arXiv subjects

H. S. Chan

Publications and source records attributed to H. S. Chan.

3 recordsLinked to original sources

arXiv2026

A grid of fast-rotating, chemically-homogeneous, supernova and/or long-GRB progenitors

The understanding of the mechanism(s) by which massive stars collapse and possibly explode is rapidly maturing. Uncertainties in the structure of the stellar core at the onset of collapse are often dominant in numerical simulations, and a limited number of progenitor grids are available. This is especially true for explosions where rotation and magnetic fields play a significant or primary role. We present a grid of 113 single-star models with initial masses $M_{\rm ZAMS}=30-90\,M_{\odot}$ and initial rigid rotation $ω_{\rm ZAMS}=0.5-0.99\,ω_{\rm crit}$ computed at $Z=0.001$ with the open-source stellar evolution code \textsc{MESA}. We adopt a 128-isotope nuclear reaction network capable of following the weak reactions deleptonizing the core during and after silicon core burning. By construction, these models experience rotationally-induced chemically-homogeneous evolution, and reach the onset of collapse ($v_{\rm infall}\lesssim -300\,\mathrm{km\ s^{-1}}$) with large and structured amounts of angular momentum, possibly sufficient to form accretion disks on a proto-compact object. Therefore, these progenitor structures provide a homogeneous set of models with updated input physics and improved algorithmic accuracy to understand stellar explosions of (some types of) stripped-envelope supernovae, possibly jetted and/or broad-lined, collapsars or magnetar-powered, and/or long $γ$-ray bursts.

astro-ph.HE↗

arXiv2021

Searching for Anomalies in the ZTF Catalog of Periodic Variable Stars

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 multi-wavelength 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 \citet{Chen_2020}. 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.

astro-ph.SR↗

arXiv2021

A Convolutional Autoencoder-Based Pipeline for Anomaly Detection and Classification of Periodic Variables

The periodic pulsations of stars teach us about their underlying physical process. We present a convolutional autoencoder-based pipeline as an automatic approach to search for out-of-distribution anomalous periodic variables within The Zwicky Transient Facility Catalog of Periodic Variable Stars (ZTF CPVS). We use an isolation forest to rank each periodic variable by its anomaly score. Our overall most anomalous events have a unique physical origin: they are mostly highly variable and irregular evolved stars. Multiwavelength data suggest that they are most likely Red Giant or Asymptotic Giant Branch stars concentrated in the Milky Way galactic disk. Furthermore, we show how the learned latent features can be used for the classification of periodic variables through a hierarchical random forest. This novel semi-supervised approach allows astronomers to identify the most anomalous events within a given physical class, significantly increasing the potential for scientific discovery.

astro-ph.SR↗