I use data and models to study how galaxies grow into their observed masses, sizes, and structural properties. Though specific-sounding, this question is central to understanding how the universe’s machines for turning gas into stars and, ultimately, life emerged from the basic laws of physics.
As they grew over the past ~12 billion years, galaxies morphed from chaotic clumps to organized disks and spheroids, and migrated from isolated to dense environments. A striking reduction in their ability to form new stars (i.e., their health) accompanied these changes, such that the universe is ~3 times less productive now than when Earth formed. While all systems have slowed, spherical objects living in groups of other galaxies are effectively dead today, while isolated disky galaxies like our Milky Way are relatively active.
Astronomers seek causation amidst these trends to build a theory of why galaxies are the way they are. Unfortunately, theories describe galaxy histories, but our data capture only single moments in their lives. To progress, we stitch together snapshots of different galaxies seen at different times to reconstruct hypothetical histories of individual (hopefully representative) objects.
As such, the central question is whether a galaxy’s observed properties—e.g., its mass and growth rate—are the result of a unique(ish) path, or are common to multiple diverging ones. My work reveals that there are a huge (infinite?) number of ways to meaningfully connect galaxies across time, such that we cannot (yet) determine if their evolution is mainly driven by events like supernovae, black hole jets, and collisions, or is predetermined by the conditions of their birth/early childhood. I favor the latter interpretation, but am now working to test it by using new Magellan observations and models developed at Carnegie to illuminate the true biographies of individual galaxies, allowing them to be much more robustly connected to plausible progenitors and ancestors in other datasets. By tightening evolutionary envelopes, we would bring our empirical inferences much closer to our physical theories, enabling us to learn (hopefully) which of these not only fit the facts, but also explain them.
PhD, Astronomy & Astrophysics --- The University of Chicago, 2015
AB, Physics --- Columbia University, 2009
• I was a postdoc at UCLA from 2015 to 2018.