Understanding how temperature varies across space and through time is fundamental to hydrologic and ecological study. Modeling within these fields requires realistic near-surface temperature reconstructions to accurately represent site-specific processes. In complex terrain, these representations rely on understanding how temperature varies with elevation and topography. On average globally, temperature decreases with elevation at about 6.5°C per km, termed the lapse rate. However, numerous studies have shown that commonly used models of lapse rate perform poorly in complex terrain, and spatial patterns of temperature vary in response to diurnal and seasonal patterns, topography, and synoptic conditions. Notably, cold air pooling (CAP; the accumulation of sinking cold air in poorly drained topographic features) is a dominant influence on night-time temperatures in mountain terrain, resulting in valley bottoms cooling significantly more than mid-slope elevations. However, the literature has yet to explore whether CAP significantly impacts snowpack development and subsequent spring melt patterns. By leveraging a dense network of temperature sensors and terrain analysis, this study aims to (1) implement and optimize an automated algorithm for mapping CAP in the well-studied East River watershed (Colorado), (2) develop a regional temperature model that accurately captures local variability and spatial patterns of CAP, and (3) integrate these temperatures into a hydrologic model to assess their impacts on snow distributions and melt. Findings will provide insight into local temperature structures relevant to ongoing ecological and hydrologic research in the region, and ultimately inform hydrologic modeling practices in mountain environments worldwide where CAP remains largely overlooked. 

Including updated thermodynamic ice polymorph properties in our planetary structure model predicts that TRAPPIST-1 h can support a subsurface liquid water layer with conduction present. TRAPPIST-1 h is of interest as it may be an ocean world with an icy surface based on observed mass, radius, and instellation. Previous research has created interior models that mathematically derive equations of state (EOS) for ice phases II through VI using ad-hoc parametrizations for density and heat capacity from various sources, which may not be applicable over such a large span of conditions. Notably these previous models predicted no liquid oceans nor ice VII within the hydrosphere. The surface pressure, mass of water, core radius, and metal-silicate core density of planet h remain unknown, leaving the question of how the hydrosphere changes when altering these parameters to reflect past and present ocean worlds. We present new predictions on the structure of TRAPPIST-1 h’s hydrosphere using, for the first time, accurate and self-consistent temperature- and pressure-dependent thermodynamic properties of water and ice polymorphs from the SeaFreeze framework to model the hydrosphere. Specifically, we compute different hydrosphere structures by iterating over a range of iron core fractions (0.05 - 0.9), and comparing models with and without a conductive layer at the top of the ice Ih crust. Results include a series of plausible hydrosphere structures that are consistent with the latest total mass and radius observations from Spitzer data of planet h. These outcomes can help interpret future spectroscopic and photometric observations.

Previous studies have established that galaxy shape and structure, otherwise known as morphology, correlate with environmental density: elliptical galaxies are more prevalent in high-density regions, and spiral galaxies are more prevalent in low-density environments. However, recent studies suggest that stellar mass may primarily drive this trend. In this work, we analyze around 3 million galaxies observed by the Hyper Suprime-Cam survey to reassess the correlation of morphology with large-scale environmental density from a quantitative perspective. The morphological measurements for our galaxies were done using the Bayesian machine learning framework Galaxy Morphology Posterior Estimation Network (GaMPEN). Our analysis employs a Monte Carlo-based framework to account for uncertainties in structural parameter measurements while investigating the correlation between bulge-to-total light ratio, the proportion of light emitted from the center of a galaxy, and environmental density. Leveraging the statistical power of our large dataset, we conclusively demonstrate that the morphology-environment correlation disappears when controlling for stellar mass. Thus, the observed trend arises predominantly because denser environments preferentially host more massive galaxies, making stellar mass the key driver of the morphology-environment relationship. Our results mark a significant advance in addressing this long-standing debate. Furthermore, the methodological framework presented provides a versatile tool for probing the interplay between galaxy properties and the large-scale structure of the universe, which will be particularly valuable in light of ongoing and forthcoming large surveys that supply high-resolution data needed to examine this relationship across extensive cosmic volumes.

Quasars are some of the most luminous objects in the universe. Through analysis of quasar spectra, outflows of gas and dust can be identified by absorption troughs. Outflows that travel at speeds greater than 10% of the speed of light are known as Extremely High Velocity Outflows (EHVOs), and while there have been fewer studies compared to those at lower speeds, they might carry out large amounts of energy due to their higher speeds. The amount of gas in these outflows can be measured and studied through their CIV absorption troughs. However, in some cases, this absorption is contaminated by absorption of other ions at lower speeds. I have developed programming tools to analyze some of these complex EHVO absorption features. I will present the results of applying these techniques to two interesting cases: (1) one of the most luminous quasars in the universe and (2) the fastest known EHVO to date. My work improves the quality of EHVO analysis, resulting in more accurate measurements of absorption of these extreme outflows. This is crucial to obtain better estimates of mass outflow rates and kinetic energies in quasars, of which EHVOs might be some of the largest contributors.

Quasars, located at the centers of distant galaxies, are among the most luminous objects in the universe due to the accretion disks surrounding their central supermassive black holes. By analyzing their spectra, we can observe outflows launched from their accretion disks which grant us insight into their physical and chemical conditions. Some of these outflows, known as Extremely High Velocity Outflows or EHVOs, have been discovered traveling at speeds greater than 10% the speed of light. Due to their extreme speeds, EHVOs carry a significant amount of kinetic energy that could potentially be impacting their host galaxies by either enhancing or quenching their star formation. While outflows traveling at lower speeds have been well studied, there is still much to learn about EHVOs. My project focuses on uncovering the mechanisms that drive EHVOs and the conditions necessary to launch them at such high speeds. To achieve this, I am collaborating with a research team at the University of Nevada, Las Vegas in a theoretical-observational partnership. They generate simulated spectral data of quasar winds using the Sirocco tool, adjusting quasar physical properties such as black hole mass to try and reproduce the conditions that generate EHVOs. We compare these results to observational data from the largest EHVO sample identified in the Sloan Digital Sky Survey’s 16th data release and provide feedback for refining theoretical inputs to better match the data. I will present the results from this work as well as what we have learned from this latest EHVO survey.

Strong gravitational lensing provides a natural magnifying effect for the study of the most distant galaxies. While there have been studies on the physical properties of star-forming clumps in strongly lensed galaxies, there is a critical need to automate the process of identifying these clusters, especially in scenarios where high flux density regions are to be discovered in large imaging surveys. Typical methods of clump identification rely on contrast enhancement through image smoothing and subtraction, followed by the use of visual and automatic source detection software. While generally effective, these approaches require careful parameter tuning and manual validation, limiting their efficiency and reproducibility. We present a novel software pipeline titled SUMAC (Software for Uniform Manifold Approximation of Clusters) that automatically processes FITS files of lensed galaxies, reduces the data using Uniform Manifold Approximation and Projection (UMAP), and outputs a topological map clustering together pixels with similar characteristics. Users can specify parameters of interest, including flux, spectral energy distribution, and morphology. We utilize JWST/NIRCam imagery of the z =2.481 lensed galaxy SGAS1110, confirming the functionality of SUMAC by automatically tagging points in the UMAP topological space, mapping them back to the imagery of the lensed galaxy to show alignment with visual star forming clusters. We additionally analyze spectroscopic data for the galaxy, ensuring pixels that SUMAC identifies as corresponding to star-forming clumps match characteristics such as age, metallicity, and emission line ratios that are indicative of star formation. SUMAC’s ability to handle large datasets efficiently, without requiring manual validation or extensive parameter tuning, ensures a more reproducible and scalable approach to high-redshift galactic analysis. SUMAC has the potential to be a valuable tool in the field of astronomical image processing, increasing the efficiency and accuracy of galactic dynamics studies.