Quantum Chromodynamics (QCD) describes the strong nuclear force governing interactions among quarks and gluons. Under extreme temperature and energy conditions, such as those created in relativistic heavy-ion collisions, matter transitions into a quark-gluon plasma (QGP), where quarks and gluons become deconfined. These conditions also enable the study of novel quantum phenomena such as the Chiral Magnetic Effect (CME), which arises from the interplay between topological gluon fields and strong magnetic fields generated in non-central collisions. In this project, we focus on simulating CME-driven charge separation using the event-by-event anomalous viscous fluid dynamics (EBE-AVFD) framework. We will analyze CME-sensitive observables, including the three-particle correlator γ, as a function of the number of spectator protons, which serves as a proxy for the magnetic field strength. This allows us to investigate the expected dependence of CME signals on the initial electromagnetic field. To disentangle possible background contributions, we will also study related observables such as elliptic flow (v₂), the two-particle correlator δ, and higher-harmonic correlators such as γ₁₃₂. These measurements help quantify non-CME background effects arising from collective flow and other sources. Simulation results will be analyzed within a ROOT-based framework and used to establish analysis strategies and benchmarks for CME-sensitive and background observables. This study paves the way for forthcoming real-data analyses at RHIC.

Next-generation gravitational wave observatories will measure signals from merging black holes with unprecedented precision, demanding equally precise theoretical models to interpret these observations. Current models include only the simplest description of how the surrounding astrophysical environment influences a merger. One important environmental effect is dynamical friction -- a gravitational drag force produced when a compact object moves through a surrounding gas or fluid. Although this effect can significantly alter the observable gravitational wave signal, theoretical predictions still largely rely on a leading-order formula first written by Chandrasekhar in the 1940s. To address this limitation, we apply powerful perturbative techniques from particle physics to construct a hydrodynamic effective field theory for dynamical friction. This framework allows the drag force to be computed systematically to any precision through a Feynman diagram expansion in graviton interactions. Using this approach, we calculate the next-to-leading-order correction to dynamical friction for the first time. Our results establish a foundation for predicting environmental effects on gravitational wave signals with the precision required for upcoming detectors. Further, this work suggests a common theoretical language for computing precision drag forces on both quarks at the Large Hadron Collider and black holes adrift in the cosmos.

A longstanding area of research in astrophysics and cosmology is how large-scale structure forms from the remnants of the Big Bang in the early universe and how it evolves over time. The latest astronomical instruments, like the James Webb Space Telescope, finally allow us to observe the earliest epochs of the universe. To understand the processes driving this evolution, it is important to develop theoretical models that describe them. The most detailed models involve the growth of dark matter structure as the main driving factor in the evolution of galaxies, especially in the first billion years of our universe’s history. Current theoretical frameworks of galaxy formation in the early universe can generate predictions that match observations of high-redshift galaxies. However, these semi-analytical models often rely on an oversimplification of the evolution of dark matter halos, using smooth fitting formulae to infer their growth rates, which then dictate the growth of their associated galaxies. In my project, I use a semi-analytical framework based on the Extended Press-Schechter formalism to explore how a more nuanced approach—accounting for dark matter halo mergers in a model that connects galaxies to these halos—can improve our understanding of galaxy formation.

Networks appear everywhere, from neuronal networks in the brain to power grids and social systems. While often studied as fixed structures, real-world networks are constantly growing and evolving. Understanding how collective behavior constrains and guides network growth is an important question across many fields. In this project, we study how synchronization guides network formation in growing networks using the Kuramoto model, a mathematical framework for describing interacting oscillators. Motivated by neuronal systems, where a balance between excitation and inhibition is essential for stable dynamics, we investigate how the ratio of excitatory and inhibitory nodes shapes network growth. We simulate network growth under a range of conditions, varying the proportion of inhibitory nodes and the target synchronization levels, examining how composition influences the ability to synchronize. Our preliminary results suggest that the balance between excitatory and inhibitory nodes plays a key role in determining whether synchronization occurs, and that this effect depends on the desired level of coherence. Rather than following a simple trend, the relationship is nuanced, highlighting how small changes in composition can lead to different behaviors. These findings provide insight into how complex systems maintain stable patterns of activity as they grow and change.

The ionosphere is the partially-ionized part of the upper atmosphere, which interacts with solar winds. We evaluate the electron temperature (Te) predictions in the polar cap ionosphere from the High-latitude Ionosphere Dynamics for Research Applications (HIDRA) model against observations from the Resolute Bay Incoherent Scatter Radar North (RISR-N) facility. Temperature of ambient plasma electrons in the ionosphere (Te) modeling is critical because of its direct relationship to the topside scale height Ht, which characterizes the vertical gradient of plasma density in the topside ionosphere. We analyze two significant geomagnetic storms — November 27–29, 2011 and March 6–8, 2016 — categorizing each into quiet and active periods by geomagnetic activity. Median profiles extracted from HIDRA over 250–400 km altitude are binned by solar zenith angle (SZA) and compared against RISR-N observations. We find that HIDRA improves from earlier ionospheric models, qualitatively preserving the shape and drop-off rate of the observed Te–SZA distribution across both storms and activity levels. However, HIDRA exhibits a systematic shift toward higher SZA. Improved understanding of the polar cap electron temperature and topside scale heights are needed for improving predictions of ion escape rates into the magnetosphere and magnetosphere-ionosphere coupling processes that affect space weather.