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Waly M Z Karim

Publications and source records attributed to Waly M Z Karim.

2 recordsLinked to original sources

The Mass Function of Neutron Stars from Core-Collapse Supernova Simulations

Using the mapping between progenitor core structure and the gravitational mass of neutron stars derived from sophisticated 3D supernova simulations, we determine the theoretical mass distribution of neutron stars at birth and compare it with neutron star mass function measurements. In the process, we explore the effects of islands of black hole formation and subsequent mass accretion. We show that supernova theory can explain the observed neutron star mass function from its lower-mass peak near $\sim$1.35 $M_{\odot}$ to its higher-mass tail. Moreover, the lower predicted kick speeds expected during the birth of lower-mass neutron stars and the higher expected kick speeds expected on average for higher mass neutron stars both sculpt the observed mass function in desired directions. The upshot of all these influences is to imprint upon the measured neutron star mass function features that reflect the varied physics of both neutron star origins and the neutron-star/black-hole dichotomy. Very approximately, we derive a black hole birth fraction of $\sim$21\%. In summary, we suggest that supernova theory can now be used to explain, however provisionally, various measured attributes of the population of compact objects and that an era of productive engagement between supernova theory and observation is at hand.

astro-ph.HE↗

Pushing the Limit of Asteroseismic Detection for Cool Dwarfs using TESS and Deep Learning

Asteroseismology provides a powerful probe of stellar interiors by detecting stellar oscillations, including solar-like oscillations, which are stochastically excited by near-surface convection. While thousands of solar-like oscillators have been identified in evolved stars, only a limited number of main-sequence cool dwarfs have confirmed oscillations due to the low amplitudes of their signals. In this work, we train a convolutional autoencoder on TESS two-minute light curves to automatically identify solar-like oscillation features in cool dwarf main sequence and sub-giant stars. Using catalogs of confirmed oscillators for training and validation, our network achieves a classification accuracy of 99.8% on the test set, along with Precision of 0.945, Recall of 0.998, and F1 Score of 0.971. From the Asteroseismic Target List, our model identifies 3463 potential solar-like oscillators (probability greater than 0.5). After further analysis, we find a list of 24 candidate stars that have the potential to exhibit solar-like oscillations. Notably, several of these candidates occupy regions of the color-magnitude diagram that are accessible only through more resource-intensive radial velocity observations, thereby has the potential of extending the detection frontier of TESS-based asteroseismology. Our candidate catalog provides a valuable foundation for follow-up efforts aimed at expanding the sample of cool-dwarf solar-like oscillators. This will ultimately improve our understanding of stellar structure and evolution across the lower main sequence and strengthen the evidence for using deep learning techniques to study stellar light curves.

astro-ph.SR↗