We present a global three-dimensional radiation magnetohydrodynamic (RMHD) simulation of a circumbinary disk (CBD) around a massive black hole binary (MBHB) with a total mass $2 \times 10^7\,M_{\odot}$ and mass ratio $0.1$, separated by $100\, GM_{\rm tot}/c^2$. The inclusion of radiation makes the disk thinner, denser, less eccentric at the inner edge, and more filamentary when compared to an otherwise identical locally isothermal MHD disk. The RMHD disk has accretion rate $\sim 0.23\,\dot{M}_{\mathrm{Edd}}$ and produces thermal emission peaking in the near-UV/optical with a luminosity of $\sim 1\, \% L_{\rm {Edd }}$. Compared with an equal-mass binary with the same total mass, the thermal emission of the CBD around the unequal-mass binary is several orders of magnitude brighter and much more variable at far-UV/soft X-rays frequencies. Similarly, we find that the light curve associated with the $0.1$ mass ratio binary exhibits dominant periodicity corresponding to 2 binary orbits, compared to the equal-mass binary that shows periodicity at 2.5-5 binary orbits. Our results highlight the importance of radiation for the structure and observational properties of MBHB circumbinary disks and have implications for detecting electromagnetic counterparts to LISA gravitational wave precursors and for the heavier binaries targeted by the Pulsar Timing Arrays.
Dust plays a critical role in the study of the interstellar medium (ISM). Extinction maps derived from optical surveys often fail to capture regions with high column density due to the limited photometric depth in optical wavelengths. To address these limitations, we developed the XPNICER method based on near-infrared (NIR) photometric survey data. This method combines the previously established PNICER and Xpercentile techniques, enabling effective mitigation of foreground contamination and improved handling of complex dust structures in the Galactic plane, which thus can provide more accurate extinction estimates, particularly in highly obscured regions. By applying XPNICER to the Galactic Plane Survey from the UKIRT Infrared Deep Sky Survey, we have generated a series of two-dimensional (2D) dust extinction maps that span roughly 1800 deg2 of the Galactic plane (0< l < 110deg and 140< l < 232deg, |b| < 5deg). These maps, with spatial resolutions between 30arcsec and 300arcsec, can trace extinction up to Av ~ 30-40 mag. This new approach offers higher spatial resolution and better detection of high-extinction regions compared to previous large-scale dust-based maps of the Galactic plane, providing an independent and complementary measure of dust column densities.
Strong gravitational lensing provides a powerful tool to directly infer the dark matter (DM) subhalo mass function (SHMF) in lens galaxies. However, comparing observationally inferred SHMFs to theoretical predictions remains challenging, as the predicted SHMF can vary significantly between galaxies - even within the same cosmological model - due to differences in the properties and environment of individual galaxies. We present a machine learning framework to infer the galaxy-specific predicted SHMF from galaxy images, conditioned on the assumed inverse warm DM particle mass $M^{-1}_{\rm DM}$. To train the model, we use 1024 high-resolution hydrodynamical zoom-in simulations from the DREAMS suite. Mock observations are generated using Synthesizer, excluding gas particle contributions, and SHMFs are computed with the Rockstar halo finder. Our neural network takes as input both the galaxy images and the inverse DM mass. This method enables scalable, image-based predictions for the theoretical DM SHMFs of individual galaxies, facilitating direct comparisons with observational measurements.
We apply the automatic stellar stream detection algorithm StarStream to Gaia Data Release 3 and identify 87 stellar streams associated with Galactic globular clusters (GCs), including 34 high-quality cases with median completeness and purity both exceeding 50%, as estimated from modeling mock streams. These detections double the number of known GC streams, and increase the fraction of GCs with tidal streams at high Galactic latitudes (|b| > 30 degree) to 75%. In contrast to visual expectations, many new streams are wide or short, or misaligned with their progenitors' orbits. Taking advantage of the unbiased density measurements enabled by our method, we also estimate the mass loss rate for the progenitor GCs. We find that several low-mass, large-size clusters have enhanced mass loss rates, indicating that they are approaching complete tidal disruption.
The Gaia mission has led to the discovery of over 100 stellar streams in the Milky Way, most of which likely originated from globular clusters (GCs). As the upcoming wide-field surveys can potentially continue to increase the number of known streams, there is a growing need to shift focus from manual detection of individual streams to automated detection methods that prioritize both quality and quantity. Traditional techniques rely heavily on the visual expectation that GC streams are dynamically cold and thin. This assumption does not hold for all streams, whose morphologies and kinematics can vary significantly with the progenitor's mass and orbit. As a result, these methods are biased toward a subset of the whole stream population, with often unquantified purity and completeness. In this work, we present StarStream, an automatic stream detection algorithm based on a physics-inspired model rather than visual expectation. Our method provides a more accurate prediction of stream stars in the multi-dimensional space of observables, while using fewer free parameters to account for the diversity of streams. Applied to a mock GC stream catalog tailored for the Gaia DR3 dataset, our algorithm achieves both purity and completeness of at least 65% at Galactic latitudes |b| > 30 degree.