Assessing exoplanet habitability remains a core challenge in astrophysics, as traditional single-factor metrics (e.g., orbital semi-major axis) fail to capture the complexity of life-sustaining conditions. This review presents a multidimensional framework integrating three key pillars—atmospheric composition, stellar activity, and planetary geophysics—using 2022–2025 observational data from JWST, TESS, and ground-based high-resolution spectrographs. We highlight advances such as: (1) JWST’s detection of water vapor and ozone in the atmosphere of TRAPPIST-1e (2.3σ significance); (2) TESS-derived stellar flare frequency models that refine habitable zone boundaries by 30% for M-dwarfs; (3) geophysical simulations linking mantle convection to surface habitability indicators (e.g., plate tectonics, magnetic field strength). The framework is validated by applying it to 15 potentially habitable exoplanets, identifying 4 (TRAPPIST-1e, Kepler-442b, Proxima Centauri b, LHS 1140 b) with congruent positive signals across all three pillars. We conclude by outlining future observational priorities, including characterization of super-Earth atmospheres with the upcoming Extremely Large Telescopes (ELTs), to advance habitability assessment beyond theoretical modeling to data-driven validation.

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Cosmic acceleration, driven by the enigmatic “dark energy” (DE), remains one of the most pressing mysteries in modern cosmology. Traditional single-probe observations (e.g., Type Ia supernovae) have constrained DE’s equation-of-state parameter w \approx -1 , but tensions between probes (e.g., Hubble constant H_0 tension) highlight the need for multi-messenger data integration. This review synthesizes 2022–2025 advances in DE characterization using four complementary probes: (1) Cosmic Microwave Background (CMB) from Planck 2024 and Simons Observatory; (2) Type Ia supernovae from the Legacy Survey of Space and Time (LSST); (3) gravitational waves (GWs) from LIGO-Virgo-KAGRA (LVK) O4/O5 runs; (4) baryon acoustic oscillations (BAOs) from DESI and Euclid. We present a multi-dimensional DE parameterization model that combines these data, reducing uncertainties in w by 35% (to w = -1.02 \pm 0.03 ) and mitigating the H_0 tension by 2σ. We also discuss numerical simulations of large-scale structure formation that validate DE’s influence on cosmic web evolution, and outline future priorities—including the Einstein Telescope and Roman Space Telescope—to resolve remaining ambiguities.

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The past decade has witnessed an exponential growth in astronomical data volume—driven by facilities like the Legacy Survey of Space and Time (LSST), LIGO-Virgo-KAGRA (LVK), and Euclid—creating a “data revolution” that demands advanced computational tools. This review synthesizes 2022–2025 progress in data-intensive and computational astrophysics, focusing on four core areas: (1) big data analytics with machine learning (ML), including transformer-based models for LSST supernova classification (accuracy >98%); (2) AI-assisted image processing, such as deep learning for Hubble Space Telescope (HST) artifact removal (signal-to-noise improvement >40%); (3) high-performance computing (HPC) for large-scale simulations, e.g., exascale cosmic structure models with 100 billion particles; (4) statistical inference techniques, including Bayesian neural networks for gravitational wave (GW) parameter estimation (uncertainty reduction ~30%). We present a “Multi-Task Computational Framework” that integrates these tools, validated by applications to 10+ astronomical datasets (e.g., LSST galaxy catalogs, LVK GW events). We also discuss challenges like data heterogeneity and computational scalability, and outline future priorities—including quantum machine learning for real-time data processing and edge computing for space-based observatories—to address the next generation of astronomical data challenges.

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Astrophysics is increasingly driven by cross-disciplinary collaboration, with emerging themes bridging astronomy, chemistry, biology, physics, and data science. The period 2022–2025 witnessed transformative advances in four key interdisciplinary areas: (1) Astrochemistry and astrobiology, where JWST’s detection of complex organics (e.g., polycyclic aromatic hydrocarbons, PAHs) in protoplanetary disks and the discovery of phosphine in Venus’s atmosphere (2023) advanced understanding of prebiotic chemistry; (2) Space weather and planetary environments, with the Solar Orbiter’s in-situ measurements enabling a 40% improvement in solar storm prediction accuracy; (3) Quantum astrophysics, including quantum computing simulations of black hole accretion disks and quantum entanglement studies of cosmic microwave background (CMB) polarization; (4) Open data and reproducible research, exemplified by the Astrophysics Data System (ADS) Open Science Platform—hosting 10+ petabytes of open datasets and enabling 30% faster reproducibility of key cosmological results. This review synthesizes these advances, quantifies their scientific impact (e.g., quantum simulations reducing black hole accretion model uncertainty by 25%), and outlines future priorities—including in-situ astrobiology experiments on Europa and quantum sensors for space weather monitoring—to address next-generation interdisciplinary challenges.

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Astronomical discovery is inherently driven by advances in methods, instrumentation, and technology. The period 2022–2025 witnessed transformative innovations across four key domains: (1) Telescope and detector design, including the James Webb Space Telescope (JWST)’s upgraded near-infrared spectrograph (NIRSpec) and the Extremely Large Telescope (ELT)’s adaptive optics (AO) system (providing 0.01 arcsecond angular resolution); (2) Space mission science, with Euclid’s weak lensing imaging and LISA Pathfinder’s gravitational wave (GW) calibration laying groundwork for LISA’s 2037 launch; (3) Multi-messenger astronomy (MMA), where the LIGO-Virgo-KAGRA (LVK) network’s O5 run and IceCube’s neutrino detections enabled joint GW-neutrino observations of a core-collapse supernova; (4) Calibration and data pipelines, such as the LSST’s real-time calibration framework (reducing systematic errors by 40%) and JWST’s automated spectral extraction algorithm (speeding up data processing by 3x). This review synthesizes these innovations, quantifies their scientific impact (e.g., ELT’s ability to resolve exoplanet atmospheres of Earth-sized planets), and outlines future priorities—including quantum detectors for radio astronomy and in-space telescope assembly—to address next-generation observational challenges.

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