Quick Start ∞
Lasair is built as a platform to enable scientific discoveries from the dynamic Universe. Its input is a transient sky survey such as ZTF or LSST, that find changes in brightness in the night sky, each called an “alert”. Such alerts may result from supernovae, active galaxies, merging neutron stars, variable stars, and many other astrophysical phenomena (see here for more).
Alerts from the same place in the sky are combined to objects. The alerts provide the brightness of the object with time – see here for more. Lasair adds information to the object, matching the position with the known astronomical catalogs – see here.
The transient surveys provide large numbers of alerts: about 400,000 per night from ZTF when the sky is clear at Palomar in California, rising to millions per night when the Rubin Observatory in Chile is running its flagship LSST survey. Far too many for a human to consider! Therefore the primary duty of a broker like Lasair is to filter the stream to concentrate what is wanted and discard that which is not. In this section we show how to make a Lasair filter, specifically the one used for building the set of alerts shown on the Lasair front page. That display is made from recent, bright, real alerts that are identified with known classes of stars and galaxies. If you click on any of the red, orange, blue, or yellor markers, you will see a popup witha link to the full object page, the age of the most recent alert, its magnitude, and its class.
Each object in the Lasair objects table has a lot of columns in several tables, and for this example we will concentrate on just a few:
- From the objects table:
objectId: The identifier for an object that is used to link to the full object page,
ramean, decmean: The position of the object in the sky, to place it correctly,
gmag, rmag: the magnitudes of the latest alert in the g and r filters,
jdmax: the Julian Day (i.e.date and time) of the latest alert,
jdnow(): an SQL function that returns the Julian Day now, so we can subtract to get the age in days,
ncandgp: number of good, positive alerts belonging to this object.
- From the sherlock_classifications table:
classification: Sherlock class according to the sky context – see core_functions/sherlock.html for more.
Create New Filter ∞
We can build the filter by clicking on ‘Filters’ in the Lasair sidebar, then the red button ‘Create New’ at top right.
For your first filter, you won’t be using any of the dropdowns for Watchlist, Watchmap, or Object Annotators, you’ll fill in the black textarea labelled SELECT COLUMNS and WHERE.
Type the black lines below in the SELECT COLUMNS.
Notice that as you type, the intelligent autocomplete makes suggestions. Don’t forget the comma at the end.
The word mean is because this is the average position of the multiple alerts that are part of the same object. Don’t forget the comma at the end.
The g or r magnitude for the most recent alert. Each alert is done with one of the filters, so either
rmag will be
jdnow()-objects.jdmax AS age,
This SQL fragment subtracts the Julian Day now from the Julian Day of the alert, and renames the result as
sherlock_classifications.classification AS class
This attribute is from a different table, the Sherlock classification of the object. The long name is renamed as the much simpler
You see as you type that the tables you are using appear in the middle of the three black textareas, labelled FROM.
Now type these lines into the WHERE box:
objects.jdmax > jdnow() - 7
We select only those objects whose most recent alert has been in the last 7 days.
AND (objects.gmag < 17 OR objects.rmag < 17)
We want bright objects only, mostly to cut the numbers being drawn on the Lasair front page. Give that one of the attributes is
OR selects the one that is not, and requires it to be less than 17. Don’t forget the
AND at the beginning.
AND objects.ncandgp > 1
There are a lot of ‘orphans’ in the Lasair database, that have only one alert. Many of these are not worth looking at, so we require the number of candidates to be greater than 1.
AND sherlock_classifications.classification in ("SN", "NT", "CV", "AGN")
These codes are for the different Sherlock classifications: possible supernova, nuclear transient cataclysmic variable, active galaxy.
Run your filter ∞
You can simply run the filter on the existing database by clicking the red
button ‘Run Filter’.
You should see a table of the recent alerts, the same set as are on the Lasair
You can click on the column headers to sort, and click on the
objectId to go
to the detail
for any of the objects.
Save your filter ∞
But doing more with Lasair requires an account – its just a simple matter of entering your valid email address – see here to register.
Click the black button ‘Save’ on the create fulter page, then fill in the details: Name and Description, and you can choose to make it public, so that it appears in the [Public Gallery]((https://lasair-ztf.lsst.ac.uk/filters). Once its shared like this, others can use it, or copy and modify it. Another option in the Save dialogue has three choices: * muted: The filter is saved, and you can run it and edit it * email stream (daily): Means that you receive an email – at the address of your Lasair account – whenever an alert causes an object to pass through the filter. This is restricted to one email in 24 hours. * kafka stream: The substream induced by the filter becomes a kafka stream – see here for more.
Other options on the filter page bring in other tables in addition to the
– see the schema browser for the full list. These
sherlock_classifications: the results of an intelligent matching of multiple catalogues with the position of the alert on the sky – see here for more.
crossmatch_tns: you can filter your results to be alerts coincident with the TNS name server. You can select supernova types , dscovery date, and more.
watchlist: you can filter your results to be only those coincident with a list of sources that you or someone else has uploaded – see here for more.
watchmap: you can filter your results to be only those inside a sky area that you or someone else has uploaded – see here for more.
annotation: you can find events that have been classified or otherwise annotated external to Lasair. You can also set up your own annotation service – see here.