Additional event information

• Special Talks

  Speaker: Vital Fernandez (STScI)
  Title and abstract are forthcoming.

  Speaker: Doyeon Avery Kim (STScI)
  Title and abstract are forthcoming.

  Speaker: Benjamin Gibson (STScI)
  Title: Bridging the Divide: Studying the Resolved and Integrated Light of Nearby Galaxies in High Resolution
  Abstract: Our unique perspective from within the Milky Way gives us the ability to take resolved images and spectra of individual stars throughout most of its bar, bulge, and disk. However, taking spectra of individual stars in the centers of other galaxies is impracticable due to crowding. As such, we must use integrated light to characterize stellar populations, which often overlap spatially. Furthermore, spectral modelling techniques must account for the variety of stellar species, gas, dust, and dynamics that affect the shape of spectra. 

  I have developed a novel framework to analyze stellar populations in M31 using high resolution integrated light spectra from the APOGEE survey. By making some crucial, physically motivated assumptions about the bulk motions of stars in M31’s various components, I measured individual metallicity gradients in M31’s classical bulge and bar, and identified a chemodynamically distinct component in M31’s disk.

  I now study some galaxies that are slightly further away: M82, NGC4449, and NGC7793. These galaxies are actively star forming and have interesting dust properties, which I will probe via SED fitting to panchromatic photometry from Hubble and JWST. In the future I plan to combine these techniques with my novel spectroscopic framework to more fully characterize the overlapping stellar populations of nearby galaxies. Such work is vital for expanding our understanding of galactic structure, formation, and evolution in the local universe.

  Speaker: John Soltis (STScI)
  Title: Learning Gaia: Working Towards a Probabilistic Gaia Foundation Model
  Abstract: Even when trained to do relatively simple tasks, like predict the next word in a sentence, machine learning models trained on very large datasets are capable of performing a plethora of complex, previously unspecified tasks. The astronomy community has long sought to develop this type of model, often called a foundation model, using astronomical data. I will discuss ongoing work to develop such a model using Gaia Data Release 3 data, with the aim of creating a model that can accurately reproduce the distribution of Gaia-observed objects. Such a model would be useful for a variety of tasks in astronomy, including analyzing the structure of the Milky Way, identifying structures like streams and clusters, and classifying other objects of interest.