![]() ![]() ![]() We can actually query and collect the quasar catalog data in one step using the SkyServer API. Additionally, we want CLASS_BEST, the source classification, to be of type 'QSO' and DR14_ZWARNING, the warning flag, to be '0' or off. We will grab the name, DR14_PLUG_RA, DR14_PLUG_DEC, MJD, redshift, CLASS_BEST, CONF_BEST and DR14_ZWARNING columns where redshift is greater than 1.3 but less than 1.5. We can get more information about the table, as well as descriptions of its columns, in the table schema. Looking through the SDSS schema browser, the spiders_quasar table suits our needs the most. We will use the SDSS SPIDERS catalog to look at quasars and their properties. Querying ¶ 3.1 Finding suitable quasars ¶įor this tutorial, we want to look at quasars within a particular redshift range. More information about astroquery can be found at the astroquery github repository. Note that these commands can also be done in a Jupyter notebook by either declaring the code cell a bash cell by pasting %%bash at the top of the cell, or preceding each line with a !. The CADC module is only available with the 'bleeding edge' master version of the astroquery module, and can be installed using the command: pip install 2.2 From source ¶Īlternatively, you can clone and install from the source: # If you have a github account: ![]() The module can be installed in two ways: 2.1 Using pip ¶ This tutorial will go through some of the basic functionalities of the CADC module in the astroquery package. More details about the MegaPipe images can found in the documentation. The goal of the MegaPipe project is to increase the usage of MegaCam data by processing and configuring the archival data, making the data much more usable. The MegaPipe image stacking pipeline at the CADC takes processed images from the Canada France Hawaii Telescope MegaCam and combines them into a single image. After finding quasars of a certain redshift, we will query the CADC database for MegaPipe images that contain the quasars. Included is a quasar catalog that describes properties of SPIDERS quasars. In this tutorial, we will be focusing on eBOSS, or Extended Baryon Oscillation Spectroscopic Survey, which has a sub-survey of X-Ray sources called SPIDERS, or SPectroscopic IDentification of ERosita Sources. The project is now in it's fourth phase, SDSS-IV, which involves three surveys: eBOSS, APOGEE-2, and MaNGA. The Sloan Digital Sky Survey (SDSS) is a survey that began operations in 2000, collecting data on more than one-third of the sky. Introduction ¶ Sloan Digital Sky Survey ¶ 3.2 Querying with the query_region functionġ.Additionally, each repository has a maximum of 100 simultaneous users. A notebook will shutdown after 10 minutes of inactivity and has a maximum of 2 GB of RAM. Warning: Binder instances have limited computational resources. To launch this notebook interactively, click the Binder button. The goal is to show some of the basic functionalities of the CADC astroquery by going through a simple end-to-end example. We will use the astropy, astroquery, and matplotlib packages in order to query, refine, and visualize the data. In this notebook, we will image quasars, grabbing our targets from the SDSS SPIDERS catalog, and collecting the images from the Canadian Astronomical Data Centre. ![]()
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