Abstract: The main goal of this tutorial is to teach participants how to use recent Virtual Observatory standards allowing exploration and querying of all-sky datasets. The Hierarchical Progressive Survey (HiPS) and the Multi-Order Coverage map (MOC) can be used by data providers to expose their datasets (images or catalogues), and astronomers can use them to perform complex queries on all-sky datasets. Participants will create image and catalogue HiPS, learn how to compare them to reference datasets, and share them in a web page. Advanced usage with the Table Access Protocol and astropy/MOCpy will also be shown.
Abstract: Attendees will be given an introduction to the MEAN stack (MongoDB, Express.js, AngularJS, and Node.js) method of web development with a focus on astronomical applications. By the end of the session they will have created from scratch a fully functional web application with a user interface that is capable of submitting a query to a database of observational sources and displaying results. In addition we will explore how we can adapt MongoDB's integrated support of advanced geolocation features to do complex positional queries and matching on the sky. Attendees should leave with an understanding of the powerful features that modern web frameworks have to offer and the confidence that they can rapidly develop and customize web applications suited to their own particular research or archival needs.
Abstract: Today, IVOA interoperability standards like Simple Cone Search (SCS), Simple Image Access (SIAP) and TAP/ADQL are used by many astronomers. Most recent surveys like Gaia are making use of these standards for publishing their data, future surveys like LSST or SKA plan to do so. Though accessing data of a single service makes a good bunch of VO use cases so far, familiarity with VO standards opens the possibility to combine and crossmatch the data of several surveys and service within a few steps: knowing how to access one service means knowing how to access all of them. The following tutorial introduces into the usage of some of these standards and how one can remotely crossmatch between different services and survey. As example we chose the use case of identifying brown dwarves in 2MASS and SDSS.
Abstract: In May 2018 STScI announced that ~110TB of Hubble's archival
observations are available in cloud storage on Amazon Web Services
(AWS). This tutorial will provide an introduction to accessing this
dataset and to the AWS cloud computing in general for members of the
astronomical community who have not previously used cloud resources. We
will demonstrate how to access the Hubble Public Dataset on AWS in order
to carry out a variety of tasks. Participants will be introduced to the
custom MAST extension of the astroquery Python library (astroquery.mast)
and the AWS client Python library boto3. We will demonstrate operations
with the data such as retrieving, displaying and running analysis on
single images. We will then show participants how to scale up their
analysis through serverless computing. Finally we will showcase some
advanced capabilities such as logging, price estimation. etc.
Level: The tutorial will be geared toward novice AWS users.
Prerequisites: Intermediate Python knowledge is strongly recommended.
Notice: Free AWS credits will be provided but participants will need a valid credit card in order to create an AWS account even though no charges will be incurred during the tutorial.