#61: Topological Noise Differentiation — Presentation Detail — Celebration of Scholars

Additional Information

More information is available at carthage.edu/celebration-scholars/. The following are members of the Research, Scholarship, and Creativity Committee who are eager to listen to ideas and answer questions:

Thomas Carr
Katherin Hilson
Kim Instenes
John Kirk
Sarah Terrill

#61: Topological Noise Differentiation

Name: Hector Rauda
Major: Physics and Mathematics
Hometown: El Salvador
Faculty Sponsor: John Kirk
Other Sponsors:
Type of research: SURE
Funding: SURE

Abstract

Topological data analysis (TDA) is a field that uses math and computers to understand data structure. It is used in many areas such as science, medicine, and business to find patterns and make predictions. TDA helps us see meaningful connections and structures in data that are not easy to see otherwise.

This study examines how noise affects different data features that change over time. It aims to understand the relationship between noisy data and "topological noise" which is temporary patterns in the data that go away when parameters change. Typically, we ignore topological noise because we consider it irrelevant. However, recent research suggests that specific short-lived patterns in chaotic systems could be significant. The goal is to develop ways to differentiate noise that is meaningful for the data's structure from random noise that does not mean anything.

Poster file

Submit date: March 27, 2024, 11:50 p.m.