Office: PAB 3-351
Phone: (310) 794 9223
I use data and models to study how many ways galaxies could have achieved their observed masses and growth rates. Though specific, this question is central to understanding how galaxies—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, spheroidal objects living in groups are effectively dead today, while isolated disky ones (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 astronomical 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 (representative) objects. As such, the question of whether a galaxy’s observed properties—e.g., mass and growth rate—are the result of a unique path, or common to multiple diverging ones becomes central.
My work reveals that there are a huge(potentially infinite) number of ways to 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 predetermined by the conditions of their birth (with the former being akin to “bad days”). I favor the latter interpretation, but am now working to test this by introducing new observations—e.g., galaxies’ chemical content—that might serve as better “tags” to shrink the inherent uncertainties in matching a galaxy with its plausible progenitors. By tightening evolutionary envelopes, we would bring our data much closer to our physical models, enabling us to learn (hopefully) which of these not only fit the facts, but also explainthem.