Spatially-resolved images of debris disks are necessary to determine disk morphological properties and the scattering phase function (SPF) which quantifies the brightness of scattered light as a function of phase angle. Current high-contrast imaging instruments have successfully resolved several dozens of debris disks around other stars, but few studies have investigated trends in the scattered-light, resolved population of debris disks in a uniform and consistent manner. We have combined Karhunen-Loeve Image Projection (KLIP) with radiative-transfer disk forward modeling in order to obtain the highest quality image reductions and constrain disk morphological properties of eight debris disks imaged by the Gemini Planet Imager at H-band with a consistent and uniformly-applied approach. In describing the scattering properties of our models, we assume a common SPF informed from solar system dust scattering measurements and apply it to all systems. We identify a diverse range of dust density properties among the sample, including critical radius, radial width, and vertical width. We also identify radially narrow and vertically extended disks that may have resulted from substellar companion perturbations, along with a tentative positive trend in disk eccentricity with relative disk width. We also find that using a common SPF can achieve reasonable model fits for disks that are axisymmetric and asymmetric when fitting models to each side of the disk independently, suggesting that scattering behavior from debris disks may be similar to Solar System dust.

We present Denario, an AI multi-agent system designed to serve as a scientific research assistant. Denario can perform many different tasks, such as generating ideas, checking the literature, developing research plans, writing and executing code, making plots, and drafting and reviewing a scientific paper. The system has a modular architecture, allowing it to handle specific tasks, such as generating an idea, or carrying out end-to-end scientific analysis using Cmbagent as a deep-research backend. In this work, we describe in detail Denario and its modules, and illustrate its capabilities by presenting multiple AI-generated papers generated by it in many different scientific disciplines such as astrophysics, biology, biophysics, biomedical informatics, chemistry, material science, mathematical physics, medicine, neuroscience and planetary science. Denario also excels at combining ideas from different disciplines, and we illustrate this by showing a paper that applies methods from quantum physics and machine learning to astrophysical data. We report the evaluations performed on these papers by domain experts, who provided both numerical scores and review-like feedback. We then highlight the strengths, weaknesses, and limitations of the current system. Finally, we discuss the ethical implications of AI-driven research and reflect on how such technology relates to the philosophy of science. We publicly release the code at this https URL. A Denario demo can also be run directly on the web at this https URL, and the full app will be deployed on the cloud.

Training-free acceleration has emerged as an advanced research area in video generation based on diffusion models. The redundancy of latents in diffusion model inference provides a natural entry point for acceleration. In this paper, we decompose the inference process into the encoding, denoising, and decoding stages, and observe that cache-based acceleration methods often lead to substantial memory surges in the latter two stages. To address this problem, we analyze the characteristics of inference across different stages and propose stage-specific strategies for reducing memory consumption: 1) Asynchronous Cache Swapping. 2) Feature chunk. 3) Slicing latents to decode. At the same time, we ensure that the time overhead introduced by these three strategies remains lower than the acceleration gains themselves. Compared with the baseline, our approach achieves faster inference speed and lower memory usage, while maintaining quality degradation within an acceptable range. The Code is available at this https URL .

The measurement of the structure of stellar populations in the Milky Way disk places fundamental constraints on models of galaxy formation and evolution. Previously, the disk's structure has been studied in terms of populations defined geometrically and/or chemically, but a decomposition based on stellar ages provides a more direct connection to the history of the disk, and stronger constraint on theory. Here, we use positions, abundances and ages for 31,244 red giant branch stars from the SDSS-APOGEE survey, spanning 3 < R_{\mathrm{gc}} < 15 kpc, to dissect the disk into mono-age and mono-[Fe/H] populations at low and high [$\alpha$/Fe]. For each population, with \Delta \mathrm{age} < 2 Gyr and \Delta \mathrm{[Fe/H]} < 0.1 dex, we measure the structure and surface-mass density contribution. We find that low [$\alpha$/Fe] mono-age populations are fit well by a broken exponential, which increases to a peak radius and decreases thereafter. We show that this profile becomes broader with age, interpreted here as a new signal of disk heating and radial migration. High [$\alpha$/Fe] populations are well fit as single exponentials within the radial range considered, with an average scale length of $1.9\pm 0.1$ kpc. We find that the relative contribution of high to low [$\alpha$/Fe] populations at $R_0$ is $f_\Sigma = 18\% \pm 5\%$; high [$\alpha$/Fe] contributes most of the mass at old ages, and low [$\alpha$/Fe] at young ages. The low and high [$\alpha$/Fe] populations overlap in age at intermediate [Fe/H], although both contribute mass at $R_{0}$ across the full range of [Fe/H]. The mass weighted scale height $h_Z$ distribution is a smoothly declining exponential function. High [$\alpha$/Fe] populations are thicker than low [$\alpha$/Fe], and the average $h_Z$ increases steadily with age, between 200 and 600 pc.

The properties of Milky Way satellite galaxies have important implications for galaxy formation, reionization, and the fundamental physics of dark matter. However, the population of Milky Way satellites includes the faintest known galaxies, and current observations are incomplete. To understand the impact of observational selection effects on the known satellite population, we perform rigorous, quantitative estimates of the Milky Way satellite galaxy detection efficiency in three wide-field survey datasets: the Dark Energy Survey Year 6, the DECam Local Volume Exploration Data Release 3, and the Pan-STARRS1 Data Release 1. Together, these surveys cover $\sim$13,600 deg$^2$ to $g \sim 24.0$ and $\sim$27,700 deg$^2$ to $g \sim 22.5$, spanning $\sim$91% of the high-Galactic-latitude sky ($|b| \geq 15^\circ$). We apply multiple detection algorithms over the combined footprint and recover 49 known satellites above a strict census detection threshold. To characterize the sensitivity of our census, we run our detection algorithms on a large set of simulated galaxies injected into the survey data, which allows us to develop models that predict the detectability of satellites as a function of their properties. We then fit an empirical model to our data and infer the luminosity function, radial distribution, and size-luminosity relation of Milky Way satellite galaxies. Our empirical model predicts a total of $265^{+79}_{-47}$ satellite galaxies with $-20 \leq M_V \leq 0$, half-light radii of $15 \leq r_{1/2} (\rm pc) \leq 3000$, and galactocentric distances of $10 \leq D_{\rm GC} (\rm kpc) \leq 300$. We also identify a mild anisotropy in the angular distribution of the observed galaxies, at a significance of $\sim$$2\sigma$, which can be attributed to the clustering of satellites associated with the LMC.

Despite the excellent real-world predictive performance of modern machine learning (ML) methods, many scientists remain hesitant to discard traditional physical-conceptual (PC) approaches due mainly to their relative interpretability, which contributes to credibility during decision-making. In this context, a currently underexplored aspect of ML is how to develop minimally-optimal representations that can facilitate better insight regarding system functioning. Regardless of how this is achieved, it is arguably true that parsimonious representations better support the advancement of scientific understanding. Our own view is that ML-based modeling of geoscientific systems should be based in the use of computational units that are fundamentally interpretable by design. This paper continues our exploration of how the strengths of ML can be exploited in the service of better understanding via scientific investigation. Here, we use the Mass Conserving Perceptron (MCP) as the fundamental computational unit in a generic network architecture consisting of nodes arranged in series and parallel to explore several generic and important issues related to the use of observational data for constructing input-state-output models of dynamical systems. In the context of lumped catchment modeling, we show that physical interpretability and excellent predictive performance can both be achieved using a relatively parsimonious distributed-state multiple-flow-path network with context-dependent gating and information sharing across the nodes, suggesting that MCP-based modeling can play a significant role in application of ML to geoscientific investigation.

We present results from a systematic search for transiting short-period Giant Exoplanets around M-dwarf Stars (GEMS; P < 10 days, $R_p \gtrsim 8~R_\oplus$) within a distance-limited 100\,pc sample of 149,316 M-dwarfs using TESS-Gaia Light Curve (TGLC) data. This search led to the discovery of one new candidate GEM, following spectroscopic vetting of 12 additional candidates to eliminate astrophysical false positives and refine our occurrence rate estimates. We describe the development and application of the \texttt{TESS-miner} package and associated vetting procedures used in this analysis. To assess detection completeness, we conducted $\sim$ 72 million injection-recovery tests across $\sim$ 26,000 stars with an average of $\sim$3 sectors of data per star, subdivided into early-type (M0--M2.5), mid-type (M2.5--M4), and late-type (M4 or later) M-dwarfs. Our pipeline demonstrates high sensitivity across all M-dwarf subtypes within the injection bounds. We estimate the occurrence rates of short-period GEMS as a function of stellar mass, and combine our measured occurrence rates with those derived for FGK stars and fit an exponential trend with stellar mass, consistent with core-accretion theory predictions. We find GEMS occurrence rates of $0.067\% \pm 0.047\%$ for early-type M-dwarfs, $0.139\% \pm 0.069\%$ for mid-type, and $0.032\% \pm 0.032\%$ for late-type M-dwarfs, with a mean rate of $0.065^{+0.025}_{-0.027}\%$ across the full M-dwarf sample. We note that while our search spanned 1.0~\mathrm{days} < P < 10.0 days, these occurrence rates were calculated using planets orbiting with 1.0~\mathrm{days} < P < 5.0 days. This work lays the foundation for future occurrence rate investigations for GEMS.

We report on the observation and measurement of astrometry, photometry, morphology, and activity of the interstellar object 3I/ATLAS, also designated C/2025 N1 (ATLAS), with the NSF-DOE Vera C. Rubin Observatory. The third interstellar object, comet 3I/ATLAS, was first discovered on UT 2025 July 1. Serendipitously, the Rubin Observatory collected imaging in the area of the sky inhabited by the object during regular commissioning activities. We successfully recovered object detections from Rubin visits spanning UT 2025 June 21 (10 days before discovery) to UT 2025 July 7. Facilitated by Rubin's high resolution and large aperture, we report on the detection of cometary activity as early as June 21st, and observe it throughout. We measure the location and magnitude of the object on 37 Rubin images in r, i, and z bands, with typical precision of about 20 mas (100 mas, systematic) and about 10 mmag, respectively. We use these to derive improved orbit solutions, and to show there is no detectable photometric variability on hourly timescales. We derive a V-band absolute magnitude of H_V = (13.7 +/- 0.2) mag, and an equivalent effective nucleus radius of around (5.6 +/- 0.7) km. These data represent the earliest observations of this object by a large (8-meter class) telescope reported to date, and illustrate the type of measurements (and discoveries) Rubin's Legacy Survey of Space and Time (LSST) will begin to provide once operational later this year.

We present the first detection of weak gravitational lensing around spectroscopically confirmed dwarf galaxies, using the large overlap between DESI DR1 spectroscopic data and DECADE/DES weak lensing catalogs. A clean dwarf galaxy sample with well-defined redshift and stellar mass cuts enables excess surface mass density measurements in two stellar mass bins ($\log \rm{M}_*=[8.2, 9.2]~M_\odot$ and $\log \rm{M}_*=[9.2, 10.2]~M_\odot$), with signal-to-noise ratios of $5.6$ and $12.4$ respectively. This signal-to-noise drops to $4.5$ and $9.2$ respectively for measurements without applying individual inverse probability (IIP) weights, which mitigates fiber incompleteness from DESI's targeting. The measurements are robust against variations in stellar mass estimates, photometric shredding, and lensing calibration systematics. Using a simulation-based modeling framework with stellar mass function priors, we constrain the stellar mass-halo mass relation and find a satellite fraction of $\simeq 0.3$, which is higher than previous photometric studies but $1.5\sigma$ lower than $\Lambda$CDM predictions. We find that IIP weights have a significant impact on lensing measurements and can change the inferred $f_{\rm{sat}}$ by a factor of two, highlighting the need for accurate fiber incompleteness corrections for dwarf galaxy samples. Our results open a new observational window into the galaxy-halo connection at low masses, showing that future massively multiplexed spectroscopic observations and weak lensing data will enable stringent tests of galaxy formation models and $\Lambda$CDM predictions.

We present the samples of galaxies and quasars used for DESI 2024 cosmological analyses, drawn from the DESI Data Release 1 (DR1). We describe the construction of large-scale structure (LSS) catalogs from these samples, which include matched sets of synthetic reference `randoms' and weights that account for variations in the observed density of the samples due to experimental design and varying instrument performance. We detail how we correct for variations in observational completeness, the input `target' densities due to imaging systematics, and the ability to confidently measure redshifts from DESI spectra. We then summarize how remaining uncertainties in the corrections can be translated to systematic uncertainties for particular analyses. We describe the weights added to maximize the signal-to-noise of DESI DR1 2-point clustering measurements. We detail measurement pipelines applied to the LSS catalogs that obtain 2-point clustering measurements in configuration and Fourier space. The resulting 2-point measurements depend on window functions and normalization constraints particular to each sample, and we present the corrections required to match models to the data. We compare the configuration- and Fourier-space 2-point clustering of the data samples to that recovered from simulations of DESI DR1 and find they are, generally, in statistical agreement to within 2\% in the inferred real-space over-density field. The LSS catalogs, 2-point measurements, and their covariance matrices will be released publicly with DESI DR1.

We present and analyze follow-up, higher resolution ($R$ $\sim$ 70) $H$ and $K$ band integral field spectroscopy of the superjovian exoplanet HIP 99770 b with SCExAO/CHARIS. Our new data recover the companion at a high signal-to-noise ratio in both bandpasses and more than double the astrometric baseline for its orbital motion. Jointly modeling HIP 99770 b's position and the star's astrometry from \textit{Hipparcos} and \textit{Gaia} yields orbital parameters consistent with those from the discovery paper, albeit with smaller errors, and a slight preference for a smaller semimajor axis ($\sim$15.7--15.8 au)and a larger eccentricity ($\sim$0.28--0.29), disfavoring a circular orbit. We revise its dynamical mass slightly downwards to 15.0$_{-4.4}^{+4.5}$ $M_{\rm Jup}$ for a flat prior and 13.1$_{-5.2}^{+4.8}$ $M_{\rm Jup}$ for a more standard log-uniform mass prior, where the inclusion of its relative radial-velocity measurement is primarily responsible for these changes. \textcolor{red}{We find consistent results for HIP 99770 b's dynamical mass including recent VLTI/GRAVITY astrometry, albeit with a slightly smaller, better constrained eccentricity of $e$ $\sim$ 0.22$^{+0.10}_{-0.13}$}. HIP 99770 b is a $\sim$ 1300 K object at the L/T transition with a gravity intermediate between that of the HR 8799 planets and older, more massive field brown dwarfs with similar temperatures but with hints of equilibrium chemistry. HIP 99770 b is particularly well suited for spectroscopic follow up with Roman CGI during the technology demonstration phase at 730 nm to further constrain its metallicity and chemistry; JWST thermal infrared observations could likewise explore the planet's carbon chemistry, metallicity, and clouds.

We present a sample of 341 "little red dots" (LRDs) spanning the redshift range $z\sim2-11$ using data from the CEERS, PRIMER, JADES, UNCOVER and NGDEEP surveys. Unlike past use of color indices to identify LRDs, we employ continuum slope fitting using shifting bandpasses to sample the same rest-frame emission blueward and redward of the Balmer break. This enables the detection of LRDs over a wider redshift range and with less contamination from galaxies with strong breaks that otherwise lack a rising red continuum. The redshift distribution of our sample increases at z&lt;8 and then undergoes a rapid decline at $z\sim4.5$, which may tie the emergence of these sources to the inside-out growth that galaxies experience during this epoch. We find that LRDs are $\sim1$ dex more numerous than X-ray and UV selected AGN at z~5-7. Within our sample, we have identified the first two X-ray detected LRDs. An X-ray spectral analysis confirms that these AGN are moderately obscured with $\log\,(N_{\rm H}/{\rm cm}^{2}$) of $23.3^{+0.4}_{-1.3}$ and $22.72^{+0.13}_{-0.16}$. Our analysis reveals that reddened AGN emission dominates their rest-optical light, while the rest-UV originates from their host galaxies. We also present NIRSpec observations from the RUBIES survey of 17 LRDs that show broad emission lines consistent with AGN activity. The confirmed AGN fraction of our sample is 71\% for sources with F444W<26.5. In addition, we find three LRDs with blue-shifted Balmer absorption features in their spectra, suggesting an outflow of high-density, low-ionization gas from near the central engine of these faint, red AGN.

Planning in textual environments have been shown to be a long-standing challenge even for current models. A recent, promising line of work uses LLMs to generate a formal representation of the environment that can be solved by a symbolic planner. However, existing methods rely on a fully-observed environment where all entity states are initially known, so a one-off representation can be constructed, leading to a complete plan. In contrast, we tackle partially-observed environments where there is initially no sufficient information to plan for the end-goal. We propose PDDLEGO that iteratively construct a planning representation that can lead to a partial plan for a given sub-goal. By accomplishing the sub-goal, more information is acquired to augment the representation, eventually achieving the end-goal. We show that plans produced by few-shot PDDLEGO are 43% more efficient than generating plans end-to-end on the Coin Collector simulation, with strong performance (98%) on the more complex Cooking World simulation where end-to-end LLMs fail to generate coherent plans (4%).

Simulating ecohydrological processes is essential for understanding complex environmental systems and guiding sustainable management amid accelerating climate change and human pressures. Process-based models provide physical realism but can suffer from structural rigidity, high computational costs, and complex calibration, while machine learning (ML) methods are efficient and flexible yet often lack interpretability and transferability. We propose a unified three-phase framework that integrates process-based models with ML and progressively embeds them into artificial intelligence (AI) through knowledge distillation. Phase I, behavioral distillation, enhances process models via surrogate learning and model simplification to capture key dynamics at lower computational cost. Phase II, structural distillation, reformulates process equations as modular components within a graph neural network (GNN), enabling multiscale representation and seamless integration with ML models. Phase III, cognitive distillation, embeds expert reasoning and adaptive decision-making into intelligent modeling agents using the Eyes-Brain-Hands-Mouth architecture. Demonstrations for the Samish watershed highlight the framework's applicability to ecohydrological modeling, showing that it can reproduce process-based model outputs, improve predictive accuracy, and support scenario-based decision-making. The framework offers a scalable and transferable pathway toward next-generation intelligent ecohydrological modeling systems, with the potential extension to other process-based domains.

The Kepler Spacecraft has discovered a large number of planets up to one-year periods and down to terrestrial sizes. While the majority of the target stars are main-sequence dwarfs of spectral type F, G, and K, Kepler covers stars with effective temperature as low as 2500 K, which corresponds to M stars. These cooler stars allow characterization of small planets near the habitable zone, yet it is not clear if this population is representative of that around FGK stars. In this paper, we calculate the occurrence of planets around stars of different spectral types as a function of planet radius and distance from the star, and show that they are significantly different from each other. We further identify two trends: First, the occurrence of Earth to Neptune-sized planets is successively higher toward later spectral types at all orbital periods probed by Kepler; Planets around M stars occur twice as frequently as around G stars, and thrice as frequently as around F stars. Second, a drop in planet occurrence is evident at all spectral types inward of a 10 day orbital period, with a plateau further out. By assigning to each spectral type a median stellar mass, we show that the distance from the star where this drop occurs is stellar mass dependent, and scales with semi-major axis as the cube root of stellar mass. By comparing different mechanisms of planet formation, trapping and destruction, we find that this scaling best matches the location of the pre-main-sequence co-rotation radius, indicating efficient trapping of migrating planets or planetary building blocks. These results demonstrate the stellar-mass dependence of the planet population, both in terms of occurrence rate and of orbital distribution. The prominent stellar-mass dependence of the inner boundary of the planet population shows that the formation or migration of planets is sensitive to the stellar parameters.