The first stars formed out of pristine gas, causing them to be so massive that none are expected to have survived until today. If their direct descendants were sufficiently low-mass stars, they could exist today and would be recognizable by having the lowest metallicity (abundance of elements heavier than helium). The lowest metallicity star currently known is a star in the thick disk of the Milky Way with total metallicity Z < 1.4 x 10^-6 (log Z/Zsun < -4.0). While other stars with lower iron abundance have been discovered, they have high carbon abundances and thus higher total metallicities (log Z/Zsun > -3). Here we present the discovery and detailed chemical analysis of the most metal-poor star yet found: the red giant star SDSS J0715-7334 with ultra-low abundances of both iron and carbon ([Fe/H]=-4.3, [C/Fe]<-0.2), resulting in total metallicity Z < 7.8 x 10^-7 (log Z/Zsun < -4.3). This star has the most pristine composition of any object known in the universe. The star's orbit indicates that it originates from the halo of the Large Magellanic Cloud. Its detailed chemical composition implies a supernova progenitor with initial mass of 30 solar masses. Current models of low-mass star formation can explain the existence of SDSS J0715-7334 only if dust cooling was already able to operate at the time of its formation. SDSS J0715-7334 is over ten times more metal-poor than the most metal-poor high-redshift galaxies found by the James Webb Space Telescope, some of which have been claimed to be potentially metal-free. Substantially deeper observations of high-redshift galaxies would be needed to prove that they are truly pristine galaxies made of metal-free stars and not metal-enriched galaxies composed of second-generation stars like SDSS J0715-7334.

The recently reported binary black hole merger, GW231123, has unusual properties that make it hard to explain astrophysically. Parameter estimation studies are consistent with maximally spinning black holes and the dimensionless spin of the more massive component is constrained to be $\chi_1\gtrsim 0.8$. Analysis of data also revealed potential systematics that could not be fully replicated with simulated studies. We explore the possibility that these measurements are biased due to unmodeled non-Gaussian noise in the detectors, and that the actual black hole spins are more modest. We present evidence for a population of \textit{microglitches} in LIGO gravitational-wave strain data that can lead to biases in the parameter estimation of short-duration signals such as GW231123. Using simulated data of a massive event like GW231123, we demonstrate how microglitches can bias our measurements of black hole spins toward $\chi\approx1$ with negligible posterior support for the true value of $\chi\approx0.7$. We develop a noise model to account for microglitches and show that this model successfully reduces biases in the recovery of signal parameters. We characterize the microglitch population in real interferometer data surrounding GW231123 and find a single detector glitch duty cycle of $0.57_{-0.19}^{+0.21}$, which implies nearly a $100\%$ probability that at least one event through the fourth gravitational wave transient catalog coincides with microglitches in two detectors. We argue that further investigations are required before we can have a confident picture of the astrophysical properties of GW231123.

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.

Cometary activity from interstellar objects provides a unique window into the environs of other stellar systems. We report blue-sensitive integral field unit spectroscopy of the interstellar object 3I/ATLAS from the Keck-II-mounted Keck Cosmic Web Imager on August 24, 2025 UT. We confirm previously reported CN and Ni outgassing, and present, for the first time, the radial profiles of Ni and CN emission in 3I/ATLAS. We find a characteristic $e$-folding radius of $593.7\pm14.8$ km for Ni and $841.0\pm15.4$ km for CN; this suggests that the Ni emission is more centrally concentrated in the nucleus of the comet and favors hypotheses involving easily dissociated species such as metal carbonyls or metal-polycyclic-aromatic-hydrocarbon molecules. Additional integral field spectroscopy after perihelion will offer a continued opportunity to determine the evolution of the radial distributions of species in interstellar comet 3I/ATLAS.

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 report the discovery of three Milky Way satellite candidates: Carina IV, Phoenix III, and DELVE 7, in the third data release of the DECam Local Volume Exploration survey (DELVE). The candidate systems were identified by cross-matching results from two independent search algorithms. All three are extremely faint systems composed of old, metal-poor stellar populations ($\tau \gtrsim 10$ Gyr, [Fe/H] $\lesssim -1.4$). Carina IV ($M_V = -2.8;\ r_{1/2} = 40 {\rm pc}$) and Phoenix III ($M_V = -1.2;\ r_{1/2} = 19 {\rm pc}$) have half-light radii that are consistent with the known population of dwarf galaxies, while DELVE 7 ($M_V = 1.2;\ r_{1/2} = 2 {\rm pc}$) is very compact and seems more likely to be a star cluster, though its nature remains ambiguous without spectroscopic followup. The Gaia proper motions of stars in Carina IV ($M_* = 2250^{+1180}_{-830} {\rm M_\odot}$) indicate that it is unlikely to be associated with the LMC, while DECam CaHK photometry confirms that its member stars are metal-poor. Phoenix III ($M_* = 520^{+660}_{-290} {\rm M_\odot}$) is the faintest known satellite in the extreme outer stellar halo ($D_{\rm GC} > 100$ kpc), while DELVE 7 ($M_* = 60^{+120}_{-40} {\rm M_\odot}$) is the faintest known satellite with $D_{\rm GC} > 20$ kpc.

We present an analysis of high-resolution mid-infrared observations at 25 and 37 $\mu m$ of the Sagittarius C Complex (Sgr C) in the Central Molecular Zone (CMZ), based on data from the SOFIA/FORCAST Galactic Center Legacy Survey. Enabled by the high bright-source limit of the FORCAST instrument, we perform a map-level dust temperature and optical depth analysis with a focus on the Sgr C HII region, which has an average dust temperature of 61 K and an average 37 $\mu m$ optical depth of 0.05. We find that the Sgr C HII region contains several high-density dust emission ridges, with lengths of up to several parsecs. Noting prior evidence for nonthermal radio emission from these density ridges, we postulate that there is an enhancement of relativistic electrons within them, possibly attributable to diffusive shock acceleration induced by the wind of a known nearby Wolf-Rayet (WR) star impacting the density ridges and the ambient gas in the surrounding photo-dissociation region. Additionally, the tangential magnetic field in the outskirts of the Sgr C HII region may serve to confine the electrons within this region. We examined the heating effect of the WR star by calculating its heating profile and performing a spectral energy distribution modelling of the HII region. We found an integrated MIR luminosity of $(1.40\pm0.19)\times10^{6} L_\odot$, which implies that presently unidentified massive stars must be present in the HII region in addition to the WR star. We also present a brief analysis of adjacent regions, such as a mid-infrared/radio source denoted "Source C" and the G359.43+0.02 young stellar object cluster near the northern end of the prominent Sgr C non-thermal filament (NTF).

Type Ia supernovae (SNe Ia) have been essential for probing the nature of dark energy; however, most SN analyses rely on the same low-redshift sample, which may lead to shared systematics. In a companion paper (arXiv:2508.10878), we introduce the Dark Energy Bedrock All-Sky Supernova (DEBASS) program, which has already collected more than 500 low-redshift SNe Ia on the Dark Energy Camera (DECam), and present an initial release of 77 SNe Ia within the Dark Energy Survey (DES) footprint observed between 2021 and 2024. Here, we examine the systematics, including photometric calibration and selection effects. We find agreement at the 10 millimagnitude level among the tertiary standard stars of DEBASS, DES, and Pan-STARRS1. Our simulations reproduce the observed distributions of DEBASS SN light-curve properties, and we measure a bias-corrected Hubble residual scatter of $0.08$ mag, which, while small, is found in 10% of our simulations. We compare the DEBASS SN distances to the Foundation sample and find consistency with a median residual offset of $0.016 \pm 0.019$ mag. Selection effects have negligible impacts on distances, but a different photometric calibration solution shifts the median residual $-0.015 \pm 0.019$ mag, highlighting calibration sensitivity. Using conservative simulations, we forecast that replacing historical low-redshift samples with the full DEBASS sample (>400 SNe Ia) will improve the statistical uncertainties on dark energy parameters $w_0$ and $w_a$ by 30% and 24% respectively, enhance the dark energy Figure of Merit by up to 60%, and enable a measurement of $f\sigma_8$ at the 25% level.

We present a novel implementation for the quadratic maximum likelihood (QML) power spectrum estimator for multiple correlated scalar fields on the sphere. Our estimator supports arbitrary binning in redshift and multipoles $\ell$ and includes cross-correlations of redshift bins. It implements a fully optimal analysis with a pixel-wise covariance model. We implement a number of optimizations which make the estimator and associated covariance matrix computationally tractable for a low-$\ell$ analysis, suitable for example for kSZ velocity reconstruction or primordial non-Gaussianity from scale-dependent bias analyses. We validate our estimator extensively on simulations and compare its features and precision with the common pseudo-$C_\ell$ method, showing significant gains at large scales. We make our code publicly available. In a companion paper, we apply the estimator to kSZ velocity reconstruction using data from ACT and DESI Legacy Survey and construct full set of QML estimators on 40 correlated fields up to $N_{\text{side}}= 32$ in timescale of an hour on a single 24-core CPU requiring &lt;256\ \mathrm{Gb} RAM, demonstrating the performance of the code.

The application of deep machine learning methods in astronomy has exploded in the last decade, with new models showing remarkably improved performance on benchmark tasks. Not nearly enough attention is given to understanding the models' robustness, especially when the test data are systematically different from the training data, or "out of domain." Domain shift poses a significant challenge for simulation-based inference, where models are trained on simulated data but applied to real observational data. In this paper, we explore domain shift and test domain adaptation methods for a specific scientific case: simulation-based inference for estimating galaxy cluster masses from X-ray profiles. We build datasets to mimic simulation-based inference: a training set from the Magneticum simulation, a scatter-augmented training set to capture uncertainties in scaling relations, and a test set derived from the IllustrisTNG simulation. We demonstrate that the Test Set is out of domain in subtle ways that would be difficult to detect without careful analysis. We apply three deep learning methods: a standard neural network (NN), a neural network trained on the scatter-augmented input catalogs, and a Deep Reconstruction-Regression Network (DRRN), a semi-supervised deep model engineered to address domain shift. Although the NN improves results by 17% in the Training Data, it performs 40% worse on the out-of-domain Test Set. Surprisingly, the Scatter-Augmented Neural Network (SANN) performs similarly. While the DRRN is successful in mapping the training and Test Data onto the same latent space, it consistently underperforms compared to a straightforward Yx scaling relation. These results serve as a warning that simulation-based inference must be handled with extreme care, as subtle differences between training simulations and observational data can lead to unforeseen biases creeping into the results.

We present constraints on models of cosmology and astrophysics using cosmic shear data vectors from three datasets: the northern and southern Galactic cap of the Dark Energy Camera All Data Everywhere (DECADE) project, and the Dark Energy Survey (DES) Year 3. These data vectors combined consist of 270 million galaxies spread across 13,000 ${\rm deg}^2$ of the sky. We first extract constraints for $\Lambda$CDM cosmology and find $S_8= 0.805^{+0.019}_{-0.019}$ and $\Omega_{\rm m} = 0.262^{+0.023}_{-0.036}$, which is consistent within $1.9 \sigma$ of constraints from the Planck satellite. Extending our analysis to dynamical dark energy models shows that lensing provides some (but still minor) improvements to existing constraints from supernovae and baryon acoustic oscillations. Finally, we study six different models for the impact of baryons on the matter power spectrum. We show the different models provide consistent constraints on baryon suppression, and associated cosmology, once the astrophysical priors are sufficiently wide. Current scale-cut approaches for mitigating baryon contamination result in a residual bias of $\approx 0.3\sigma$ in the $S_8, \Omega_{\rm m}$ posterior. Using all scales with dedicated baryon modeling leads to negligible improvement as the new information is used solely to self-calibrate the baryon model on small scales. Additional non-lensing datasets, and/or calibrations of the baryon model, will be required to access the full statistical power of the lensing measurements. The combined dataset in this work represents the largest lensing dataset to date (most galaxies, largest area) and provides an apt testing ground for analyses of upcoming datasets from Stage IV surveys. The DECADE shear catalogs, data vectors, and likelihoods are made publicly available.

Time-domain surveys such as the Zwicky Transient Facility (ZTF) have opened a new frontier in the discovery and characterization of transients. While photometric light curves provide broad temporal coverage, spectroscopic observations remain crucial for physical interpretation and source classification. However, existing spectral analysis methods -- often reliant on template fitting or parametric models -- are limited in their ability to capture the complex and evolving spectra characteristic of such sources, which are sometimes only available at low resolution. In this work, we introduce SpectraNet, a deep convolutional neural network designed to learn robust representations of optical spectra from transients. Our model combines multi-scale convolution kernels and multi-scale pooling to extract features from preprocessed spectra in a hierarchical and interpretable manner. We train and validate SpectraNet on low-resolution time-series spectra obtained from the Spectral Energy Distribution Machine (SEDM) and other instruments, demonstrating state-of-the-art performance in classification. Furthermore, in redshift prediction tasks, SpectraNet achieves a root mean squared relative redshift error of 0.02, highlighting its effectiveness in precise regression tasks as well.

Understanding the astrophysical origins of binary black holes requires accurate and flexible modeling of multi-dimensional population properties. In this paper, using a data-driven framework based on binned Gaussian processes, we characterize the joint distribution of BBH primary masses, mass ratios, and effective inspiral spins. We identify three distinct subpopulations in the GWTC-4 sample of observations and investigate their astrophysical origins. We find that only one of the three subpopulations exhibits the $35M_{\odot}$ peak, which is characterized by a strong preference for equal mass systems and isotropic spin orientations. Our inferred distributions are consistent with a predominantly dynamical origin of this feature. By comparing with theoretical simulations, we further show that the subpopulation that exhibits the $35M_{\sun}$ peak can exclusively comprise dynamically assembled systems in globular clusters, specifically if black hole birth spins are in the range~$(0.1-0.2)$, whereas the other two subpopulations require substantial contributions from alternative formation channels. We constrain the \textit{lower bound} on the merger rate of BBHs in globular clusters to be $0.69^{+0.23}_{-0.33} \rm{Gpc}^{-3}\rm{yr}^{-1}$, which is consistent with theoretical predictions. We conclude that dynamical formation in globular clusters remains a strong candidate for the origin of this excess near $30-40M_{\odot}$ and that more data and targeted parametric models are necessary to rigorously establish this interpretation.

Next-generation surveys are expected to uncover thousands of globular cluster (GC) stellar streams, motivating the need for a theoretical framework that produces realistic GC streams in a fully cosmological, Milky Way-like environment. We present $\textsf{CosmoGEMS}$, a star-by-star cosmological GC stream framework that self-consistently links small-scale cluster physics with large-scale Galactic dynamics. The initial phase-space positions of stream stars are informed by post-processed GC populations within the FIRE cosmological simulation. Escaped stars are orbit-integrated from their time of escape to the present day in a time-evolving Galactic potential extracted from the same simulation using a basis function expansion. We explore two example streams on different orbits. One forms a long, thin stream with a velocity dispersion consistent with Milky Way GC streams. However, it exhibits a clump and orbital-phase-dependent misalignments due to the evolving potential. The other stream develops both a thin component and a diffuse, shell-like structure, similar to features observed in streams like Jhelum. These results highlight the power of fully cosmological models in producing realistic stream morphologies and kinematics. Unlike idealized simulations, our models naturally incorporate time-dependent changes in the progenitor's orbit, including orbital plane evolution, which significantly affects stream structure. This challenges common assumptions in stream-finding algorithms and interpretation. $\textsf{CosmoGEMS}$ provides a key step toward connecting future stellar stream observations with the physics of globular cluster evolution and hierarchical galaxy formation in a cosmological context.

The origin of black hole (BH) spins remains one of the least understood aspects of BHs. Despite many uncertainties, it is commonly assumed that if BHs originated from isolated massive star binaries, their spins should be aligned with the orbital angular momentum of the binary system. This assumption stems from the notion that BHs inherit their spins from their progenitor stars. In this study, we relax this long-held viewpoint and explore various mechanisms that can spin up BHs before or during their formation. In addition to natal spins, we discuss physical processes that can spin BHs isotropically, parallel to natal kicks, and perpendicular to natal kicks. These different mechanisms leave behind distinct imprints on the observable distributions of spin magnitudes, spin-orbit misalignments and the effective inspiral spin of merging binaries. In particular, these mechanisms allow even the binaries originating in the field to exhibit precession and retrograde spin (\chi_{\rm eff}&lt;0). This broadens the parameter space allowed for isolated binary evolution into regimes which were previously thought to be exclusive to dynamically assembled binaries.

We combine new spectroscopic observations of the ultra faint dwarf galaxy (UFD) Boötes I (Boo I) from the Southern Stellar Stream Spectroscopic Survey ($S^{5}$) with $\sim$15 years of archival spectroscopic data to create the largest sample of stellar kinematics and metallicities to date in any Milky Way UFD. Our combined sample includes 148 members extending out to $\sim$7 half-light radii ($r_h$), including 24 newly confirmed members, 18 binary candidates, 15 RR Lyrae stars, and 92 [Fe/H] measurements. Using this larger and more spatially extended sample, we provide updated constraints on Boo I's systemic properties, including its radial population gradients. Properly accounting for perspective rotation effects in a UFD for the first time, we detect a $4\sigma$ line-of-sight velocity gradient of $1.2\pm0.3$ km s$^{-1}$ $r_h^{-1}$ aligned along Boo I's orbit and discuss its potential tidal origins. We also infer a metallicity gradient of $-0.10\pm0.02$ dex $r_h^{-1}$ in agreement with previous studies. Using an axisymmetric Jeans model, we provide updated constraints on Boo I's dark matter density profile, which weakly favor a cusped ($\gamma=1.0^{+0.5}_{-0.6}$) dark matter profile. Lastly, we re-analyze Boo I's metallicity distribution function with a one-zone galactic chemical evolution model and place new constraints on its rapid, inefficient star formation and strong galactic outflows.

We present a simulation-based forward-modeling framework for cosmological inference from optical galaxy-cluster samples, and apply it to the abundance and weak-lensing signals of DES-Y1 redMaPPer clusters. The model embeds cosmology-dependent optical selection using a counts-in-cylinders approach, while also accounting for cluster miscentering and baryonic feedback in lensing. Applied to DES-Y1, and assuming a flat $\Lambda$CDM cosmology, we obtain $\Omega_m=0.254^{+0.026}_{-0.020}$ and $\sigma_8=0.826^{+0.030}_{-0.034}$, consistent with a broad suite of low-redshift structure measurements, including recent full-shape analyses, the DES/KiDS/HSC 3$\times$2 results, and most cluster-abundance studies. Our results are also consistent with \textit{Planck}, with the difference being significant at $2.58\sigma$. These results establish simulation-based forward-modeling of cluster abundances as a promising new tool for precision cosmology with Stage~IV survey data.

Future space observatories that seek to perform imaging and spectroscopy of faint astronomical sources will require ultra-low-noise detectors that are sensitive over a broad wavelength range. Silicon charge-coupled devices (CCDs), such as EMCCDs, skipper CCDs, multi-amplifier sensing (MAS) CCDs, and single-electron sensitive read out (SiSeRO) CCDs have demonstrated the ability to detect and measure single photons from X-ray energies to near the silicon band gap (~1.1 $\mu$m), making them candidate technologies for this application. In this context, we study a relatively unexplored source of low-energy background coming from Cherenkov radiation produced by energetic cosmic rays traversing a silicon detector. We present a model for Cherenkov photon production and absorption that is calibrated to laboratory data, and we use this model to characterize the residual background rate for ultra-low-noise silicon detectors in space. We study how the Cherenkov background rate depends on detector thickness, variations in solar activity, and the contribution of heavy cosmic ray species (Z > 2). We find that for thick silicon detectors, such as those required to achieve high quantum efficiency at long wavelengths, the rate of cosmic-ray-induced Cherenkov photon production is comparable to other detector and astrophysical backgrounds. We apply our Cherenkov background model to simulated spectroscopic observations of extra-solar planets, and we find that thick detectors continue to outperform their thinner counterparts at longer wavelengths despite a larger Cherenkov background rate. Furthermore, we find that minimal masking of cosmic-ray tracks continues to maximize the signal-to-noise of very faint sources despite the existence of extended halos of Cherenkov photons.

The detection of gravitational waves has brought to light a population of binary black holes that merge within a Hubble time. Multiple formation channels can contribute to this population, making it difficult to definitively associate particular population features with underlying stellar physics. Black hole spins are considered an important discriminator between various channels, but they are less well-measured than masses, making conclusive astrophysical statements using spins difficult thus far. In this paper, we consider the distribution of the effective inspiral spin $\chi_{\rm eff}$ -- a quantity much better measured than individual component spins. We show that non-Gaussian features like skewness, asymmetry about zero, and multimodality can naturally arise in the $\chi_{\rm eff}$ distribution when multiple channels contribute to the population. Searching for such features, we find signs of skewness and asymmetry already in the current catalogs, but no statistically significant signs of bimodality. These features provide robust evidence for the presence of a subpopulation with spins preferentially aligned to the binary's orbital angular momentum; and we conservatively estimate the fraction of this subpopulation to be at least $12 \% - 17\%$ (at $90\%$ credibility). Our models do not find an excess of non-spinning systems and instead find that at least $\sim 20 \%$ of the binaries have some degree of negative $\chi_{\rm eff}$. The data also suggest that, if preferentially aligned mergers form a significant fraction of the population, they must have small spins.