The COVID-19 pandemic spotlighted Asian American (AA) mental health and their foreign position, underscored by anti-Chinese rhetoric and policies and the concurrent surge in hate incidents, crimes and xenophobia against AAs. However, framing COVID-19 as the sudden cause of anti-Asian hate facilitates misconceptions about AAs: it undermines a storied history in which AAs have long survived and challenged their treatment as “forever foreigners.” To understand the extent to which anti-Asian hate and mental health challenges among AAs during COVID-19 was a unique event in AA racialization, it is paramount to understand AA marginalization and its implications for mental health prior to COVID-19. To do so, I use the 2016 Collaborative Multiracial Postelection Survey (CMPS). The survey gathers data on respondent attitudes across a diverse set of political, psychological and socio-demographic measures and is ideal for Asian ethnic subgroups and intersectional analyses of gender, age and immigration generation. My analysis includes bivariate and multivariate regression analyses using the sample of AAs (N= 3006). Thus far, I have found that a substantial proportion of AAs indicate low feelings of belonging in the U.S., personal experiences of racial discrimination, and depressive symptoms. For the project’s next step, I will examine the extent to which AAs share a common foreign experience prior to COVID-19 and how this contributes to depressive symptoms. I expect that there will be significant subgroup differences at the intersections of ethnicity, gender, and immigration generation in the relationship between experiences of racial discrimination, feelings of belonging, and depressive symptoms when community involvement and local contextual factors are considered. This work helps situate anti-Asian hate during the pandemic as part of a more extensive history of racism in the U.S. and broadly, contributes to more informed mental health services by highlighting the implications of racism for mental health.

Modern astronomical survey telescopes, like the Vera C. Rubin Observatory and Extremely Large Telescope, are projected to produce terabytes of data each observing night, raising the need for efficient machine learning algorithms to flag astronomical data for further study. One such algorithm is the Random Forest (RF) algorithm, which has previously been demonstrated to process some 2.36 million different galaxy spectra data (in 12 hours over 128 CPUs) for any potentially unique or even undiscovered phenomena. However, this previous demonstration used spectra taken from the combined light of a given galaxy. RF algorithms are ensembles of structures called “decision trees,” which categorize data points with value-comparison questions. This method can be extended to create a metric to calculate how unusual a data point is compared to other points in the dataset. Our project will extend this RF algorithm for the Mapping of Nearby GAlaxies survey (MaNGA), which accounts for the fact that spectra vary with regions of a galaxy. We explore the behavior of the RF algorithms when accounting for these spatially-varying features. Our methods include the generation of synthetic data to train random forest algorithms, RF model hyperparameter searches, and comparison of models. Furthermore, our project compares the similarity of our results to the results from Baron and Poznanski (2016), which previously applied the RF algorithms to the 2.36 million spectra data. We present the conclusions from the RF algorithms and whether prevalent emission lines are flagged by the algorithms, such as hydrogen-alpha and O-III lines. Our project also discusses the features characteristic of outlier galactic region spectra data. Successful implementation of the RF algorithms to process data pipelines from upcoming large surveys has the potential to accelerate the rate of astronomical discoveries to unprecedented levels.

The largest scale structure in the universe creates a cosmic web. Nodes of the web are connected by mega-parsec scale filaments of warm gas, galaxies, and cold dark matter. Cosmic filaments are typically assumed to form and cool uniformly in many cosmological models. Recent works suggest that the internal structure, particularly with regard to the cooling mechanism, may be more complex. Gas clouds larger than a characteristic length appear to shatter as they cool below 106 K, fragmenting into smaller cloudlets. Other factors may also contribute to a more turbulent and irregular structure. The behavior of these filaments has implications for many of the issues at the forefront of astronomy. Cold dense clouds could create Lyman limit systems in the early universe, restricting the the distance that ionizing photons can travel. Further, the structure of these filaments may influence the mass of dark matter and constraints on cosmological models. To study these systems, I am simulating the formation of a single cosmic filament using a cosmological hydrodynamics code. Together with my mentor, I derived functions for the initial displacement and velocities of the particles from theory to create the desired collapse of matter into a filament. I then run the simulations with these initial conditions on a supercomputer, enabling simulations with tens of millions of particles. The simulations have mass resolutions that have not been previously achieved, allowing us to better understand the internal behavior. Preliminary findings indicate that there is indeed a complicated structure within the filaments.

Massive stars place powerful constraints on stellar evolution and exhibit a wide range of exotic evolutionary phases. They play a crucial role in regulating their environments, driving the chemical evolution of host galaxies, and establishing energy equilibrium through feedback processes. Stellar variability, notably, acts as a profound probe into the poorly-constrained physics of massive star evolution, illuminating intrinsic properties such as surface gravity. Drawing upon the collective insights from past literature on the dynamics of stellar rotation and surface gravity, this project delves into the correlation between variability metrics from the Zwicky Transient Facility (ZTF) and surface gravity measurements from the Apache Point Observatory Galactic Evolution Experiment (APOGEE) data. By merging these datasets, our aim is to use modern statistical methods to decode the relationship between observed variability and surface gravity in massive stars, shedding light on their rotational behaviors and structural changes over time. This endeavor not only seeks to deepen our understanding of stellar mechanisms but also to improve the precision in classifying stellar masses by utilizing variability as a key diagnostic tool. We endeavor to connect empirical observations with theoretical frameworks, paving the way for future advancements in our comprehension of stellar dynamics and their cosmic significance. Our results will place key constraints on the study of massive stars.

 The Circumgalactic Medium (CGM) is an extended structure surrounding galaxies, populated with hot diffuse gas and cold dense clouds of gas. The CGM acts as an intermediary between gas within galaxies (Interstellar Medium, or ISM) and gas between galaxies (Intergalactic Medium, or IGM). The mass of a given galaxy's ISM at the time of its formation is much too small to fuel star formation over galactic timescales, so gas must be accreted from the CGM to continue fueling star formation, and insights into the properties of this gas could improve our understanding of galaxy evolution.  During galaxy formation, gas heats up as it falls into dark matter halos from the cosmic web, forming the hot CGM. This gas then cools radiatively (through metal lines and Bremsstrahlung emission in the UV and X-Ray). The time required for this gas to cool and collapse depends on the properties observed from emission—hotter gas has more energy to radiate, high density or metal-rich gas cools more efficiently. If the cooling time is short, heating is required to prevent the gas from collapsing onto the galaxy; one such mechanism is the galactic winds driven by supermassive black holes and supernovae. The details of these heating processes are still uncertain and are a topic of active research in the field. For example, recent observations from the X-Ray telescope eROSITA have been shown to conflict with numerical hydrodynamic simulations of galaxy formation.  In our work we construct analytical models of the CGM with parameters such as density and temperature distribution and spatial extent. We calculate the gas mass and luminosity of the CGM to constrain the reasonable values of the model parameters. We will then use detailed spectral models of gas emission to compare our calculated luminosities to those observed and relate them to heating mechanisms.

Recently there has been interest in two possible sources of mass in the outer solar system. First, observations of recently discovered remote outer solar system objects have suggested the presence of a ninth planet. Different numerical simulations have suggested either a less massive (1.5-3 Earth masses) planet with a semimajor axis of 250-500 AU from the Sun (the Earth orbits at 1 AU), or a more massive (5-15 Earth masses) planet at 400-800 AU. Second, data from the New Horizons spacecraft has suggested that there may be an additional roughly circular belt of objects, similar to the Kuiper Belt, beyond 60 AU. This raises the question of whether this belt would be compatible with some or all of the proposed forms of planet 9. To answer this question, I ran a series of orbital dynamics simulations with randomly generated test particles representing the proposed second Kuiper Belt, and different masses and orbital parameters for planet 9. By looking at how planet 9 changed the orbits of the test particles over the period of the simulation, I concluded that although planet 9 would not significantly affect objects orbiting at 60-100 AU, in the most extreme cases, it would significantly broaden the distribution of orbital inclinations of objects beyond 100 AU. Astronomical deep and wide surveys conducted over the next few years have the potential to detect both planet 9, and objects beyond the Kuiper Belt. If second Kuiper Belt objects are discovered, these objects having a wider-than-expected range or orbital inclinations would point to gravitational disturbances, such as those caused by planet 9. Alternatively, if planet 9 is discovered, these simulations suggest that a second Kuiper Belt would need to be more inclined than has been so far assumed.

Active galactic nuclei (AGNs) are galaxies that consist of a supermassive black hole at the center. Quasars are a type of AGN, which are up to thousands of times more luminous than entire galaxies due to the active accretion of gas onto the supermassive black hole. When active accretion shuts off, a quasar could change into a more normal galaxy. According to standard theory, such significant changes in accretion processes are predicted to occur over timescales of 10,000 to 100,000 years. However, recent studies have discovered a new and unusual type of quasars that undergo dramatic change on timescales of decades to years, contrary to predicted timescales. These objects are dubbed “changing-look quasars” (CLQs). To better understand the physical processes behind CLQs, more CLQ spectra must be identified and examined to perform further statistical analyses. This study analyzes on the order of 102 quasar spectra from the Sloan Digital Sky Survey (SDSS). For each object analyzed, we first assess whether the SDSS spectroscopic pipeline got the classification and redshift correct. In multi-epoch spectra, we then identify spectral features that change over time to distinguish CLQs from regular quasars with relatively constant accretion. This is done by looking at changes in velocity widths of emission lines in at least one of the common quasar emission lines. By producing a larger statistical sample of new CLQs, this study can reveal new information about accretion changes in AGNs, improve our understanding of accretion physics near supermassive black holes, and potentially challenge the standard theory of accretion.

The backsplash galaxies of the Milky Way are galaxies that have once entered the virial radius of the Milky Way but reside outside of which today. As a backsplash galaxy enters the Milky Way, its gravitational interaction with the Milky Way causes its star forming material to be stripped away and causes it to appear to be more diffused and older. The evolution and properties of a backsplash galaxy depend significantly on the properties of its dark matter halo as it makes up the majority of its mass. In my research, I use cosmological simulations of Cold Dark Matter (CDM) and Self-Interacting Dark Matter (SIDM) of Near-Milky Way halos done by my mentors and their colleagues to identify and analyze the properties of backsplash halos during their evolution and compare the results across the two dark matter models. Significant differences between the results from the CDM and the SIDM models are anticipated, with the major difference caused by the interactions between the SIDM particles allowing the exchange of energy and momentum between particles, causing the energy to transfer between regions of the halo, resulting in altered density profiles which influences the tidal evolution history. After the analysis of both models are completed, the results can be compared and matched to observational data of the candidates of backsplash galaxies of the Milky Way, and conclude in each model’s ability to make accurate predictions. This research contributes to the ongoing investigation of the properties of dark matter particles and the analysis of the evolution of backsplash galaxies.

The Monte Carlo Physarum Machine (MCPM) is a modeling program constructed similarly to the more traditional Monte Carlo Markov Chain. The program is modeled after the Physarum slime mold, an organism which seeks out nutrients to absorb and consume. When applied to matter distribution in the universe, MCPM is designed to seek out the largest density objects, primarily in the form of galaxies and galactic clusters. Given previous applications of the program, I wanted to know whether it could be put into further use to produce accurate predictions of the current structure of matter in the universe. We call these large structures of matter that make up the universe Large Scale Structures (LSS). To determine the accuracy for LSS environment predictions via MCPM, I am cross comparing the results from the Value Added Catalog (VAC), obtained using MCPM, with the results from the most recent Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) catalog. The VAC, which was the first catalog of matter dense galaxies obtained using MCPM, and MaNGA both possess matter density data that I have compared to determine if the same galactic structures were documented. The step that I am currently working on is seeing if I can replicate the VAC results with data compiled in the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX) catalog. I am currently still working with and sorting through the data from HETDEX to produce the most accurate results. If I can determine with certainty MCPM produces the same results as other methods, its use as a primary data modeling process in cosmology would establish a concrete method for mapping the cosmos with unprecedented accuracy and reliability. Continued work with MCPM will allow for further advancements in its modeling and mapping procedures and push the boundaries of cosmological modeling and prediction.

The expected launch of the Nancy Grace Roman Space Telescope (Roman), a next-generation space-based infrared observatory, will soon allow us to observe fields in minutes that would previously have taken months to cover. As such, it will advance our knowledge of galactic structure and evolution at a rapid rate. To better leverage the influx of science that will come out of the launch and commissioning of Roman, we are developing a pipeline that simulates observational images taken by Roman and then performs photometry on the images. As a first step, we are testing methods for recovering dwarf galaxies from photometry catalogs that contain both a dwarf and a surrounding stellar halo. In order to produce mock Roman observations, we use the Space Telescope Science Institute's Space Telescope Image Product Simulator (STScI-STIPS) software tools, which are able to add background galaxies, realistic background levels, and noise along with source catalogs to produce simulated images. Our early testing uses input catalogs designed to simulate typical dwarf galaxy characteristics added to stellar catalogs generated from numerical simulations that mimic the nearby spiral galaxy M81. The ultimate goal of this pipeline software is to determine the observational strategy to resolve structures in extended stellar halos of nearby galaxies. Such measurements would allow us to distinguish between formation and evolution scenarios for these halos. This work is part of the Roman Infrared Nearby Galaxies Survey (RINGS), a large Roman Wide-Field Science (WFS) program funded by NASA under grant 80NSSC24K0084.

Local Group galaxies are the closest ones we can study in detail to decipher the processes that shape the universe around us. An interesting property of these galaxies is their star formation history (SFH), which provides a fossil record of when stars were formed in a galaxy. The process by which this occurs is a complex interplay between the gas, the interstellar medium (ISM), and the energy from newly formed stars. By pairing SFH measurements with data on the galaxy’s gas content, we can investigate the timescales on which young massive stars affect the structure of the surrounding gas in the (ISM) as well as its ability to form more stars. Furthermore, since star formation is closely linked to the properties of the gas in a galaxy, such as metallicity and extinction, the SFH also probes these properties. By utilizing resolved stellar photometry from the Hubble Space Telescope (HST), I measured the SFH for four Local Group galaxies (IC10, IC1613, WLM, and NGC 6822) that already have detailed imaging of their gas content from radio observations. First I measured the colors and brightnesses of resolved stars in each galaxy from the HST imaging. Next, I generated and processed a set of artificial stars using the same photometry pipeline as the real observations to provide statistical measures of our data quality. With the processed artificial stars and the original photometry, I then fitted a series of model Hess diagrams for a range of ages and metallicities to obtain each galaxy’s SFH. These measurements allow us to pair this SFH with other observational data. For example, we can map star formation and compare it with observations such as supernovae locations, and we can explore links between the star formation and the ISM as measured through emission from neutral and ionized hydrogen.