Gravitational waves (GWs) from compact binaries are excellent probes of gravity in the strong- and dynamical-field regime. We report a test of general relativity (GR) with the third GW Transient Catalog (GWTC-3) using the recently developed neural post-Einsteinian framework, both on individual events and at the population level through hierarchical modeling. We find no significant violation of GR and place a constraint that, for the first time, efficiently covers non-GR theories characterized by not only post-Newtonian deviations but also those beyond under the same theory-agnostic framework.

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.

We present measurements of the temperature and E-mode polarization angular power spectra of the cosmic microwave background (CMB) from observations of 4% of the sky with SPT-3G, the current camera on the South Pole Telescope (SPT). The maps used in this analysis are the deepest used in a CMB TT/TE/EE analysis to date. The maps and resulting power spectra have been validated through blind and unblind tests. The measurements of the lensed EE and TE spectra are the most precise to date at l=1800-4000 and l=2200-4000, respectively. Combining our TT/TE/EE spectra with previously published SPT-3G CMB lensing results, we find parameters for the standard LCDM model consistent with Planck and ACT-DR6 with comparable constraining power. We report a Hubble constant of $H_0=66.66\pm0.60$ km/s/Mpc from SPT-3G alone, 6.2 sigma away from local measurements from SH0ES. For the first time, combined ground-based (SPT+ACT) CMB primary and lensing data have reached Planck's constraining power on some parameters, a milestone for CMB cosmology. The combination of these three CMB experiments yields the tightest CMB constraints to date, with $H_0=67.24\pm0.35$ km/s/Mpc, and the amplitude of clustering $\sigma_8=0.8137\pm0.0038$. CMB data alone show no evidence for physics beyond LCDM; however, we observe a 2.8 sigma difference in LCDM between CMB and baryon acoustic oscillation (BAO) results from DESI-DR2, which is relaxed in extended models. The combination of CMB and BAO yields 2-3 sigma shifts from LCDM in the curvature of the universe, the amplitude of CMB lensing, or the dark energy equation of state. It also drives mild preferences for models that address the Hubble tension through modified recombination or variations in the electron mass in a non-flat universe. This work highlights the growing power of ground-based CMB experiments and lays a foundation for further cosmological analyses with SPT-3G.

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 compare the results of Eulerian hydrodynamic simulations of cluster formation against virial scaling relations between four bulk quantities: the cluster mass, the dark matter velocity dispersion, the gas temperature and the cluster luminosity. The comparison is made for a large number of clusters at a range of redshifts in three different cosmological models (CHDM, CDM and OCDM). We find that the analytic formulae provide a good description of the relations between three of the four numerical quantities. The fourth (luminosity) also agrees once we introduce a procedure to correct for the fixed numerical resolution. We also compute the normalizations for the virial relations and compare extensively to the existing literature, finding remarkably good agreement. The Press-Schechter prescription is calibrated with the simulations, again finding results consistent with other authors. We also examine related issues such as the size of the scatter in the virial relations, the effect of metallicity with a fixed pass-band, and the structure of the halos. All of this is done in order to establish a firm groundwork for the use of clusters as cosmological probes. Implications for the models are briefly discussed.

Machine-learning-based surrogate models offer significant computational efficiency and faster simulations compared to traditional numerical methods, especially for problems requiring repeated evaluations of partial differential equations. This work introduces the Geometry-Informed Neural Operator Transformer (GINOT), which integrates the transformer architecture with the neural operator framework to enable forward predictions on arbitrary geometries. GINOT employs a sampling and grouping strategy together with an attention mechanism to encode surface point clouds that are unordered, exhibit non-uniform point densities, and contain varying numbers of points for different geometries. The geometry information is seamlessly integrated with query points in the solution decoder through the attention mechanism. The performance of GINOT is validated on multiple challenging datasets, showcasing its high accuracy and strong generalization capabilities for complex and arbitrary 2D and 3D geometries.

The next generation of wide-field cosmic microwave background (CMB) surveys are uniquely poised to open a new window for time-domain astronomy in the millimeter band. Here we explore the discovery phase space for extragalactic transients with near-term and future CMB experiments to characterize the expected population. We use existing millimeter-band light curves of known transients (gamma-ray bursts, tidal disruption events, fast blue optical transients, neutron star mergers) and theoretical models, in conjunction with known and estimated volumetric rates. Using Monte Carlo simulations of various CMB survey designs (area, cadence, depth, duration) we estimate the detection rates and the resulting light curve characteristics. We find that existing and near-term surveys will find tens to hundreds of long-duration gamma-ray bursts (LGRBs), driven primarily by detections of the reverse shock emission, and including off-axis LGRBs. Next-generation experiments (CMB-S4, CMB-HD) will find tens of fast blue optical transients (FBOTs) in the nearby universe and will detect a few tidal disruption events. CMB-HD will additionally detect a small number of short gamma-ray bursts, where these will be discovered within the detection volume of next generation gravitational wave experiments like Cosmic Explorer.

We investigate a cosmological model in which a fraction of the dark matter is atomic dark matter (ADM). This ADM consists of dark versions of the electron and of the proton, interacting with each other and with dark photons just as their light sector versions do, but interacting with everything else only gravitationally. We find constraints given current cosmic microwave background (CMB) and baryon acoustic oscillation (BAO) data, with and without an $H_0$ prior, and with and without enforcing a big bang nucleosynthesis consistent helium abundance. We find that, at low dark photon temperature, one can have consistency with BAO and CMB data, with a fraction of dark matter that is ADM ($f_{\rm adm}$) as large as $\sim 0.1$. Such a large $f_{\rm adm}$ leads to a suppression of density fluctuations today on scales below about 60 Mpc that may be of relevance to the $\sigma_8$ tension. Our work motivates calculation of nonlinear corrections to matter power spectrum predictions in the ADM model. We forecast parameter constraints to come from future ground-based CMB surveys, and find that if ADM is indeed the cause of the $\sigma_8$ tension, the influence of the ADM, primarily on CMB lensing, will likely be detectable at high significance.

We present a catalog of continuum and emission line properties for 750,414 broad-line quasars included in the Sloan Digital Sky Survey Data Release 16 quasar catalog (DR16Q), measured from optical spectroscopy. These quasars cover broad ranges in redshift ($0.1\lesssim z\lesssim 6$) and luminosity ($44\lesssim \log (L_{\rm bol}/{\rm erg\,s^{-1}})\lesssim 48$), and probe lower luminosities than an earlier compilation of SDSS DR7 quasars. Derived physical quantities such as single-epoch virial black hole masses and bolometric luminosities are also included in this catalog. We present improved systemic redshifts and realistic redshift uncertainties for DR16Q quasars using the measured line peaks and correcting for velocity shifts of various lines with respect to the systemic velocity. About 1%, 1.4%, and 11% of the original DR16Q redshifts deviate from the systemic redshifts by $|\Delta V|>1500\,{\rm km\,s^{-1}}$, $|\Delta V|\in [1000,1500]\,{\rm km\,s^{-1}}$, and $|\Delta V|\in [500,1000]\,{\rm km\,s^{-1}}$, respectively; about $1900$ DR16Q redshifts were catastrophically wrong ($|\Delta V|>10,000\,{\rm km\,s^{-1}}$). We demonstrate the utility of this data product in quantifying the spectral diversity and correlations among physical properties of quasars with large statistical samples.

As photometric surveys reach unprecedented statistical precision, systematic uncertainties increasingly dominate large-scale structure probes relying on galaxy number density. Defining the final survey footprint is critical, as it excludes regions affected by artefacts or suboptimal observing conditions. For galaxy clustering, spatially varying observational systematics, such as seeing, are a leading source of bias. Template maps of contaminants are used to derive spatially dependent corrections, but extreme values may fall outside the applicability range of mitigation methods, compromising correction reliability. The complexity and accuracy of systematics modelling depend on footprint conservativeness, with aggressive masking enabling simpler, robust mitigation. We present a unified approach to define the DES Year 6 joint footprint, integrating observational systematics templates and artefact indicators that degrade mitigation performance. This removes extreme values from an initial seed footprint, leading to the final joint footprint. By evaluating the DES Year 6 lens sample MagLim++ plus plus on this footprint, we enhance the Iterative Systematics Decontamination (ISD) method, detecting non-linear systematic contamination and improving correction accuracy. While the mask's impact on clustering is less significant than systematics decontamination, it remains non-negligible, comparable to statistical uncertainties in certain w(theta) scales and redshift bins. Supporting coherent analyses of galaxy clustering and cosmic shear, the final footprint spans 4031.04 deg2, setting the basis for DES Year 6 1x2pt, 2x2pt, and 3x2pt analyses. This work highlights how targeted masking strategies optimise the balance between statistical power and systematic control in Stage-III and -IV surveys.

NEXUS is a JWST Multi-Cycle (Cycles 3-5; 368 primary hrs) GO Treasury imaging and spectroscopic survey around the North Ecliptic Pole. It contains two overlapping tiers. The Wide tier ($\sim 400~{\rm arcmin}^2$) performs NIRCam/WFSS 2.4-5 micron grism spectroscopy with three epochs over 3 years (final continuum {\rm S/N/pixel>3} at F444W<22.2). The Deep tier ($\sim 50~{\rm arcmin}^2$) performs high-multiplexing NIRSpec 0.6-5.3 micron MOS/PRISM spectroscopy for $\sim 10,000$ targets, over 18 epochs with a 2-month cadence (epoch/final continuum {\rm S/N/pixel>3} at F200W$\lesssim 27/29$). All epochs have simultaneous multi-band NIRCam and MIRI imaging ($5\sigma$ final depths of $\sim 28-29$ in NIRCam and $\sim 25$ in MIRI). The field is within the continuous viewing zone of JWST, and is fully covered by the Euclid Ultra-Deep Field, with 0.9-2 micron deep Euclid spectroscopy and cadenced photometry. NEXUS has three science pillars. First, with its massive and nearly complete (flux-limited) spectroscopic samples and deep photometry, it will perform efficient classification and physical characterization of galaxies and AGNs from $z\sim 1$ to Cosmic Dawn. With the large contiguous area coverage, it will measure the spatial clustering and demography of the first galaxies and SMBHs at z>6. Second, multi-epoch observations enable systematic time-domain investigations, focusing on z>3 transients and low-mass AGN reverberation mapping. Third, the comprehensive data set will enable knowledge transfer to other legacy fields, create data challenges, and initiate benchmark work for future space missions. With rapid public releases of processed data and an open invitation for collaboration, NEXUS aims for broad and swift community engagement, to become a powerhouse to drive transformative advancements in multiple key science areas of astronomy.

We forecast constraints on cosmological parameters enabled by three surveys conducted with SPT-3G, the third-generation camera on the South Pole Telescope. The surveys cover separate regions of 1500, 2650, and 6000 ${\rm deg}^{2}$ to different depths, in total observing 25% of the sky. These regions will be measured to white noise levels of roughly 2.5, 9, and 12 $\mu{\rm K-arcmin}$, respectively, in CMB temperature units at 150 GHz by the end of 2024. The survey also includes measurements at 95 and 220 GHz, which have noise levels a factor of ~1.2 and 3.5 times higher than 150 GHz, respectively, with each band having a polarization noise level ~$\sqrt{\text{2}}$ times higher than the temperature noise. We use a novel approach to obtain the covariance matrices for jointly and optimally estimated gravitational lensing potential bandpowers and unlensed CMB temperature and polarization bandpowers. We demonstrate the ability to test the $\Lambda{\rm CDM}$ model via the consistency of cosmological parameters constrained independently from SPT-3G and Planck data, and consider the improvement in constraints on $\Lambda{\rm CDM}$ extension parameters from a joint analysis of SPT-3G and Planck data. The $\Lambda{\rm CDM}$ cosmological parameters are typically constrained with uncertainties up to ~2 times smaller with SPT-3G data, compared to Planck, with the two data sets measuring significantly different angular scales and polarization levels, providing additional tests of the standard cosmological model.

Periodic variability in active galactic nuclei (AGNs) is a promising method for studying sub-parsec supermassive black hole binaries (SMBHBs), which are a challenging detection target. While extensive searches have been made in the optical, X-ray and gamma-ray bands, systematic infrared (IR) studies remain limited. Using data from the Wide-field Infrared Survey Explorer (WISE), which provides unique decade-long mid-IR light curves with a six-month cadence, we have conducted the first systematic search for SMBHB candidates based on IR periodicity. Analyzing a parent sample of 48,932 objects selected from about half a million AGNs, we have identified 28 candidate periodic AGNs with periods ranging from 1,268 to 2,437 days (in the observer frame) by fitting their WISE light curves with sinusoidal functions. However, our mock simulation of the parent sample indicates that stochastic variability can actually produce a similar number of periodic sources, underscoring the difficulty in robustly identifying real periodic signals with WISE light curves, given their current sampling. Notably, we found no overlap between our sample and optical periodic sources, which can be explained by a distinct preference for certain periods due to selection bias. By combining archived data from different surveys, we have identified SDSS J140336.43+174136.1 as a candidate exhibiting periodic behavior in both optical and IR bands, a phenomenon that warrants further validation through observational tests. Our results highlight the potential of IR time-domain surveys, including future missions such as the Nancy Grace-Roman Space Telescope, for identifying periodic AGNs, but complementary tests are still needed to determine their physical origins such as SMBHBs.

Neural operators have emerged as powerful tools for learning nonlinear mappings between function spaces, enabling real-time prediction of complex dynamics in diverse scientific and engineering applications. With their growing adoption in engineering design evaluation, a wide range of neural operator architectures have been proposed for various problem settings. However, model selection remains challenging due to the absence of fair and comprehensive comparisons. To address this, we propose and standardize six representative 3D industry-scale engineering design datasets spanning thermal analysis, linear elasticity, elasto-plasticity, time-dependent plastic problems, and computational fluid dynamics. All datasets include fully preprocessed inputs and outputs for model training, making them directly usable across diverse neural operator architectures. Using these datasets, we conduct a systematic comparison of four types of neural operator variants, including Branch-Trunk-based Neural Operators inspired by DeepONet, Graph-based Neural Operators inspired by Graph Neural Networks, Grid-based Neural Operators inspired by Fourier Neural Operators, and Point-based Neural Operators inspired by PointNet. We further introduce practical enhancements to adapt these models to different engineering settings, improving the fairness of the comparison. Our benchmarking study evaluates each model strengths and limitations in terms of predictive performance, computational efficiency, memory usage, and deployment complexity. The findings provide actionable insights to guide future neural operator development.

Strong gravitational lensing of active galactic nuclei (AGN) enables measurements of cosmological parameters through time-delay cosmography (TDC). With data from the upcoming LSST survey, we anticipate using a sample of O(1000) lensed AGN for TDC. To prepare for this dataset and enable this measurement, we construct and analyze a realistic mock sample of 1300 systems drawn from the OM10 (Oguri & Marshall 2010) catalog of simulated lenses with AGN sources at z<3.1 in order to test a key aspect of the analysis pipeline, that of the lens modeling. We realize the lenses as power law elliptical mass distributions and simulate 5-year LSST i-band coadd images. From every image, we infer the lens mass model parameters using neural posterior estimation (NPE). Focusing on the key model parameters, $\theta_E$ (the Einstein Radius) and $\gamma_{lens}$ (the projected mass density profile slope), with consistent mass-light ellipticity correlations in test and training data, we recover $\theta_E$ with less than 1% bias per lens, 6.5% precision per lens and $\gamma_{lens}$ with less than 3% bias per lens, 8% precision per lens. We find that lens light subtraction prior to modeling is only useful when applied to data sampled from the training prior. If emulated deconvolution is applied to the data prior to modeling, precision improves across all parameters by a factor of 2. Finally, we combine the inferred lens mass models using Bayesian Hierarchical Inference to recover the global properties of the lens sample with less than 1% bias.

We perform a search for stellar streams around the Milky Way using the first three years of multi-band optical imaging data from the Dark Energy Survey (DES). We use DES data covering $\sim 5000$ sq. deg. to a depth of g > 23.5 with a relative photometric calibration uncertainty of < 1 \%. This data set yields unprecedented sensitivity to the stellar density field in the southern celestial hemisphere, enabling the detection of faint stellar streams to a heliocentric distance of $\sim 50$ kpc. We search for stellar streams using a matched-filter in color-magnitude space derived from a synthetic isochrone of an old, metal-poor stellar population. Our detection technique recovers four previously known thin stellar streams: Phoenix, ATLAS, Tucana III, and a possible extension of Molonglo. In addition, we report the discovery of eleven new stellar streams. In general, the new streams detected by DES are fainter, more distant, and lower surface brightness than streams detected by similar techniques in previous photometric surveys. As a by-product of our stellar stream search, we find evidence for extra-tidal stellar structure associated with four globular clusters: NGC 288, NGC 1261, NGC 1851, and NGC 1904. The ever-growing sample of stellar streams will provide insight into the formation of the Galactic stellar halo, the Milky Way gravitational potential, as well as the large- and small-scale distribution of dark matter around the Milky Way.

We investigate how differences in the stellar feedback produce disks with different morphologies in Milky Way-like progenitors over 1 $\leq z \leq 5$, using eight state-of-the-art cosmological hydrodynamics simulation codes in the \textit{AGORA} project. In three of the participating codes, a distinct, rotation-dominated inner core emerges with a formation timescale of $\lesssim 300$ Myr, largely driven by a major merger event, while two other codes exhibit similar signs of wet compaction -- gaseous shrinkage into a compact starburst phase -- at earlier epochs. The remaining three codes show only weak evidence of wet compaction. Consequently, we divide the simulated galaxies into two groups: those with strong compaction signatures and those with weaker ones. Galaxies in these two groups differ in size, stellar age gradients, and disk-to-total mass ratios. Specifically, codes with strong wet compaction build their outer disks in an inside-out fashion, leading to negative age gradients, whereas codes with weaker compaction feature flat or positive age gradients caused primarily by outward stellar migration. Although the stellar half-mass radii of these two groups diverge at $z \sim 3$, the inclusion of dust extinction brings their sizes and shapes in mock observations closer to each other and to observed galaxies. We attribute the observed morphological differences primarily to variations in the stellar feedback implementations -- such as delayed cooling timescales, and feedback strengths -- that regulate both the onset and duration of compaction. Overall, our results suggest that disk assembly at high redshifts is highly sensitive to the details of the stellar feedback prescriptions in simulations.

Gravitational wave observations have great potential to reveal new information about the fundamental nature of gravity, but extracting that information can be difficult. One popular technique is the parametrized inspiral test of general relativity (a realization of the parametrized post-Einsteinian framework), where the gravitational waveform, as calculated in Einstein's theory as a series expansion in the orbital velocity, is parametrically deformed at a given set of orders in velocity. However, most current approaches usually only analyze the data while considering a single, specific modification at a time. Are then constraints placed with a single modification robust to our ignorance of higher post-Newtonian order corrections? We show here that for a wide class of theories, specifically those that admit a post-Newtonian expansion, single-parameter tests are indeed robust. In particular, through a series of full Bayesian parameter estimation studies on several different sets of synthetic data, we show that single-parameter constraints are not degraded but rather are improved by the inclusion of multiple parameters, provided one includes information about the mathematical structure of the series. We then exemplify this with a specific theory of gravity, shift-symmetric scalar Gauss-Bonnet theory, where the waveform has been calculated to higher post-Newtonian orders than leading. We show that the inclusion of these higher order terms strengthens single-parameter constraints, instead of weakening them, and that the strengthening is very mild. This analysis therefore provides strong evidence that single-parameter post-Einsteinian tests of general relativity are robust to ignorance of high post-Newtonian order terms in the general relativistic deformations.