Search for: All records

Abstract Deriving high-quality light curves for asteroids and other periodic sources from survey data is challenging owing to many factors, including the sparsely sampled observational record and diurnal aliasing, which is a signature imparted into the periodic signal of a source that is a function of the observing schedule of ground-based telescopes. In this paper we examine the utility of combining asteroid observational records from the Zwicky Transient Facility and the Transiting Exoplanet Survey Satellite, which are the ground- and space-based facilities, respectively, to determine to what degree the data from the space-based facility can suppress diurnal aliases. Furthermore, we examine several optimizations that are used to derive the rotation periods of asteroids, which we then compare to the reported rotation periods in the literature. Through this analysis we find that we can reliably derive the rotation periods for ∼85% of our sample of 222 objects that are also reported in the literature and that the remaining ∼15% are difficult to reliably derive, as many are asteroids that are insufficiently elongated, which produces a light curve with an insufficient amplitude and, consequently, an incorrect rotation period. We also investigate a binary classification method that biases against reporting incorrect rotation periods. We conclude the paper by assessing the utility of using other ground- or space-based facilities as companion telescopes to the forthcoming Rubin Observatory. 

Abstract The Solar system Notification Alert Processing System (snaps) is a Zwicky Transient Facility (ZTF) and Rubin Observatory alert broker that will send alerts to the community regarding interesting events in the solar system.snapsis actively monitoring solar system objects and one of its functions is to compare objects (primarily main belt asteroids) to one another to find those that are outliers relative to the population. In this paper, we use theSNAPShot1data set, which contains 31,693 objects from ZTF, and derive outlier scores for each of these objects.snapsemploys an unsupervised approach; consequently, to derive outlier rankings for each object, we propose four different outlier metrics such that we can explore variants of the outlier scores and add confidence to the outlier rankings. We also provide outlier scores for each object in each permutation of 15 feature spaces, between two and 15 features, which yields 32,752 total feature spaces. We show that we can derive population outlier rankings each month at Rubin Observatory scale using four Nvidia A100 GPUs, and present several avenues of scientific investigation that can be explored using population outlier detection.