2021 CoolSci at JHU/STScI

Wed 27 Jan 2021

This colloquium is hosted by STScI and will be held as a fully virtual event.


3:00 PM - 4:00 PM

Contact Information:

Have questions? Please contact STScI.


Featuring Aleksandra Hamanowicz (STScI) on A 3D View on the Cosmic Baryon Cycle and Rick White (STScI) on The Hubble Image Similarity Project.


All 2021 CoolSci at JHU/STScI Colloquium virtual talks are held on Wednesdays at 3:00 PM.

Please direct questions or comments to contact above. The 2021 committee members are Ramona Augustin, Andres del Pino Molina, and Raymond Simons (STScI).

Name: Aleksandra Hamanowicz
Title: A 3D View on the Cosmic Baryon Cycle
Abstract: Galaxies are constantly fed by the diffuse material from the intergalactic medium through the Circum-Galactic Medium (CGM).We can probe these vast gaseous halos around galaxies by studying absorbers detected in the spectra of bright background quasars. To understand the dynamics of the system we combine the physical properties from the absorption features with the broader view of the absorber’s host and its environment by emission diagnostics, using IFU spectroscopy. Gas travelling through the CGM enters a galaxy and replenishes the gas reservoirs which further transforms into molecular phase — the direct fuel of the star formation. Recent studies have suggested a possible link between the cosmic density of H— the most abundant molecule in the Universe — and the Star Formation History of the Universe. The second most abundant molecule, still linked to star formation, is CO and its rotational transitions are bright and relatively easy to observe with ALMA, allowing us to probe the molecular content of whole populations of galaxies.

In my talk, I will present the two surveys probing the gaseous content of galaxies in different phases: molecular within the galaxies and diffuse in the CGM. We combined MUSE and ALMA to understand the properties of host galaxies of quasar absorbers in the MUSE-ALMA Haloes Survey. Surprisingly, we found large fraction of groups associated with absorbers, which introduces a challenge in connecting CGM detected in absorption to a particular galaxy. Addressing the molecular gas content of galaxies, we turned towards the archival observations of ALMA calibrators, constructing ALMACAL-CO, blind CO emission-line survey. A survey is a part of the extensive science project ALMACAL, utilizing ALMA calibration data for scientific purposes. Thanks to a uniqueness of the ALMACAL dataset we are able to study galaxies over a wide area, and are not sensitive to the effects of cosmic variance. The results of the survey, confirm findings of other blind emission line searches: the shape of the molecular gas mass function mirrors star formation history of the Universe, suggesting that the molecular gas content of galaxies is closely linked to the evolution of SFH.

Name: Rick White
Title: The Hubble Image Similarity Project
Abstract: Archives of astronomical images allow users to find images by metadata: what camera, what filter, what PI, what declination. Catalogs of the objects contained in those images provide a limited search of the data itself: position, magnitude, position angle, Sersic index. We are harnessing neural networks to answer a much harder question: If I have a complex image, how can I find all the images in the archive that look like it? Using convolutional neural networks trained on terrestrial images, we can find edge-on galaxies with dust lanes, star fields, and rich scenes of star formation.

A major hindrance to our work is the difficulty of comparing algorithms to determine which one is best. At its core the question is whether the groups of images identified by one algorithm are more similar than the groups from another algorithm. Our new Hubble Image Similarity Project aims to create a large database of similarity information between segments of Hubble images. The images are compared by humans in a citizen science project, where they are asked to select similar images from a comparison sample. We also designed the project for community impact: our citizen scientists are service-industry professionals from the local area near STScI in Baltimore who were impacted by the Covid-19 pandemic. They are paid a fair wage for their work through the Amazon Mechanical Turk system.

The comparison measurements are analyzed to compute a distance matrix between all the pairs of images, and that distance matrix can subsequently be utilized to assess the accuracy of algorithms based on computer vision methods. The image similarity matrix shows that the collective visual wisdom of our neighbors matches the accuracy of the trained eye, with even subtle differences among images faithfully reflected in the distances. We will publish the data and the images for use as a test set.