Session C: 1:45PM – 3:15PM

Sciences. Session C – Oral Presentations, Henriksen, Alumni House

SESSION C (1:45-3:15PM)
Location: Henriksen Room, Alumni House


A catalog of nearby accelerating star candidates in Gaia DR3
Joshua Hill, University of Utah
Marc Whiting, University of Utah

Faculty Mentor Ben Bromley, University of Utah

SESSION C 1:45-2:00PM
Henriksen Room (1st floor), Alumni House
Science and Technology

We describe a new catalog of accelerating star candidates with Gaia G ≤ 17.5 and distances d ≤ 100 pc. Designated as Gaia Nearby Accelerating Star Catalog (GNASC), it contains 28,218 members identified using a supervised machine-learning algorithm trained on the Hipparcos-Gaia Catalog of Accelerations, Gaia Data Release 2, and Gaia Early Data Release 3. We take advantage of the difference in observation timelines of the two Gaia catalogs and information about the quality of the astrometric modeling based on the premise that acceleration will correlate with astrometric uncertainties. Catalog membership is based on whether constant proper motion over three decades can be ruled out at high confidence (greater than 99.9%). Test data suggests that catalog members each have a 68% likelihood of true astrometric acceleration; subsets of the catalog perform even better, with the
likelihood exceeding 85%. We compare the GNASC with Gaia Data Release 3 and its table of stars for which acceleration is detected at high confidence based on precise astrometric fits. Our catalog, derived without this information, captured over 96% of sources in the table that meet our selection criteria. In addition, the GNASC contains bright, nearby candidates that were not in the original Hipparcos survey, including members of known binary systems as well as stars with companions yet to be identified. It thus extends the Hipparcos-Gaia Catalog of Accelerations and demonstrates the potential of the machine-learning approach to discover hidden partners of nearby stars in future astrometric surveys.



Bronco: A programming language for generating stories
Jonas Knochelmann, University of Utah

Faculty Mentor Rogelio Cardona-Rivera, University of Utah

SESSION C 2:05-2:20PM
Henriksen Room (1st floor), Alumni House
Science and Technology

We present Bronco: an in-development authoring language for Turing-complete procedural text generation. Our language emerged from a close examination of existing tools. This analysis led to our desire of supporting users in specifying yielding grammars, a formalism we invented that is more expressive than what several popular and available solutions offer. With this formalism as our basis, we detail the qualities of Bronco that expose its power in author-focused ways.


A Review of The Use of Machine Learning in Cybersecurity and Cyber Attacks
Connor Scott, Utah Valley University

Faculty Mentor Sayeed Sajal, Utah Valley University

SESSION C 2:25-2:40PM
Henriksen Room (1st floor), Alumni House
Science and Technology

As technology has improved cyber criminals have developed newer and more sophisticated methods of attack. As a result cybersecurity professionals have needed to adapt and improve their own methods of defending against cyber threats. One technology that is increasingly being leveraged against such threats is machine learning. Machine learning is an aspect of artificial intelligence (AI) and computer science that uses data and algorithms to build models of underlying patterns allowing for the prediction of future data and classifying current data. In the context of cybersecurity machine learning can be used in a variety of ways including monitoring activity within a network in order to detect malicious or abnormal activity, monitoring background activity of individual computers in order to detect malware, as well as other uses. By collecting data and building a model of normal patterns, machine learning allows cybersecurity professionals to automate the process of monitoring systems in real time in order to immediately detect abnormal activity and rapidly respond to threats and breaches. Its use however is not limited to cybersecurity professionals but also by cyber criminals. Cyber attacks are now being executed using machine learning as well. Increasingly sophisticated machine learning models are being used to execute attacks in new ways. Some ways machine learning is being used in cyber attacks include using it to evade web filters, bypass CAPTCHA checks, and creating targeted phishing emails and messages. As the technology has evolved newer methods are being used both in offense and defense and recent trends indicate that it will continue to be an important subject in regards to the future of the cybersecurity field. In this paper I will endeavor to create a comprehensive review and explanation of how this technology is being used both by cybersecurity professionals and cyber criminals, its strengths and weaknesses in regards to current practices and future trends


Constructing Time  in a  Closed Dynamical System
Zachary Zito, Utah State University

Faculty Mentor Charles Torre, Utah State University

SESSION C 2:45-3:00PM
Henriksen Room (1st floor), Alumni House
Science and Technology

The role of a time parameter is vital to a study of Physics, yet is often taken for granted. The traditional use of a constant, immutable time variable necessarily relies upon notions that are fundamentally unmeasurable and must, therefore, be assumed. Here, a simple, classical system is canonically approached and subsequently reformulated to preclude the ideal element of assumed time, retaining only an ideal element related to space. Time is then shown to have not been vital to the formulation originally, appearing as an emergent property rather than a fundamental axiom. A one dimensional, two particle system in a timeless framework – inspired by the models developed by Barbour and Bertotti -is presented. The system’s Langrangian is defined in terms of position and momentum and the equations of motion are stated. An observable quantity T, constructed from observables in the system, serves as a relative time parameter and replaces the postulated absolute time τ, allowing for a system fully characterized by measurable, concrete quantities. Along with two other observables, T serves as the independent variable with respect to which relational properties of the entire system may be established. The physical and philosophic justifications and implications are expounded and examined. Time, it seems, is a concept abstracted from paths in configuration space and can be viewed as analogous to Mach’s principle of universal inertial reference frames.


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Utah Conference on Undergraduate Research 2023 - Program by Office of Undergraduate Research is licensed under a Creative Commons Attribution 4.0 International License, except where otherwise noted.

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