Galactic bars are key non-axisymmetric structures in disk galaxies, driving angular-momentum redistribution and contributing to secular evolution, central mass build-up, and the formation of nuclear structures. Robust and homogeneous measurements of bar length, however, remain challenging, particularly for large imaging surveys, where manual estimates are time-consuming and sensitive to methodological choices. We introduce elma, a standalone, pip-installable Python package for automated bar-length estimation in galaxies already identified as candidate barred systems. The method operates directly on two-dimensional imaging data, using iterative elliptical-isophote fitting to trace the radial ellipticity profile and identify a projected bar-length estimate from the semi-major axis associated with the local maximum in ellipticity. Using the image WCS information and a user-supplied redshift, elma converts angular measurement into a projected physical length. We demonstrate the package on JWST/NIRCam imaging of barred galaxies in the GOODS-South field. The code is released under the MIT license at a repository in Github.

Advancing Astrophysics with the SKA II (AASKAII), written by our science community, outlines the transformative scientific advances that will be enabled by the SKA telescopes. In the decade since the publication of the previous edition, telescope designs have matured, construction has commenced, and the SKA Organisation has evolved into the SKA Observatory (SKAO). At the same time, observations from SKA precursor and pathfinder telescopes have provided new insights into longstanding scientific challenges while revealing entirely new phenomena. Published in advance of the first science verification campaign for the SKA Observatory, this volume looks ahead to the coming decades of discovery and innovation in radio astronomy. AASKAII spans the broad range of scientific research enabled by the SKA telescopes, SKA-Mid and SKA-Low. The contributions are organised into six thematic categories according to their scientific focus. The opening section presents overview chapters from the SKA Science Working Groups, around which our community is organised. Each overview provides the broader context that connects the contributions in this volume to the key scientific questions being pursued by their respective communities.

Multi-band photometry traces diverse physical processes across a wide range of wavelengths. In recent decades, this field has been driven by the rapid growth of multi-imaging datasets, from high-resolution observation from Hubble Space Telescope and James Webb Space Telescope to the forthcoming large-scale surveys enabled by the Roman Space Telescope and Rubin Observatory, for example. In this work, we present lightstack, a Python package for combining standalone images into photometric data cubes. The workflow consists of three main steps: cropping a region of interest from a mosaic across all available filters; stacking the images to construct the data cube; and performing PSF matching on the cube. This package is intended for preparing data for studies involving multi-band photometry. The code is released under an MIT license and is available on GitHub together with a Jupyter tutorial notebook. The version used for this publication (v0.2.1) is archived on Zenodo.

The successful detection of X-ray polarization from many celestial sources belonging to different classes by the IXPE mission has opened a new window in X-ray astronomy. While an impressive number of scientific topics have already been addressed by IXPE, many of them would benefit from a new class of instrumentation that could be launched on a relatively short time scale. In this contribution, we present the development activities of a focal-plane polarimeter whose goal is to extend the energy range of IXPE up to tens of keV, with better sensitivity and lower background. Our design is based on the use of multilayer mirrors and stacked instrumentation, comprising either a low- or medium-energy imaging photoelectric polarimeter and an active Compton polarimeter. Such an approach relies on hardware with flight heritage and -- although still under development for the specific application in X-ray polarimetry -- it has the potential to answer compelling scientific questions and to soon become competitive from the point of view of feasibility for space applications.

The Extremely Large Telescope (ELT) is, to date, the most ambitious ground-based telescope under construction. MOSAIC is a multi-objects spectrograph (MOS) that aims to make full use of the largest telescope in the world. At its heart, about 300 robotic positioners will pick-off skylight from the focal surface of the ELT to feed it to its Near Infrared (NIR) and visible (VIS) spectrographs. The gigantic scale of the ELT presents three main challenges for MOSAIC positioners: (1) the light beams on the focal surface cannot be focused in a single fiber, similarly to other MOS instruments, involving a design with relay mirrors patrolling the field of view, and reimaging the sub-field on 2 fixed fiber bundles located 600 mm behind the ELT focal plane (2) The positioner needs to adapt to the local telecentricity, which means it has to point at the ELT pupil center located 37.868 m away from the focal plane (3) The Atmospheric Dispersion Corrector (ADC) needed to cover the whole focal surface of the ELT is impossible to build to this scale; hence each positioner needs its own ADC. EPFL is responsible for designing and supervising the mass manufacturing of the positioners. This paper aims to present its initial design and prototypes.

Recent developments in widefield radio telescopes have enabled searches of a new region of parameter space in the time domain: timescales of seconds to minutes, that have been overlooked in traditional surveys. These searches have revealed a new population of sources: long period transients, which typically show periodic behaviour of minutes to hours. In addition they have detected phenomena ranging from extreme scintillation to stellar radio bursts. However, almost all searches to date have involved archival data that has been processed in offline, batch mode. In this context, we present VASTER, the first short-timescale imaging and transient detection pipeline running in real time on a widefield radio telescope. VASTER has been running on the Australian SKA Pathfinder (ASKAP) since July 2025, and images most of the ASKAP survey project data on timescales of 15 minutes. In this paper we describe the VASTER system, and present the results from the first two weeks of operation, including the discovery of two long period transients: ASKAP~J165130.3$-$450520 with a period of 6.48 hours and ASKAP~J170036.6$-$445758 with a period of 4.69 hours. The detection of these two sources adds to the small, but growing, population of long period transients, as well as demonstrating the potential of VASTER to explore this region of transient parameter space.

The gravitational effects of seismic waves, so-called Newtonian noise, will likely limit the low-frequency sensitivity of future ground-based gravitational wave detectors, such as the Einstein Telescope. It has been proposed to mitigate this noise source by predicting it from measurements of the surrounding seismic displacement field using an array of seismometers. In this paper, we investigate the Newtonian noise prediction abilities of neural networks based on synthetic data from such seismometer arrays and compare the results with the Wiener filter as benchmark. We developed a simulation that generates density fluctuations of random plane waves and Gaussian wave packets, and that calculates the resulting Newtonian noise and displacement field. We investigate the performance on approximately stationary wave fields and single dominating long- and short-term events. For the first case, we observe comparable performance of neural networks and the Wiener filter with the networks performing slightly better. For the second case, however, we find that convolutional neural networks and graph neural networks can outperform the Wiener filter by factors of 15-80, depending on the frequency and the array configuration, and that they can reduce the corresponding Newtonian noise amplitude spectral density by factors of 10-30.

Dual-bandpass spatial heterodyne spectrometers (DB-SHS) enable simultaneous high-resolution measurements of widely separated passbands, providing powerful diagnostics of astrophysical and planetary environments. However, DB-SHS instruments require a single incident beam to span two adjacent diffraction gratings with distinct ruling densities and blaze angles, resulting in a large gap between ruled sections that reduces throughput. Dual-ruled gratings solve this problem by integrating multiple ruled panels onto a single substrate, minimizing the dead space between ruled sections. We present experimental validation of a first-generation symmetric-facet dual-ruled grating manufactured by Bach Research, mechanically ruled at $800$ and $\mathrm{2000\;gr\;mm^{-1}}$ with a $13.8^\circ$ blaze angle. Using a stabilized deuterium source alongside a Czerny-Turner monochromator, we measured diffraction efficiencies into the $m = 0, \pm1, \pm2$ orders from $200$ to $\mathrm{700\;nm}$. We compare these results with theoretical predictions from rigorous coupled-wave analysis (RCWA), inferring a facet asymmetry of $\lesssim1^\circ$ and $\sim70\%$ facet duty cycle indicative of minor manufacturing defects. This work demonstrates the viability of mechanically ruled, symmetric-facet, dual-ruled gratings and lays the foundation for laboratory validation of the first DB-SHS, ultimately enabling high-resolution spectroscopy of distinct spectral regions relevant to astrophysical and planetary remote sensing.

Many modern near-infrared instruments employ HAWAII-2RG (H2RG) detectors with integration times that can reach 300-600s. Up-the-ramp (UTR) sampling offers advantages over Fowler sampling, including superior cosmic ray rejection and noise reduction, but requires fitting linear ramps from 30-60 reads. Ground-based K-band sky brightness has been reported to vary by 3-10% on timescales of minutes, potentially introducing systematic errors and compromising photometric accuracy. Additionally, UTR data formats involve higher-dimensional FITS files with larger file sizes impacting observatory operations. We present a feasibility study using the GIRMOS Data Simulator with high-fidelity flux budgets and empirical K-band sky variations estimated, for Mauna Kea, from Gemini-NIRI at 10-20s cadence. Using a Monte Carlo approach we assess whether linear ramp fitting remains viable under variable sky conditions, quantify SNRs and systematic biases, and report nightly data volume estimates. Our results show that the advantages of the UTR readout hold for read-noise-limited targets placed in the inter-line regions, translating into 4-10% savings in observing time. Over the sky emission lines, UTR fitting remains possible but its performance is compromised, both by a degradation in SNR and by a high rate of pixels falsely flagged by the cosmic ray rejection algorithm. Both effects are driven by the higher signal level rather than by sky variability and the latter could be mitigated by adapting CR rejection thresholds to the local signal level. These findings address how ground-based conditions affect UTR implementation in near-infrared spectrographs, with GIRMOS as a concrete case of study.

Modeling of complex microlensing events suffers from many difficult-to-disentangle degeneracies. This is especially the case for orbital motion of the source in a binary system, the so-called xallarap effect. To address the degeneracies inherent in xallarap modeling, we developed a novel approach that directly samples the physical parameters of the source stars (initial mass, evolutionary phase, metallicity, distance, and reddening) during MCMC fitting. In our approach the physical parameters of the source are estimated using MIST stellar evolution models. This parametrization imposes astrophysical constraints that help identify the physically most probable solutions. We test our method on the complex microlensing event OGLE-2017-BLG-0114, which exhibits signatures that can be traced to the complexity of the source system. We successfully constrained the microlensing models, achieving improvements in the Einstein ring radius estimates by up to an order of magnitude in the case of binary source models.

Astrochemical modeling is a key tool for the understanding of the formation and destruction of molecules in the dense gas of the interstellar medium, as observed by modern day observational facilities. UCLCHEM is a comprehensive astrochemical modeling framework that can model the interstellar medium ranging from extra-galactic to protoplanetary disks scales. The framework consists of a core routine that solves chemical reaction networks as a function of time. The chemistry includes a description of gas and ice grain chemistry and the interactions between the two. The physical modeling includes parametrizations for modelling cloud collapse, protostellar cores and shocks as well as the ability to provide user defined inputs. This manuscript provides an overview of the physics and chemistry included in UCLCHEM, as well as the inner workings of the solver routine and the programming interface.

Modern telescopes observing the Cosmic Microwave Background (CMB) polarization require an exquisite control of systematics to target Inflationary Gravitational Waves (IGW), Cosmic Birefringence (CB), and Primordial Magnetic Fields (PMF). The absolute polarization angle of the detectors is a critical parameter to disentangle the $E$-modes and $B$-modes of the CMB, allowing a correct detection of primordial $B$-modes as well as testing Cosmic Birefringence theories. To this end, we discuss the current status of the POLOCALC project, an ERC Advanced Grant that aims to develop air-borne calibration sources for CMB small-aperture telescopes. The main scientific objective of POLOCALC is to enable a direct calibration of the absolute polarization angle of CMB polarimeters with an accuracy of $0.01 \degree $. We present the latest developments regarding the calibration source, the calibration strategies designed to use drone-based calibrators, and the application to modern ground-based experiments.

We present a simulation-driven assessment of the performance of the SPHEREx pipeline for galaxy cluster science, focusing on photometry, source blending, survey depth, and photometric redshift accuracy. To do that, we compile a sample of eight galaxy clusters spanning a wide redshift range ($z \approx 0.02$-$1.1$) and develop an end-to-end pipeline. We use the ancillary data from the DESI Legacy Survey and COSMOS survey, and generate realistic mock SPHEREx observations with the SPHEREx Sky Simulator. By performing forced photometry on these images with The Tractor, we quantify the characteristic biases and uncertainties relevant to cluster science. We find that the photometry is generally unbiased, but source blending is the primary driver of catastrophic outliers, particularly when the combined flux of neighbors is comparable to the flux of targets. Measuring the effective survey depth, we find that SPHEREx detects members down to $K_{s}\approx 20$ AB ($5σ$), 7-9 mag fainter than the brightest cluster galaxy (BCG) in nearby clusters but only 1-2 mag for clusters at $z \sim 1$, where the BCG itself has faded close to this depth. Despite these challenges, we demonstrate that SPHEREx can achieve a photometric redshift precision of $σ_{\mathrm{NMAD}}\approx 0.003$-$0.01$ for cluster galaxies with an appropriate sample selection based on brightness or signal-to-noise. Combining the redshifts of quality-selected members, we recover cluster redshifts with a bias of $|Δz|/(1+z) < 0.002$ and a scatter of $σ\approx 0.002$ at $z \lesssim 0.5$, meeting the precision required for cluster cosmology.

SED fitting is the most common technique to recover galaxy physical properties from observed photometry. However, SED fitting requires many assumptions that essentially collapse a galaxy from a three-dimensional spatially varying object with complex structure into a scalar point. Moreover, modern inference techniques are computationally intensive, which presents a unique challenge in the era of extremely large datasets. We present \textsc{Phot-Gal}, a new galaxy SED modeling tool that solves the inverse problem of SED fitting by training a machine learning model on photometry generated from 3D radiative transfer of simulated galaxies with a wide range of implemented physics. \textsc{Phot-Gal} is designed to accept an arbitrary amount of input photometry by utilizing a $K$-nearest neighbors imputation strategy. Our fiducial model predicts redshift, stellar mass, dust mass, and star formation rate with uncertainties based on the provided input photometry. We evaluate the performance of \textsc{Phot-Gal} relative to the commonly-used SED fitting tool \textsc{prospector} in successfully recovering each of these properties with several metrics for the inferred values and uncertainties and find that it outperforms the accuracy of standard SED fitting software on the testing set. However, with fewer photometric constraints, \textsc{Phot-Gal} is more likely to have output uncertainties that do not reflect the offset from the ground truth. We dissect the components of \textsc{Phot-Gal} to find reasonable physical justifications for the photometry it relies on most, understand how each step in its workflow contributes to the eventual output posterior, and evaluate its ability to generalize to novel data.

We present a new framework for BayeSN, the hierarchical Bayesian SED model for type Ia supernovae (SNe Ia), incorporating cross-calibration of samples observed across heterogeneous telescopes. This framework is the first to parametrise the filter wavelength and zero-point offsets commonly used in SN~Ia cosmology within SN SED model training, enabling additional constraint on cross-calibration from SNe beyond the standard stellar-based cross-calibration pipeline. We apply this framework to train a new G26 BayeSN model on the same SED model training sample used in recent cosmological analyses, an order-of-magnitude increase over previous BayeSN training samples, and include a novel training methodology to leverage high-redshift SNe Ia in BayeSN training. We present the G26 model and apply it to the DES-SN5YR sample to assess performance, finding a 12 per cent reduction in $σ_{\rm NMAD}$ scatter when compared with SALT3.Dovekie; 0.164 mag compared with 0.185 mag for a sample of likely SNe Ia at $z < 0.7$, without bias corrections. We additionally present constraints on cross-calibration wavelength and zero-point shifts from our framework when using the latest `Dovekie' calibration constraints as a prior. This work is a key step towards a full end-to-end cosmological analysis with BayeSN; the new G26 model is incorporated within the public BayeSN code.

Phenomenological model building has traditionally relied on human reasoning: equations are proposed from theoretical intuition, analogy, or empirical convenience, and only then tested against data. Here we show that this cycle can be recast as an iterative AI reasoning process for dynamical dark energy. Our framework uses a large language model to propose equations of state together with cosmological rationales, grounded by retrieval from the dark-energy literature and refined through autonomous evaluation. Each candidate is embedded in a cosmological model, optimized against observations, and assessed using likelihood performance and theoretical consistency. An independent language-model critic scores the physical motivation, novelty, clarity, stability and implementation validity of both the equation and its rationale, allowing subsequent proposals to evolve jointly in mathematical structure and physical reasoning. Applied to cosmological data combinations including supernovae, baryon acoustic oscillations and Planck likelihoods, the framework identifies two parameterizations that, to the best of our knowledge, have not previously been explored and that are competitive with established forms. For Pantheon+ supernovae, DESI DR2 baryon acoustic oscillations and the full Planck 2018 temperature, polarization, and lensing likelihoods, the best AI-selected model attains larger Bayesian evidence than the traditional parameterizations considered here by more than one unit. These results show that AI-guided reasoning can complement physical model building by proposing and evaluating interpretable phenomenological parameterizations for dynamical dark energy.

Past and recent observations suggest that many planetary mass or dwarf planet objects may exist in the outer Solar System. Gravitational perturbations may occasionally bring some of them into the inner Solar System. The early, rare collision between the early Earth and a Mars sized body is generally invoked to explain the formation of the Moon. More probable than a direct impact, are grazing or near Earth flybys of similar objects. Such passages may have left strong tidal signatures: giant waves, large volcanic episodes, sea regressions, coherent meteor showers, and major climatic perturbations. These mechanisms could have contributed to several major biological mass extinctions over the past $600$ million years, as suggested by peculiar correlations in the geological record. Similar events may have occurred several times during the earlier history of Earth. Accretion of mini planets by largest planets and in particular by the Sun may also have occurred many more times over the last four billion years. Possibly producing additional temperature variations on planets and Earth.

The pulsar magnetosphere has only recently been addressed using Physics-Informed Neural Networks (PINNs), by deploying a domain-decomposition approach and treating the separatrix and equatorial current sheet as infinitesimally thin discontinuities. However, this baseline requires extensive manual hyperparameter tuning, achieves limited final accuracy and demands several hours of training. We refine this framework by introducing domain-specific neural architectures based on Kolmogorov-Arnold networks, an automated adaptive training pipeline and a physics-based convergence criterion that eliminate the need for manual calibration. The proposed methodology delivers self-consistent axisymmetric magnetosphere solutions with mean squared errors of the PDE residuals at O(1e-6) in double precision - an improvement of two orders of magnitude over the baseline - while achieving convergence in under 20 minutes in single precision. Importantly, the method reliably resolves stellar radii reduced by up to 80% compared to the baseline, overcoming the severe spatial scale disparities that also challenge traditional solvers. Furthermore, by varying the flux that opens to infinity, we provide a correction to the equation that connects it to the equatorial T-point's position. The complete framework is released as the open-source library PulsarX.

Rare $α$-particle-induced charge clusters appear in LSST images as compact, PSF-like sources with a median FWHM of $0.\!\!^{\prime\prime}95$ and median ellipticity consistent with zero, closely resembling unresolved astrophysical point sources. These events are detected in both dark and science exposures at a rate of approximately $10^{-12}\ \mathrm{pixel}^{-1}\ \mathrm{s}^{-1}$. Their collected charge and morphology are consistent with energy deposition from $\sim$5 MeV $α$-particles in silicon CCDs, and their spatial distribution across the focal plane suggests a localized material origin, plausibly associated with trace radioactive contamination in the cryostat aluminum. Despite their deceptive appearance, we demonstrate that a simple broadness statistic based on fourth-order moments cleanly separates these events from stellar PSFs, enabling efficient rejection in coadded images and real-time alert streams. Such charge clusters do not impose an intrinsic bright-end contamination floor for Rubin transient searches, as genuine fast astrophysical events would exhibit characteristically different morphological signatures.

In this paper, we introduce the radio frequency interference monitor deployed at the Dominion Radio Astrophysical Observatory. It provides 2 GHz of instantaneous bandwidth, supporting channel bandwidths as fine as ~100 Hz for 1 s integrations, or integration times as low as ~50 ms for the standard 3.33 kHz channel bandwidth. After operating as a prototype instrument for several years, the monitor was commissioned to improve the calibration method, analog section temperature, and gain stability. It now operates both as a transient detector and as a long-term radio environment characterization tool. We introduce novel applications for the monitor and derive a new method for calculating the effect of gain drift on integrated data.

A primary goal of the Habitable Worlds Observatory (HWO) is to detect and measure the abundance of biosignature molecules, such as water (H2O) and oxygen (O2), in the atmosphere of Earth analogs. This is expected to require deep spectroscopic observations lasting hundreds of hours per planet. In this context, it is essential to optimize the spectral resolution of the spectrograph to both maximize the number of planets that can be studied over the lifetime of the mission, and also to reduce the risks of false detections. The purpose of this work is to provide a framework to explore the spectral resolution design trade-space for HWO. This framework must be valid and comparable across all spectral resolutions from low (R<100) to high resolutions (R>10,000), and account for the spectral correlation of the residual starlight (i.e., speckle noise chromaticity). Leveraging the concept of "template matching", we develop a simulation toolkit based on the Python package EXOSIMS to compute the detection significance of planets and molecules. We then simulate observations of Earth analogs around 164 stars using representative mission parameters to explore the effects of the detector noise and the correlated speckle noise floor. Our findings suggest that a moderate or high resolution spectrograph (R>1,000) will provide higher sensitivity to critical molecules compared to a low resolution spectroscopy mode (e.g., R~140). The correlated speckle noise may also entirely suppress our ability to detect bio-signatures at low spectral resolutions. We conclude that a more comprehensive study combined with detailed models of its stability, and other sources of correlated noise, is necessary to fully explore the trade space of spectral resolution and detectability of key species.

We present a summary of gravitational-wave (GW) follow-up using the Las Cumbres Observatory global network of telescopes during the third (O3) and fourth (O4) observing runs of the GW detectors. As in O2, we implemented the Gehrels et al. 2016 galaxy-targeted strategy. Here we test its efficacy in O3 and O4 and analyze the Las Cumbres Observatory response time and depth for nine GW alerts that showed a possibility of having an electromagnetic counterpart (GW190425, GW190426_152155, S190510g, GW190728_064510, GW190814, S190822c, GW191216_213338, S240422ed and S250206dm). We find that Las Cumbres Observatory is able to begin observations in response to GW alerts within minutes of the alert, with the observations being deep enough to detect possible GW170817-like kilonovae out to a median distance of 250 Mpc. In this sense a global rapid-response network of telescopes like Las Cumbres is an excellent GW follow-up facility. However, the galaxy-targeted follow-up strategy was much less efficient in O3 and O4 than originally predicted, given the larger than assumed GW localizations. We conclude that coordination between various facilities to include both wide-field and rapid-response capabilities is required to achieve efficient and comprehensive follow-up of GW events.

The Majoron is a hypothetical (pseudo) Nambu-Goldstone boson arising from the spontaneous breaking of a global lepton number symmetry, and is known as a candidate for dark matter in our Universe. In this paper, we investigate the possibility of probing the Majoron dark matter with a linear optical cavity used in the interferometric gravitational wave detectors. We consider a scenario in which the Majoron dark matter couples to photons through a QED anomaly, leading to an oscillatory photon birefringence induced by the coherent dark matter background. The anomaly coefficient is fixed by requiring the model to simultaneously reproduce the electroweak Higgs scale and a typical right-handed Majorana neutrino mass scale, and the resulting dark matter-photon coupling naturally falls within the sensitivity range of optical interferometers. By incorporating additional optics to extract the birefringence signal, we find that ground-based laser interferometers such as Advanced LIGO, KAGRA, as well as future detectors, can probe a region of the parameter space of Majoron dark matter.

This work presents the design and performance characterization of the optical calibration systems produced for the Pacific Ocean Neutrino Experiment (P-ONE), which target gain, energy and time calibration in the detector. These systems include novel light-pulse driver circuitry based on gallium nitride field-effect transistor technology and its application to directional and isotropic, self-monitoring optical calibration instruments. A total of 330 directional light pulsers and two isotropic, 17-inch calibration modules (P-CALs) were produced for the first P-ONE line. We present the designs and performance of both the directional and isotropic calibration devices and perform detailed optical characterizations of both full-production batches. In a wavelength range of $365 - 520\,$nm, our developed driver circuits achieve emission intensities up to $10^{11}\,$photons and pulse widths as small as $1.4\,$ns, respectively. Light-pulse drivers and self-monitoring electronics in the P-CAL were characterized using the same experimental setup, and the instrument's optical-isotropy design was optimized in combination with a dedicated GEANT4-based simulation framework. The optimized P-CAL achieves a simulated isotropy grade of $1.00 \pm 0.01$ across the entire $4π\,$solid angle range. These simulation investigations were explicitly confirmed by dedicated measurements in both air and water using two independent experimental setups, and we report the results. With this, a detailed performance estimate for deployed P-CAL modules in P-ONE was possible.

Our understanding of the early Universe has long been limited by biased galaxy samples selected through various color criteria. With deep JWST infrared imaging, mass-complete galaxy samples can now be studied up to $z \sim 8$ for the first time. However, recent work has revealed systematic uncertainties in measuring physical properties of galaxies based solely on JWST/NIRCam and HST photometry, due to their limited wavelength coverage. This highlights the need for supplementary data, particularly in the rest-frame UV and near-infrared. Here we present the ULTIMATE-deblending project, which will eventually deliver self-consistent UV to radio photometry for galaxies detected in deep JWST surveys, including both NIRCam and MIRI data. In this first paper, we release a 50-band photometric catalog spanning CFHT/U to JWST/MIRI F1800W, covering a total of 627.1 arcmin$^2$ across two JWST/PRIMER fields. We detail the reduction of the JWST imaging data, the photometric procedures, and the spectral-energy-distribution-fitting methodology used to derive the galaxy properties. Compared with photometry including only HST and JWST bands, the inclusion of deblended low-resolution photometry from ground-based telescopes improves the accuracy of photometric redshifts by $\sim$40%, while reducing the outlier fraction by $\sim$60%. This galaxy sample can serve as a key reference for statistical studies of galaxy formation and evolution in the early Universe. The UV-to-MIR catalogs and JWST mosaics from the ULTIMATE-deblending project have been made publicly available.

Wide-field and high-cadence sky surveys are the first step in the chain of discovery and characterisation of astrophysical transients such as supernovae, kilonovae, and tidal disruption events, each linked to the varied demise of stellar systems. The Gravitational-wave Optical Transient Observer (GOTO) is a telescope array of thirty-two 40 cm unit telescopes split over two almost antipodal sites. It performs a regular time-domain sky-survey in the optical to ~20 mag in addition to immediate scheduling of follow-up observations at the locations of external multi-wavelength and -messenger triggers. To facilitate the timely recovery of optical counterparts to these triggers, as well as the presence of serendipitous discoveries of astrophysical transients in the regular sky-survey, a low-latency data pipeline and workflow was developed. The implementation of this workflow is described herein and the quality of GOTO data delivered by it assessed, alongside its performance for prompt transient recovery. Utilising difference image analysis to identify candidate discoveries, the process is typically complete ~7 minutes after shutter close on the telescope. We further describe later processing of these candidates -- both automated and human-in-the-loop -- including reporting to the wider community and the triggering of more detailed observations, with a focus on immediate, intra-night characterisation. The workflow is meeting the needs of GOTO to promptly discover, report and characterise infant transients. Nevertheless, areas for further development and improvements are also highlighted.

This work investigates symbolic regression (SR) as an interpretable alternative to black-box machine learning for the classification of stars, galaxies, and quasars in the Sloan Digital Sky Survey Data Release 17 (SDSS DR17). We conduct a systematic comparative study of four state-of-the-art SR frameworks: {\tt PySR}, Exhaustive Symbolic Regression ({\tt ESR}) with MDL-based selection, Physical Symbolic Optimization ({\tt PhySO}) using deep reinforcement learning, and Multi-View Symbolic Regression ({\tt MvSR}). By deriving compact analytic functions (complexity $\leq$ 10) on a representative training subset and subsequently evaluating them via an 80,000-sample 5-fold cross-validation threshold optimization phase and a subsequent 10,000-sample unseen hold-out test set, we map spectroscopic redshift ($z$) to continuous classification scores. Our results demonstrate that these low-complexity expressions achieve high predictive reliability, with {\tt MvSR} reaching a cross-validation Cohen's Kappa of 0.8956 (0.8876 on the hold-out set) and {\tt PhySO} achieving exceptional parametric stability ($σ< 0.002$). We note however that the resulting equations returned by Symbolic regression are purely empirical and no physical significance should be ascribed to these equations.

Astrophysics has experienced an overwhelming increase in research output, as is evident from the year-over-year increase in the number of research papers submitted to the online repository arXiv. As a result, keeping up with progress happening outside our respective sub-fields can be exhausting. While it is impossible to be informed on every single aspect of every sub-field, this paper aims to be the next best thing. We present a summary of statistics for every paper uploaded onto the Astrophysics arXiv over the past year - 2025. We analyse a host of metrics like the most used keywords, subfields and telescopes, the distribution of journals, the most studied astrophysical objects like GW, GRB, FRB events, exoplanets and much more. We also indexed the authors' affiliations to put into context the global distribution of research and collaboration. Combining this data with the citation information of each paper allows us to understand how influential different papers have been on the progress of the field this year. We also present a first of its kind Astrophysical Spectral Fingerprint showing the distribution of research across the electromagnetic spectrum as well as the distribution of research by redshift. Overall, these statistics highlight the general current state of the field, the hot topics people are working on and the different research communities across the globe and how they function. We hope that this is helpful for both students and professionals alike to adapt their current trajectories to better benefit the field.

The data post-processing gain is an important parameter for exposure time calculations used to inform the design of the Habitable Worlds Observatory (HWO). Assuming azimuthally symmetric noise properties is a common simplifying assumption for such simulations, which neglects the patchy nature of the residual diffracted starlight; i.e., speckles. Fortunately, patchiness might prove to be an opportunity that improves the overall sensitivity of observatory assuming photon-noise limited speckle subtraction. We illustrate this effect in the context of angular differential imaging (ADI), which is one of the possible observing strategies being considered for the detection and characterization of exo-Earth with HWO. We show that combining observations of two observatory roll angles leads to a gain in signal-to-noise greater than $\sqrt{2}$ when the patchy starlight dominates other noise sources. The gain can be closer to x2 when the starlight dominates the noise budget by more than an order of magnitude. In other words, combining good and bad observations is better than combining two average ones. This statement is very general as it is a direct consequence of combining data with a weighted mean. It applies more broadly to any combination of observations with varying noise level.

The 1.3 mm ground-based very long baseline interferometry (VLBI) array Event Horizon Telescope (EHT), is limited by Earth's diameter, restricting its black hole shadow imaging to only M87* and Sgr A*. Extending baselines to the Moon would achieve ~0.7 uas angular resolution at 230 GHz, enabling shadow detection for a much larger sample of supermassive black holes (SMBHs). The concept is motivated by space VLBI missions and lunar exploration, including the ongoing Lunar Orbit VLBI EXperiment (LOVEX) aboard QueQiao-2 (Chang'E-7) and the planned International Lunar Research Station (ILRS). We assess shadow detectability for 31 SMBHs with predicted large angular sizes, exploring different telescope location and antenna size. Assuming a telescope at the lunar antipode, we simulate the Moon-Earth (u,v) coverage and show that sources with direction near the Moon's orbital plane yield projected baselines spanning from short to long, enabling sampling of the first visibility null - a key shadow signature. Using a geometric ring model, we identify six shadow-detectable candidates for Moon-Earth VLBI. Among these, M104, NGC 5077, and NGC 1052 are detectable with a 5 m lunar-based telescope; PGC 049940 requires 10 m; NGC 524 requires 20 m; and NGC 5252 requires 40 m. Furthermore, if space telescopes fill the baseline coverage gaps between Moon and Earth, the n=2 photon ring region is detectable for Sgr A*, M87* with a 10 m lunar-based telescope, and 12 candidates are detectable for the n=1 photon ring region using a lunar-based telescope of up to 40 m. These results provide a clear scientific and technical motivation for lunar-based telescopes in future black hole shadow studies.