Madeline Schemel | Searcharxiv

arXiv2020

Zwicky Transient Facility Observations of Trojan Asteroids: A Thousand Colors, Rotation Amplitudes, and Phase Functions

We introduce a new method for analyzing sparse photometric data of asteroids and apply it to Zwicky Transient Facility observations of the Jupiter Trojan asteroids. The method relies on the creation of a likelihood model that includes the probability distribution of rotational brightness variations at an unknown rotation phase. The likelihood model is analyzed via a Markov Chain Monte Carlo to quantify the uncertainty in our parameter estimates. Using this method, we provide color, phase parameter, absolute magnitude and amplitude of rotation measurements for 1049 Jupiter Trojans. We find that phase parameter is correlated with color and the distribution of Trojan asteroid rotational amplitudes is indistinguishable from that of main-belt asteroids.

astro-ph.EP↗

arXiv2020

A Method to Distinguish Quiescent and Dusty Star-forming Galaxies with Machine Learning

Large photometric surveys provide a rich source of observations of quiescent galaxies, including a surprisingly large population at z>1. However, identifying large, but clean, samples of quiescent galaxies has proven difficult because of their near-degeneracy with interlopers such as dusty, star-forming galaxies. We describe a new technique for selecting quiescent galaxies based upon t-distributed stochastic neighbor embedding (t-SNE), an unsupervised machine learning algorithm for dimensionality reduction. This t-SNE selection provides an improvement both over UVJ, removing interlopers which otherwise would pass color selection, and over photometric template fitting, more strongly towards high redshift. Due to the similarity between the colors of high- and low-redshift quiescent galaxies, under our assumptions t-SNE outperforms template fitting in 63% of trials at redshifts where a large training sample already exists. It also may be able to select quiescent galaxies more efficiently at higher redshifts than the training sample.

astro-ph.GA↗