Physicshttp://hdl.handle.net/1773/49562023-12-08T17:11:28Z2023-12-08T17:11:28ZThe Influence of Dark and Ordinary Matter Physics on Galaxy FormationCruz, Akaxia Danaehttp://hdl.handle.net/1773/509002023-09-29T11:22:40ZThe Influence of Dark and Ordinary Matter Physics on Galaxy Formation
Cruz, Akaxia Danae
Overwhelming observational evidence suggests that 85$\%$ of all the matter in the universe is dark matter (DM), a particle whose microscopic properties remain poorly constrained over many orders of magnitude. The current, widely assumed paradigm of a collisionless, cold DM (CDM) and dark energy cosmology called $\Lambda$CDM has proven to be very successful on large scales. Yet, observed galaxies are generally less dense than simple CDM-only predictions, and while CDM is often assumed to be a single, collisionless particle species, there are no Standard Model particles that are similarly collisionless. These discrepancies suggest small-scale problems for the $\Lambda$CDM paradigm and have ignited the astrophysical community to consider models of DM which abandon the collisionless assumption. This thesis details the use of hydrodynamic simulations, analytic and numerical methods, and observations to examine the fundamental nature of DM by asking how altering its microscopic properties can influence the largest scales, with an emphasis on galaxy formation. In particular, using analytic and numerical minimization methods we show that if DM is charged, collective plasma processes may dominate momentum exchange over direct, short-range particle collisions. Using cosmological hydrodynamic simulations, we find that self-interacting DM with an interaction cross-section of $\sigma_{\rm SI} = 1 $cm$^2$/g delays supermassive black hole growth through mergers by billions of years compared to CDM growth. With the same simulations, we show slow accretion of cold clumps through the circumgalactic medium and onto galaxies is an important process that fuels star formation, independent of background DM.
Thesis (Ph.D.)--University of Washington, 2023
Laser Cooling and Trapping of 6Li: Experimental Tools for Many-Body Fermionic Dynamics and Ring TrapTang, Xinxinhttp://hdl.handle.net/1773/509012023-09-29T11:22:41ZLaser Cooling and Trapping of 6Li: Experimental Tools for Many-Body Fermionic Dynamics and Ring Trap
Tang, Xinxin
This thesis delves into the laser cooling and trapping of 6Li, a fermionic atom, with the aimof creating experimental tools for the exploration of many-body fermionic dynamics in quantum
degeneracy, specifically within ring traps. We provide an overview of 6Li properties,
the theoretical underpinnings of many-body fermionic dynamics in cold atom traps, and the
principles of AC Stark shift in far-off-resonance light fields. Our experimental apparatus,
capable of trapping Yb and Li, and completed experiments on YbLi Magnetic Feshbach
Resonance (MFR) and Yb bosonic dynamics are detailed, along with technical adaptations
for laser cooling and trapping of 6Li towards a single-species Fermi condensate. We discuss
our sub-Doppler cooling system, magnetic field stabilization method, and progress towards
quantum degeneracy, as well as a developed method of optical pulses to induce dynamics
in the paired Fermi condensate. Our work on ring traps and entrainment, including the
development of a ring beam setup using a Digital Micromirror Device (DMD), is also elaborated.
The thesis concludes by underscoring the significance of our developments in the
context of many-body fermionic dynamics in quantum degeneracy and the use of ring traps
for studying dual-superfluid systems, contributing to the broader field of ultracold atomic
physics.
Thesis (Ph.D.)--University of Washington, 2023
Beam Dynamics Challenges in the Muon g-2 ExperimentMacCoy, Brynnhttp://hdl.handle.net/1773/508992023-09-29T11:22:38ZBeam Dynamics Challenges in the Muon g-2 Experiment
MacCoy, Brynn
The muon's anomalous magnetic moment $a_{\mu}$ has hinted at physics beyond the standard model for nearly 20 years. The Muon $g-2$ experiment at Fermilab aims to measure $a_{\mu}$ to 140 parts per billion (ppb) precision. The 460 ppb result from its first data run (Run-1), released in 2021, agreed with the previous 2006 Brookhaven Muon $g-2$ result. The experimental average stands in tension with the standard model theory $a_{\mu}$ prediction by $4.2 \sigma$. The result of Run-2/3 data analysis is set be released in summer 2023, and will improve on the Run-1 precision by a factor of two. With the data collected in all six runs, the experiment is on track to produce a 140 ppb measurement of $a_{\mu}$. If the experiment and theory central values are both unchanged, the tension would exceed $5 \sigma$. The measurement is accomplished by injecting muons into a magnetic storage ring and precisely measuring two observable frequencies: $\omega_a$, the muons' anomalous precession frequency, and $\tilde{\omega}'_p$, the precession frequency of protons which determines the magnetic field strength experienced by the muons. This thesis presents a selection of muon beam dynamics effects which are critical for reaching the experiment precision goal. A system of detectors assists with the challenging beam injection into the storage ring, and a measurement of the injected beam provides input for simulating the stored beam dynamics. A new method is introduced to reduce a critical systemic caused by time dependence in the stored beam momentum, enabled by a detector which directly profiles the stored beam. Finally the analysis of $\tilde{\omega}'_p$, the muon-weighted magnetic field, for the Run-2/3 result is presented. Systematics of $\tilde{\omega}'_p$ due to beam effects are evaluated in detail, and shown to be sub-dominant.
Thesis (Ph.D.)--University of Washington, 2023
Machine Learning for Aero-Optical Wavefront Characterization and ForecastingSahba, Shervinhttp://hdl.handle.net/1773/508982023-09-29T11:22:30ZMachine Learning for Aero-Optical Wavefront Characterization and Forecasting
Sahba, Shervin
The laser is a masterwork of the previous century of physics, but to harness its power coherently in the turbulent wilds of the sky remains a challenge. Free-space lasing hosts myriad applications, from direct energy transmissions for defense to secure communication channels that robustly support quantum entanglement. Aero-optics is the multi-disciplinary field underpinning such optical transmissions through atmosphere and fluid flows. Turbulent variations in the index of refraction, like those forming around boundary layers of airborne optical platforms, manifest aberrated wavefronts. Forecasting these rapid phase distortions allows us to rectify laser transmissions via adaptive optic engineering. Doing so hinges on the development of low-latency predictive techniques, for which we turn to advancements in data-driven algorithms and deep learning. This thesis thus introduces key concepts of aero-optics, wavefront sensing, and machine learning for data-driven physics. We then demonstrate three machine learning methodologies - optimized Dynamic Mode Decomposition, sensor fusion through shallow decoder networks, and forecasting via recurrent neural networks with shallow decoder outputs — for robust aero-optical wavefront sensing.
Thesis (Ph.D.)--University of Washington, 2023