We present a study of 323 photometrically variable young stellar objects that are likely members of the North America and Pelican nebulae star-forming region. To do so, we utilize over two years of data in the g and r photometric bands from the Zwicky Transient Facility. We first investigate periodic variability, finding 46 objects (∼15% of the sample) with significant periods that phase well and can be attributed to stellar rotation. We then use the quasiperiodicity (Q) and flux asymmetry (M) variability metrics to assign morphological classifications to the remaining aperiodic light curves. Another ∼39% of the variable star sample beyond the periodic (low Q) sources are also flux-symmetric, but with a quasiperiodic (moderate Q) or stochastic (high Q) nature. Concerning flux-asymmetric sources, our analysis reveals ∼14% bursters (high negative M) and ∼29% dippers (high positive M). We also investigate the relationship between variability slopes in the g versus g − r color–magnitude diagram, and the light-curve morphological classes. Burster-type objects have shallow slopes, while dipper-type variables tend to have higher slopes that are consistent with extinction-driven variability. Our work is one of the earliest applications of the Q and M metrics to ground-based data. We therefore contrast the Q values of high-cadence and high-precision space-based data, for which these metrics were designed, with Q determinations resulting from degraded space-based light curves that have the cadence and photometric precision characteristic of ground-based data.

Astronomy is presently experiencing profound growth in the deployment of machine learning to explore large datasets. However, transient quasi-periodic oscillations (QPOs) which appear in power density spectra of many X-ray binary system observations are an intriguing phenomena heretofore not explored with machine learning. In light of this, we propose and experiment with novel methodologies for predicting the presence and properties of QPOs to make the first ever detections and characterizations of QPOs with machine learning models. We base our findings on raw energy spectra and processed features derived from energy spectra using an abundance of data from the NICER and Rossi X-ray Timing Explorer space telescope archives for two black hole low mass X-ray binary sources, GRS 1915+105 and MAXI J1535-571. We advance these non-traditional methods as a foundation for using machine learning to discover global inter-object generalizations between–and provide unique insights about–energy and timing phenomena to assist with the ongoing challenge of unambiguously understanding the nature and origin of QPOs. Additionally, we have developed a publicly available Python machine learning library, QPOML, to enable further Machine Learning aided investigations into QPOs.

Dozens of exotic materials are found only in meteorites. These “meteorite minerals” are formed in the solar system's cold, long-lived, proto-planetary disk, in the slowly cooling cores of planetesimals, and in high-speed collisions. To the best of our knowledge no recent published work has aggregated information about minerals only found in meteorites in a comprehensive and machine readable manner. Thus, we have compiled a preliminary catalog of 81 known meteorite minerals from the literature to serve as a stepping stone for a future, more extensive effort. We also explore the distribution of these meteorite minerals by meteorite type.