A simulation of two massive galaxies merging about 820 million years after the Big Bang (Lower et al, 2022 in prep). On the right is the rate of star formation over time; after the merger occurs, a burst of star formation happens as a results of huge inflows of gas - the fuel for star formation - are funneled into the galaxy.

My research interests are broadly described as galaxy formation and evolution. Most of my undergraduate research focused on dusty galaxies in the early Universe, studying their properties, learning a lot about galaxy spectral energy distribution (SED) modeling, and figuring out how to think like a scientist.

At University of Florida, I've been working on my PhD with Desika Narayanan. I dabbled a bit more in SED modeling and published two papers using cosmological simulations to 'ground-truth' SED modeling techniques by understanding the assumptions we make when asking a simple question like “How do we measure the stellar mass of this galaxy?”

As part of my thesis, my current work is again focused on the first dusty galaxies in the Universe and understanding the origin of massive galaxies. Namely, asking questions like how they achieved huge star formation rates, how they formed huge reservoirs of dust, and what is their fate as they evolve over cosmic time. I spend my time analyzing hydrodynamic simulation outputs, thinking about how to visualize large and multi-dimensional datasets, and finding causal connections between the complex physical systems that drive galaxy formation and evolution.

Below you'll find more in depth descriptions of the projects I've lead or co-lead at UIUC and UF.


Improving Galaxy SED Modeling With Cosmological Simulations

The primary method for inferring the stellar mass of a galaxy is through spectral energy distribution (SED) modeling. However, the technique rests on assumptions such as the galaxy star formation history (SFH) and dust attenuation law that can severely impact the accuracy of derived physical properties from SED modeling. I'm interested in examining the impact of various model assumptions on inferred galaxy properties, and the projects I'm working on are summarized below:

Nonparametric SFHs: First, in Lower et al. 2020, I examine the efficacy of non-parametric SFHs, that is, models for the star formation history of a galaxy that do not explicitly assume a shape, and the contribution to the uncertainty on inferred galaxy stellar masses from the SFH alone.

Mirkwood ML-Powered SED Modeling: In Gilda, Lower, & Narayanan 2021, we develop a machine learning framework, trained on a suite of 3 cosmological hydrodynamical simulations, to infer galaxy physical properties from broadband photometry. Our fiducial model outperforms state-of-the-art Bayesian inference based SED fitting, paving the way for next-generation machine learning based SED modeling.

Accounting for Geometry in Dust Attenuation Models: My current work expands on Lower et al. 2020 by incorporating the impact of diffuse dust and developing a model for dust attenuation that includes a term to describe the star-dust geometry of a galaxy. Namely, we show that allowing deviations from a uniform screen model, in which all stellar light is attenuated by equal optical depth, our ability to infer the true galaxy attenuation curves, and most importantly, dust-corrected star formation rates is greatly improved over traditional methods.

Infrared and Radio Properties of High Redshift Dusty Star Forming Galaxies

My first experiences with research in undergrad at UIUC were centered around galaxies discovered with the South Pole Telescope (SPT). These galaxies comprise a catalog of systematically selected gravitationally lensed dusty galaxies. Gravitational lensing results from massive foreground galaxies or galaxy clusters bending light emitted from background galaxies towards Earth, magnifying the brightness which allows fainter and higher redshift galaxies to be seen. As a consequence, these sources are selected to be at high redshift and are very luminous at sub-mm wavelengths. Under the supervision of Dr. Joaquin Vieira, my work broadly involved characterizing these sources in terms of their physical properties. I estimated the number density of the SPT source catalog, fit modified blackbody SEDs to their photometry to estimate dust properties, and analyzed radio data from ATCA to determine the far-infrared radio correlation at high redshift.

Publications & Press

First and Second Author

  • How Well Can We Measure Galaxy Dust Attenuation Curves? The Impact of the Assumed Star-Dust Geometry Model in SED Fitting, Sidney Lower, Desika Narayanan, Joel Leja, Benjamin D. Johnson, Charlie Conroy, Romeel Davé; 2022, accepted ApJ, arXiv:2203.00074

  • Mirkwood: Fast and Accurate SED Modeling Using Machine Learning, Sankalp Gilda, Sidney Lower, Desika Narayanan; 2021, ApJ, 916, 43

  • How Well Can We Measure the Stellar Mass of a Galaxy: The Impact of the Assumed Star Formation History Model in SED Fitting, Sidney Lower, Desika Narayanan, Joel Leja, Benjamin D. Johnson, Charlie Conroy, Romeel Davé; 2020, ApJ, 904, 33

Collaborating Author

  • Quenching and the UVJ Diagram in the SIMBA Cosmological Simulation, Hollis Akins, Desika Narayanan, Katherine Whitaker, Romeel Davé, Sidney Lower, Rachel Bezanson, Robert Feldmann, Mariska Kriek; 2022, ApJ, 929, 94

  • SQuIGGLE: Studying Quenching in Intermediate-z Galaxies— Gas, AnguLar Momentum, and Evolution, Wren Suess, Mariska Kriek, Rachel Bezanson, Jenny Greene, David Setton, Justin Spilker, Robert Feldmann, Andy Goulding, Benjamin D. Johnson, Joel Leja, Desika Narayanan, Khalil Hall-Hooper, Qiana Hunt, Sidney Lower, Margaret Verrico; 2022, ApJ, 926, 89S

  • POWDERDAY: Dust Radiative Transfer for Galaxy Simulations, Narayanan, D., Turk, M. J., Robitaille, T., Kelly, A. J., et al., including S. Lower, 2021, ApJS, 252, 12

  • Galaxy Growth in a Massive Halo in the First Billion Years of Cosmic History, Marrone, D. P., J. S. Spilker, C. C. Hayward, J. D. Vieira, et al., including S. Lower, 2018, Nature, 553, 51