115 citations · 282 across the 12 of their papers we have counts for
13 papers
Incorporating Measurement Error in Astronomical Object Classification
Sarah Shy, Hyungsuk Tak, Eric D. Feigelson +2
Most general-purpose classification methods, such as support-vector machine (SVM) and random forest (RF), fail to account for an unusual characteristic of astronomical data: known…
A Statistician Teaches Deep Learning
G. Jogesh Babu, David Banks, Hyunsoon Cho +3
Deep learning (DL) has gained much attention and become increasingly popular in modern data science. Computer scientists led the way in developing deep learning techniques, so the…
21st Century Statistical and Computational Challenges in Astrophysics
Eric D. Feigelson, Rafael S. de Souza, Emille E. O. Ishida +1
Modern astronomy has been rapidly increasing our ability to see deeper into the universe, acquiring enormous samples of cosmic populations. Gaining astrophysical insights from thes…
Algorithms and Statistical Models for Scientific Discovery in the Petabyte Era
Brian Nord, Andrew J. Connolly, Jamie Kinney +35
The field of astronomy has arrived at a turning point in terms of size and complexity of both datasets and scientific collaboration. Commensurately, algorithms and statistical mode…
AutoRegressive Planet Search: Application to the Kepler Mission
Gabriel A. Caceres, Eric D. Feigelson, G. Jogesh Babu +5
The 4-year light curves of 156,717 stars observed with NASA's Kepler mission are analyzed using the AutoRegressive Planet Search (ARPS) methodology described by Caceres et al. (201…
Autoregressive Times Series Methods for Time Domain Astronomy
Eric D. Feigelson, G. Jogesh Babu, Gabriel A. Caceres
Celestial objects exhibit a wide range of variability in brightness at different wavebands. Surprisingly, the most common methods for characterizing time series in statistics -- pa…