Kepler-107 | Kepler-107 b |    0.01104 |     0.137 | 2019-02          |
| Kepler-107 | Kepler-107 b |  nan       |     0.142 | 2018-10          |
| Kepler-107 | Kepler-107 b |  nan       |   nan     | 2018-08-16       |
| Kepler-107 | Kepler-107 b |  nan       |   nan     | 2017-08-31 00:00 |
| Kepler-107 | Kepler-107 b |  nan       |   nan     | 2016-07          |
| Kepler-107 | Kepler-107 b |  nan       |     0.138 | 2016-05          |
| Kepler-107 | Kepler-107 b |  nan       |   nan     | 2015-09-24       |
| Kepler-107 | Kepler-107 b |  nan       |     0.141 | 2015-08          |
| Kepler-107 | Kepler-107 b |  nan       |   nan     | 2014-12-18       |
| Kepler-107 | Kepler-107 b |  nan       |   nan     | 2014-12-04       |
| Kepler-107 | Kepler-107 b |  nan       |     0.139 | 2014-03          |
| Kepler-107 | Kepler-107 b |  nan       |   nan     | 2014-01-08       |
+------------+--------------+------------+-----------+------------------+

That is something you need to know about NASA’s database, otherwise you might fetch obsolete/inaccurate data for your research.

Furthermore, if you’ll need the latest pl_radj value, then you would execute the following query:

SELECT TOP 1 pl_radj
FROM ps
WHERE pl_name = 'Kepler-107 b' AND pl_radj IS NOT NULL
ORDER BY pl_pubdate DESC

…you would, but the results will surprise you once again, because NASA’s TOP clause is broken!

It becomes especially complicated if you need to get the latest value of a parameter for all the planets in the system. For now, I’ve come up with the following CTE (common table expression) using ranking:

SELECT * FROM
(WITH latestEntries AS
    (SELECT hostname, pl_name, pl_radj, pl_pubdate,
    ROW_NUMBER() OVER(
        PARTITION BY pl_name ORDER BY CASE WHEN pl_radj IS NULL THEN 1 ELSE 0 END, pl_pubdate DESC
    ) AS rank
    FROM ps WHERE hostname = 'Kepler-107')
SELECT hostname, pl_name, pl_radj, pl_pubdate FROM latestEntries WHERE rank = 1 ORDER BY pl_name)
Results found: 4
+------------+--------------+-----------+--------------+
| hostname   | pl_name      |   pl_radj | pl_pubdate   |
|------------+--------------+-----------+--------------|
| Kepler-107 | Kepler-107 b |     0.137 | 2019-02      |
| Kepler-107 | Kepler-107 c |     0.142 | 2019-02      |
| Kepler-107 | Kepler-107 d |     0.077 | 2019-02      |
| Kepler-107 | Kepler-107 e |     0.259 | 2019-02      |
+------------+--------------+-----------+--------------+

Looks pretty complex, but I am not sure if there is any other way.

The documentation says that there is default_flag field/column, which you could think to use to query the latest data, but (and I wonder if database maintainers are aware of this) this obviously won’t work in case when a planet has its parameters spread across several publications. For example:

SELECT hostname, pl_name, pl_massj, pl_radj, pl_pubdate, default_flag
FROM ps
WHERE pl_name = 'HR 8799 b'
ORDER BY pl_pubdate DESC
Results found: 4
+------------+-----------+------------+-----------+--------------+----------------+
| hostname   | pl_name   |   pl_massj |   pl_radj | pl_pubdate   |   default_flag |
|------------+-----------+------------+-----------+--------------+----------------|
| HR 8799    | HR 8799 b |        nan |     nan   | 2016-08      |              0 |
| HR 8799    | HR 8799 b |        nan |     nan   | 2016-03      |              0 |
| HR 8799    | HR 8799 b |        nan |       0.6 | 2016-03      |              0 |
| HR 8799    | HR 8799 b |          7 |       1.2 | 2008-11      |              1 |
+------------+-----------+------------+-----------+--------------+----------------+

So if you trusted blindly the default_flag and took the values from 2008-11 publication, then you’d miss the newer pl_radj value from 2016-03 publication.

Planets from the same system have different stellar data

If we take HR 8799 system as an example, then its planet HR 8799 b has st_teff value of 7204.58 from the publication on 2016-08:

SELECT * FROM
(WITH latestEntries AS
    (SELECT hostname, pl_name, st_teff, pl_pubdate,
    ROW_NUMBER() OVER(
        PARTITION BY pl_name ORDER BY CASE WHEN st_teff IS NULL THEN 1 ELSE 0 END, pl_pubdate DESC
    ) AS rank
    FROM ps WHERE hostname = 'HR 8799')
SELECT hostname, pl_name, st_teff, pl_pubdate FROM latestEntries WHERE rank = 1 ORDER BY pl_name)
Results found: 4
+------------+-----------+-----------+--------------+
| hostname   | pl_name   |   st_teff | pl_pubdate   |
|------------+-----------+-----------+--------------|
| HR 8799    | HR 8799 b |   7204.58 | 2016-08      |
| HR 8799    | HR 8799 c |   7204.58 | 2016-08      |
| HR 8799    | HR 8799 d |   7204.58 | 2016-08      |
| HR 8799    | HR 8799 e |   7400    | 2019-03      |
+------------+-----------+-----------+--------------+

As this is a stellar parameter, one would expect it to be the same for every planet in the system, but as you can see other planets in that system, such as HR 8799 e, have different values (there also might be no value at all). How to know which one is the right one? Because pl_pubdate filed name implies that it applies to planetary parameters, and there are no host_pubdate or star_pubdate fields in the table.

So we can only assume that pl_pubdate applies to stellar parameters too, and that means that if you are working with just HR 8799 b, and you are not interested in other planets, still you cannot just take 7204.58 value, you’ll need to execute an additional query to get the first not null st_teff value ordered by publication date (yes, also with CTE and ranking, because TOP is broken) from all the planets in the system.

No way to get the latest stellar data

Following the previous issue, there actually seems to no way to get the actually latest stellar data. For instance, on this page we can see that this very same HR 8799 star has even newer publication from Swastik et al. 2021, which states st_teff value of 7339:

Unavailable st_teff values in NASA database

How can one get this value from via TAP interface? Or the 7376.66 value from “Gaia DR2”? Neither of these are available in the ps table. Is there some other table with all the stellar data? And can it be joined with ps table by the hostname or some other identifier? I didn’t find answers to that.

TOP clause is broken

So yeah, here are the radius values for the Kepler-107 b planet by publication date:

SELECT pl_radj, pl_pubdate
FROM ps
WHERE pl_name = 'Kepler-107 b'
ORDER BY pl_pubdate DESC
Results found: 13
+-----------+------------------+
|   pl_radj | pl_pubdate       |
|-----------+------------------|
|   nan     | 2019-03          |
|     0.137 | 2019-02          |
|     0.142 | 2018-10          |
|   nan     | 2018-08-16       |
|   nan     | 2017-08-31 00:00 |
|   nan     | 2016-07          |
|     0.138 | 2016-05          |
|   nan     | 2015-09-24       |
|     0.141 | 2015-08          |
|   nan     | 2014-12-18       |
|   nan     | 2014-12-04       |
|     0.139 | 2014-03          |
|   nan     | 2014-01-08       |
+-----------+------------------+

As you want to get the latest value, that would be the query you’d normally execute:

SELECT TOP 1 pl_radj, pl_pubdate
FROM ps
WHERE pl_name = 'Kepler-107 b' AND pl_radj IS NOT NULL
ORDER BY pl_pubdate DESC

but here are the results that you will actually get:

Results found: 1
+-----------+--------------+
|   pl_radj | pl_pubdate   |
|-----------+--------------|
|     0.138 | 2016-05      |
+-----------+--------------+

I know, right, why in the ass did it return 0.138 value from 2016-05 instead of 0.137 from 2019-02? Go figure!

I sent a detailed bugreport with steps to reproduce the problem to their support more than a month ago, but the issue is still not fixed. I did, however, received two e-mails from the support system with notifications that my bugreport is still being processed, so hopefully eventually this will be fixed. And who knows for how long it has been present already (from the very beginning, I imagine)? Could it even be that I’m the first one who noticed and/or bothered to report it?

As a workaround one can use the following CTE with ranking:

SELECT * FROM
(WITH latestEntries AS
    (SELECT pl_radj, pl_pubdate,
    ROW_NUMBER() OVER(
        PARTITION BY pl_name ORDER BY CASE WHEN pl_radj IS NULL THEN 1 ELSE 0 END, pl_pubdate DESC
    ) AS rank
    FROM ps WHERE pl_name = 'Kepler-107 b')
SELECT pl_radj, pl_pubdate FROM latestEntries WHERE rank = 1)
Results found: 1
+-----------+--------------+
|   pl_radj | pl_pubdate   |
|-----------+--------------|
|     0.137 | 2019-02      |
+-----------+--------------+

Yes, it is overcomplicated, but it works.

Missing uncertainties columns

Documentation claims that there are uncertainties values for parameters like ra, dec, glat, glon and others:

NASA, missing uncertainties columns

However, in reality you will get the following error trying to SELECT them:

ORA-00904: 'DECERR1': invalid identifier

And indeed, there are no such columns in the table:

SELECT column_name, datatype, description
FROM tap_schema.columns
WHERE table_name = 'ps' AND
(
    column_name LIKE 'ra%'
    OR column_name LIKE 'dec%'
    OR column_name LIKE 'glat%'
    OR column_name LIKE 'glon%'
    OR column_name LIKE 'elat%'
    OR column_name LIKE 'elon%'
)
ORDER BY column_name
+---------------+------------+--------------------------+
| column_name   | datatype   | description              |
|---------------+------------+--------------------------|
| dec           | double     | Dec [decimal]            |
| elat          | double     | Ecliptic Latitude [deg]  |
| elon          | double     | Ecliptic Longitude [deg] |
| glat          | double     | Galactic Latitude [deg]  |
| glon          | double     | Galactic Longitude [deg] |
| ra            | double     | RA [decimal]             |
+---------------+------------+--------------------------+

Why then they are listed in the documentation?

Paris Astronomical Data Centre

The PADC database has issues too.

Obsolete data

Quite often the data is just obsolete. If you compare mass, radius and other parameters for the same planets in PADC and then in NASA, you’ll see that PADC data lags behind (with years of delay).

Let’s take Kepler-11 d planet as an example. Here are its mass and radius from PADC:

SELECT granule_uid, mass, radius
FROM exoplanet.epn_core
WHERE granule_uid = 'Kepler-11 d'
+---------------+--------+----------+
| granule_uid   |   mass |   radius |
|---------------+--------+----------|
| Kepler-11 d   |  0.023 |    0.278 |
+---------------+--------+----------+

and here are its mass and radius from NASA:

SELECT pl_name, pl_massj, pl_radj, pl_pubdate
FROM ps
WHERE pl_name = 'Kepler-11 d' AND (pl_massj IS NOT NULL OR pl_radj IS NOT NULL)
ORDER BY pl_pubdate DESC
+-------------+------------+-----------+--------------+
| pl_name     |   pl_massj |   pl_radj | pl_pubdate   |
|-------------+------------+-----------+--------------|
| Kepler-11 d |   nan      |     0.3   | 2018-10      |
| Kepler-11 d |     0.0214 |     0.294 | 2017-07      |
| Kepler-11 d |   nan      |     0.301 | 2016-05      |
| Kepler-11 d |     0.0228 |   nan     | 2014-08      |
| Kepler-11 d |     0.021  |     0.265 | 2014-05      |
| Kepler-11 d |     0.023  |     0.278 | 2013-06      |
| Kepler-11 d |     0.019  |     0.306 | 2011-02      |
+-------------+------------+-----------+--------------+

So what PADC has is the data from 2013-06 publication, while there were at least 5 new publications with updated data since then!

Here’s another example - planet Kepler-10 b. Data from PADC:

SELECT granule_uid, mass, radius
FROM exoplanet.epn_core
WHERE granule_uid = 'Kepler-10 b'
+---------------+--------+----------+
| granule_uid   |   mass |   radius |
|---------------+--------+----------|
| Kepler-10 b   | 0.0145 |    0.132 |
+---------------+--------+----------+

and data from NASA:

SELECT pl_name, pl_massj, pl_radj, pl_pubdate
FROM ps
WHERE pl_name = 'Kepler-10 b' AND (pl_massj IS NOT NULL OR pl_radj IS NOT NULL)
ORDER BY pl_pubdate DESC
+-------------+------------+-----------+--------------+
| pl_name     |   pl_massj |   pl_radj | pl_pubdate   |
|-------------+------------+-----------+--------------|
| Kepler-10 b |    0.01123 |     0.133 | 2019-09      |
| Kepler-10 b |  nan       |     0.133 | 2018-10      |
| Kepler-10 b |    0.01019 |   nan     | 2017-10      |
| Kepler-10 b |  nan       |     0.135 | 2016-05      |
| Kepler-10 b |    0.0117  |     0.131 | 2016-03      |
| Kepler-10 b |  nan       |     0.131 | 2015-08      |
| Kepler-10 b |    0.0145  |     0.132 | 2015-05      |
| Kepler-10 b |    0.01    |     0.131 | 2014-07      |
| Kepler-10 b |    0.01447 |     0.13  | 2014-02      |
| Kepler-10 b |    0.014   |     0.126 | 2011-03      |
+-------------+------------+-----------+--------------+

Again, PADC’s data is from 2015-05, and since then there were at least 6 new publications with updated data!

There are many planets whose data is like that in PADC. To make things worse, there is no way to determine in automated manner how old the data in PADC is, because it doesn’t have a field similar to NASA’s pl_pubdate. There are updated and modification_date fields, which look promising, but here’s what they return for the Kepler-10 b planet:

SELECT granule_uid, mass, radius, updated, modification_date
FROM exoplanet.epn_core
WHERE granule_uid = 'Kepler-10 b'
+---------------+--------+----------+-----------+---------------------+
| granule_uid   |   mass |   radius | updated   | modification_date   |
|---------------+--------+----------+-----------+---------------------|
| Kepler-10 b   | 0.0145 |    0.132 |           | 2021-02-05T07:00:00 |
+---------------+--------+----------+-----------+---------------------+

So updated is just empty, and modification_date states a date that is later than the latest pl_pubdate from NASA, but still PADC stores values that have been obsolete for several years, so you cannot rely on this field either.

Untrustworthy data

With K2-266 b planet as an example, there is mass value of 0.0355:

SELECT granule_uid, mass
FROM exoplanet.epn_core
WHERE granule_uid = 'K2-266 b'
+---------------+--------+
| granule_uid   |   mass |
|---------------+--------|
| K2-266 b      | 0.0355 |
+---------------+--------+

but there is no referred article, so it is unknown where this value originates from.

If we query the same data from NASA, then it returns null/nan, which is correct, as there is no publication stating this mass:

SELECT pl_name, pl_massj
FROM ps
WHERE pl_name = 'K2-266 b'
+-----------+------------+
| pl_name   |   pl_massj |
|-----------+------------|
| K2-266 b  |        nan |
+-----------+------------+

So where did PADC got the 0.0355 value from?

Or here’s another example, with Kepler-167 e planet. This one does have a source article, but in there it’s only a theory that this planet mass might be in a range from 0.3 to 50 mass of Jupiter, and yet PADC states that the mass is 4.0 (why on earth exactly 4.0?) with the error of -3.7/+46.0:

SELECT granule_uid, mass, mass_error_min, mass_error_max
FROM exoplanet.epn_core
WHERE granule_uid = 'Kepler-167 e'
+---------------+--------+------------------+------------------+
| granule_uid   |   mass |   mass_error_min |   mass_error_max |
|---------------+--------+------------------+------------------|
| Kepler-167 e  |      4 |              3.7 |               46 |
+---------------+--------+------------------+------------------+

Here’s what NASA has for this planet:

SELECT pl_name, pl_massj, pl_massjerr2, pl_massjerr1, pl_pubdate
FROM ps
WHERE pl_name = 'Kepler-167 e'
ORDER BY pl_pubdate DESC
+--------------+------------+----------------+----------------+------------------+
| pl_name      |   pl_massj |   pl_massjerr2 |   pl_massjerr1 | pl_pubdate       |
|--------------+------------+----------------+----------------+------------------|
| Kepler-167 e |        nan |            nan |            nan | 2019-03          |
| Kepler-167 e |        nan |            nan |            nan | 2018-10          |
| Kepler-167 e |        nan |            nan |            nan | 2018-08-16       |
| Kepler-167 e |        nan |            nan |            nan | 2017-08-31 00:00 |
| Kepler-167 e |        nan |            nan |            nan | 2016-04          |
| Kepler-167 e |        nan |            nan |            nan | 2014-12-18       |
| Kepler-167 e |        nan |            nan |            nan | 2014-12-04       |
| Kepler-167 e |        nan |            nan |            nan | 2014-01-08       |
+--------------+------------+----------------+----------------+------------------+

null/nan values for mass and errors, as it should be, because neither of the publications states these values.

Different names for star and planets

In NASA database there is a system Kepler-89. It’s a binary system, meaning that it has 2 stars, Kepler-89 B and KOI-94:

Binary system in NASA

If you query planets from KOI-94 system in NASA database, then you’ll get this:

SELECT pl_name
FROM ps
WHERE hostname = 'KOI-94'
GROUP BY pl_name
ORDER BY pl_name
+-----------+
| pl_name   |
|-----------|
| KOI-94 b  |
| KOI-94 c  |
| KOI-94 d  |
| KOI-94 e  |
+-----------+

But if you query the same from PADC:

SELECT granule_uid
FROM exoplanet.epn_core
WHERE star_name = 'KOI-94'
ORDER BY granule_uid
+---------------+
| granule_uid   |
|---------------|
| Kepler-89 b   |
| Kepler-89 c   |
| Kepler-89 d   |
| Kepler-89 e   |
+---------------+

then all of the sudden the planets names are different. Wut.

And so if you’ll want to cross-check or enrich the data you got from one database with the data from another, you’ll need to keep in mind that that the planets might have different names there. For automated processing that means that you’ll need to keep a list of such mismatches to map names between databases (as if you didn’t have enough things to worry about already).

Overall

Pretty concerning, right, that being a astronomer / planetary scientist you don’t have a reliable source of data about stars and planets.

Why there is more than one database about space objects, each containing intersecting/overlapping data? Why it is not one common database instead?

Why these databases first of all have different numbers of stars and planets, and secondly have different data about the same stars and planets? How can researcher know which one to use, which one has the most recent / trustworthy data? Why there is no even an agreement about naming, as different databases have different names for the same stars and planets?

C такими базулями нам не летать к далёкому космосу.

On the bright side, though, having at least these databases free to use for any researcher is tremendously better than having closed/private databases or no databases at all. And hopefully the problems that I described in the article will eventually find a solution.