We are happy to share with you the latest developments at Astro Data Lab in this November 2023 newsletter!
In this newsletter
- Data Lab tutorial at ADASS 2023
- Retirement of Legacy Surveys DR3 through DR7
- specClient replaced by SPARCL
- New Jupyter notebooks
Data Lab tutorial at ADASS 2023
Data Lab team members will be offering a tutorial at this year’s Astronomical Data Analysis Software and Systems (ADASS) conference in Tucson, Arizona on Sunday November 5, 2023 from 3:10 p.m. to 5 p.m. in the North Ballroom of the Student Union on the University of Arizona campus. Registration for ADASS is necessary to attend the tutorial, please register by Friday November 3, 2023 here.
In this tutorial we will teach participants how to use data-proximate science platforms to conduct astronomy research. Using the Astro Data Lab science platform and the new SPARCL (SPectra Analysis and Retrievable Catalog Lab) service for spectroscopy, participants will first learn how to find documentation, information about all of Astro Data Lab’s data holdings of over 100 TB of wide-field survey catalogs, 2.5 PB of imagery, and millions of spectra from DESI and SDSS, and how to access help from the Astro Data Lab team. We will then teach the group in an interactive mode how to use various data services and analysis tools at Data Lab, including how to crossmatch tables, build and submit catalog queries, search for images and create cutouts, search for and download spectra, and how to use the Astro Data Lab Jupyter notebook server. The participants will execute and modify a number of science-case example notebooks from various domains of astronomy focusing on data analysis. The tutorial will also make use of some amenities on science platforms, including remote file storage and remote user-owned database tables.
Tutorial notebooks are available here: https://github.com/astro-datalab/Tutorial-ADASS-2023
See the full tutorial description here: https://adass2023.lpl.arizona.edu/events/tutorial-t003
Retirement of Legacy Surveys DR3 through DR7
Recently, the Legacy Surveys Data Releases 3 through 7 were retired at Astro Data Lab. This includes all tables, image cutout endpoints, and file services. These older DRs have been superseded by Legacy Surveys DRs 8, 9, and 10, which are available for users at Data Lab. For more information about the Legacy Surveys at Data Lab, see the Legacy Surveys landing page.
specClient replaced by SPARCL
The specClient package for accessing spectroscopic data at Astro Data Lab was recently retired. Users are instead encouraged to use SPARCL (SPectra Analysis and Retrievable Catalog) for their spectroscopic research needs. SPARCL allows users to discover and retrieve spectroscopic data, currently from SDSS DR16, BOSS DR16, and DESI EDR. The SPARCL Python client can be easily installed locally:
pip install sparclclient
It is also installed on Astro Data Lab's Jupyter notebook server. The client can be loaded within a Python session or program via:
from sparcl.client import SparclClient
client = SparclClient()
Additionally, Astro Data Lab's database now holds the sparcl.main table, which enables discovery of spectra using SQL queries. Use the retrieved spectrum IDs to fetch the corresponding spectra with SPARCL. For more details, see the How to use SPARCL Jupyter notebook and the Obtain and plot spectra data using SPARCL, prospect, and specutils notebook.
New Jupyter notebooks at Data Lab
Data Lab curates an extensive collection of notebooks for our user community, which range from introductory, over technical, educational, to entire science use cases. Several new notebooks were recently added to the suite:
This notebook shows how to obtain DESI EDR, SDSS DR16, and (e)BOSS DR16 spectra using the NOIRLab SPARCL spectrum service, convert data to specutils objects as needed, and use prospect to display the data. Prospect allows multiple, independent spectra visualizations to coexist within the same notebook.
This notebook explores the DESI Early Data Release (DESI EDR) at Astro Data Lab. Information about the release can be found here. The notebook shows how to access the redshift catalog from the Data Lab database, how to separate sources based on the DESI targeting information, how to access all the available spectra for a given object using SPARCL (SPectra Analysis and Retrievable Catalog Lab), and finally how to plot the "best" spectrum. This notebook is similar to the DESI EDR tutorial notebook, but it has been modified such that installing the DESI software (desispec) is not necessary to run it. As a result, it can be run using a Python 3 kernel with only the Data Lab and SPARCL dependencies (which are installed at Data Lab).
This notebook demonstrates a detection of the baryonic acoustic oscillation (BAO) peak by constructing a simple 3d co-moving spatial correlation function using data from The Baryon Oscillation Spectroscopic Survey (BOSS) released a part of SDSS DR12. In particular, it uses the BOSS catalogs and randoms collected by Florian Beutler here. It generally follows the procedure described in Eisenstein et al 2005, which was the first study to measure the BAO peak in data from SDSS. We are able to discern a BAO imprint at the typical distance scale of 100 h-1 Mpc. This notebook was originally generated as part of the final project for ASTR285 "Science with Large Astronomical Surveys" [Syllabus] at the University of Chicago, which was using Astro Data Lab.
Using the photometric properties of the Milky Way as described in Licquia et al. 2016, this notebook scrapes the Legacy Surveys Data Release 9 (LS DR9) North catalog for possible Milky Way analogues. Similar studies have been performed on SDSS galaxies, but the Legacy Surveys provide much deeper imaging data. The notebook also leverages the photometric redshift catalog created for LS DR9 to locate Milky Way analogues to higher redshifts than many SDSS-identified Milky Way analogues.
This notebook was originally generated as part of the final project for ASTR285 "Science with Large Astronomical Surveys" [Syllabus] at the University of Chicago, which was using Astro Data Lab.
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