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The 230 GHz lightcurves of Sagittarius A* (Sgr A*) predicted by general relativistic magnetohydrodynamics and general relativistic ray-tracing (GRRT) models by the Event Horizon Telescope Collaboration have higher variabilityM_ΔTcompared to observations. In this series of papers, we explore the origin of such large brightness variability. In this first paper, we performed large GRRT parameter surveys that span from the optically thin to the optically thick regimes, covering the ion-to-electron temperature ratio under strongly magnetized conditions,R_Low, from 1 to 60. We find that increasingR_Lowcan lead to either an increase or a reduction inM_ΔTdepending on the other model parameters, making it consistent with the observed variability of Sgr A* in some cases. Our analysis of GRRT image snapshots finds that the major contribution to the largeM_ΔTfor theR_Low= 1 models comes from the photon ring. However, secondary contributions from the accretion flow are also visible depending on the spin parameter. Our work demonstrates the importance of the electron temperature used for modeling radiatively inefficient accretion flows and places new constraints on the ion-to-electron temperature ratio. A more in-depth analysis for understanding the dependencies ofM_ΔTonR_Lowwill be performed in subsequent papers.

 

Abstract Using the second data release from the Zwicky Transient Facility (ZTF), Chen et al. created a ZTF Catalog of Periodic Variable Stars (ZTF CPVS) of 781,602 periodic variables stars (PVSs) with 11 class labels. Here, we provide a new classification model of PVSs in the ZTF CPVS using a convolutional variational autoencoder and hierarchical random forest. We cross-match the sky-coordinate of PVSs in the ZTF CPVS with those presented in the SIMBAD catalog. We identify non-stellar objects that are not previously classified, including extragalactic objects such as Quasi-Stellar Objects, Active Galactic Nuclei, supernovae and planetary nebulae. We then create a new labeled training set with 13 classes in two levels. We obtain a reasonable level of completeness (≳90%) for certain classes of PVSs, although we have poorer completeness in other classes (∼40% in some cases). Our new labels for the ZTF CPVS are available via Zenodo. 

Periodic variables illuminate the physical processes of stars throughout their lifetime. Wide-field surveys continue to increase our discovery rates of periodic variable stars. Automated approaches are essential to identify interesting periodic variable stars for multiwavelength and spectroscopic follow-up. Here we present a novel unsupervised machine-learning approach to hunt for anomalous periodic variables using phase-folded light curves presented in the Zwicky Transient Facility Catalogue of Periodic Variable Stars by Chen et al. We use a convolutional variational autoencoder to learn a low-dimensional latent representation, and we search for anomalies within this latent dimension via an isolation forest. We identify anomalies with irregular variability. Most of the top anomalies are likely highly variable red giants or asymptotic giant branch stars concentrated in the Milky Way galactic disk; a fraction of the identified anomalies are more consistent with young stellar objects. Detailed spectroscopic follow-up observations are encouraged to reveal the nature of these anomalies.

 

This data release contains 730,184 periodic transients with the new class labels in a csv file and the cross-match results of periodic variable stars (PVSs) in the ZTF CPVS with the SIMBAD catalog. Classifications and details with this data set are available in Cheung et al. (2021) and Chan et al. (2021)