8 papers
Improving the Precision of Line-by-Line Radial Velocities: A Data-Driven Iterative Algorithm for Spectral Line Selection
Kanishk Pandey, Eric A. Ford, Joseph M. Salzer +17
Independent analysis of individual spectral lines, or line-by-line (LBL) analyses, can improve upon standard cross-correlation function (CCF) methods for measuring radial velocitie…
Confidence regions for a persistence diagram of a single image with one or more loops
Susan Glenn, Jessi Cisewski-Kehe, Jun Zhu +1
Topological data analysis (TDA) uses persistent homology to quantify loops and higher-dimensional holes in data, making it particularly relevant for examining the characteristics o…
Tensor Computation of Euler Characteristic Functions and Transforms
Jessi Cisewski-Kehe, Brittany Terese Fasy, Alexander McCleary +1
The weighted Euler characteristic transform (WECT) and Euler characteristic function (ECF) have proven to be useful tools in a variety of applications. However, current methods for…
GJ 523b is a Massive, 170 Myr-old Mega-Earth, Likely on a Polar Orbit
Maxwell A. Kroft, Thomas G. Beatty, Joseph M. Salzer +18
We use WIYN/NEID radial velocity measurements to confirm the planetary nature and measure the mass of the TESS transiting exoplanet candidate around the mid-K dwarf GJ 523 ($V=9.23…
Tracking Temporal Evolution of Topological Features in Image Data
Susan Glenn, Jessi Cisewski-Kehe, Jun Zhu +1
Topological Data Analysis (TDA) can be used to detect and characterize holes in an image, such as zero-dimensional holes (connected components) or one-dimensional holes (loops). Ho…
MaxTDA: Robust Statistical Inference for Maximal Persistence in Topological Data Analysis
Sixtus Dakurah, Jessi Cisewski-Kehe
Persistent homology is an area within topological data analysis (TDA) that can uncover different dimensional holes (connected components, loops, voids, etc.) in data. The holes are…