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Isolated black holes (BHs) and neutron stars (NSs) are largely undetectable across the electromagnetic spectrum. For this reason, our only real prospect of observing these isolated compact remnants is via microlensing; a feat recently performed for the first time. However, characterization of the microlensing events caused by BHs and NSs is still in its infancy. In this work, we perform N -body simulations to explore the frequency and physical characteristics of microlensing events across the entire sky. Our simulations find that every year we can expect |$88_{-6}^{+6}$| BH, |$6.8_{-1.6}^{+1.7}$| NS, and |$20^{+30}_{-20}$| stellar microlensing events which cause an astrometric shift larger than 2 mas. Similarly, we can expect |$21_{-3}^{+3}$| BH, |$18_{-3}^{+3}$| NS, and |$7500_{-500}^{+500}$| stellar microlensing events which cause a bump magnitude larger than 1 mag. Leveraging a more comprehensive dynamical model than prior work, we predict the fraction of microlensing events caused by BHs as a function of Einstein time to be smaller than previously thought. Comparison of our microlensing simulations to events in Gaia finds good agreement. Finally, we predict that in the combination of Gaia and Gaia NIR data there will be |$14700_{-900}^{+600}$| BH and |$1600_{-200}^{+300}$| NS events creating a centroid shift larger than 1 mas and |$330_{-120}^{+100}$| BH and |$310_{-100}^{+110}$| NS events causing bump magnitudes >1. Of these, <10 BH and |$5_{-5}^{+10}$| NS events should be detectable using current analysis techniques. These results inform future astrometric mission design, such as Gaia NIR, as they indicate that, compared to stellar events, there are fewer observable BH events than previously thought. [ABSTRACT FROM AUTHOR]

The advent of massive broad-band photometric surveys enabled photometric redshift estimates for unprecedented numbers of galaxies and quasars. These estimates can be improved using better algorithms or by obtaining complementary data such as narrow-band photometry, and broad-band photometry over an extended wavelength range. We investigate the impact of both approaches on photometric redshifts for quasars using data from Southern Photometric Local Universe Survey (S-PLUS) DR4, Galaxy Evolution Explorer (GALEX) DR6/7, and the unWISE catalog for the Wide-field Infrared Survey Explorer (WISE) in three machine learning methods: Random Forest, Flexible Conditional Density Estimation (FlexCoDE), and Bayesian Mixture Density Network (BMDN). Including narrow-band photometry improves the root-mean-square error by 11 per cent in comparison to a model trained with only broad-band photometry. Narrow-band information only provided an improvement of 3.8 per cent when GALEX and WISE colours were included. Thus, narrow bands play a more important role for objects that do not have GALEX or WISE counterparts, which respectively makes 92 per cent and 25 per cent of S-PLUS data considered here. Nevertheless, the inclusion of narrow-band information provided better estimates of the probability density functions obtained with FlexCoDE and BMDN. We publicly release a value-added catalogue of photometrically selected quasars with the photo- z predictions from all methods studied here. The catalogue provided with this work covers the S-PLUS DR4 area (∼3000 square degrees), containing 645 980, 244 912, 144 991 sources with the probability of being a quasar higher than, 80 per cent, 90 per cent, 95 per cent up to r < 21.3 and good photometry quality in the detection image. More quasar candidates can be retrieved from the S-PLUS data base by considering less restrictive selection criteria. [ABSTRACT FROM AUTHOR]

With the plentiful information available in the Gaia BP/RP spectra, there is significant scope for applying discriminative models to extract stellar atmospheric parameters and abundances. We describe an approach to leverage an 'Uncertain Neural Network' model trained on APOGEE data to provide high-quality predictions with robust estimates for per-prediction uncertainty. We report median formal uncertainties of 0.068 dex, 69.1 K, 0.14 dex, 0.031 dex, 0.040 dex, and 0.029 dex for [Fe/H], T eff, log  g , [C/Fe], [N/Fe], and [α/M], respectively. We validate these predictions against our APOGEE training data, LAMOST, and Gaia GSP- phot stellar parameters, and see a strong correlation between our predicted parameters and those derived from these surveys. We investigate the information content of the spectra by considering the 'attention' our model pays to different spectral features compared to expectations from synthetic spectra calculations. Our model's predictions are applied to the Gaia data set, and we produce a publicly available catalogue of our model's predictions. [ABSTRACT FROM AUTHOR]

The spatial–kinematic structure of 48 young star clusters and associations is investigated. Moran's I statistic is used to quantify the degree of kinematic substructure in each region, and the results are compared to those expected assuming the hierarchical or monolithic models of star cluster formation. Of the observed regions, 39 are found to have significant kinematic substructure, such that they are compatible with the hierarchical model and incompatible with the monolithic model. This includes multiple regions whose Q parameter shows the region to be centrally concentrated and clustered. The remaining nine are compatible with both models. From this, it is concluded that the kinematic substructure of the observed star clusters represents strong evidence in favour of the hierarchical model of star cluster formation over the monolithic model. [ABSTRACT FROM AUTHOR]

We apply the hierarchical probabilistic spectral energy distribution (SED) model bayesn to analyse a sample of 475 type Ia supernovae (0.015 < z < 0.4) from Foundation, DES3YR and PS1MD to investigate the properties of dust in their host galaxies. We jointly infer the dust law RV population distributions at the SED level in high- and low-mass galaxies simultaneously with dust-independent, intrinsic differences. We find an intrinsic mass step of −0.049 ± 0.016 mag, at a significance of 3.1σ, when allowing for a constant intrinsic, achromatic magnitude offset. We additionally apply a model allowing for time- and wavelength-dependent intrinsic differences between SNe Ia in different mass bins, finding ∼2σ differences in magnitude and colour around peak and 4.5σ differences at later times. These intrinsic differences are inferred simultaneously with a difference in population mean RV of ∼2σ significance, demonstrating that both intrinsic and extrinsic differences may play a role in causing the host galaxy mass step. We also consider a model which allows the mean of the RV distribution to linearly evolve with redshift but find no evidence for any evolution – we infer the gradient of this relation η R  = −0.38 ± 0.70. In addition, we discuss in brief a new, GPU-accelerated python implementation of bayesn suitable for application to large surveys which is publicly available and can be used for future cosmological analyses; this code can be found here: https://github.com/bayesn/bayesn. [ABSTRACT FROM AUTHOR]

We use the emulation framework CosmoPower to construct and publicly release neural network emulators of cosmological observables, including the cosmic microwave background (CMB) temperature and polarization power spectra, matter power spectrum, distance-redshift relation, baryon acoustic oscillation (BAO) and redshift-space distortion (RSD) observables, and derived parameters. We train our emulators on Einstein–Boltzmann calculations obtained with high-precision numerical convergence settings, for a wide range of cosmological models including ΛCDM, w CDM, ΛCDM +  N eff, and ΛCDM + Σ m ν. Our CMB emulators are accurate to better than 0.5 per cent out to ℓ = 104, which is sufficient for Stage-IV data analysis, and our P (k) emulators reach the same accuracy level out to |$k=50 \, \, \mathrm{Mpc}^{-1}$|⁠ , which is sufficient for Stage-III data analysis. We release the emulators via an online repository (CosmoPower Organisation), which will be continually updated with additional extended cosmological models. Our emulators accelerate cosmological data analysis by orders of magnitude, enabling cosmological parameter extraction analyses, using current survey data, to be performed on a laptop. We validate our emulators by comparing them to class and camb and by reproducing cosmological parameter constraints derived from Planck TT, TE, EE, and CMB lensing data, as well as from the Atacama Cosmology Telescope Data Release 4 CMB data, Dark Energy Survey Year-1 galaxy lensing and clustering data, and Baryon Oscillation Spectroscopic Survey Data Release 12 BAO and RSD data. [ABSTRACT FROM AUTHOR]

Galaxy morphologies and their relation with physical properties have been a relevant subject of study in the past. Most galaxy morphology catalogues have been labelled by human annotators or by machine learning models trained on human-labelled data. Human-generated labels have been shown to contain biases in terms of the observational properties of the data, such as image resolution. These biases are independent of the annotators, that is, are present even in catalogues labelled by experts. In this work, we demonstrate that training deep learning models on biased galaxy data produces biased models, meaning that the biases in the training data are transferred to the predictions of the new models. We also propose a method to train deep learning models that considers this inherent labelling bias, to obtain a de-biased model even when training on biased data. We show that models trained using our deep de-biasing method are capable of reducing the bias of human-labelled data sets. [ABSTRACT FROM AUTHOR]

Machine learning (ML) algorithms are becoming popular in cosmology for extracting valuable information from cosmological data. In this paper, we evaluate the performance of a convolutional neural network (CNN) trained on matter density snapshots to distinguish clustering dark energy (DE) from the cosmological constant scenario and to detect the speed of sound (c s) associated with clustering DE. We compare the CNN results with those from a Random Forest (RF) algorithm trained on power spectra. Varying the DE equation of state parameter w DE within the range of −0.7 to −0.99 while keeping |$c_s^2 = 1$|⁠ , we find that the CNN approach results in a significant improvement in accuracy over the RF algorithm. The improvement in classification accuracy can be as high as 40 per cent depending on the physical scales involved. We also investigate the ML algorithms' ability to detect the impact of the speed of sound by choosing |$c_s^2$| from the set {1, 10−2, 10−4, 10−7} while maintaining a constant w DE for three different cases: w DE ∈ {−0.7, −0.8, −0.9}. Our results suggest that distinguishing between various values of |$c_s^2$| and the case where |$c_s^2=1$| is challenging, particularly at small scales and when w DE ≈ −1. However, as we consider larger scales, the accuracy of |$c_s^2$| detection improves. Notably, the CNN algorithm consistently outperforms the RF algorithm, leading to an approximate 20 per cent enhancement in |$c_s^2$| detection accuracy in some cases. [ABSTRACT FROM AUTHOR]

This paper investigates the directional distribution of extraterrestrial objects (meteors, fireballs) impacting Earth's surface and forming craters. It also introduces a novel directional statistical mixture model to analyze their falls, validated through rigorous testing. First, we address whether these falls follow non-uniform directional patterns by explicitly employing directional statistical tools for analysing such data. Using projection techniques for longitude and latitude and more importantly, a general spherical statistical approach, we statistically investigate the suitability of the von Mises distribution and its spherical version, the von Mises–Fisher distribution, (a maximum entropy distribution for directional data). Moreover, leveraging extensive data sets encompassing meteor falls, fireball showers, and craters, we propose and validate a novel mixture von Mises–Fisher model for comprehensively analysing extraterrestrial object falls. Our study reveals distinct statistical characteristics across data sets: fireball falls exhibit non-uniformity, while meteor craters suggest a potential for both uniform and von Mises distributions with a preference for the latter after further refinement. Meteor landings deviate from a single-directional maximum entropic distribution; we demonstrate the effectiveness of an optimal 13-component mixture von Mises–Fisher distribution for accurate modelling. Similar analyses resulted in 3- and 6-component partitions for fireball and crater data sets. This research presents valuable insights into the spatial patterns and directional statistical distribution models governing extraterrestrial objects' fall on Earth, useful for various future works. [ABSTRACT FROM AUTHOR]

Large ground-based solar telescopes are equipped with adaptive optics systems to correct wavefront distortions induced in the turbulent atmosphere. The design of the adaptive optics system strongly depends on the vertical profiles of the optical turbulence. In particular, the characteristics of the optical turbulence determine the design of tomographic adaptive optics systems, which provide image correction within a wide field of view. In the article, a new method to estimate reference optical turbulence characteristics from Era-5 reanalysis assimilated data is presented. This method is based on the dependence of the air refractive index structure constant |$C_n^2$| on the vertical shears of wind speed as well as the outer scale of turbulence L0. The L0 parameter is estimated by minimization of the dispersion between the modeled and measured values of the refractive index structure constant |$C_n^2$| within the surface layer. For the first time, parametrization coefficients and reference profiles of optical turbulence averaged for the period 1940–2022 are calculated for the Large Solar Vacuum Telescope (LSVT) site. The calculated optical turbulence profiles are representative; these profiles correspond to typical changes of the measured values of the Fried parameter, the isoplanatic angle, and the outer scale of turbulence at the LSVT site. The model turbulence profiles are verified taking into account the Shack–Hartmann wavefront sensor measurements at the LSVT. The higher accuracy of estimation of the optical turbulence characteristics makes it possible to refine parameters relevant to the LSVT adaptive optics system. The obtained results can be used in order to develop high-resolution solar adaptive optics technologies as applied to ground-based telescopes including those using the principles of atmospheric tomography. [ABSTRACT FROM AUTHOR]

Leveraging the data sets of galaxy triplets and large-scale filaments obtained from the Sloan Digital Sky Survey, we scrutinize the alignment of the three sides of the triangles formed by galaxy triplets and the normal vectors of the triplet planes within observed large-scale filaments. Our statistical investigation reveals that the longest and median sides of the galaxy triplets exhibit a robust alignment with the spines of their host large-scale filaments, while the shortest sides show no or only weak alignment with the filaments. Additionally, the normal vectors of triplets tend to be perpendicular to the filaments. The alignment signal diminishes rapidly with the increasing distance from the triplet to the filament spine, and is primarily significant for triplets located within distances shorter than 0.2 Mpc |$/h$|⁠ , with a confidence level exceeding |$20\sigma$|⁠. Moreover, in comparison to compact galaxy triplets, the alignment signal is more conspicuous among the loose triplets. This alignment analysis contributes to the formulation of a framework depicting the clustering and relaxation of galaxies within cosmological large-scale filament regimes, providing deeper insights into the intricate interactions between galaxies and their pivotal role in shaping galaxy groups. [ABSTRACT FROM AUTHOR]

We present the first fully simulation-based hierarchical analysis of the light curves of a population of low-redshift type Ia supernovæ (SNæ Ia). Our hardware-accelerated forward model, released in the Python package slicsim , includes stochastic variations of each SN's spectral flux distribution (based on the pre-trained BayeSN model), extinction from dust in the host and in the Milky Way, redshift, and realistic instrumental noise. By utilizing truncated marginal neural ratio estimation (TMNRE), a neural network-enabled simulation-based inference technique, we implicitly marginalize over 4000 latent variables (for a set of ≈100 SNæ Ia) to efficiently infer SN Ia absolute magnitudes and host-galaxy dust properties at the population level while also constraining the parameters of individual objects. Amortization of the inference procedure allows us to obtain coverage guarantees for our results through Bayesian validation and frequentist calibration. Furthermore, we show a detailed comparison to full likelihood-based inference, implemented through Hamiltonian Monte Carlo, on simulated data and then apply TMNRE to the light curves of 86 SNæ Ia from the Carnegie Supernova Project, deriving marginal posteriors in excellent agreement with previous work. Given its ability to accommodate arbitrarily complex extensions to the forward model, e.g. different populations based on host properties, redshift evolution, complicated photometric redshift estimates, selection effects, and non-Ia contamination, without significant modifications to the inference procedure, TMNRE has the potential to become the tool of choice for cosmological parameter inference from future, large SN Ia samples. [ABSTRACT FROM AUTHOR]

The pervasive presence of warm gas in galaxy haloes suggests that the circumgalactic medium (CGM) is multiphase in its ionization structure and complex in its kinematics. Some recent state-of-the-art cosmological galaxy simulations predict an azimuthal dependence of CGM metallicities. We investigate the presence of such a trend by analysing the distribution of gas properties in the CGM around 47 z < 0.7 galaxies from the Multiphase Galaxy Halos Survey determined using a cloud-by-cloud, multiphase, ionization modelling approach. We identify three distinct populations of absorbers: cool clouds (T ∼ 104.1 K) in photoionization equilibrium, warm–hot collisionally ionized clouds (T ∼ 104.5–105 K) affected by time-dependent photoionization, and hotter clouds (T ∼ 105.4–106 K) with broad O  vi and Ly α absorption consistent with collisional ionization. We find that fragmentation can play a role in the origin of cool clouds, that warm–hot clouds are out of equilibrium due to rapid cooling, and that hotter clouds are representative of virialized halo gas in all but the lowest mass galaxies. The metallicities of clouds do not depend on the azimuthal angle or other galaxy properties for any of these populations. At face value, this disagrees with the simplistic model of the CGM with bipolar outflows and cold-mode planar accretion. However, the number of clouds per sightline is significantly larger close to the minor and major axes. This implies that the processes of outflows and accretion are contributing to these CGM cloud populations, and our sightlines are probing gas of mixed origins at all azimuthal angles in these low-redshift galaxies. [ABSTRACT FROM AUTHOR]

L 98-59b is a transiting exoplanet with radius |${0.85^{+0.061}_{-0.047}}$| R ⊕ and orbital period |${2.2531136^{+1.2e-06}_{-1.5e-06}}$| d. In 2021, a Doppler detection of  L 98-59b was announced, with radial velocity (RV) semi-amplitude |$K_{\rm b}=0.46^{+0.20}_{-0.17}$| |$\textrm {m}\, \textrm {s}^{-1}$| inferred from 164 High Accuracy Radial velocity Planet Searcher and 66 Échelle SPectrograph for Rocky Exoplanets and Stable Spectroscopic Observation RVs. This translated into a mass |$M_{\rm b}=0.40^{+0.16}_{-0.15}$| M ⊕, or half the mass of Venus, making  L 98-59b then (by far) the lowest mass planet with a putative RV detection. To illuminate the difficulties in securely detecting <1  |$\textrm {m}\, \textrm {s}^{-1}$|  signals, we argue here that the published RV detection of  L 98-59b is not supported by sufficient statistical evidence. We show, under the published modelling assumptions, that Bayesian model comparison (BMC) suggests an |$\sim 50~{{\ \rm per\ cent}}$| false-detection probability. We also often infer ∼40  |$\textrm {cm}\, \textrm {s}^{-1}$| semi-amplitudes for Keplerians with periods not corresponding to any known planet; importantly, though, BMC rejects all these 'detections'. By implementing a more sophisticated stellar activity model and more realistic parameter priors, we infer a mass M b = 0.47 ± 0.14 M ⊕ from semi-amplitude K b = 0.56 ± 0.16  |$\textrm {m}\, \textrm {s}^{-1}$|⁠ , with BMC now indicating substantial yet still not decisive evidence for the detection. Definitive detection and characterization may require many additional RVs, alongside careful modelling and stringent statistical analysis. Our case study has implications for current extreme-precision RV campaigns and the longer term quest to discover the first Earth-twins. [ABSTRACT FROM AUTHOR]

This work explores the relationships between galaxy sizes and related observable galaxy properties in a large volume cosmological hydrodynamical simulation. The objectives of this work are to develop a better understanding of both the correlations between galaxy properties and the influence of environment on galaxy physics in order to build an improved model for the galaxy sizes, building off of the fundamental plane. With an accurate intrinsic galaxy size predictor, the residuals in the observed galaxy sizes can potentially be used for multiple cosmological applications, including making measurements of galaxy velocities in spectroscopic samples, estimating the rate of cosmic expansion, and constraining the uncertainties in the photometric redshifts of galaxies. Using projection pursuit regression, the model accurately predicts intrinsic galaxy sizes and have residuals which have limited correlation with galaxy properties. The model decreases the spatial correlation of galaxy size residuals by a factor of ∼5 at small scales compared to the baseline correlation when the mean size is used as a predictor. [ABSTRACT FROM AUTHOR]

The green valley represents the population of galaxies that are transitioning from the actively star-forming blue cloud to the passively evolving red sequence. Studying the properties of the green valley galaxies is crucial for our understanding of the exact mechanisms and processes that drive this transition. The green valley does not have a universally accepted definition. The boundaries of the green valley are often determined by empirical lines that are subjective and vary across studies. We present an unambiguous definition of the green valley in the colour–stellar mass plane using the entropic thresholding. We first divide the galaxy population into the blue cloud and the red sequence based on a colour threshold that minimizes the intraclass variance and maximizes the interclass variance. Our method splits the region between the mean colours of the blue cloud and the red sequence into three parts by maximizing the total entropy of that region. We repeat our analysis in a number of independent stellar mass bins to define the boundaries of the green valley in the colour–mass diagram. Our method provides a robust and natural definition of the green valley. [ABSTRACT FROM AUTHOR]

In recent years, there has been a remarkable development of simulation-based inference (SBI) algorithms, and they have now been applied across a wide range of astrophysical and cosmological analyses. There are a number of key advantages to these methods, centred around the ability to perform scalable statistical inference without an explicit likelihood. In this work, we propose two technical building blocks to a specific sequential SBI algorithm, truncated marginal neural ratio estimation (TMNRE). In particular, first, we develop autoregressive ratio estimation with the aim to robustly estimate correlated high-dimensional posteriors. Secondly, we propose a slice-based nested sampling algorithm to efficiently draw both posterior samples and constrained prior samples from ratio estimators, the latter being instrumental for sequential inference. To validate our implementation, we carry out inference tasks on three concrete examples: a toy model of a multidimensional Gaussian, the analysis of a stellar stream mock observation, and finally, a proof-of-concept application to substructure searches in strong gravitational lensing. In addition, we publicly release the code for both the autoregressive ratio estimator and the slice sampler. [ABSTRACT FROM AUTHOR]

We use rest-frame optical and near-infrared (NIR) observations of 42 Type Ia supernovae (SNe Ia) from the Carnegie Supernova Project at low- z and 37 from the RAISIN (SNIA in the IR) Survey at high- z to investigate correlations between SN Ia host galaxy dust, host mass, and redshift. This is the first time the SN Ia host galaxy dust extinction law at high- z has been estimated using combined optical and rest-frame NIR data (YJ band). We use the BayeSN hierarchical model to leverage the data's wide rest-frame wavelength range (extending to ∼1.0–1.2 μ m for the RAISIN sample at 0.2 ≲ z ≲ 0.6). By contrasting the RAISIN and Carnegie Supernova Project (CSP) data, we constrain the population distributions of the host dust R V parameter for both redshift ranges. We place a limit on the difference in population mean R V between RAISIN and CSP of −1.16 < Δμ(R V) < 1.38 with 95 per cent posterior probability. For RAISIN we estimate μ(R V) = 2.58 ± 0.57, and constrain the population standard deviation to σ(R V) < 0.90 [2.42] at the 68 [95] per cent level. Given that we are only able to constrain the size of the low- to high- z shift in μ(R V) to ≲1.4 – which could still propagate to a substantial bias in the equation-of-state parameter w – these and other recent results motivate continued effort to obtain rest-frame NIR data at low- and high-redshifts (e.g. using the Roman Space Telescope). [ABSTRACT FROM AUTHOR]

Atacama Large Millimetre/submillimetre Array (ALMA) observations of the thermal emission from protoplanetary disc dust have revealed a wealth of substructures that could evidence embedded planets, but planet-driven spirals, one of the more compelling lines of evidence, remain relatively rare. Existing works have focused on detecting these spirals using methods that operate in image space. Here, we explore the planet detection capabilities of fitting planet-driven spirals to disc observations directly in visibility space. We test our method on synthetic ALMA observations of planet-containing model discs for a range of disc/observational parameters, finding it significantly outperforms image residuals in identifying spirals in these observations and is able to identify spirals in regions of the parameter space in which no gaps are detected. These tests suggest that a visibility-space fitting approach warrants further investigation and may be able to find planet-driven spirals in observations that have not yet been found with existing approaches. We also test our method on six discs in the Taurus molecular cloud observed with ALMA at 1.33 mm, but find no evidence for planet-driven spirals. We find that the minimum planet masses necessary to drive detectable spirals range from ≈0.03 to |$0.5 \, M_{\text{Jup}}$| over orbital radii of 10–100 au, with planet masses below these thresholds potentially hiding in such disc observations. Conversely, we suggest that planets ≳0.5–1  M Jup can likely be ruled out over orbital radii of ≈20– |$60 \, \text{au}$| on the grounds that we would have detected them if they were present. [ABSTRACT FROM AUTHOR]

We present gausSN , a Bayesian semiparametric Gaussian Process (GP) model for time-delay estimation with resolved systems of gravitationally lensed supernovae (glSNe). gausSN models the underlying light curve non-parametrically using a GP. Without assuming a template light curve for each SN type, gausSN fits for the time delays of all images using data in any number of wavelength filters simultaneously. We also introduce a novel time-varying magnification model to capture the effects of microlensing alongside time-delay estimation. In this analysis, we model the time-varying relative magnification as a sigmoid function, as well as a constant for comparison to existing time-delay estimation approaches. We demonstrate that gausSN provides robust time-delay estimates for simulations of glSNe from the Nancy Grace Roman Space Telescope and the Vera C. Rubin Observatory's Legacy Survey of Space and Time (Rubin-LSST). We find that up to 43.6 per cent of time-delay estimates from Roman and 52.9 per cent from Rubin-LSST have fractional errors of less than 5  per cent. We then apply gausSN to SN Refsdal and find the time delay for the fifth image is consistent with the original analysis, regardless of microlensing treatment. Therefore, gausSN maintains the level of precision and accuracy achieved by existing time-delay extraction methods with fewer assumptions about the underlying shape of the light curve than template-based approaches, while incorporating microlensing into the statistical error budget. gausSN is scalable for time-delay cosmography analyses given current projections of glSNe discovery rates from Rubin-LSST and Roman. [ABSTRACT FROM AUTHOR]

It has long been known that galaxy shapes align coherently with the large-scale density field. Characterizing this effect is essential to interpreting measurements of weak gravitational lensing, the deflection of light from distant galaxies by matter overdensities along the line of sight, as it also produces coherent galaxy alignments that we wish to interpret in terms of a cosmological model. Existing direct measurements of intrinsic alignments using galaxy samples with high-quality shape and redshift measurements typically use well-understood but sub-optimal projected estimators, which do not make good use of the information in the data when comparing those estimators to theoretical models. We demonstrate a more optimal estimator, based on a multipole expansion of the correlation functions or power spectra, for direct measurements of galaxy intrinsic alignments. We show that even using the lowest order multipole alone increases the significance of inferred model parameters using simulated and real data, without any additional modelling complexity. We apply this estimator to measurements of parameters of the non-linear alignment model using data from the Sloan Digital Sky survey, demonstrating consistent results with a factor of ∼2 greater precision in parameter fits to intrinsic alignments models. This result is functionally equivalent to quadrupling the survey area, but without the attendant costs – thereby demonstrating the value in using this new estimator in current and future intrinsic alignments measurements using spectroscopic galaxy samples. [ABSTRACT FROM AUTHOR]

The existence of a radio synchrotron background (RSB) excess is implied by a number of measurements, including excess emission seen by the Absolute Radiometer for Cosmology, Astrophysics, and Diffuse Emission (ARCADE 2) and Long Wavelength Array (LWA) experiments. Highly sensitive wideband radio arrays, of the kind used to measure the cosmic 21 cm signal, provide a promising way to further constrain the RSB excess through its anisotropy, providing additional insight into its origin. We present a framework for evaluating the potential of 21 cm arrays to disentangle different components of the diffuse radio sky based on the combination of their frequency spectrum and angular power spectrum. The formalism is designed to calculate uncertainties due to the intrinsic cosmic variance alone or together with instrumental noise. In particular, we predict the potential for measuring the anisotropy of a broad generalized class of excess radio background models using the low-frequency Hydrogen Epoch of Reionization Array (HERA) as an example. We find that a HERA-like array can distinguish an RSB excess from other sky components based on its angular clustering and spectral dependence, even if these are quite similar to one or more of the other components – but only in the case that the RSB excess is relatively bright. [ABSTRACT FROM AUTHOR]

Massive neutrinos have non-negligible impact on the formation of large-scale structures. We investigate the impact of massive neutrinos on the halo assembly bias effect, measured by the relative halo bias |$\hat{b}$| as a function of the curvature of the initial density peak |$\hat{s}$|⁠ , neutrino excess ϵν, or halo concentration |$\hat{c}$|⁠ , using a large suite of Σ M ν = 0.0 and 0.4 eV simulations with the same initial conditions. By tracing dark matter haloes back to their initial density peaks, we construct a catalogue of halo 'twins' that collapsed from the same peaks but evolved separately with and without massive neutrinos, thereby isolating any effect of neutrinos on halo formation. We detect a 2 per cent weakening of the halo assembly bias as measured by |$\hat{b}(\epsilon _\nu)$| in the presence of massive neutrinos. As there exists a significant correlation between |$\hat{s}$| and ϵν (r cc = 0.319), the impact of neutrinos persists at a reduced level (0.1 per cent) in the halo assembly bias measured by |$\hat{b}(\hat{s})$|⁠. However, we do not detect any neutrino-induced impact on |$\hat{b}(\hat{c})$|⁠ , consistent with earlier studies and the lack of correlation between |$\hat{c}$| and ϵν (r cc = 0.087). We also discover an analogous assembly bias effect for the 'neutrino haloes', whose concentrations are anticorrelated with the large-scale clustering of neutrinos. [ABSTRACT FROM AUTHOR]

We analyse the volume-limited subsamples extracted from the sixteenth data release of the Sloan Digital Sky Survey-IV (SDSS-IV) eBOSS quasar survey spanning a redshift interval of 0.8 < z < 2.2, to estimate the scale of transition to homogeneity in the Universe. The multifractal analysis used for this purpose considers the scaling behaviour of different moments of quasar distribution in different density environments. This analysis gives the spectrum of generalized dimension Dq , where positive values of q characterize the scaling behaviour in overdense regions and the negative ones in underdense regions. We expect fractal correlation dimension Dq (r) = 3, for a homogeneous, random point distribution in 3-Dimensions. The fractal correlation dimension Dq (r), corresponding to q  = 2 obtained in our study stabilizes in the range (2.8–2.9) for scales r > 80 h −1 Mpc. The observed quasar distribution shows consistency with the simulated mock data and the random distribution of quasars within one sigma. Further, the generalized dimension spectrum Dq (r) also reveals transition to homogeneity beyond >110  h −1 Mpc, and the dominance of clustering at small scales r < 80  h −1 Mpc. Consequently, our study provides strong evidence for the homogeneity in SDSS quasar distribution, offering insights into large-scale structure properties and, thus can play a pivotal role in scrutinizing the clustering properties of quasars and its evolution in various upcoming surveys such as Dark Energy Spectroscopic Instrument and Extremely Large Telescope. [ABSTRACT FROM AUTHOR]

We present astrolink , an efficient and versatile clustering algorithm designed to hierarchically classify astrophysically relevant structures from both synthetic and observational data sets. We build upon clustar-nd , a hierarchical galaxy/(sub)halo finder, so that astrolink now generates a 2D representation of the implicit clustering structure as well as ensuring that clusters are statistically distinct from the noisy density fluctuations implicit within the n -dimensional input data. This redesign replaces the three cluster extraction parameters from clustar-nd with a single parameter, S – the lower statistical significance threshold of clusters, which can be automatically and reliably estimated via a dynamical model-fitting process. We demonstrate the robustness of this approach compared to astrolink 's predecessors by applying each algorithm to a suite of simulated galaxies defined over various feature spaces. We find that astrolink delivers a more powerful clustering performance while being |$\sim 27~{{\ \rm per \, cent}}$| faster and using less memory than clustar-nd. With these improvements, astrolink is ideally suited to extracting a meaningful set of hierarchical and arbitrarily shaped astrophysical clusters from both synthetic and observational data sets – lending itself as a great tool for morphological decomposition within the context of hierarchical structure formation. [ABSTRACT FROM AUTHOR]

The Large Area Telescope (LAT) onboard the Fermi gamma-ray observatory continuously scans the sky in an energy range from 50 MeV to 1 TeV. The telescope has identified over 6000 gamma-ray emitting sources, approximately half of which are classified as active galactic nuclei (AGN). However, not all of these gamma-ray sources have known redshift values for the reason that redshift estimation following traditional methods can be an expensive, challenging task. Alternatively, as an effort to robustly predict the AGN redshift values, many researchers have recently turned to machine learning methods. However, while the focus has primarily been on predicting specific values, real-world data often allows us only to predict conditional probability distributions, constrained by conditional entropy [ H (Y | X)]. In our study, we employ the Hierarchical Correlation Reconstruction approach to inexpensively predict complex conditional probability distributions, including multimodal ones. This is achieved through independent Mean Squared Error estimation of multiple moment-like parameters, combined into reconstruction of the conditional distribution. By employing linear regression for this purpose, we can develop interpretable models where coefficients describe the contributions of features to conditional moments. This article extends the original approach by incorporating Canonical Correlation Analysis for feature optimization and l1 'lasso' regularization. Our primary focus is on the practical problem of predicting the redshift of AGN using data from the Fourth Fermi -LAT Data Release 3 (4LAC-DR3) data set. [ABSTRACT FROM AUTHOR]

The arrival of the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST), Euclid-Wide and Roman wide-area sensitive surveys will herald a new era in strong lens science in which the number of strong lenses known is expected to rise from |$\mathcal {O}(10^3)$| to |$\mathcal {O}(10^5)$|⁠. However, current lens-finding methods still require time-consuming follow-up visual inspection by strong lens experts to remove false positives which is only set to increase with these surveys. In this work, we demonstrate a range of methods to produce calibrated probabilities to help determine the veracity of any given lens candidate. To do this we use the classifications from citizen science and multiple neural networks for galaxies selected from the Hyper Suprime-Cam survey. Our methodology is not restricted to particular classifier types and could be applied to any strong lens classifier which produces quantitative scores. Using these calibrated probabilities, we generate an ensemble classifier, combining citizen science, and neural network lens finders. We find such an ensemble can provide improved classification over the individual classifiers. We find a false-positive rate of 10−3 can be achieved with a completeness of 46 per cent, compared to 34 per cent for the best individual classifier. Given the large number of galaxy–galaxy strong lenses anticipated in LSST, such improvement would still produce significant numbers of false positives, in which case using calibrated probabilities will be essential for population analysis of large populations of lenses and to help prioritize candidates for follow-up. [ABSTRACT FROM AUTHOR]

We present a novel methodology for exploring local features directly in the primordial power spectrum using a genetic algorithm pipeline coupled with a Boltzmann solver and Cosmic Microwave Background data (CMB). After testing the robustness of our pipeline using mock data, we apply it to the latest CMB data, including Planck 2018 and CamSpec PR4. Our model-independent approach provides an analytical reconstruction of the power spectra that best fits the data, with the unsupervised machine learning algorithm exploring a functional space built off simple 'grammar' functions. We find significant improvements upon the simple power-law behaviour, by Δχ2 ≲ −21, consistently with more traditional model-based approaches. These best-fits always address both the low-ℓ anomaly in the TT spectrum and the residual high-ℓ oscillations in the TT, TE, and EE spectra. The proposed pipeline provides an adaptable tool for exploring features in the primordial power spectrum in a model-independent way, providing valuable hints to theorists for constructing viable inflationary models that are consistent with the current and upcoming CMB surveys. [ABSTRACT FROM AUTHOR]

Context. The variety and complexity of long duration gamma-ray burst (LGRB) light curves (LCs) encode a wealth of information about the way LGRB engines release their energy following the collapse of the progenitor massive star. Thus far, attempts to characterise GRB LCs have focused on a number of properties, such as the minimum variability timescale and power density spectra (both ensemble average and individual), or considering different definitions of variability. In parallel, a characterisation as a stochastic process has been pursued by studying the distributions of waiting times, peak flux, and fluence of individual peaks that can be identified within GRB time profiles. However, an important question remains as to whether the diversity of GRB profiles can be described in terms of a common stochastic process. Aims. Here, we address this issue by extracting and modelling, for the first time, the distribution of the number of peaks within a GRB profile. Methods. We analysed four different GRB catalogues: CGRO/BATSE, Swift/BAT, BeppoSAX/GRBM, and Insight-HXMT. The statistically significant peaks were identified by means of well tested and calibrated algorithm MEPSA and further selected by applying a set of thresholds on the signal-to-noise ratio. We then extracted the corresponding distributions of number of peaks per GRB. Results. Among the different models considered (power-law, simple or stretched exponential), we find that only a mixture of two exponentials was able to model all the observed distributions. This suggests the existence of two distinct behaviours: (i) an average number of 2.1 ± 0.1 peaks per GRB ("peak-poor"), accounting for about 80% of the observed population of GRBs; and (ii) an average number of 8.3 ± 1.0 peaks per GRB ("peak-rich"), accounting for the remaining 20% of the observed population. Conclusions. We associate the class of peak-rich GRBs with the presence of sub-second variability, which appears to be surprisingly absent among peak-poor GRBs. The two classes could result from two distinct regimes in which the inner engines of GRBs release their energy or otherwise dissipate that energy as gamma rays. [ABSTRACT FROM AUTHOR]

Context. Eclipsing binary systems provide the opportunity to measure the fundamental parameters of their component stars in a stellar-model-independent way. This makes them ideal candidates for testing and calibrating theories of stellar structure and (tidal) evolution. Large photometric (space) surveys provide a wealth of data for both the discovery and the analysis of these systems. Even without spectroscopic follow-up there is often enough information in their photometric time series to warrant analysis, especially if there is an added value present in the form of intrinsic variability, such as pulsations. Aims. Our goal is to implement and validate a framework for the homogeneous analysis of large numbers of eclipsing binary light curves, such as the numerous high-duty-cycle observations from space missions like TESS. The aim of this framework is to be quick and simple to run and to limit the user's time investment when obtaining, amongst other parameters, orbital eccentricities. Methods. We developed a new and fully automated methodology for the analysis of eclipsing binary light curves with or without additional intrinsic variability. Our method includes a fast iterative pre-whitening procedure that results in a list of extracted sinusoids that is broadly applicable for purposes other than eclipses. After eclipses are identified and measured, orbital and stellar parameters are measured under the assumption of spherical stars of uniform brightness. Results. We tested our methodology in two settings: a set of synthetic light curves with known input and the catalogue of Kepler eclipsing binaries. The synthetic tests show that we can reliably recover the frequencies and amplitudes of the sinusoids included in the signal as well as the input binary parameters, albeit to varying degrees of accuracy. Recovery of the tangential component of eccentricity is the most accurate and precise. Kepler results confirm a robust determination of orbital periods, with 80.5% of periods matching the catalogued ones. We present the eccentricities for this analysis and show that they broadly follow the theoretically expected pattern as a function of the orbital period. Conclusions. Our analysis methodology is shown to be capable of analysing large numbers of eclipsing binary light curves with no user intervention, and in doing so provide a basis for a further in-depth analysis of systems of particular interest as well as for statistical analysis at the sample level. Furthermore, the computational performance of the frequency analysis, extracting hundreds of sinusoids from Kepler light curves in a few hours, demonstrates its value as a tool for a field like asteroseismology. [ABSTRACT FROM AUTHOR]

Context. Galaxy clusters are an essential tool to understand and constrain the cosmological parameters of our universe. Thanks to its multi-band design, J-PAS offers a unique group and cluster detection window using precise photometric redshifts and sufficient depths. Aims. We produced galaxy cluster catalogues from miniJPAS, which is a pathfinder survey for the wider J-PAS survey, using the PZWav algorithm. Methods. Relying only on photometric information, we provide optical mass tracers for the identified clusters, including richness, optical luminosity, and stellar mass. By reanalysing the Chandra mosaic of the AEGIS field, alongside the overlapping XMM-Newton observations, we produced an X-ray catalogue. Results. The analysis revealed the possible presence of structures with masses of 4 × 1013 M⊙ at redshift 0.75, highlighting the depth of the survey. Comparing results with those from two other cluster catalogues provided by AMICO and VT, we found 43 common clusters with cluster centre offsets of 100 ± 60 kpc and redshift differences below 0.001. We provide a comparison of the cluster catalogues with a catalogue of massive galaxies and report on the significance of cluster selection. In general, we were able to recover approximately 75% of the galaxies with M⋆ > 2 × 1011 M⊙. Conclusions. This study emphasises the potential of the J-PAS survey and the employed techniques, including down to group scales. [ABSTRACT FROM AUTHOR]

Aims. We investigated the theoretical biases affecting the asteroseismic grid-based estimates of stellar mass, radius, and age in the presence of a mismatch between the heavy element mixture of observed stars and stellar models. Methods. We performed a controlled simulation adopting a stellar effective temperature, [Fe/H], an average large frequency spacing, and a frequency of maximum oscillation power as observational constraints. Synthetic stars were sampled from grids of stellar models computed with different [α/Fe] values from 0.0 to 0.4. The mass, radius, and age of these objects were then estimated by adopting a grid of models with a fixed [α/Fe] value of 0.0. The experiment was repeated assuming different sets of observational uncertainties. In the reference scenario, we adopted an uncertainty of 1.5% in seismic parameters, 50 K in effective temperature, and 0.05 dex in [Fe/H]. A higher uncertainty in the atmospheric constraints was also adopted in order to explore the impact on the precision of the observations of the estimated stellar parameters. Results. Our Monte Carlo experiment showed that estimated parameters are biased up to 3% in mass, 1.5% in radius, and 4% in age when the reference uncertainty scenario was adopted. These values correspond to 45%, 48%, and 16% of the estimated uncertainty in the stellar parameters. These non-negligible biases in mass and radius disappear when adopting larger observational uncertainties because of the possibility of the fitting algorithm exploring a wider range of possible solutions. However, in this scenario, the age is significantly biased by −8%. Finally, we verified that the stellar mass, radius, and age can be estimated with a high accuracy by adopting a grid with the incorrect value of [α/Fe] if the metallicity [Fe/H] of the target is adjusted to match the Z in the fitting grid. In this scenario, the maximum bias in the age was reduced to 1.5%. [ABSTRACT FROM AUTHOR]

Context. ALMA observations show that dusty, distant, massive (M* ≳ 1011 M⊙) galaxies usually have a remarkable star-formation activity, contributing of the order of 25% of the cosmic star-formation rate density at z ≈ 3–5, and up to 30% at z ∼ 7. Nonetheless, they are elusive in classical optical surveys, and current near-IR surveys are able to detect them only in very small sky areas. Since these objects have low space densities, deep and wide surveys are necessary to obtain statistically relevant results about them. Euclid will potentially be capable of delivering the required information, but, given the lack of spectroscopic features at these distances within its bands, it is still unclear if Euclid will be able to identify and characterise these objects. Aims. The goal of this work is to assess the capability of Euclid, together with ancillary optical and near-IR data, to identify these distant, dusty, and massive galaxies based on broadband photometry. Methods. We used a gradient-boosting algorithm to predict both the redshift and spectral type of objects at high z. To perform such an analysis, we made use of simulated photometric observations that mimic the Euclid Deep Survey, derived using the state-of-the-art Spectro-Photometric Realizations of Infrared-selected Targets at all-z (SPRITZ) software. Results. The gradient-boosting algorithm was found to be accurate in predicting both the redshift and spectral type of objects within the simulated Euclid Deep Survey catalogue at z > 2, while drastically decreasing the runtime with respect to spectral-energy-distribution-fitting methods. In particular, we studied the analogue of HIEROs (i.e. sources selected on the basis of a red H − [4.5]> 2.25), combining Euclid and Spitzer data at the depth of the Deep Fields. These sources include the bulk of obscured and massive galaxies in a broad redshift range, 3 < z < 7. We find that the dusty population at 3 ≲ z ≲ 7 is well identified, with a redshift root mean squared error and catastrophic outlier fraction of only 0.55 and 8.5% (HE ≤ 26), respectively. Our findings suggest that with Euclid we will obtain meaningful insights into the impact of massive and dusty galaxies on the cosmic star-formation rate over time. [ABSTRACT FROM AUTHOR]

Context. Sagittarius A* (Sgr A*) exhibits frequent flaring activity across the electromagnetic spectrum. Signatures of an orbiting hot spot have been identified in the polarized millimeter wavelength light curves observed with ALMA in 2017 immediately after an X-ray flare. The nature of these hot spots remains uncertain. Aims. We expanded existing theoretical hot-spot models created to describe the Sgr A* polarized emission at millimeter wavelengths. We sampled the posterior space, identifying best-fitting parameters and characterizing uncertainties. Methods. Using the numerical radiative transfer code ipole, we defined a semi-analytical model describing a ball of plasma orbiting Sgr A*, threaded with a magnetic field and emitting synchrotron radiation. We then explored the posterior space in the Bayesian framework of dynesty. We fit the static background emission separately, using a radiatively inefficient accretion flow model. Results. We considered eight models with a varying level of complexity, distinguished by choices regarding dynamically important cooling, non-Keplerian motion, and magnetic field polarity. All models converge to realizations that fit the data, but one model without cooling, non-Keplerian motion, and magnetic field pointing toward us improved the fit significantly and also matched the observed circular polarization. Conclusions. Our models represent observational data well and allow testing various effects in a systematic manner. From our analysis, we have inferred an inclination of ∼155 − 160 deg, which corroborates previous estimates, a preferred period of ∼90 min, and an orbital radius of 9 − 12.0 gravitational radii. Our non-Keplerian models indicate a preference for an orbital velocity of 0.6–0.9 times the Keplerian value. Last, all our models agree on a high dimensionless spin value (a* >  0.8), but the impact of spin on the corresponding light curves is subdominant with respect to other parameters. [ABSTRACT FROM AUTHOR]

The mysterious nature of the dark sector of the Λ-cold-dark-matter (ΛCDM) model is one of the main motivators behind the study of alternative cosmological models. A central quantity of interest for these models is the matter power spectrum, which quantifies structure formation on various scales and can be cross-validated through theory, simulations, and observations. Here, we present a tool that can be used to create emulators for the non-linear matter power spectrum, and similar global clustering statistics, for models beyond ΛCDM with very little computation effort and without the need for supercomputers. We use fast approximate N-body simulations to emulate the boost, B(k, z) = Pbeyond − ΛCDM(k, z)/PΛCDM(k, z), and then rely on existing high-quality emulators made for ΛCDM to isolate Pbeyond − ΛCDM(k, z). Since both the ΛCDM and beyond-ΛCDM models are simulated in our approach, some of the lack of power on small scales due to the low force-resolution in the simulations is factored out, allowing us to extend the emulator to k ∼ 3 − 5 h Mpc−1 and still maintain good accuracy. In addition, errors from the simulation and emulation process can easily be estimated and factored into the covariance when using the emulator on data. As an example of using the pipeline, we create an emulator for the well-studied f(R) model with massive neutrinos, using approximately 3000 CPU hours of computation time. Provided with the paper is a fully functioning pipeline that generates parameter samples, runs a Boltzmann solver to produce initial conditions, runs the simulations, and then gathers all the data and runs it through a machine learning module to develop the emulator. This tool, named Sesame, can be used by anyone to generate a power spectrum emulator for the cosmological model of their choice. [ABSTRACT FROM AUTHOR]

Context. High-resolution 3D maps of interstellar dust are critical for probing the underlying physics shaping the structure of the interstellar medium, and for foreground correction of astrophysical observations affected by dust. Aims. We aim to construct a new 3D map of the spatial distribution of interstellar dust extinction out to a distance of 1.25 kpc from the Sun. Methods. We leveraged distance and extinction estimates to 54 million nearby stars derived from the Gaia BP/RP spectra. Using the stellar distance and extinction information, we inferred the spatial distribution of dust extinction. We modeled the logarithmic dust extinction with a Gaussian process in a spherical coordinate system via iterative charted refinement and a correlation kernel inferred in previous work. In total, our posterior has over 661 million degrees of freedom. We probed the posterior distribution using the variational inference method MGVI. Results. Our 3D dust map has an angular resolution of up to 14′ (Nside = 256), and we achieve parsec-scale distance resolution, sampling the dust in 516 logarithmically spaced distance bins spanning 69 pc to 1250 pc. We generated 12 samples from the variational posterior of the 3D dust distribution and release the samples alongside the mean 3D dust map and its corresponding uncertainty. Conclusions. Our map resolves the internal structure of hundreds of molecular clouds in the solar neighborhood and will be broadly useful for studies of star formation, Galactic structure, and young stellar populations. It is available for download in a variety of coordinate systems online and can also be queried via the publicly available dustmaps Python package. [ABSTRACT FROM AUTHOR]

The number of discovered exoplanets now exceeds 5500 allowing statistical analyses of planetary systems. Multi-planet systems are mini-laboratories of planet formation and evolution, and analysing their system architectures can help us to constrain the physics of these processes. Recent works have found evidence of significant intrasystem uniformity in planet properties such as radius, mass, and orbital spacing, collectively termed 'peas in a pod' trends. In particular, correlations in radius and mass have been interpreted as implying uniformity in planet bulk density and composition within a system. However, the samples used to assess trends in mass tend to be small and biased. In this paper, we re-evaluate correlations in planet properties in a large sample of systems with at least two planets for which mass and radius have been directly measured, and therefore bulk density can be calculated. Our sample was assembled using the most up-to-date exoplanet catalogue data, and we compute the relevant statistics while using a procedure to 'weight' the data points according to measurement precision. We find a moderate correlation in radius and a weak correlation in the densities of adjacent planets. However, masses of neighbouring planets show no overall correlation in our main sample and a weak correlation among pairs of planets similar in size or pairs restricted to Mp<100 M⊕, Rp<10 R⊕. Similarly, we show that the intrasystem dispersion in radius is typically less than that in mass and density. We identify ranges in stellar host properties that correlate with stronger uniformity in pairs of adjacent planets: low Teff for planet masses, and low metallicity and old age for planet densities. Furthermore, we explore whether peas in a pod trends extend into planet compositions or interior structures. For small neighbouring planets with similar radii, we show that their masses and interior structures are often disparate, indicating that even within the same system, similarity in radii is not necessarily a good proxy for similarity in composition or the physical nature of the planets. [ABSTRACT FROM AUTHOR]

Optical phase curves of hot Jupiters can reveal global scattering properties. We implemented a Bayesian inference framework for optical phase curves with flux contributions from: reflected light from a potentially inhomogeneous atmosphere, thermal emission, ellipsoidal variations, Doppler beaming, and stellar rotation via a Gaussian process in the time domain. We probed for atmospheric homogeneity and time variability using the reflected light inferences for highly precise Kepler light curves of five hot Jupiters. We also investigated the scattering properties that constrain the most likely condensates in the inhomogeneous atmospheres. Cross validation prefers inhomogeneous albedo distributions for Kepler-7 b and Kepler-41 b, and a weak preference for inhomogeneity for KOI-13 b. None of the five planets exhibit significant variations in geometric albedo on 1-yr timescales, in agreement with theoretical expectations. We show that analytic reflected light phase curves with isotropic multiple scattering are in excellent agreement with full Rayleigh multiple scattering calculations, allowing for accelerated and analytic inference. In a case study of Kepler-41 b, we identified perovskite, forsterite, and enstatite as possible scattering species consistent with the reflected light phase curves, with condensate particle radii in the range 0.01–0.1 µm. [ABSTRACT FROM AUTHOR]

Ultra-high energy cosmic rays are the most extreme energetic particles detected on Earth, however, their acceleration sites are still mysterious. We explore the contribution of low-luminosity gamma-ray bursts to the ultra-high energy cosmic ray flux, since they form the bulk of the nearby population. We analyse a representative sample of these bursts detected by BeppoSAX, INTEGRAL , and Swift between 1998–2016, and found that in order to reconcile our theoretical flux with the observed flux, these bursts should accelerate at most 10−13 M⊙ of ultra-high energy cosmic rays. [ABSTRACT FROM AUTHOR]

Radio galaxies exhibit a rich diversity of morphological characteristics, which make their classification into distinct types a complex challenge. To address this challenge effectively, we introduce an innovative approach for radio galaxy classification using COSFIRE filters. These filters possess the ability to adapt to both the shape and orientation of prototype patterns within images. The COSFIRE approach is explainable, learning-free, rotation-tolerant, efficient, and does not require a large training set. To assess the efficacy of our method, we conducted experiments on a benchmark radio galaxy data set comprising of 1180 training samples and 404 test samples. Notably, our approach achieved an average accuracy rate of 93.36 per cent. This achievement outperforms contemporary deep learning models, and it is the best result ever achieved on this data set. Additionally, COSFIRE filters offer better computational performance, ∼20 × fewer operations than the DenseNet-based competing method (when comparing at the same accuracy). Our findings underscore the effectiveness of the COSFIRE filter-based approach in addressing the complexities associated with radio galaxy classification. This research contributes to advancing the field by offering a robust solution that transcends the orientation challenges intrinsic to radio galaxy observations. Our method is versatile in that it is applicable to various image classification approaches. [ABSTRACT FROM AUTHOR]

Black hole low-mass X-ray binaries (BH-LMXBs) are excellent observational laboratories for studying many open questions in accretion physics. However, determining the physical properties of BH-LMXBs necessitates knowing their distances. With the increased discovery rate of BH-LMXBs, many canonical methods cannot produce accurate distance estimates at the desired pace. In this study, we develop a versatile statistical framework to obtain robust distance estimates soon after discovery. Our framework builds on previous methods where the soft spectral state and the soft-to-hard spectral state transitions, typically present in an outbursting BH-LMXB, are used to place constraints on mass and distance. We further develop the traditional framework by incorporating general relativistic corrections, accounting for spectral/physical parameter uncertainties, and employing assumptions grounded in current theoretical and observational knowledge. We tested our framework by analysing a sample of 50 BH-LMXB sources using X-ray spectral data from the Swift /XRT, MAXI /GSC, and RXTE /PCA missions. By modelling their spectra, we applied our framework to 26 sources from the 50. Comparison of our estimated distances to previous distance estimates indicates that our findings are dependable and in agreement with the accurate estimates obtained through parallax and H  i absorption methods. Investigating the accuracy of our constraints, we have found that estimates obtained using both the soft and transition spectral information have a median uncertainty (1σ) of 20 per cent, while estimates obtained using only the soft spectral state spectrum have a median uncertainty (1σ) of around 50 per cent. Furthermore, we have found no instrument-specific biases. [ABSTRACT FROM AUTHOR]

The recent discovery of 1-propanol (CH3CH2CH2OH) in the interstellar medium (ISM) is of tremendous interest since fatty alcohols have been proposed as constituents of proto-cell membranes. Motivated by this discovery, we present the laboratory mid-infrared (MIR) and vacuum ultraviolet (VUV) absorption spectra of 1-propanol ice under astrochemical conditions, mimicking an icy mantle on cold dust in the ISM. Both MIR and VUV spectra were recorded at ultrahigh vacuum of ∼10-9 mbar and at temperatures ranging from 10 K to sublimation. The morphology of the 1-propanol ice deposited at 10 K was amorphous. By warming the ice to temperatures of 140 K and above, with subsequent recording of IR spectra, we observe complete sublimation of 1-propanol molecules from the substrate around 170 K. No amorphous-to-crystalline phase change was observed upon warming to higher temperatures. Additionally, we observe the IR and VUV signatures of 1-propanol ice on the substrate well beyond its melting point (147 K). To the best of our knowledge, this is the first reported observation of a molecular ice staying well beyond its melting point under such conditions. This result shows that the morphology of icy mantles on ISM cold dust grains is more complex than previously thought. Our atomistic molecular dynamics simulations capture the experimental trends and shed light on the microscopic origin of this unusual phase behaviour of 1-propanol. [ABSTRACT FROM AUTHOR]

We present a novel approach to identify galaxy clusters that are undergoing a merger using a deep learning approach. This paper uses massive galaxy clusters spanning 0 ≤ z ≤ 2 from The Three Hundred project, a suite of hydrodynamic resimulations of 324 large galaxy clusters. Mock, idealized Compton- y and X-ray maps were constructed for the sample, capturing them out to a radius of 2 R 200. The idealized nature of these maps mean they do not consider observational effects such as foreground or background astrophysical objects, any spatial resolution limits or restriction on X-ray energy bands. Half of the maps belong to a merging population as defined by a mass increase Δ M/M ≥ 0.75, and the other half serves as a controlled, relaxed population. We employ a convolutional neural network architecture and train the model to classify clusters into one of the groups. A best-performing model was able to correctly distinguish between the two populations with a balanced accuracy (BA) and recall of 0.77, ROC-AUC of 0.85, PR-AUC of 0.55, and F 1 score of 0.53. Using a multichannel model relative to a single-channel model, we obtain a 3 per cent improvement in BA score, and a 6 per cent improvement in F 1 score. We use a saliency interpretation approach to discern the regions most important to each classification decision. By analysing radially binned saliency values we find a preference to utilize regions out to larger distances for mergers with respect to non-mergers, greater than ∼1.2 R 200 and ∼0.7 R 200 for SZ and X-ray, respectively. [ABSTRACT FROM AUTHOR]

GRB 230812B is a bright and relatively nearby (z = 0.36) long gamma-ray burst (GRB) that has generated significant interest in the community and has thus been observed over the entire electromagnetic spectrum. We report over 80 observations in X-ray, ultraviolet, optical, infrared, and submillimetre bands from the GRANDMA (Global Rapid Advanced Network for Multimessenger Addicts) network of observatories and from observational partners. Adding complementary data from the literature, we then derive essential physical parameters associated with the ejecta and external properties (i.e. the geometry and environment) of the GRB and compare with other analyses of this event. We spectroscopically confirm the presence of an associated supernova, SN2023pel, and we derive a photospheric expansion velocity of v ∼ 17 × 103 km s−1. We analyse the photometric data first using empirical fits of the flux and then with full Bayesian inference. We again strongly establish the presence of a supernova in the data, with a maximum (pseudo-)bolometric luminosity of 5.75 × 1042 erg s−1, at |$15.76^{+0.81}_{-1.21}$|  d (in the observer frame) after the trigger, with a half-max time width of 22.0 d. We compare these values with those of SN1998bw, SN2006aj, and SN2013dx. Our best-fitting model favours a very low density environment (⁠|$\log _{10}({n_{\rm ISM}/{\rm cm}^{-3}}) = -2.38^{+1.45}_{-1.60}$|⁠) and small values for the jet's core angle |$\theta _{\rm core} = 1.54^{+1.02}_{-0.81} \ \rm {deg}$| and viewing angle |$\theta _{\rm obs} = 0.76^{+1.29}_{-0.76} \ \rm {deg}$|⁠. GRB 230812B is thus one of the best observed afterglows with a distinctive supernova bump. [ABSTRACT FROM AUTHOR]

We present the first comprehensive halo occupation distribution (HOD) analysis of the Dark Energy Spectroscopic Instrument (DESI) One-Percent Survey luminous red galaxy (LRG) and Quasi Stellar Object (QSO) samples. We constrain the HOD of each sample and test possible HOD extensions by fitting the redshift-space galaxy 2-point correlation functions in 0.15 < r < 32  h −1 Mpc in a set of fiducial redshift bins. We use AbacusSummit cubic boxes at Planck 2018 cosmology as model templates and forward model galaxy clustering with the AbacusHOD package. We achieve good fits with a standard HOD model with velocity bias, and we find no evidence for galaxy assembly bias or satellite profile modulation at the current level of statistical uncertainty. For LRGs in 0.4 < z < 0.6, we infer a satellite fraction of |$f_\mathrm{sat} = 11\pm 1~{y{\ \mathrm{per\,cent}}}$|⁠ , a mean halo mass of |$\log _{10}\overline{M}_h/M_\odot =13.40^{+0.02}_{-0.02}$|⁠ , and a linear bias of |$b_\mathrm{lin} = 1.93_{-0.04}^{+0.06}$|⁠. For LRGs in 0.6 < z < 0.8, we find |$f_\mathrm{sat}=14\pm 1~{{\ \mathrm{per\,cent}}}$|⁠ , |$\log _{10}\overline{M}_h/M_\odot =13.24^{+0.02}_{-0.02}$|⁠ , and |$b_\mathrm{lin}=2.08_{-0.03}^{+0.03}$|⁠. For QSOs, we infer |$f_\mathrm{sat}=3^{+8}_{-2}\mathrm{per\,cent}$|⁠ , |$\log _{10}\overline{M}_h/M_\odot = 12.65^{+0.09}_{-0.04}$|⁠ , and |$b_\mathrm{lin} = 2.63_{-0.26}^{+0.37}$| in redshift range 0.8 < z < 2.1. Using these fits, we generate a large suite of high fidelity galaxy mocks, forming the basis of systematic tests for DESI Y1 cosmological analyses. We also study the redshift-evolution of the DESI LRG sample from z  = 0.4 up to z  = 1.1, revealling significant and interesting trends in mean halo mass, linear bias, and satellite fraction. [ABSTRACT FROM AUTHOR]

In this article, over three thousand meteor spectra recorded in Saitama, Japan, between 2018 and 2021 are analysed. We performed a principal component analysis of the intensity of the sodium and magnesium lines and the iron band of each meteor in combination with the orbital elements and the kinematic parameters such as the geocentric velocity and the initial and final altitudes to determine the relative importance of each of these variables in the composition of a meteor's spectrum. We found that for meteor showers such as GEM, LEO, QUA, and PER, the orbital elements or kinematic parameters are important parameters that determine the intensity of these spectral lines, which could indicate the influence of gravitational and/or non-gravitational forces in distinguishing the composition or variations in the strength of the meteoroid material in each meteor shower. The normal spectral type in our sample ranges from 39.5 per cent to 96.8 per cent, corresponding to GEM and PER, respectively. The COM meteors have a similar sodium content to the components of the LEO and PER showers, suggesting a comet as a possible parent body. The sodium and magnesium content of the iron-class meteoroids is the lowest of all our samples, but it is not zero, suggesting that there are no pure iron meteoroids. The sodium content of the GEM meteor shower shows temporal variations in 2021 compared to the 2019 and 2020 observing seasons. We found no correlation between the sodium content and the perihelion distance for other meteor showers. [ABSTRACT FROM AUTHOR]

In this paper, we set out to construct a set of reference mock galaxy redshift surveys (MGRSs) for the future Chinese Space-station Survey Telescope (CSST) observation, where subsequent survey selection effects can be added and evaluated. This set of MGRSs is generated using the dark matter subhaloes extracted from a high-resolution Jiutian N -body simulation of the standard lambda-cold dark matter cosmogony with Ω m  = 0.3111, ΩΛ = 0.6889, and σ8 = 0.8102. The simulation has a box size of 1  h −1Gpc, and consists of 61443 particles with mass resolution |$3.723 \times 10^{8} \:h^{-1}\rm M_\odot$|⁠. In order to take into account the effect of redshift evolution, we first use all 128 snapshots in the Jiutian simulation to generate a light-cone halo/subhalo catalogue. Next, galaxy luminosities are assigned to the main and subhalo populations using the subhalo abundance matching (SHAM) method with the DESI (Dark Energy Spectroscopic Instrument) z -band luminosity functions at different redshifts. Multiband photometries, as well as images, are then assigned to each mock galaxy using a 3D parameter space nearest-neighbour sampling of the DESI LS (Legacy Imaging Survey) observational galaxies and groups. Finally, the CSST and DESI LS survey geometry and magnitude limit cuts are applied to generate the required MGRSs. As we have checked, this set of MGRSs can generally reproduce the observed galaxy luminosity/mass functions within 0.1 dex for galaxies with L > 108   h -2 L⊙ (or M * > 108.5  h -2M⊙) and within 1σ level for galaxies with L < 108   h -2 L⊙ (or M * < 108.5  h -2 M⊙). Together with the CSST slitless spectra and redshifts for our DESI LS seed galaxies that are under construction, we will set out to test various slitless observational selection effects in subsequent probes. [ABSTRACT FROM AUTHOR]

Statistical analysis of samples of the orbits of celestial bodies is complicated by the fact that the Keplerian orbit is a multidimensional object, the coordinate representation of which non-linearly depends on the choice of orbital elements. In this work, using the construction of the Fréchet mean, concepts of mean orbit and dispersion of the orbit family are introduced, consistent with the distance function on the orbit set. The introduced statistical characteristics serve as analogues of sample mean and variance of a one-dimensional random variable. Exact formulas for calculating the elements of mean orbits and dispersion quantities with respect to two metrics on the orbit space are derived. For a large sample of meteoroid orbits from the Geminid stream, numerical simulations of orbit evolution over 20 000 yr in the past were conducted. By analysing the dependency of statistical characteristics on time, estimates for the age of the stream and the gas outflow velocity are obtained under the assumption of the birth of the Geminids due to the rapid destruction of the cometary nucleus. The estimate of the age of the stream lies in the interval from 1200 to 2400 yr, and the speed of gas outflow at perihelion should have been more than 1.2 km s−1. [ABSTRACT FROM AUTHOR]

We present a Wide-band tapered gridded estimator (TGE), which incorporates baseline migration and variation of the primary beam pattern for neutral hydrogen (⁠|$H\, {\small I}$|⁠) 21-cm intensity mapping (IM) with large frequency bandwidth radio-interferometric observations. Here we have analysed |$394-494 \, {\rm MHz}$| (z  = 1.9–2.6) uGMRT data to estimate the Multifrequency Angular Power Spectrum (MAPS) C ℓ(Δν) from which we have removed the foregrounds using the polynomial fitting (PF) and Gaussian Process Regression (GPR) methods developed in our earlier work. Using the residual C ℓ(Δν) to estimate the mean-squared 21-cm brightness temperature fluctuation Δ2(k), we find that this is consistent with 0 ± 2σ in several k bins. The resulting 2σ upper limit |$\Delta ^2(k) \lt (4.68)^2 \, \rm {mK^2}$| at |$k=0.219\, \rm {Mpc^{-1}}$| is nearly 15 times tighter than earlier limits obtained from a smaller bandwidth (⁠|$24.4 \, {\rm MHz}$|⁠) of the same data. The 2σ upper limit |$[\Omega _{H\, {\small I}} b_{H\, {\small I}}] \lt 1.01 \times 10^{-2}$| is within an order of magnitude of the value expected from independent estimates of the |$H\, {\small I}$| mass density |$\Omega _{H\, {\small I}}$| and the |$H\, {\small I}$| bias |$b_{H\, {\small I}}$|⁠. The techniques used here can be applied to other telescopes and frequencies, including |$\sim 150 \, {\rm MHz}$| Epoch of Reionization observations. [ABSTRACT FROM AUTHOR]

Cosmological galaxy formation simulations are powerful tools to understand the complex processes that govern the formation and evolution of galaxies. However, evaluating the realism of these simulations remains a challenge. The two common approaches for evaluating galaxy simulations is either through scaling relations based on a few key physical galaxy properties, or through a set of pre-defined morphological parameters based on galaxy images. This paper proposes a novel image-based method for evaluating the quality of galaxy simulations using unsupervised deep learning anomaly detection techniques. By comparing full galaxy images, our approach can identify and quantify discrepancies between simulated and observed galaxies. As a demonstration, we apply this method to SDSS imaging and NIHAO simulations with different physics models, parameters, and resolution. We further compare the metric of our method to scaling relations as well as morphological parameters. We show that anomaly detection is able to capture similarities and differences between real and simulated objects that scaling relations and morphological parameters are unable to cover, thus indeed providing a new point of view to validate and calibrate cosmological simulations against observed data. [ABSTRACT FROM AUTHOR]