Research Project: Examining the Assembly History of the Universe Using Large Grism Datasets
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Astronomy is entering an age of large surveys and massive data sets. It is important that we adapt and develop methods that will work with these data sets in a statistically sophisticated way. The spatially resolved data sets of space-based slitless grism spectra in the near-IR, taken from the Hubble Space Telescope (HST) and the next generation of space telescopes (JWST and WFIRST), provide highly valuable data on distant galaxies. Gathering this data from the ground is especially challenging given that all rest-optical features shift above ~ 1 µm where studies from ground-based telescopes are subject to higher backgrounds and a high density of telluric emission lines in wavelength space. Slitless grism data provide spectral data from each object in the field, allowing us to constrain stellar population parameters with higher confidence with population statistics. The CANDELS Lyman-a Emission At Reionization survey (PI: C. Papovich) covers 12 fields in the GOODS-North and GOODS-South Deep regions of CANDELS, averaging ~ 400 objects per field. The methods I developed in Estrada-Carpenter et al. 2019 show that grism data on its own is capable of providing constraints on the stellar populations of massive quiescent galaxies. The studies I propose include measuring the (light- weighted) ages, star-formation, and chemical evolution histories of quiescent and star-forming galaxies at z > 1 using existing HST/ WFC3 G102/G141 grism data and photometric data that already exist in the CANDELS/GOODS fields (and possibly other fields). These data will allow me to study the variation of stellar population parameters as a function of mass and activity. I then plan to use these constraints to derive at what redshift this population of quiescent galaxies would have formed and quenched, identify the star- forming progenitors and use analyses of both populations to understand how galaxies form stars, quench, and assemble.
I am an expert in grism analysis and am very familiar with all the difficulties that come from working with these complex data sets. I have already built a similar tools to analyze HST/WFC3 G102 grism spectra for my previous project. My fitting methods includes applying a forward modeling technique which accounts for the morphological broadening seen in grism data. I will update these methods to accommodate the inclusion of additional datasets. For my proposed research I will use the high-performance cluster computers at Texas A&M, which I have previous experience with. I will use Flexible Stellar Population Synthesis models to model my galaxies. These models include the ability to create non-parametric start-formation and chemical evolution histories. In my previous work I used a predefined grid which I marginalized over. I am currently working with a nested sampling algorithm, which allows me to fit many more parameters. All the HST data needed for this study are available (and my adviser is the PI of the main dataset). I will analyze these data, and also adapt the fitting tools to work with data from JWST and WFIRST, which will move this analysis method into the 21st century and enable a larger range of science.
This proposal supports the mission of NASA by achieving the goal to "Explore the origin and evolution of the galaxies, stars and planets that make up our universe" as well as seeking to "understand how the universe has evolved since the Big Bang, and how its constituents were produced" (as seen in Chapter 4.4 of the NASA SMD 2014 Science Plan).
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