Mass Loss and Subsequent Thermal Evolution of Surviving Helium White Dwarfs Shocked by Thermonuclear Supernovae

Kavli Affiliate: Lars Bildsten | First 5 Authors: Tin Long Sunny Wong, Tin Long Sunny Wong, , , | Summary: Following a type Ia supernova (SN Ia) in a double white dwarf (WD) binary, a surviving WD companion leaves at its orbital velocity $approx 1$,000 – 3,000 km/s. The Gaia mission has discovered seven such […]


Continue.. Mass Loss and Subsequent Thermal Evolution of Surviving Helium White Dwarfs Shocked by Thermonuclear Supernovae

Type IIb Supernova Progenitors in 3D: Variability and Episodic Mass Loss revealed by Radiation-Hydrodynamics Simulations

Kavli Affiliate: Lars Bildsten | First 5 Authors: Jared A. Goldberg, Jared A. Goldberg, , , | Summary: We present the first 3D Radiation-Hydrodynamics simulations of partially-stripped ($M_mathrmcoresim10M_odot$, $M_mathrmenvsim0.1-1M_odot$) Yellow Supergiant ($Lsim10^5$, $T_mathrmeffapprox5000-8000$K) envelopes, constructed with Athena++. These envelope models represent the progenitors of Type IIb supernovae (SNe-IIb), which have lost a substantial fraction of […]


Continue.. Type IIb Supernova Progenitors in 3D: Variability and Episodic Mass Loss revealed by Radiation-Hydrodynamics Simulations

ATLAS: A Self-Supervised and Cross-Stage Netlist Power Model for Fine-Grained Time-Based Layout Power Analysis

Kavli Affiliate: Jing Wang | First 5 Authors: Wenkai Li, Wenkai Li, , , | Summary: Accurate power prediction in VLSI design is crucial for effective power optimization, especially as designs get transformed from gate-level netlist to layout stages. However, traditional accurate power simulation requires time-consuming back-end processing and simulation steps, which significantly impede design […]


Continue.. ATLAS: A Self-Supervised and Cross-Stage Netlist Power Model for Fine-Grained Time-Based Layout Power Analysis

Exploring Self-Supervised Audio Models for Generalized Anomalous Sound Detection

Kavli Affiliate: Jia Liu | First 5 Authors: Bing Han, Bing Han, , , | Summary: Machine anomalous sound detection (ASD) is a valuable technique across various applications. However, its generalization performance is often limited due to challenges in data collection and the complexity of acoustic environments. Inspired by the success of large pre-trained models […]


Continue.. Exploring Self-Supervised Audio Models for Generalized Anomalous Sound Detection