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See newsletters from before April 2019
Interested in sharpening your TESS data skills? The TESS Interactive Data Workshop will take place on Saturday, 7 January 2023, from 9am-5pm PST at AAS 241 in Seattle, WA. Registration is available via the AAS portal. MAST staff will be there!
Updates were made to the several of our TESS-based collections. Additional updates were made to the HST ULLYSES catalog.
As part of the ongoing JWebbinar series, three members of the MAST staff did live walkthroughs to an audience of about 100 community members. Topics ranged from an introduction to common MAST vocubulary to downloading transiting exoplanet data from JWST. Be sure to check out the recordings and Jupyter Notebooks!
As of Nov 1st, the HST Classic search form will no longer be available, after being replaced with the modernized Hubble Search form. We have a number of guides and tutorials to ease the transition, and we're happy to answer your questions and hear your feedback!
New High-Level Science Products at MAST include a study of the Bootes I dwarf galaxy, the first JWST early-release observations, preliminary data for a spectroscopic study of gravitationally lensed galaxies with JWST, and an improved all-sky catalog of source classification and redshift. Updates were also made to several TESS-based collections and updated redshifts for CANDELS field sources.
A new dedicated search form is now live for the ULLYSES High-Level Science Product collection. This provides users with specialized features and data retrieval options for this library of young star standards.
This month saw updates for three separate TESS High-Level Science Products: TASOC, TESS-SPOC, and TICA. This increases the number of TESS light curves in MAST by several million! Updates were also made to the ILLUSTRIS collection, including improved models and simulated images.
Starting on November 1st, the Classic HST Search Form will no longer be available and MAST users will be re-directed to the new Hubble Search. This streamlined, moderized form retains all of the features of the Classic Search; it also incoporates some new features that we're excited to share with our users!
With data from JWST releasing frequently, MAST users can use the Portal Subscriptions system to receive various notifications when data of interest are released or updated.
High-Level Science Products at MAST now include a new catalog of simulated high-z galaxy images for major observatories, along with a new collection of processed imaging of a dark vortex on Neptune. Updates were also made to the MSTARPANSPEC and TICA collections.
The first science data files used to create JWST's incredible First Images are now available through MAST! This process is ongoing in an effort to make all files available to the public as soon as possible, but there are a number of available options to download this first batch right away.
The first JWST science data will be released to users at MAST in stages over two days starting on July 13, 2022. In many cases, MAST will also provide bulk download scripts which pull the data from the Amazon Web Services (AWS) cloud.
New High-Level Science Products at MAST include simulations of proper motions within stellar clusters, new SED models based on Spitzer calibration, predicted products for the Roman Space Telescope supernova survey, and a catalog of eclipsing binary stars found with TESS. Updates were made to the TICA collection of quick-look TESS data, and the newest data release for the ULLYSES collection.
New algorithms using data from Gaia EDR3 have been applied to the Pan-STARRS1 catalog at MAST to calculate improved positions and new proper motions for 1.7 billion objects having at least 3 detections in PS1 single-epoch images.
Hello Universe is a new project at MAST designed to help astronomers develop machine learning methods for astronomical discovery by providing combinations of data and tutorial code. Machine learning will be an essential tool for analyzing the rich data sets of the upcoming decade, and Hello Universe provides a framework for mastering machine learning algorithms and testing new techniques.