We present an updated catalog of TeV gamma-ray sources based on the fifth pass of data from the High-Altitude Water Cherenkov (HAWC) Observatory. This release benefits from improved event reconstruction and nearly three additional years of observations. It also incorporates a systematic multi-source fitting framework, enabling more flexible and accurate modeling of the gamma-ray sky. This fitting procedure was modeled after the manual approach used in HAWC analyses of individual sources and regions, as well as other gamma-ray catalogs, like the 4FGL. In addition to more varied modeling of source morphology and spectral parameters compared to previous HAWC catalogs, this catalog uses a robust modeling of Galactic diffuse TeV emission. The fitting procedure uses both point-like and symmetric Gaussian spatial templates to model the source morphology. The spectral shape of the emission is modeled with either a simple power-law or log-parabola to explore curvature in the spectral energy distribution. We report 85 sources at the 4{\sigma} level, including 11 sources not associated with any TeVCat source using a distance-based association criterion. Distance-based association with the 1LHAASO catalog results in 22 4HWC sources without a counterpart. Additionally, there are 12 sources not associated with any physical counterpart in the Low- or High-Mass X-Ray Binary, the ATNF, or Fermi Pulsar, or SNR catalogs of sources. Five of the aforementioned sources have no counterpart in any of the catalogs searched and represent an opportunity for follow-up observations.
We present a gas kinematic study of the massive protocluster G318.049+00.086. The protocluster is reported to contain 12 prestellar core candidates and 4 protostellar cores. Filamentary structures are identified using the 1.3 mm dust continuum map, with four of them converge into a dense central region, forming a hub-filament system (HFS). High velocity gradients (10 - 20 km s$^{-1}$ pc$^{-1}$) derived from PV analysis of H$^{13}$CO$^{+}$ emission along three of those filaments are suggestive of mass inflow onto the central hub. A mass inflow rate higher than $10^{3}$ M$_{\odot}$ Myr$^{-1}$ along the filaments is indicating that the central hub is capable of forming massive star(s). Investigation of H$^{13}$CO$^{+}$ and CCH spectral profiles revealed the majority of the cores having the characteristic blue asymmetric line profiles, typical signature of gravitational collapse. The remaining few cores showed red asymmetric profiles, indicative of gas expansion. Also, the derived mass infall rates for the protostellar cores in hub-region is significantly higher in comparison to those located along the filaments. The mass-radius relationship of the cores revealed that the cores with red profiles reside in the massive star formation regime. However, the global velocity gradient along the filaments suggests that these particular cores are losing material to the hub. Our results are supporting a competitive accretion scenario of massive star formation where gas is expected to be funnelled from less gravitationally dominant cores to the cores located at the gravitationally favorable position.
The direct detection of gravitational waves (GWs) by LIGO has strikingly confirmed general relativity (GR), but testing GR via GWs requires estimating parameterized post-Einsteinian (ppE) deviation parameters in waveform models. Traditional Bayesian inference methods like Markov chain Monte Carlo (MCMC) provide reliable estimates but suffer from prohibitive computational costs, failing to meet the real-time demands and surging data volume of future GW detectors. Here, we propose a factorized neural posterior estimation framework: we construct independent normalizing flow models for each of the nine ppE deviation parameters and effectively integrate prior information from other source parameters via a conditional embedding network. Leveraging a hybrid neural network with a convolutional neural network and a Residual Neural Network for feature extraction, our method performs rapid and statistically reliable posterior inference directly from binary black hole signals. Compared to conventional MCMC, our approach achieves millisecond-scale inference time with a speedup factor of $9 \times 10^4$. Comprehensive validations show that the posterior estimates pass the Kolmogorov-Smirnov test and achieve empirical coverage probabilities close to theoretical targets. This work demonstrates the great potential of deep learning for GW parameter estimation and provides a viable technical solution for real-time GR tests with next-generation detectors.
We present three-dimensional simulations of the ultra-hot Jupiter (UHJ) WASP-121b from the planetary surface to extended outflows, coupling hydrodynamics with consistent non-equilibrium thermochemistry, ray-tracing radiative transfer, and hydrodynamics using the GPU-accelerated Kratos framework. The fiducial model exhibits several atmospheric layers, including the lower atmospheres controlled by day-night circulation, and transonic photoevaporative outflows at higher altitudes shaped into two spiral arms by the stellar gravity and orbital motion effects. Different species could trace different regions: Fe probes rotation-dominated inner layers, Na maps dense spiral arms where recombination balances photoionization, and H$\alpha$ and He $10830~{\rm A}$ features trace progressively more extended, ionized gas. With spiral arm velocities reaching $\sim 40~{\rm km\ s}^{-1}$ projected along the line of sight, this morphology explains observed high-velocity Na and H$\alpha$ absorption features without requiring significant super-rotation jet streams. Parametric studies reveal complex dependencies on stellar irradiation: enhanced FUV intensifies outflows and extends spiral arms spatially and kinematically, while EUV and X-ray expands spiral structures into attenuated, ionized regions. Stellar wind confinement compresses the dayside outflow and enhances metastable helium absorption. This work demonstrates that current and future transmission spectral observations that probe multiple species can provide important constraints on astrophysical environments of UHJs by comparing state-of-the-art simulations.
We investigate the chemical abundances and star-formation histories (SFH) of ten massive ($\mathrm{log}_{10} (M_{\star}/\mathrm{M}_{\odot})>10.5$) quiescent galaxies at $3<z<4$ using deep, medium-resolution spectroscopic data obtained as part of the \textit{JWST DeepDive} Cycle 2 GO program. Our \textit{DeepDive} sample demonstrates early formation and quenching times inferred from spectro-photometric fitting, with most galaxies having formed 50\% of their stellar mass by $z \sim 5$, and quenching by $z \sim 4$, showing good agreement across the various SFH parameterizations explored in this work. Though they differ slightly between SFH parameterizations, the inferred formation timescales for the {\it DeepDive} sample span both rapid ($\lesssim$ 100 Myr) and more extended ($\gtrsim$ 200 Myr) episodes, corresponding to star formation occurring over a few to several dynamical times given their compact sizes and high densities at $z\sim3-4$. On average, massive quiescent galaxies at $3<z<4$ are $\alpha$-enhanced ($\langle [\alpha/\mathrm{Fe}]\rangle$= $0.22^{+0.22}_{-0.17}$), although there is strong diversity ($\sim0.3$ dex in scatter) among individual [$\alpha$/Fe] values. Our results for $\alpha$-enhancement are consistent with lower-redshift studies, implying weak evolution in [$\alpha$/Fe] from $z \sim 4$ to $z\sim 1$. The SFH timescales associated with the low [$\alpha$/Fe] measurements suggest longer formation timescales, potentially pointing to earlier enrichment by Type Ia supernovae, or metals preferentially being removed via outflows driven either by powerful early active galactic nuclei or supernovae. Overall, this work represents the first, statistically representative combined study of the star-formation histories and chemical abundances of massive quiescent galaxies at $z>3$.
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this http URL , La Paz: Vision Cultural, pp. 99-109, 2024