This page describes how to use the cone search query service of the LCC server to search for a collection of objects in a specified sky region, and optionally filtering the matching objects by various database columns.

The web interface

The cone-search query form
The cone-search query form


The cone-search query service requires a central coordinate specification string, along with an optional search radius in arcminutes, using any of the formats below.

HH:MM:SS[.ssss] [+-]DD:MM:SS.ssss [DD.dddd]
HH MM SS[.ssss] [+-]DD MM SS.ssss [DD.dddd]
DDD[.dddd] [+-]DDD[.ddd] [DD.ddd]

The maximum search radius is 60 arcminutes or 1 degree.

The cone-search service also takes several optional inputs. These include:

  1. Collection to search in: specified using the select control in the top-left portion of the cone-search tab. Multiple collections can be selected by holding Ctrl (Linux/Windows) or Cmd (MacOS) when clicking on the options in the control. By default, the service will search all collections available on the LCC server until it finds a match. Results will always be returned with the name of the collection any matches were found in.

  2. Columns to retrieve: specified using the select control in the bottom-left portion of the cone-search tab. This select control will update automatically to present only the columns available in all of the collections that are selected to search in. The service will return the objectid, ra, and decl columns for any matches by default, so these don't need to be selected. Specify any additional columns to retrieve by selecting them in this control, holding Ctrl (Linux/Windows) or Cmd (MacOS) when clicking on the options to select multiple columns.

  3. Filters on database columns: specified using the controls just under the main coordinates input box. First, choose a column to filter on using the left select control, then choose the operator to use from the middle select control, and finally type the value to filter on in the input box on the right. Hit Enter or click on the + button to add the filter to the list of active filters. After the first filter is added, you can add an arbitrary number of additional filters, specifying the logical method to use (AND/OR) to chain them. This effectively builds up a WHERE SQL clause that will be applied to the objects that match the initial coordinate search specification.

You may also choose a column to sort the results by and the desired sort order using the select boxes under the active column filters list. Finally, you may also restrict the number of rows returned or ask for a random sample of rows from the database matching your search conditions. If requested, these operations are carried out in the following order.

random sampling search results → sorting search results → limiting search result rows

Execute the cone-search query by clicking on the Search button or just by hitting Enter once you're done typing into the main coordinate input box. The query will enter the run-queue and begin executing as soon as possible. If it does not finish within 30 seconds, either because the query itself took too long or if the light curve collection for the matching objects took too long, it will be sent to a background queue. In either case, you will be informed of the permanent URL where the results of the query will appear as a dataset. From the dataset page associated with this query, you can view and download a data table CSV file generated based on the columns specified. You may also download light curves (individually or in a collected ZIP file) of all matching objects.


Example 1: Search for any objects within 15 arcminutes of the coordinates (α, δ) = (290.0,45.0):

Cone search query example 1 input
Cone search query example 1 input

Example 2: Search for objects within 60 arcminutes of the coordinates (α, δ) = (290.0,45.0), but restrict the query to just the hatnet_keplerfield collection, return the columns: sdssg, sdssr, sdssi, propermotion, and return only objects that match the condition: (sdssr < 13.0) and (propermotion > 50.0):

Cone search query example 2 input
Cone search query example 2 input


The cone search query service accepts HTTP requests to its endpoint:


See the general API page for how to handle the responses from the query service. This service requires an API key; see the API key docs for how to request and use these.


The search query arguments are encoded as URL query string parameters as described in the table below.

Parameter Required Default Description
coords yes The center coordinates and search radius as specified previously. These can be in either decimal or sexagesimal format. The search radius is in arcminutes and is optional.
visibility yes unlisted public means the resulting dataset will be public and visible on the Recent Datasets page. unlisted means the resulting dataset will only be accessible to people who know its URL. private means the dataset and any associated products will only be visible and accessible to the user running the search.
filters no Database column filters to apply to the search results. This is a string in SQL format specifying the columns and operators to use to filter the results. You will have to use special codes for mathematical operators since non-text symbols are automatically stripped from the query input:
< → lt
> → gt
≤ → le
≥ → ge
= → eq
≠ → ne
contains → ct
sortspec no ['matchdist','asc'] This is a string of the form: "['column to sort by','asc|desc']" indicating the database column to sort the results by and the desired sort order: asc for ascending, desc for descending order.
samplespec no If this is specified, then random sampling for the search result is turned on. This parameter then indicates the number of rows to return in a uniform random sample of the search results.
limitspec no If this is specified, then row limits for the search result are turned on. This parameter then indicates the number of rows to return from the search results. If random sampling is also turned on, the rows will be random sampled returning samplespec rows before applying the row limit in limitspec.
collections[] no Collections to search in. Specify this multiple times to indicate multiple collections to search. If this is not specified, all LCC-Server collections will be searched.
columns[] no Columns to retrieve. The database object names, right ascensions, and declinations are returned automatically. Columns used for filtering and sorting are NOT returned automatically (this is a convenience for the browser UI only). Specify them here if you want to see them in the output.

API usage example

Run the query from Example 2 above, using the Python Requests1 package:

import requests, json

# get an API key
apikey_info = requests.get('')
apikey = apikey_info.json()['result']['apikey']

# build up the params
params = {'coords':'290.0 45.0 60.0',
          'filters':'(sdssr lt 13.0) and (propermotion gt 50.0)',

# this is the URL to hit
url = ''

# fire the request
# this will block until the server either finishes or sends work to background
resp =,
                     headers={'Authorization':'Bearer %s' % apikey})

# parse the line-delimited JSON
textlines = resp.text.split('\n')[:-1]  # the last line is empty so remove it
jsonlines = [json.loads(x) for x in textlines]

# get the status and URL of the dataset created as a result of our query
# the last item in the list of JSON strings tells us what happened
query_status = jsonlines[-1]['status']
dataset_seturl = jsonlines[-1]['result']['seturl'] if query_status == 'ok' else None

if dataset_seturl:

    # can now get the dataset as JSON if needed
    resp = requests.get(dataset_seturl,{'json':1,'strformat':1},
                        headers={'Authorization':'Bearer %s' % apikey})

    # this is now a Python dict
    dataset = resp.json()

    # these are links to the dataset products - add to the front
    # of these to make a full URL that you can simply wget or use requests again.

    # these are the columns of the dataset table

    # these are the rows of the dataset table's current page

  1. The Requests package makes HTTP requests from Python code a relatively simple task. To install it, use pip or conda