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

Understanding the Milky Way disc formation requires characterising its structural and kinematic properties as functions of stellar age. Using red giant stars from APOGEE DR17 and Gaia DR3, we model the age-dependent stellar kinematics with a quasi-isothermal distribution function and fit disc parameters as a function of age using non-parametric splines. We identify a transition from thick to thin disc populations around 10 Gyr ago. Stars older than this have short scale lengths ($\sim$1.7 kpc), typical of the thick disc, while younger stars exhibit increasing scale length with decreasing age, consistent with inside-out formation of the thin disc. This transition coincides with the end of the starburst triggered by the Gaia-Sausage-Enceladus (GSE) merger. Stars formed around 10 Gyr ago exhibit a dip in scale length, even shorter than that of the thick disc. Comparison with an Auriga simulation suggests that this scale-length dip reflects gas disc shrinking caused by the transition from a cold to hot gas accretion mode. We propose the following disc formation scenario: (1) the thick disc formed under cold-mode accretion; (2) the GSE merger triggered a starburst and increased the total mass of the Galaxy, causing the transition to hot-mode accretion; (3) rapid gas consumption led to temporary shrinking of the star-forming gas disc; and then (4) thin disc grows in an inside-out fashion, as the size of the star-forming gas disc grows via hot-mode smooth gas accretion.

The Euclid mission aims to measure the positions, shapes, and redshifts of over a billion galaxies to provide unprecedented constraints on the nature of dark matter and dark energy. Achieving this goal requires a continuous reassessment of the mission's scientific performance, particularly in terms of its ability to constrain cosmological parameters, as our understanding of how to model large-scale structure observables improves. In this study, we present the first scientific forecasts using CLOE (Cosmology Likelihood for Observables in Euclid), a dedicated Euclid cosmological pipeline developed to support this endeavour. Using advanced Bayesian inference techniques applied to synthetic Euclid-like data, we sample the posterior distribution of cosmological and nuisance parameters across a variety of cosmological models and Euclid primary probes: cosmic shear, angular photometric galaxy clustering, galaxy-galaxy lensing, and spectroscopic galaxy clustering. We validate the capability of CLOE to produce reliable cosmological forecasts, showcasing Euclid's potential to achieve a figure of merit for the dark energy parameters $w_0$ and $w_a$ exceeding 400 when combining all primary probes. Furthermore, we illustrate the behaviour of the posterior probability distribution of the parameters of interest given different priors and scale cuts. Finally, we emphasise the importance of addressing computational challenges, proposing further exploration of innovative data science techniques to efficiently navigate the Euclid high-dimensional parameter space in upcoming cosmological data releases.

We compare the performance of the flat-sky approximation and Limber approximation for the clustering analysis of the photometric galaxy catalogue of Euclid. We study a 6 bin configuration representing the first data release (DR1) and a 13 bin configuration representative of the third and final data release (DR3). We find that the Limber approximation is sufficiently accurate for the analysis of the wide bins of DR1. Contrarily, the 13 bins of DR3 cannot be modelled accurately with the Limber approximation. Instead, the flat-sky approximation is accurate to below $5\%$ in recovering the angular power spectra of galaxy number counts in both cases and can be used to simplify the computation of the full power spectrum in harmonic space for the data analysis of DR3.

We produce a clean and well-characterised catalogue of objects within 100\,pc of the Sun from the \G\ Early Data Release 3. We characterise the catalogue through comparisons to the full data release, external catalogues, and simulations. We carry out a first analysis of the science that is possible with this sample to demonstrate its potential and best practices for its use. The selection of objects within 100\,pc from the full catalogue used selected training sets, machine-learning procedures, astrometric quantities, and solution quality indicators to determine a probability that the astrometric solution is reliable. The training set construction exploited the astrometric data, quality flags, and external photometry. For all candidates we calculated distance posterior probability densities using Bayesian procedures and mock catalogues to define priors. Any object with reliable astrometry and a non-zero probability of being within 100\,pc is included in the catalogue. We have produced a catalogue of \NFINAL\ objects that we estimate contains at least 92\% of stars of stellar type M9 within 100\,pc of the Sun. We estimate that 9\% of the stars in this catalogue probably lie outside 100\,pc, but when the distance probability function is used, a correct treatment of this contamination is possible. We produced luminosity functions with a high signal-to-noise ratio for the main-sequence stars, giants, and white dwarfs. We examined in detail the Hyades cluster, the white dwarf population, and wide-binary systems and produced candidate lists for all three samples. We detected local manifestations of several streams, superclusters, and halo objects, in which we identified 12 members of \G\ Enceladus. We present the first direct parallaxes of five objects in multiple systems within 10\,pc of the Sun.

We present the largest, most homogeneous catalogue of merging galaxies in the nearby universe obtained through the Galaxy Zoo project - an interface on the world-wide web enabling large-scale morphological classification of galaxies through visual inspection of images from the Sloan Digital Sky Survey (SDSS). The method converts a set of visually-inspected classifications for each galaxy into a single parameter (the `weighted-merger-vote fraction,' $f_m$) which describes our confidence that the system is part of an ongoing merger. We describe how $f_m$ is used to create a catalogue of 3003 visually-selected pairs of merging galaxies from the SDSS in the redshift range 0.005 < z <0.1. We use our merger sample and values of $f_m$ applied to the SDSS Main Galaxy Spectral sample (MGS) to estimate that the fraction of volume-limited (M_r < -20.55) major mergers (1/3 < {M}^*_1/{M}^*_2 < 3) in the nearby universe is $1 - 3 \times C$ where $C \sim 1.5$ is a correction factor for spectroscopic incompleteness. Having visually classified the morphologies of the constituent galaxies in our mergers, we find that the spiral-to-elliptical ratio of galaxies in mergers is higher by a factor $\sim 2$ relative to the global population. In a companion paper, we examine the internal properties of these merging galaxies and conclude that this high spiral-to-elliptical ratio in mergers is due to a longer time-scale over which mergers with spirals are detectable compared to mergers with ellipticals.

The Mid-Infrared Instrument (MIRI) on the James Webb Space Telescope (JWST), has imaging, four coronagraphs and both low and medium resolution spectroscopic modes . Being able to simulate MIRI observations will help commissioning of the instrument, as well as get users familiar with representative data. We designed the MIRI instrument simulator (MIRISim) to mimic the on-orbit performance of the MIRI imager and spectrometers using the Calibration Data Products (CDPs) developed by the MIRI instrument team. The software encorporates accurate representations of the detectors, slicers, distortions, and noise sources along the light path including the telescope's radiative background and cosmic rays. The software also includes a module which enables users to create astronomical scenes to simulate. MIRISim is a publicly available Python package that can be run at the command line, or from within Python. The outputs of MIRISim are detector images in the same uncalibrated data format that will be delivered to MIRI users. These contain the necessary metadata for ingestion by the JWST calibration pipeline.

We present a sample-variance-limited measurement of the temperature power spectrum ($TT$) of the cosmic microwave background (CMB) using observations of a $\sim\! 1500 \,\mathrm{deg}^2$ field made by SPT-3G in 2018. We report multifrequency power spectrum measurements at 95, 150, and 220GHz covering the angular multipole range 750 \leq \ell < 3000. We combine this $TT$ measurement with the published polarization power spectrum measurements from the 2018 observing season and update their associated covariance matrix to complete the SPT-3G 2018 $TT/TE/EE$ data set. This is the first analysis to present cosmological constraints from SPT $TT$, $TE$, and $EE$ power spectrum measurements jointly. We blind the cosmological results and subject the data set to a series of consistency tests at the power spectrum and parameter level. We find excellent agreement between frequencies and spectrum types and our results are robust to the modeling of astrophysical foregrounds. We report results for $\Lambda$CDM and a series of extensions, drawing on the following parameters: the amplitude of the gravitational lensing effect on primary power spectra $A_\mathrm{L}$, the effective number of neutrino species $N_{\mathrm{eff}}$, the primordial helium abundance $Y_{\mathrm{P}}$, and the baryon clumping factor due to primordial magnetic fields $b$. We find that the SPT-3G 2018 $T/TE/EE$ data are well fit by $\Lambda$CDM with a probability-to-exceed of $15\%$. For $\Lambda$CDM, we constrain the expansion rate today to $H_0 = 68.3 \pm 1.5\,\mathrm{km\,s^{-1}\,Mpc^{-1}}$ and the combined structure growth parameter to $S_8 = 0.797 \pm 0.042$. The SPT-based results are effectively independent of Planck, and the cosmological parameter constraints from either data set are within <1\,\sigma of each other. (abridged)

We present a systematic search for transiting giant planets ($0.6 R_{\rm J} \leq R_{\rm P} \leq 2.0 R_{\rm J}$) orbiting nearby low-mass stars ($M_{*} \leq 0.71 M_{\odot}$). The formation of giant planets around low-mass stars is predicted to be rare by the core-accretion planet formation theory. We search 91,306 low-mass stars in the TESS 30 minute cadence photometry detecting fifteen giant planet candidates, including seven new planet candidates which were not known planets or identified as TOIs prior to our search. Our candidates present an exciting opportunity to improve our knowledge of the giant planet population around the lowest mass stars. We perform planet injection-recovery simulations and find that our pipeline has a high detection efficiency across the majority of our targeted parameter space. We measure the occurrence rates of giant planets with host stars in different stellar mass ranges spanning our full sample. We find occurrence rates of $0.137 \pm 0.097$% (0.088 - 0.26 $M_{\odot}$), $0.108 \pm 0.083$% (0.26 - 0.42 $M_{\odot}$), and $0.29 \pm 0.15$% (0.42 - 0.71 $M_{\odot}$). For our full sample (0.088 - 0.71 $M_{\odot}$) we find a giant planet occurrence rate of $0.194 \pm 0.072$%. We have measured for the first time the occurrence rate for giant planets orbiting stars with $M_{*} \leq 0.4 M_{\odot}$ and we demonstrate this occurrence rate to be non-zero. This result contradicts currently accepted planet formation models and we discuss some possibilities for how these planets could have formed.

We present the results and conclusions from the citizen science competition `Observing Dark Worlds', where we asked participants to calculate the positions of dark matter halos from 120 catalogues of simulated weak lensing galaxy data, using computational methods. In partnership with Kaggle (this http URL), 357 users participated in the competition which saw 2278 downloads of the data and 3358 submissions. We found that the best algorithms improved on the benchmark code, LENSTOOL by > 30% and could measure the positions of > 3x10^14MSun halos to less than 5'' and < 10^14MSun to within 1'. In this paper, we present a brief overview of the winning algorithms with links to available code. We also discuss the implications of the experiment for future citizen science competitions.

Understanding the nature of dark matter in the Universe is an important goal of modern cosmology. A key method for probing this distribution is via weak gravitational lensing mass-mapping - a challenging ill-posed inverse problem where one infers the convergence field from observed shear measurements. Upcoming stage IV surveys, such as those made by the Vera C. Rubin Observatory and Euclid satellite, will provide a greater quantity and precision of data for lensing analyses, necessitating high-fidelity mass-mapping methods that are computationally efficient and that also provide uncertainties for integration into downstream cosmological analyses. In this work we introduce MMGAN, a novel mass-mapping method based on a regularised conditional generative adversarial network (GAN) framework, which generates approximate posterior samples of the convergence field given shear data. We adopt Wasserstein GANs to improve training stability and apply regularisation techniques to overcome mode collapse, issues that otherwise are particularly acute for conditional GANs. We train and validate our model on a mock COSMOS-style dataset before applying it to true COSMOS survey data. Our approach significantly outperforms the Kaiser-Squires technique and achieves similar reconstruction fidelity as alternative state-of-the-art deep learning approaches. Notably, while alternative approaches for generating samples from a learned posterior are slow (e.g. requiring $\sim$10 GPU minutes per posterior sample), MMGAN can produce a high-quality convergence sample in less than a second.

From the decades of the theoretical studies, it is well known that the formation of the bar triggers the gas funnelling into the central sub-kpc region and leads to the formation of a kinematically cold nuclear stellar disc (NSD). We demonstrate that this mechanism can be used to identify the formation epoch of the Galactic bar, using an N-body/hydrodynamics simulation of an isolated Milky Way-like galaxy. As shown in many previous literature, our simulation shows that the bar formation triggers an intense star formation for ~1 Gyr in the central region, and forms a NSD. As a result, the oldest age limit of the NSD is relatively sharp, and the oldest population becomes similar to the age of the bar. Therefore, the age distribution of the NSD tells us the formation epoch of the bar. We discuss that a major challenge in measuring the age distribution of the NSD in the Milky Way is contamination from other non-negligible stellar components in the central region, such as a classical bulge component. We demonstrate that because the NSD is kinematically colder than the other stellar populations in the Galactic central region, the NSD population can be kinematically distinguished from the other stellar populations, if the 3D velocity of tracer stars are accurately measured. Hence, in addition to the line-of-sight velocities from spectroscopic surveys, the accurate measurements of the transverse velocities of stars are necessary, and hence the near-infrared space astrometry mission, JASMINE, would play a crutial role to identify the formation epoch of the Galactic bar. We also discuss that the accuracy of stellar age estimation is also crucial to measure the oldest limit of the NSD stellar population.

The dSphs around the Milky Way are commonly considered as systems that are supported by velocity dispersion against self-gravitation. They have been long accounted among the best targets to search for indirect DM signatures in the GeV-to-TeV gamma-rays due to absence of astrophysical gamma-ray foreground or background emission. We present forecasts on the sensitivity of the future CTAO for the search for annihilating or decaying DM in such targets. We perform an original selection of candidates out of the current catalog of known objects, including both classical and ultra-faint targets. For each of them, we calculate the expected amount of DM using the most updated and complete available samples of photometric and spectroscopic data of member stars, adopting a common framework of data treatment for both classes of objects. In this way, we are able to generate novel astrophysical factor profiles for general indirect DM searches that we compare with the current literature. Out of a starting sample of 64 dSphs, we highlight the 8 most promising targets - DraI, CBe, UMaII, UMi and Wil1 in the Northern hemisphere; RetII, Scl and SgrII in the Southern hemisphere - for which different DM density models (either cored or cuspy) lead to similar expectations, at variance with what happens for other DM targets - thus resulting in more robust predictions. We find that CTAO will provide the strongest limits above ~10 TeV, down to values of velocity-averaged annihilation cross section of ~5$\times 10^{-25}$ cm$^3$ s$^{-1}$ and up to decay lifetimes of ~10$^{26}$ s for combined limits on the best targets. We argue that the largest source of inaccuracy is due to the still imprecise determination of the DM content, especially for ultra-faint dSphs. We propose possible strategies of observation for CTAO, either optimized on a deep focus on the best known candidates, or on the diversification of targets.

The precision of Stage IV cosmic shear surveys will enable us to probe smaller physical scales than ever before, however, model uncertainties from baryonic physics and non-linear structure formation will become a significant concern. The $k$-cut method -- applying a redshift-dependent $\ell$-cut after making the Bernardeau-Nishimichi-Taruya transform -- can reduce sensitivity to baryonic physics; allowing Stage IV surveys to include information from increasingly higher $\ell$-modes. Here we address the question of whether it can also mitigate the impact of making the reduced shear approximation; which is also important in the high-$\kappa$, small-scale regime. The standard procedure for relaxing this approximation requires the repeated evaluation of the convergence bispectrum, and consequently can be prohibitively computationally expensive when included in Monte Carlo analyses. We find that the $k$-cut cosmic shear procedure suppresses the $w_0w_a$CDM cosmological parameter biases expected from the reduced shear approximation for Stage IV experiments, when $\ell$-modes up to $5000$ are probed. The maximum cut required for biases from the reduced shear approximation to be below the threshold of significance is at $k = 5.37 \, h{\rm Mpc}^{-1}$. With this cut, the predicted $1\sigma$ constraints increase, relative to the case where the correction is directly computed, by less than $10\%$ for all parameters. This represents a significant improvement in constraints compared to the more conservative case where only $\ell$-modes up to 1500 are probed, and no $k$-cut is used. We also repeat this analysis for a hypothetical, comparable kinematic weak lensing survey. The key parts of code used for this analysis are made publicly available.

(Abridged) The abundance ratios between key elements such as iron and alpha-process elements carry a wealth of information on the star formation history (SFH) of galaxies. So far, simple chemical evolution models have linked [alpha/Fe] with the SFH timescale, correlating large abundance ratios with short-lived SFH. We provide an empirical correlation between [alpha/Fe] (measured from spectral indices) and the SFH (determined via a non-parametric spectral-fitting method). We offer an empirical version of the iconic outline of Thomas et al. (2005), relating star formation timescale with galaxy mass, although our results suggest, in contrast, a significant population of old (>10Gyr) stars even for the lowest mass ellipticals. In addition, the abundance ratio is found to be strongly correlated with the time to build up the stellar component, showing that the highest [alpha/Fe] (>+0.2) are attained by galaxies with the shortest half-mass formation time (<2Gyr), or equivalently, with the smallest (<40%) fraction of populations younger than 10Gyr. These observational results support the standard hypothesis that star formation incorporates the Fe-enriched interstellar medium into stars, lowering the high abundance ratio of the old populations.

To date, galaxy image simulations for weak lensing surveys usually approximate the light profiles of all galaxies as a single or double Sérsic profile, neglecting the influence of galaxy substructures and morphologies deviating from such a simplified parametric characterization. While this approximation may be sufficient for previous data sets, the stringent cosmic shear calibration requirements and the high quality of the data in the upcoming Euclid survey demand a consideration of the effects that realistic galaxy substructures have on shear measurement biases. Here we present a novel deep learning-based method to create such simulated galaxies directly from HST data. We first build and validate a convolutional neural network based on the wavelet scattering transform to learn noise-free representations independent of the point-spread function of HST galaxy images that can be injected into simulations of images from Euclid's optical instrument VIS without introducing noise correlations during PSF convolution or shearing. Then, we demonstrate the generation of new galaxy images by sampling from the model randomly and conditionally. Next, we quantify the cosmic shear bias from complex galaxy shapes in Euclid-like simulations by comparing the shear measurement biases between a sample of model objects and their best-fit double-Sérsic counterparts. Using the KSB shape measurement algorithm, we find a multiplicative bias difference between these branches with realistic morphologies and parametric profiles on the order of $6.9\times 10^{-3}$ for a realistic magnitude-Sérsic index distribution. Moreover, we find clear detection bias differences between full image scenes simulated with parametric and realistic galaxies, leading to a bias difference of $4.0\times 10^{-3}$ independent of the shape measurement method. This makes it relevant for stage IV weak lensing surveys such as Euclid.

In a previous work [arXiv:2009.03428] we proposed a new model for Quantum GRavity(QGR) and cosmology, dubbed $SU(\infty)$-QGR. One of the axioms of this model is that Hilbert spaces of the Universe and its subsystems represent $SU(\infty)$ symmetry group. In this framework, the classical spacetime is interpreted as being the parameter space characterizing states of the $SU(\infty)$ representing Hilbert spaces. Using quantum uncertainty relations, it is shown that the parameter space - the spacetime - has a 3+1 dimensional Lorentzian geometry. Here after a review of $SU(\infty)$-QGR, including the demonstration that its classical limit is Einstein gravity, we compare it with several QGR proposals, including: string and M-theories, loop quantum gravity and related models, and QGR proposals inspired by holographic principle and quantum entanglement. The purpose is to find their common and analogous features, even if they apparently seem to have different roles and interpretations. The hope is that such exercise gives a better understanding of gravity as a universal quantum force and clarifies the physical nature of the spacetime. We identify several common features among the studied models: importance of 2D structures; algebraic decomposition to tensor products; special role of $SU(2)$ group in their formulation; necessity of a quantum time as a relational observable. We discuss how these features can be considered as analogous in different models. We also show that they arise in $SU(\infty)$-QGR without fine-tuning, additional assumptions, or restrictions.