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The M81 galaxy group is surrounded by an HI debris field scattered by the tidal interactions of its galaxies, a situation that has obvious similarities to the Magellanic stream and illuminates the formation of in-situ stars in stellar halos during galaxy collisions. Using observations of stars across the M81 group from the Subaru Hyper Suprime-Cam, and observations of the neutral HI from the Very Large Array, we find that within this HI debris the density of young stars broadly correlates with the density of gas, as expected given the Schmidt-Kennicutt star formation law and the results of previous work. Yet, there are regions that have systematically different behaviors in distributions of stars and gas. We focus on two stretches of HI coming off NGC 3077: the Southern tidal bridge (between M81 and NGC 3077) and the Northern tidal bridge (from NGC 3077 in the direction of M82). The Southern bridge has a narrow strip of young stars down its center, and the Northern bridge is mostly devoid of stars. While the driver(s) for this kind of behavior remain uncertain, our analysis of star formation in galaxy group-scale mergers from the TNG50 hydrodynamical galaxy simulations shows that the differences between projected line-of-sight distances of the gas may be an important consideration. 

Research in astronomy is undergoing a major paradigm shift, transformed by the advent of large, automated, sky-surveys into a data-rich field where multi-TB to PB-sized spatio-temporal data sets are commonplace. For example the Legacy Survey of Space and Time; LSST) is about to begin delivering observations of >10^10 objects, including a database with >4 x 10^13 rows of time series data. This volume presents a challenge: how should a domain-scientist with little experience in data management or distributed computing access data and perform analyses at PB-scale? We present a possible solution to this problem built on (adapted) industry standard tools and made accessible through web gateways. We have i) developed Astronomy eXtensions for Spark, AXS, a series of astronomy-specific modifications to Apache Spark allowing astronomers to tap into its computational scalability ii) deployed datasets in AXS-queriable format in Amazon S3, leveraging its I/O scalability, iii) developed a deployment of Spark on Kubernetes with auto-scaling configurations requiring no end-user interaction, and iv) provided a Jupyter notebook, web-accessible, front-end via JupyterHub including a rich library of pre-installed common astronomical software (accessible at http://hub.dirac.institute). We use this system to enable the analysis of data from the Zwicky Transient Facility, presently the closest precursor survey to the LSST, and discuss initial results. To our knowledge, this is a first application of cloud-based scalable analytics to astronomical datasets approaching LSST-scale. The code is available at https://github.com/astronomy-commons.