The conversion of interstellar gas into stars provides the energy, momentum and chemical enrichment that help drive the evolution of galaxies across cosmic time. Observational limitations have previously made it difficult to obtain a comprehensive understanding of the star formation process (and its role on environment) due to the large dynamic range in scales over which it is relevant. However, pioneering new observational facilities are now moving the field from case studies to big data, enabling measurements across statistically significant samples of galaxies at very high resolution. This allows us for the first time to directly investigate how the small-scale (< 100 pc) physics of star formation couples to large-scale (1-10 kpc) galactic dynamics and environment.
In this presentation, I will highlight recent and current progress toward a more complete picture of star formation in the local Universe. I will show how new population synthesis models for young stellar populations can bridge the gap from Milky Way to extragalactic star formation studies. I will also present the results of the first molecular cloud-scale study of molecular clouds beyond the Local Group of galaxies. Finally, I will review some first results from two large observational campaigns through which we are tracking molecular gas and young stars at the cloud scale across dozens of nearby galaxies. This includes the systematic investigation of important physical quantities including gas conversion efficiency, molecular cloud densities and dynamics, and star formation timescales across multiple galactic environments.
The rise of algorithmic analysis has been met by a rise in the interest in storytelling, suggesting that we are most human in the stories we tell, and that the stories we tell cannot be readily rendered into numbers. And so data scientists and digital humanities scholars have turned their attention to narrative forms in hopes of at least sketching out a computational model of narrative which might reveal how narratives work, at least as texts, if not also as vehicles for the delivery of meaning. Much of this work has, however, focused on texts like novels, skipping over the kinds of texts that most of us produce each and every day, both online and off.
This presentation surveys recent work in corpus stylistics, digital humanities, and information and data sciences, and then sketches out what might be a way to discern the shape of small stories. Examples are drawn from local legends about treasure, the clown legend cascade of 2016 and select literary works, among other things.
Dr. John Laudun, professor of English at the University of Louisiana at Lafayette, is “fascinated by how humans create their world with relatively simple resources.” His current work in culture analytics has brought collaborations with physicists and other scientists seeking to understand how texts can be modeled computationally in order to better describe their functions and features.