High-precision regression of physical parameters from black hole images generated by General Relativistic Ray Tracing (GRRT) is essential for investigating spacetime curvature and advancing black hole astrophysics. However, due to limitations in observational resolution, high observational costs, and imbalanced distributions of positive and negative samples, black hole images often suffer from data scarcity, sparse parameter spaces, and complex structural characteristics. These factors pose significant challenges to conventional regression methods based on simplified physical models. To overcome these challenges, this study introduces Multiscale Adaptive Network (MANet) , a novel regression framework grounded in deep learning. MANet integrates an Adaptive Channel Attention (ACA) module to selectively enhance features in physically informative regions. Meanwhile, a Multiscale Enhancement Feature Pyramid (MEFP) is employed to capture fine-grained spatial structures such as photon rings and accretion disks, while alleviating information loss due to downsampling. Experimental evaluations on GRRT-simulated datasets demonstrate that MANet substantially improves parameter estimation accuracy and generalization capability in high-dimensional parameter spaces, outperforming existing baseline approaches. This framework presents a promising avenue for high-precision parameter regression in Event Horizon Telescope (EHT) data analysis and broader astrophysical imaging applications characterized by sparse and noisy data.
Parameter estimation (PE) for compact binary coalescence (CBC) events observed by gravitational wave (GW) laser interferometers is a core task in GW astrophysics. We present a method to compute the posterior distribution efficiently without relying on stochastic samplers. First, we show how to select sets of intrinsic and extrinsic parameters that efficiently cover the relevant phase space. We then show how to compute the likelihood for all combinations of these parameters using dot products. We describe how to assess and tune the integration accuracy, making the outcome predictable and adaptable to different applications. The low computational cost allows full PE in minutes on a single CPU, with the potential for further acceleration using multiple CPUs or GPUs. We implement this method in the $\texttt{dot-PE}$ package, enabling sensitive searches using the full evidence integral for precessing CBCs and supporting large waveform banks ($\sim10^6$ waveforms), regardless of waveform generation cost.
While cold dark matter is widely supported by a range of cosmological observations, it encounters several difficulties at smaller scales. These issues have prompted the investigation of various alternative dark matter candidates, leaving the question "What is dark matter?" still open. In this work, we propose a new cosmological model that considers dark matter as a barotropic fluid with a constant equation of state parameter and interprets dark energy as the phenomenological emergent dark energy rather than a cosmological constant. We then place constraints on our new model using the Planck 2018 Cosmic Microwave Background (CMB) anisotropy measurements, Baryon Acoustic Oscillation (BAO) measurements from the Dark Energy Spectroscopic Instrument (DESI), the Pantheon Plus (PP) compilation of Type Ia supernovae (Ia SNe), and the Redshift Space Distortions (RSD) data from Gold2018. The results show statistically significant signal for positive dark matter equation of state and square of sound speed $w_{\rm dm}=c_{\rm s,dm}^2$ ($10^{7}w_{\rm dm}$ = $4.0^{+2.5}_{-2.3}$ at the 95\% confidence level) for the data combination CMB+DESI+PP+RSD. However, Bayesian evidence indicates that this data combination favors the $\Lambda$CDM model with very strong evidence.
Although many cases of stellar spin-orbit misalignment are known, it is usually unclear whether a single planet's orbit was tilted or if the entire protoplanetary disk was misaligned. Measuring stellar obliquities in multi-transiting planetary systems helps to distinguish these possibilities. Here, we present a measurement of the sky-projected spin-orbit angle for TOI-880 c (TOI-880.01), a member of a system of three transiting planets, using the Keck Planet Finder (KPF). We found that the host star is a K-type star ($T_{\rm eff}=5050 \pm 100$ K). Planet b (TOI-880.02) has a radius of $2.19\pm0.11\mathrm{R_{\oplus}}$ and an orbital period of $2.6$ days; planet c (TOI-880.01) is a Neptune-sized planet with $4.95\pm0.20\mathrm{R_{\oplus}}$ on a $6.4$-day orbit; and planet d (TOI-880.03) has a radius of $3.40_{-0.21}^{+0.22}\mathrm{R_{\oplus}}$ and a period of $14.3$ days. By modeling the Rossiter-McLaughlin (RM) effect, we found the sky-projected obliquity to be $|\lambda_c| = 7.4_{-7.2}^{+6.8}$$^{\circ}$, consistent with a prograde, well-aligned orbit. The lack of detectable rotational modulation of the flux of the host star and a low $\rm v\sin{i_\star}$ (1.6~km/s) imply slow rotation and correspondingly slow nodal precession of the planetary orbits and the expectation that the system will remain in this coplanar configuration. TOI-880 joins a growing sample of well-aligned, coplanar, multi-transiting systems. Additionally, TOI-880 c is a promising target for JWST follow-up, with a transmission spectroscopy metric (TSM) of $\sim 170$. We could not detect clear signs of atmospheric erosion in the H$\alpha$ line from TOI-880 c, as photoevaporation might have diminished for this mature planet.
This paper investigates the utility of Fast Radio Bursts (FRBs) as novel observational probes to constrain models of interacting dark energy (IDE). By leveraging FRB dispersion measures (DMs) and redshifts, we perform a comprehensive analysis of three IDE models: gamma_m IDE, gamma_x IDE, and xi IDE, using Markov Chain Monte Carlo (MCMC) methods based on 86 localized FRBs and simulated datasets containing 2500 to 10000 mock events. By disentangling the contributions to the observed DMs from the Milky Way, host galaxies, and the intergalactic medium (IGM), key cosmological parameters are constrained, including the Hubble constant (H0), matter density (Omega_m), the dark energy equation of state (omega_x), and interaction strengths (gamma_m, gamma_x, xi). The best-fit values of the gamma_m IDE model indicate a potential alleviation of the cosmic coincidence problem. Subsequently, we utilize information criteria (IC) to conduct a comparative assessment of the three IDE models. When applied to the current sample of observed FRBs, the xi IDE model yields slightly lower IC values than the gamma_m IDE and gamma_x IDE models across all three criteria, although the differences are not statistically significant. These results underscore the value of FRB measurements as complementary probes that provide further constraints on alternative cosmological models.
The Lunar Orbital VLBI Experiment (LOVEX) is a scientific component of the Chinese Lunar Exploration Project (CLEP) Chang'E-7. The spaceborne component of LOVEX is implemented onboard the relay satellite QueQiao-2, which was launched on 2024 March 20, and later placed into an elliptical selenocentric orbit. The LOVEX-specific payload consists of an X-band cryogenic receiver, a hydrogen maser frequency standard, and VLBI data formatting and acquisition electronics. Several components of the QueQiao-2 nominal onboard instrumentation, such as the 4.2-meter antenna, the data storage device, and the downlink communication system, contribute to the overall spaceborne VLBI instrumentation. This allows us to form a space radio telescope capable of co-observing with Earth-based radio telescopes in VLBI mode. In this space VLBI system, the length of the baseline extends up to approximately 380,000 km. This paper presents the LOVEX scientific objectives, architecture, instrumentation, pre-launch tests, in-flight verification and calibration, and the first in-flight detections of interferometric response (''fringes'') achieved through observations of the quasar AO 0235+164 and the Chang'E-6 orbital module, positioned at the Sun-Earth Lagrange point L2. These initial results demonstrate the successful performance of LOVEX, verifying its capability for both astronomical and spacecraft tracking observations at ultra-long VLBI baselines.
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