See more information about the ALeRCE broker, including its frontend, in http://alerce.science/
A collection of jupyter notebooks and scripts on how to access the ALeRCE database, API, etc.
For faster cloning please use: git clone --depth=1 https://github.com/alercebroker/usecases.git
The ALeRCE broker is a Chilean-led initiative to build a community broker for LSST and other large etendue survey telescopes which produce public streams of alerts. ALeRCE is developed by many institutions in Chile and in the U.S., taking advantage of the large network of collaborators which we have established around data science and astronomy during the last decade. The main motivation of ALeRCE is to facilitate the follow-up and the exploration of the LSST and other observatories alert streams.
ALeRCE aims to facilitate the study of stationary (non--moving) variable and transient objects. We will do this by providing real-time filtered streams of aggregated, annotated and classified alerts, but also by providing alert exploration and analysis tools that can help researchers look for patterns and outliers within large populations of events. We also aim to provide forecasting tools which can help with the optimization of follow--up resources.
In this repository we show how to access the ALeRCE ZTF database, focused on different science cases. You can find several introductory jupyter notebooks in the notebooks directory, which connect to the database, query some tables and does some processing and visualization of the data. If you would like to beta test this please note that ALeRCE is still under development, with very simple taxonomy and classification models.
LSST PCW 2020: note that we are undergoing major changes in our database design and we are beta releasing some notebooks which use this new API, which is much richer than our stable version. These notebooks have the "newDB" string on them.
Please report any problems to francisco dot forster at gmail dot com.
The success of ALeRCE depends on being able to build a community of users which can connect LSST with the different follow up resources. Therefore, we are very happy to receive and publish contributed notebooks in this repository showing how your science case can benefit from having a real-time access to an annotated and classified database of ZTF alerts. Please feel free to join this repository and add your science cases!