New research challenges assumptions about subsystem independence in experiments probing quantum gravity through entanglement, revealing potential pitfalls in current detection strategies.
Researchers have developed a novel method for identifying entanglement in quantum systems that goes beyond traditional Gaussian-based techniques.
![The analysis of AT 2018cqh’s multi-epoch optical spectra-comparing observations from 2000, early 2021, and late 2023-reveals evolving spectral features, including prominent coronal lines like $6374 \text{Å}$ [Fex] and the Balmer series (${\rm H}\alpha$, ${\rm H}\beta$, ${\rm H}\gamma$, ${\rm H}\delta$, and ${\rm H}\varepsilon$), alongside forbidden lines of [Oiii], [Nii], and [Sii], demonstrating the transient nature of accretion events and the ephemeral quality of any model attempting to fully capture such phenomena.](https://arxiv.org/html/2512.16568v1/x4.png)
Detailed observations of the AT 2018cqh event confirm it as a rare tidal disruption event powered by an intermediate-mass black hole, offering a unique window into the behavior of these elusive cosmic objects.
Researchers are exploring modifications to Einstein’s theory of gravity to address discrepancies in measurements of the universe’s expansion rate and matter distribution.
Researchers have demonstrated a novel approach to building more stable and long-lived discrete time crystals using precisely controlled Rydberg atom arrays.
A new framework enables accurate and stable calculations of vacuum polarization effects, crucial for understanding the spectra of hydrogen-like ions.
Researchers have developed a novel observing cadence for the Legacy Survey of Space and Time (LSST) designed to minimize systematic errors and unlock the full potential of cosmological data.
Researchers have introduced a demanding new benchmark to test how well AI can navigate the complexities of real-world web searches, moving beyond simple question answering.
New research reveals that overparameterized neural networks exhibit surprisingly different extrapolation behaviors depending on their proximity to the training data’s origin.
New research demonstrates how information theory can dramatically improve the efficiency of identifying underlying systems from observational data.