This research endeavor explores the 1964 reasoning of Irish physicist John Bell and how it pertains to the provoking Einstein-Podolsky-Rosen Paradox. It is necessary to establish the machinations of formalisms ranging from conservation laws to quantum mechanical principles. The notion that locality is unable to be reconciled with the quantum paradigm is upheld through analysis and the subsequent Aspect experiments in the years 1980-1982. No matter the complexity, any local hidden variable theory is incompatible with the formulation of standard quantum mechanics. A number of strikingly ambiguous and abstract concepts are addressed in this pursuit to deduce quantum's validity, including separability and reality. `Elements of reality' characteristic of unique spaces are defined using basis terminology and logic from EPR. The discussion draws directly from Bell's succinct 1964 Physics 1 paper as well as numerous other useful sources. The fundamental principle and insight gleaned is that quantum physics is indeed nonlocal; the door into its metaphysical and philosophical implications has long since been opened. Yet the nexus of information pertaining to Bell's inequality and EPR logic does nothing but assert the impeccable success of quantum physics' ability to describe nature.

The Star Planet Activity Research CubeSat (SPARCS) will be a 6U CubeSat devoted to photometric monitoring of M dwarfs in the far-ultraviolet (FUV) and near-ultraviolet (NUV) (160 and 280 nm respectively), measuring the time-dependent spectral slope, intensity and evolution of M dwarf stellar UV radiation. The delta-doped detectors baselined for SPARCS have demonstrated more than five times the in-band quantum efficiency of the detectors of GALEX. Given that red:UV photon emission from cool, low-mass stars can be million:one, UV observation of thes stars are susceptible to red light contamination. In addition to the high efficiency delta-doped detectors, SPARCS will include red-rejection filters to help minimize red leak. Even so, careful red-rejection and photometric calibration is needed. As was done for GALEX, white dwarfs are used for photometric calibration in the UV. We find that the use of white dwarfs to calibrate the observations of red stars leads to significant errors in the reported flux, due to the differences in white dwarf and red dwarf spectra. Here we discuss the planned SPARCS calibration model and the color correction, and demonstrate the importance of this correction when recording UV measurements of M stars taken by SPARCS.

The research presented in this Honors Thesis provides development in machine learning models which predict future states of a system with unknown dynamics, based on observations of the system. Two case studies are presented for (1) a non-conservative pendulum and (2) a differential game dictating a two-car uncontrolled intersection scenario. In the paper we investigate how learning architectures can be manipulated for problem specific geometry. The result of this research provides that these problem specific models are valuable for accurate learning and predicting the dynamics of physics systems.<br/><br/>In order to properly model the physics of a real pendulum, modifications were made to a prior architecture which was sufficient in modeling an ideal pendulum. The necessary modifications to the previous network [13] were problem specific and not transferrable to all other non-conservative physics scenarios. The modified architecture successfully models real pendulum dynamics. This case study provides a basis for future research in augmenting the symplectic gradient of a Hamiltonian energy function to provide a generalized, non-conservative physics model.<br/><br/>A problem specific architecture was also utilized to create an accurate model for the two-car intersection case. The Costate Network proved to be an improvement from the previously used Value Network [17]. Note that this comparison is applied lightly due to slight implementation differences. The development of the Costate Network provides a basis for using characteristics to decompose functions and create a simplified learning problem.<br/><br/>This paper is successful in creating new opportunities to develop physics models, in which the sample cases should be used as a guide for modeling other real and pseudo physics. Although the focused models in this paper are not generalizable, it is important to note that these cases provide direction for future research.

With the extreme strides taken in physics in the early twentieth century, one of the biggest questions on the minds of scientists was what this new branch of quantum physics would be able to be used for. The twentieth century saw the rise of computers as devices that significantly aided in calculations and performing algorithms. Because of the incredible success of computers and all of the groundbreaking possibilities that they afforded, research into using quantum mechanics for these systems was proposed. Although theoretical at the time, it was found that a computer that had the ability to leverage quantum mechanics would be far superior to any classical machine. This sparked a wave of interest in research and funding in this exciting new field. General-use quantum computers have the potential to disrupt countless industries and fields of study, like physics, medicine, engineering, cryptography, finance, meteorology, climatology, and more. The supremacy of quantum computers has not yet been reached, but the continued funding and research into this new technology ensures that one day humanity will be able to unlock the full potential of quantum computing.