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Context. Photometric redshifts for galaxies hosting an accreting supermassive black hole in their center, known as active galactic nuclei (AGNs), are notoriously challenging. At present, they are most optimally computed via spectral energy distribution (SED) fittings, assuming that deep photometry for many wavelengths is available. However, for AGNs detected from all-sky surveys, the photometry is limited and provided by a range of instruments and studies. This makes the task of homogenizing the data challenging, presenting a dramatic drawback for the millions of AGNs that wide surveys such as SRG/eROSITA are poised to detect. Aims. This work aims to compute reliable photometric redshifts for X-ray-detected AGNs using only one dataset that covers a large area: The tenth data release of the Imaging Legacy Survey (LS10) for DESI. LS10 provides deep grizW1-W4 forced photometry within various apertures over the footprint of the eROSITA-DE survey, which avoids issues related to the cross-calibration of surveys. Methods. We present the results from CIRCLEZ, a machine-learning algorithm based on a fully connected neural network. CIRCLEZ is built on a training sample of 14 000 X-ray-detected AGNs and utilizes multi-Aperture photometry, mapping the light distribution of the sources. Results. The accuracy (ÏÃ Â NMAD) and the fraction of outliers (η) reached in a test sample of 2913 AGNs are equal to 0.067 and 11.6%, respectively. The results are comparable to (or even better than) what was previously obtained for the same field, but with much less effort in this instance. We further tested the stability of the results by computing the photometric redshifts for the sources detected in CSC2 and Chandra-COSMOS Legacy, reaching a comparable accuracy as in eFEDS when limiting the magnitude of the counterparts to the depth of LS10. Conclusions. The method can be applied to fainter samples of AGNs using deeper optical data from future surveys (for example, LSST, Euclid), granting LS10-like information on the light distribution beyond the morphological type. Along with this paper, we have released an updated version of the photometric redshifts (including errors and probability distribution functions) for eROSITA/eFEDS.

ABSTRACT: Periodograms are widely employed for identifying periodicity in time series data, yet they often struggle to accurately quantify the statistical significance of detected periodic signals when the data complexity precludes reliable simulations. We develop a data-driven approach to address this challenge by introducing a null-signal template (NST). The NST is created by carefully randomizing the period of each cycle in the periodogram template, rendering it non-periodic. It has the same frequentist properties as a periodic signal template, and we show with simulations that the distribution of false positives is the same as with the original periodic template, regardless of the underlying data. Thus, performing a periodicity search with the NST acts as an effective simulation of the null (no-signal) hypothesis, without having to simulate the noise properties of the data. We apply the NST method to the supermassive black hole binaries (SMBHB) search in the Palomar Transient Factory (PTF), where Charisi et al. had previously proposed 33 high signal-to-noise candidates utilizing simulations to quantify their significance. Our approach reveals that these simulations do not capture the complexity of the real data. There are no statistically significant periodic signal detections above the non-periodic background. To improve the search sensitivity, we introduce a Gaussian quadrature based algorithm for the Bayes Factor with correlated noise as a test statistic. We show with simulations that this improves sensitivity to true signals by more than an order of magnitude. However, the Bayes Factor approach also results in no statistically significant detections in the PTF data.

Simulations of the dark matter distribution throughout the Universe are essential in order to analyse data from cosmological surveys. N -body simulations are computationally expensive, and many cheaper alternatives (such as lognormal random fields) fail to reproduce accurate statistics of the smaller, non-linear scales. In this work, we present Psi-GAN (power-spectrum-informed generative adversarial network), a machine learning model that takes a two-dimensional lognormal dark matter density field and transforms it into a more realistic field. We construct Psi-GAN so that it is continuously conditional, and can therefore generate realistic realizations of the dark matter density field across a range of cosmologies and redshifts in |$z \in [0, 3]$|⁠. We train Psi-GAN as a generative adversarial network on |$2\, 000$| simulation boxes from the Quijote simulation suite. We use a novel critic architecture that utilizes the power spectrum as the basis for discrimination between real and generated samples. Psi-GAN shows agreement with N -body simulations over a range of redshifts and cosmologies, consistently outperforming the lognormal approximation on all tests of non-linear structure, such as being able to reproduce both the power spectrum up to wavenumbers of |$1~h~\mathrm{Mpc}^{-1}$|⁠ , and the bispectra of target N -body simulations to within |${\sim }5$|  per cent. Our improved ability to model non-linear structure should allow more robust constraints on cosmological parameters when used in techniques such as simulation-based inference. [ABSTRACT FROM AUTHOR]

The primary formation channel for the stellar-mass Binary Black Holes which have been detected merging by the LIGO–Virgo–KAGRA (LVK) collaboration is yet to be discerned. One of the main reason is that the detection of an Electromagnetic counterpart to such Gravitational Wave (GW) events, which could signpost their formation site, has so far been elusive. Recently, 20 Active Galactic Nuclei flaring activities detected by the Zwicky Transient Facility have been investigated as potential counterparts of GW events by Graham et al. We present the results of a spatial correlation analysis that involves such events and uses the up-to-date posterior samples of 78 mergers, detected during the third observing run of the LVK collaboration. We apply a likelihood method which takes into account the exact position of the flares within the 3D sky map of the GW events. We place an upper limit of 0.155 at a 90 per cent credibility level on the fraction of the detected coalescences that are physically related to an observed flare, whose posterior probability distribution peaks at a null value. Finally, we show that the typically larger values of the masses of the GW-events, which host at least one flare in their localization volume, are also consistent with the no-connection hypothesis. This is because of a positive correlation between binary masses and the size of the localization uncertainties. [ABSTRACT FROM AUTHOR]

The Epoch of Reionization (EoR) is a key period of cosmological history in which the intergalactic medium (IGM) underwent a major phase change from being neutral to almost completely ionized. Gamma-ray bursts (GRBs) are luminous and unique probes of their environments that can be used to study the timeline for the progression of the EoR. Here, we present a detailed analysis of the European Southern Observatory Very Large Telescope X-shooter spectrum of GRB 210905A, which resides at a redshift of |$z\sim 6.3$|⁠. We focus on estimating the fraction of neutral hydrogen, |$x_{\rm H \, {\small I}}$|⁠ , on the line of sight to the host galaxy of GRB 210905A by fitting the shape of the Lyman- |$\alpha$| damping wing of the afterglow spectrum. The X-shooter spectrum has a high signal-to-noise ratio, but the complex velocity structure of the host galaxy limits the precision of our conclusions. The statistically preferred model suggests a low neutral fraction with a 3 |$\sigma$| upper limit of |$x_{\rm H \, {\small I}} \lesssim 0.15$| or |$x_{\rm H \, {\small I}} \lesssim 0.23$|⁠ , depending on the absence or presence of an ionized bubble around the GRB host galaxy, indicating that the IGM around the GRB host galaxy is mostly ionized. We discuss complications in current analyses and potential avenues for future studies of the progression of the EoR and its evolution with redshift. [ABSTRACT FROM AUTHOR]

We apply and extend standard tools for void statistics to cosmological simulations that solve Einstein's equations with numerical relativity (NR). We obtain a simulated void catalogue without Newtonian approximations, using a new watershed void finder that operates on fluid-based NR simulations produced with the Einstein Toolkit. We compare and contrast measures of void size and void fraction, and compare radial stacked density profiles to empirically-derived Hamaus–Sutter–Wandelt (HSW) density profiles and profiles based on distance to void boundaries. We recover statistics roughly consistent with Newtonian N -body simulations where such a comparison is meaningful. We study the variation of dynamical spatial curvature and local expansion explicitly demonstrating the spatial fluctuations of these quantities in void regions. We find that voids in our simulations expand ∼10–30 per cent faster than the global average, and the spatial curvature density parameter in the centre of voids reaches ∼60–80 per cent. [ABSTRACT FROM AUTHOR]

We present a neural network emulator to constrain the thermal parameters of the intergalactic medium (IGM) at |$5.4 \le z \le 6.0$| using the Lyman- |$\alpha$| (Ly  |$\alpha$|⁠) forest flux autocorrelation function. Our autodifferentiable JAX -based framework accelerates the surrogate model generation process using approximately 100 sparsely sampled Nyx hydrodynamical simulations with varying combinations of thermal parameters, i.e. the temperature at mean density |$T_0$|⁠ , the slope of the temperature–density relation |$\gamma$|⁠ , and the mean transmission flux |$\langle F \rangle$|⁠. We show that this emulator has a typical accuracy of 1.0 per cent across the specified redshift range. Bayesian inference of the IGM thermal parameters, incorporating emulator uncertainty propagation, is further expedited using NumPyro Hamiltonian Monte Carlo. We compare both the inference results and computational cost of our framework with the traditional nearest-neighbour interpolation approach applied to the same set of mock Ly  |$\alpha$| flux. By examining the credibility contours of the marginalized posteriors for |$T_0, \gamma , \text{and}~\langle F \rangle$| obtained using the emulator, the statistical reliability of measurements is established through inference on 100 realistic mock data sets of the autocorrelation function. [ABSTRACT FROM AUTHOR]

Semi-analytic modelling furnishes an efficient avenue for characterizing dark matter haloes associated with satellites of Milky Way-like systems, as it easily accounts for uncertainties arising from halo-to-halo variance, the orbital disruption of satellites, baryonic feedback, and the stellar-to-halo mass (SMHM) relation. We use the SatGen semi-analytic satellite generator, which incorporates both empirical models of the galaxy–halo connection as well as analytic prescriptions for the orbital evolution of these satellites after accretion onto a host to create large samples of Milky Way-like systems and their satellites. By selecting satellites in the sample that match observed properties of a particular dwarf galaxy, we can infer arbitrary properties of the satellite galaxy within the cold dark matter paradigm. For the Milky Way's classical dwarfs, we provide inferred values (with associated uncertainties) for the maximum circular velocity |$v_\text{max}$| and the radius |$r_\text{max}$| at which it occurs, varying over two choices of baryonic feedback model and two prescriptions for the SMHM relation. While simple empirical scaling relations can recover the median inferred value for |$v_\text{max}$| and |$r_\text{max}$|⁠ , this approach provides realistic correlated uncertainties and aids interpretability. We also demonstrate how the internal properties of a satellite's dark matter profile correlate with its orbit, and we show that it is difficult to reproduce observations of the Fornax dwarf without strong baryonic feedback. The technique developed in this work is flexible in its application of observational data and can leverage arbitrary information about the satellite galaxies to make inferences about their dark matter haloes and population statistics. [ABSTRACT FROM AUTHOR]

We present a catalogue of photometric redshifts for galaxies from Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys, which includes |$\sim 0.18$| billion sources covering 14 000 |$\deg ^2$|⁠. The photometric redshifts, along with their uncertainties, are estimated through galaxy images in three optical bands (g, r , and z) from DESI and two near-infrared bands (⁠|$W1$| and |$W2$|⁠) from Wide-field Infrared Survey Explorer (WISE) using a Bayesian neural network (BNN). The training of BNN is performed by above images and their corresponding spectroscopic redshifts given in DESI Early Data Release (EDR). Our results show that categorizing galaxies into individual groups based on their inherent characteristics and estimating their photo- z s within their group separately can effectively improve the performance. Specifically, the galaxies are categorized into four distinct groups based on DESI's target selection criteria: bright galaxy sample (BGS), luminous red galaxies (LRG), emission-line galaxies (ELG), and a group comprising the remaining sources, referred to as NON. As measured by outliers of |$|\Delta z| \gt 0.15 (1 + z_{\rm true})$|⁠ , accuracy |$\sigma _{\rm NMAD}$|⁠ , and mean uncertainty |$\overline{E}$| for BNN, we achieve low outlier percentage, high accuracy, and low uncertainty: 0.14  per cent, 0.018, and 0.0212 for BGS and 0.45  per cent, 0.026, and 0.0293 for LRG respectively, surpassing results without categorization. However, the photo- z s for ELG cannot be reliably estimated, showing result of |$\gt 15~\,\mathrm{ per\,cent}$|⁠ , |$\sim 0.1$|⁠ , and |$\sim 0.1$| irrespective of training strategy. On the other hand, NON sources can reach 1.9  per cent, 0.039, and 0.0445 when a magnitude cut of |$z\lt 21.3$| is applied. Our findings demonstrate that estimating photo- z s directly from galaxy images is significantly potential, and to achieve high-quality photo- z measurement for ongoing and future large-scale imaging survey, it is sensible to implement categorization of sources based on their characteristics. [ABSTRACT FROM AUTHOR]

We do a careful investigation of the prospects of dark energy (DE) interacting with cold dark matter in alleviating the |$S_8$| clustering tension. To this end, we consider various well-known parametrizations of the DE equation of state (EoS) and consider perturbations in both the dark sectors, along with an interaction term. Moreover, we perform a separate study for the phantom and non-phantom regimes. Using cosmic microwave background (CMB), baryon acoustic oscillations, and Type Ia supernovae data sets, constraints on the model parameters for each case have been obtained and a generic reduction in the |$H_0 \!\!-\!\! \sigma _{8,0}$| correlation has been observed, both for constant and dynamical DE EoS. This reduction, coupled with a significant negative correlation between the interaction term and |$\sigma _{8,0}$|⁠ , contributes to easing the clustering tension by lowering |$\sigma _{8,0}$| to somewhere in between the early CMB and late-time clustering measurements for the phantom regime, for almost all the models under consideration. Additionally, this is achieved without exacerbating the Hubble tension. In this regard, the interacting Chevallier–Polarski–Linder and Jassal–Bagla–Padmanabhan models perform the best in relaxing the |$S_8$| tension to |$<\!\! 1\sigma$|⁠. However, for the non-phantom regime the |$\sigma _{8,0}$| tension tends to have worsened, which reassures the merits of phantom DE from latest data. We further investigate the role of redshift space distortion data sets and find an overall reduction in tension, with a |$\sigma _{8,0}$| value relatively closer to the CMB value. We finally check whether further extensions of this scenario, such as the inclusion of the sound speed of DE and warm dark matter interacting with DE, can have some effects. [ABSTRACT FROM AUTHOR]

Non-Gaussian statistics of the projected weak lensing field are powerful estimators that can outperform the constraining power of the two-point functions in inferring cosmological parameters. This is because these estimators extract the non-Gaussian information contained in the small scales. However, fully leveraging the statistical precision of such estimators is hampered by theoretical uncertainties, such as those arising from baryonic physics. Moreover, as non-Gaussian estimators mix different scales, there exists no natural cut-off scale below which baryonic feedback can be completely removed. We therefore present a Bayesian solution for accounting for baryonic feedback uncertainty in weak lensing non-Gaussianity inference. Our solution implements Bayesian model averaging (BMA), a statistical framework that accounts for model uncertainty and combines the strengths of different models to produce more robust and reliable parameter inferences. We demonstrate the effectiveness of this approach in a Stage IV convergence peak count analysis, including three baryonic feedback models. We find that the resulting BMA posterior distribution safeguards parameter inference against biases due to baryonic feedback, and therefore provides a robust framework for obtaining accurate cosmological constraints at Stage IV precision under model uncertainty scenarios. [ABSTRACT FROM AUTHOR]

A major aim of cosmological surveys is to test deviations from the standard |$\Lambda$| CDM model, but the full scientific value of these surveys will only be realized through efficient simulation methods that keep up with the increasing volume and precision of observational data. N -body simulations of modified gravity (MG) theories are computationally expensive since highly non-linear equations must be solved. This represents a significant bottleneck in the path to reach the data volume and resolution attained by equivalent |$\Lambda$| CDM simulations. We develop a field-level neural network-based emulator that generates density and velocity divergence fields under the |$f(R)$| gravity MG model from the corresponding |$\Lambda$| CDM simulated fields. Using attention mechanisms and a complementary frequency-based loss function, our model is able to learn this intricate mapping. We use the idea of latent space extrapolation to generalize our emulator to |$f(R)$| models with differing field strengths. The predictions of our emulator agree with the |$f(R)$| simulations to within 5 per cent for matter density and to within 10  per cent for velocity divergence power spectra up to |$k \sim 2\, h$| |$\mathrm{Mpc}^{-1}$|⁠. But for a few select cases, higher order statistics are reproduced with |$\lesssim$| 10 per cent agreement. Latent extrapolation allows our emulator to generalize to different parametrizations of the |$f(R)$| model without explicitly training on those variants. Given a |$\Lambda$| CDM simulation, the GPU-based emulator can reproduce the equivalent |$f(R)$| realization |$\sim$| 600 times faster than full N -body simulations. This lays the foundations for a valuable tool for realistic yet rapid mock field generation and robust cosmological analyses. [ABSTRACT FROM AUTHOR]

Planet formation in the Solar system was started when the first planetesimals were formed from the gravitational collapse of pebble clouds. Numerical simulations of this process, especially in the framework of streaming instability, produce various power laws for the initial mass function for planetesimals. While recent advances have shed light on turbulence and its role in particle clustering, a comprehensive theoretical framework linking turbulence characteristics to particle cluster properties and planetesimal mass function remains incomplete. Recently, a kinetic field theory (KFT) for ensembles of point-like classical particles in or out of equilibrium has been applied to cosmic structure formation. This theory encodes the dynamics of a classical particle ensemble by a generating functional specified by the initial probability distribution of particles in phase space and their equations of motion. Here, we apply KFT to planetesimal formation. A model for the initial probability distribution of dust particles in phase space is obtained from a quasi-initial state for a three-dimensional streaming-instability simulation that is a particle distribution with velocities for gas and particles from the Nakagawa relations. The equations of motion are chosen for the simplest case of freely streaming particles. We calculate the non-linearly evolved density power spectrum of dust particles and find that it develops a universal |$k^{-3}$| tail at small scales, suggesting scale-invariant structure formation below a characteristic and time-dependent length-scale. Thus, the KFT analysis indicates that the initial state for streaming instability simulations does not impose a constraint on structure evolution during planetesimal formation. [ABSTRACT FROM AUTHOR]

In an accompanying publication, the MeerKAT Pulsar Timing Array (MPTA) Collaboration reports tentative evidence for the presence of a stochastic gravitational wave background, following observations of similar signals from the European and Indian Pulsar Timing Arrays, the North American Nanohertz Observatory for Gravitational Waves, the Parkes Pulsar Timing Array, and the Chinese Pulsar Timing Array. If such a gravitational wave background signal originates from a population of inspiraling supermassive black hole binaries, the signal may be anisotropically distributed in the sky. In this paper, we evaluate the anisotropy of the MPTA signal using a spherical harmonic decomposition. We discuss complications arising from the covariance between pulsar pairs and the regularization of the Fisher matrix. Applying our method to the |$4.5 \hbox{-}\text{yr}$| data set, we obtain two forms of sky maps for the three most sensitive MPTA frequency bins between |$7 \ {\rm and} \ 21 \, {\rm nHz}$|⁠. Our 'clean maps' estimate the distribution of gravitational wave strain power with minimal assumptions. Our radiometer maps answer the question: Is there a statistically significant point source? We find a noteworthy hotspot in the |$7 \, \mathrm{nHz}$| clean map with a p -factor of |$p=0.015$| (not including trial factors). Future observations are required to determine if this hotspot is of astrophysical origin. [ABSTRACT FROM AUTHOR]

In this paper, we explore the detectability of polycyclic aromatic hydrocarbons (PAHs) under diverse planetary conditions, aiming to identify promising targets for future observations of planetary atmospheres. Our primary goal is to determine the minimum detectable mass fractions of PAHs on each studied planet. We integrate the one-dimensional self-consistent model petitcode with petitradtrans , a radiative transfer model, to simulate the transmission spectra of these planets. Subsequently, we employ the pandexo noise simulator using the NIRSpec PRISM instrument aboard the JWST to assess the observability. Then, we conduct a Bayesian analysis through the multinest code. Our findings illustrate that variations in C/O ratios and planet temperatures significantly influence the transmission spectra and the detectability of PAHs. Our results show that planets with [Fe/H] = 0 and 1, C/O = 0.55, and temperatures around 1200 K are the most promising for detecting PAHs, with detectable mass fractions as low as 10 |$^{-7}$|⁠ , or one thousandth of the interstellar medium abundance level. For colder planets with lower metallicities and C/O ratios, as well as hotter planets with carbon-rich atmospheres, PAHs can be detected at abundances around 10 |$^{-6}$|⁠. These results aid our strategy for selecting targets to study PAHs in the atmospheres of exoplanets. [ABSTRACT FROM AUTHOR]

We present a novel maximum a posteriori estimator to jointly estimate band powers and the covariance of the three-dimensional power spectrum (P3D) of Ly  |$\alpha$| forest flux fluctuations, called MAPLE. Our Wiener-filter based algorithm reconstructs a window-deconvolved P3D in the presence of complex survey geometries typical for Ly  |$\alpha$| surveys that are sparsely sampled transverse to and densely sampled along the line of sight. We demonstrate our method on idealized Gaussian random fields with two selection functions: (i) a sparse sampling of 30 background sources per square degree designed to emulate the current Dark Energy Spectroscopic Instrument; (ii) a dense sampling of 900 background sources per square degree emulating the upcoming Prime Focus Spectrograph Galaxy Evolution Survey. Our proof-of-principle shows promise, especially since the algorithm can be extended to marginalize jointly over nuisance parameters and contaminants, i.e. offsets introduced by continuum fitting. Our code is implemented in JAX and is publicly available on GitHub. [ABSTRACT FROM AUTHOR]

The stellar mass distribution in star-forming regions, stellar clusters and associations, the initial mass function (IMF), appears to be invariant across different star-forming environments, and is consistent with the IMF observed in the Galactic field. Deviations from the field, or standard, IMF, if genuine, would be considered strong evidence for a different set of physics at play during the formation of stars in the birth region in question. We analyse N -body simulations of the evolution of spatially and kinematically substructured star-forming regions to identify the formation of binary star clusters, where two (sub)clusters which form from the same Giant Molecular Cloud orbit a common centre of mass. We then compare the mass distributions of stars in each of the subclusters and compare them to the standard IMF, which we use to draw the stellar masses in the star-forming region from which the binary cluster(s) form. In each binary cluster that forms, the mass distributions of stars in one subcluster deviates from the standard IMF, and drastically so when we apply similar mass resolution limits as for the observed binary clusters. Therefore, if a binary subcluster is observed to have an unusual IMF, this may simply be the result of dynamical evolution, rather than different physical conditions for star formation in these systems. [ABSTRACT FROM AUTHOR]

We apply probabilistic generative modelling of colour–magnitude diagrams to six young Galactic open star clusters and determine their mass functions, binary mass-ratio distributions, and the frequencies of binary stars. We find that younger clusters tend to exhibit a higher incidence of binaries than their older counterparts. The mass-ratio distribution is fairly flat for the clusters, with one exception that exhibits a sharp increase for |$q\gtrsim 0.9$|⁠. The ratio of the number of cluster binaries for which |$q\gt 0.75$| to the number of binaries for which |$q\gt 0.5$| (referred to as |$FQ_{75}$|⁠) ranges from |$\sim 0.4 \,{\text{to}}\, 0.8$|⁠. This metric increases with the binary-star frequency of a cluster but declines with cluster age. This may be due to non-ionizing three-body dynamical processing of a primordial population of close binaries with initial mass ratios, |$q \simeq 1$|⁠. [ABSTRACT FROM AUTHOR]

The latest improvements in the scale and calibration of Type Ia supernova (SN Ia) catalogues allow us to constrain the specific nature and evolution of dark energy through its effect on the expansion history of the Universe. We present the results of Bayesian model comparison on the SNe Ia catalogue Pantheon+, where flat Lambda cold dark matter (⁠|$\Lambda$| CDM) is preferred by the data over all other models and we find moderate evidence (⁠|$\Delta \log \mathcal {Z} \sim 2.5$|⁠) to reject a number of alternate dark energy models. The effect of peculiar velocity corrections on model comparison is analysed, where we find that removing peculiar velocity corrections lessens the |$\Delta \log \mathcal {Z}$| of each cosmology against |$\Lambda$| CDM and overall reduces the strength of |$\Lambda$| CDM in the model comparison. As well as comparing cosmological models, the Bayesian methodology is extended to testing for non-gaussianity in the distribution of the Pantheon+ Hubble residuals. We find that adding a scale parameter to the Pantheon+ covariances, or alternately using a multivariate Student's t -distribution fits the data better than the fiducial analysis, producing a marginalized evidence increase of |$\Delta \log \mathcal {Z} = 1.70$| and 2.70, respectively. This improved treatment of the scatter produces a 5 per cent decrease in the uncertainty of the Hubble constant as compared to SH0ES, finding |$H_0 = 73.67 \pm 0.99$| |${\rm km}\,{\rm s}^{-1}\,{\rm Mpc}^{-1}$| in |$5.6\sigma$| tension with Planck, guided by the evidence when marginalizing over both cosmological and scatter models. We also explore |$M_B$| transition models as a potential solution for the Hubble tension, finding no evidence to support these models among the SN data. [ABSTRACT FROM AUTHOR]

Given the growth in the variety and precision of astronomical data sets of interest for cosmology, the best cosmological constraints are invariably obtained by combining data from different experiments. At the likelihood level, one complication in doing so is the need to marginalize over large-dimensional parameter models describing the data of each experiment. These include both the relatively small number of cosmological parameters of interest and a large number of 'nuisance' parameters. Sampling over the joint parameter space for multiple experiments can thus become a very computationally expensive operation. This can be significantly simplified if one could sample directly from the marginal cosmological posterior distribution of preceding experiments, depending only on the common set of cosmological parameters. We show that this can be achieved by emulating marginal posterior distributions via normalizing flows. The resulting trained normalizing flow models can be used to efficiently combine cosmological constraints from independent data sets without increasing the dimensionality of the parameter space under study. The method is able to accurately describe the posterior distribution of real cosmological data sets, as well as the joint distribution of different data sets, even when significant tension exists between experiments. The resulting joint constraints can be obtained in a fraction of the time it would take to combine the same data sets at the level of their likelihoods. We construct normalizing flow models for a set of public cosmological data sets of general interests and make them available, together with the software used to train them, and to exploit them in cosmological parameter inference. [ABSTRACT FROM AUTHOR]

We present a statistical and multiwavelength photometric study of the young open cluster NGC 6193. To determine cluster membership, we used an unsupervised ensemble-based machine-learning approach and identified 246 cluster members with a search radius of 30 arcmin using |$Gaia$| DR3 astrometry data. From isochrone fitting using asteca , we found that NGC 6193 is a very young open cluster with an age of 4.22 Myr at a distance of 1.295 Kpc having metallicity, z |$\sim$| 0.023 and an extinction of |$A_v$| |$\sim$| 1.452. We analysed the mass function (MF) for NGC 6193 using the Monte Carlo method. The MF is found to be segmented at mass |$\sim$| 0.69 M |$_{\odot }$| and MF slope for the low-mass end, |$\Gamma _\mathit{ B}$|  = 2.09, and for the high-mass end |$\Gamma _\mathit{ A}$|  = −1.09. The young stellar objects (YSOs) have been identified using the |$(J-H)\ \mathrm{ versus}\ (H-K)$| colour–colour diagram and the identified YSOs have masses 0.15 to 6.0 M |$_{\odot }$| and ages |$\le$| 3 Myr. We determined the extinction using the PNICER technique, which is found to be consistent with the dust structure obtained from the 22 |$\mu \mathrm{m}$| dust continuum emissions map of WISE for the cluster region. We found that NGC 6193 is a dynamically young cluster with mass segregation, which may have resulted from either primordial star formation processes or early dynamical evolution. We also performed orbit analysis using galpy and the cluster is found to be within the solar circle. Finally, at the end, we discussed the star formation scenario in the cluster region. [ABSTRACT FROM AUTHOR]

Statistical analyses based on the spatial correlation between the sky maps of gravitational wave (GW) events and the positions of potential host environments are a powerful tool to infer the origin of the black hole binary mergers that have been detected by the LIGO, Virgo, and KAGRA instruments. In this paper, we tighten our previous constraints on the fraction of detected GW events that may have originated from active galactic nuclei (AGNs). We consider 159 mergers detected not later than 1st June, 2024, and the all-sky quasar catalogue Quaia. We increase by a factor of 5.3 and 114, the number of considered GW sources and AGN, respectively, also extending our analysis from redshift 0.3 to 1.5. This is possible thanks to the uniformity of the AGN catalogue and its high level of completeness, which we estimate as a function of redshift and luminosity. We find at a 95 per cent credibility level that un-obscured AGN with a bolometric luminosity higher than |$10^{44.5}\,{\rm erg\ s}^{-1}$| (⁠|$10^{45}\,{\rm erg\ s}^{-1}$|⁠) do not contribute to more than the 21 (11) per cent of the detected GW events. [ABSTRACT FROM AUTHOR]

Polycyclic aromatic hydrocarbons (PAHs) have been detected throughout the Universe where they play essential roles in the evolution of their environments. For example, they are believed to affect atmospheric loss rates of close-in planets and might contribute to the pre-biotic chemistry and emergence of life. Despite their importance, the study of PAHs in exoplanet atmospheres has been limited. We aim to evaluate the possibility of detecting PAHs on exoplanets considering future observations using JWST 's Near-Infrared Spectrograph PRISM mode. The hot Saturn WASP-6 b shows properties that are consistent with a potential PAH presence and is thus used as a case study for this work. Here, we compare the likelihoods of various synthetic haze species and their combinations with the influence of PAHs on the transmission spectrum of WASP-6 b. This is possible by applying the atmospheric retrieval code petitradtrans to a collection of data from previous observations. Subsequently, by exploring synthetic, single transit JWST spectra of this planet that include PAHs, we assess whether these molecules can be detected in the near future. Previous observations support the presence of cloud/haze species in the spectrum of WASP-6 b. While this may include PAHs, the current data do not confirm their existence unambiguously. Our research suggests that utilizing the JWST for future observations could lead to a notable advancement in the study of PAHs. Employing this telescope, we find that a PAH abundance of approximately 0.1 per cent of the interstellar medium value could be robustly detectable. [ABSTRACT FROM AUTHOR]

The latest improvements in the scale and calibration of Type Ia supernova (SN Ia) catalogues allow us to constrain the specific nature and evolution of dark energy through its effect on the expansion history of the Universe. We present the results of Bayesian model comparison on the SNe Ia catalogue Pantheon+, where flat Lambda cold dark matter (⁠|$\Lambda$| CDM) is preferred by the data over all other models and we find moderate evidence (⁠|$\Delta \log \mathcal {Z} \sim 2.5$|⁠) to reject a number of alternate dark energy models. The effect of peculiar velocity corrections on model comparison is analysed, where we find that removing peculiar velocity corrections lessens the |$\Delta \log \mathcal {Z}$| of each cosmology against |$\Lambda$| CDM and overall reduces the strength of |$\Lambda$| CDM in the model comparison. As well as comparing cosmological models, the Bayesian methodology is extended to testing for non-gaussianity in the distribution of the Pantheon+ Hubble residuals. We find that adding a scale parameter to the Pantheon+ covariances, or alternately using a multivariate Student's t -distribution fits the data better than the fiducial analysis, producing a marginalized evidence increase of |$\Delta \log \mathcal {Z} = 1.70$| and 2.70, respectively. This improved treatment of the scatter produces a 5 per cent decrease in the uncertainty of the Hubble constant as compared to SH0ES, finding |$H_0 = 73.67 \pm 0.99$| |${\rm km}\,{\rm s}^{-1}\,{\rm Mpc}^{-1}$| in |$5.6\sigma$| tension with Planck, guided by the evidence when marginalizing over both cosmological and scatter models. We also explore |$M_B$| transition models as a potential solution for the Hubble tension, finding no evidence to support these models among the SN data. [ABSTRACT FROM AUTHOR]

Our current understanding is that an environment – mainly consisting of gas or stars – is required to bring massive black hole binaries (MBHBs) with total redshifted mass |$M_z\sim [10^{4},10^7]~{\rm M}_{\odot }$| to the LISA band from parsec separation. Even in the gravitational wave (GW) dominated final inspiral, realistic environments can non-negligibly speed up or slow down the binary evolution, or leave residual, measurable eccentricity in the LISA band. Despite this fact, most of the literature does not consider environmental effects or orbital eccentricity in modelling GWs from near-equal mass MBHBs. Considering either a circular MBHB embedded in a circumbinary disc or a vacuum eccentric binary, we explore if ignoring either secular gas effects (migration and accretion) or eccentric corrections to the GW waveform can mimic a failure of general relativity (GR). We use inspiral-only aligned-spin 3.5 post-Newtonian (PN) waveforms, a complete LISA response model, and Bayesian inference to perform a parameterized test of GR. For a 4-yr LISA observation of an MBHB with |$M_z=10^{5}~{\rm M}_{\odot }$|⁠ , primary-to-secondary mass ratio |$q=8$|⁠ , and component BHs' dimensionless spins |$\chi _{1,2}=0.9$| at redshift |$z=1$|⁠ , even a moderate gas–disc imprint (Eddington ratio |${\it f}_{\rm Edd}\sim 0.1$|⁠) or low initial eccentricity (⁠|$e_0\sim 10^{-2.5}$|⁠) causes a false violation of GR in several PN orders. However, correctly modelling either effect can mitigate systematics while avoiding significant biases in vacuum circular systems. The adoption of LISA makes it urgent to consider gas imprints and eccentricity in waveform models to ensure accurate inference for MBHBs. [ABSTRACT FROM AUTHOR]

The Muztagh-Ata site, an excellent high-altitude ground-based astronomical observing site was discovered and monitored in the eastern part of the Pamir Plateau in the southwestern Xinjiang Uygur Autonomous Region of China. This site has been systematically monitored using various observational parameters since the spring of 2017. Yet, the site lacks long-term monitoring and statistical characterization of key variables such as: precipitable water vapour (PWV) and air temperature. These factors directly influence whether a site is suitable for hosting large ground-based telescopes across optical, infrared, submillimeter, and millimeter wavelengths in later stages. In this study, we utilized atmospheric reanalysis data sets from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) and ERA5, the fifth edition of the European Centre for Medium-Range Weather Forecasts (ECMWF). These data sets were combined with local observations from the weather station at the Muztagh-Ata site. Following the validation of ground-based and satellite data, we conducted a comparative analysis of PWV and temperature trends at the Muztagh-Ata site over a period of 24 yr. The weighted annual mean nighttime temperature and PWV increase at rates of 0.18 |$\sim$| 0.38 |$^{\circ }$| C decade |$^{-1}$| and 0.02 |$\sim$| 0.15 mm decade |$^{-1}$|⁠ , respectively. The nighttime PWV slightly decrease during the winter with rates of |$-$| 0.01 |$\sim \, -$| 0.03 mm decade |$^{-1}$|⁠. This findings reveal that the PWV and temperature variation patterns at the Muztagh-Ata site are consistently stable, particularly in the results derived from the ERA5 data set. The comprehensive conditions at this site are highly suitable and advantageous for hosting large optical, infrared, submillimeter, and millimeter wavelengths telescope installations. [ABSTRACT FROM AUTHOR]

Characterizing and distinguishing gamma-ray bursts (GRBs) has interested astronomers for many decades. While some authors have found two or three groups of GRBs by analysing only a few parameters, recent work identified five ellipsoidally shaped groups upon considering nine parameters |$T_{50}, T_{90}, F_1, F_2, F_3, F_4, P_{64}, P_{256}, P_{1024}$|⁠. Yet others suggest subclasses within the two or three groups found earlier. Using a mixture model of Gaussian factor analysers, we analysed 1150 GRBs, that had nine parameters observed, from the current Burst and Transient Source Experiment (BATSE) catalogue, and again established five ellipsoidal-shaped groups to describe the GRBs. These five groups are characterized in terms of their average duration, fluence, and spectrum as shorter-faint-hard, long-intermediate-soft, long-intermediate-intermediate, long-bright-intermediate, and short-faint-hard. The use of factor analysers in describing individual group densities allows for a more thorough group-wise characterization of the parameters in terms of a few latent features. However, given the discrepancy with many other existing studies that advocated for two or three groups, we also performed model-estimated overlap-based syncytial clustering that successively merges poorer separated groups. The five ellipsoidal groups merge into three and then into two groups, one with GRBs of low durations and the other having longer duration GRBs. These groups are also characterized in terms of a few latent factors made up of the nine parameters. Our analysis provides context for all three sets of results, and in doing so, details a multilayered characterization of the BATSE GRBs, while also explaining the structure in their variability. [ABSTRACT FROM AUTHOR]

We predict the surface density and clustering bias of H  |$\alpha$| emitting galaxies for the Euclid and Nancy Grace Roman Space Telescope redshift surveys using a new calibration of the galform galaxy formation model. We generate 3000 galform models to train an ensemble of deep learning algorithms to create an emulator. We then use this emulator in a Markov Chain Monte Carlo (MCMC) parameter search of an eleven-dimensional parameter space, to find a best-fitting model to a calibration data set that includes local luminosity function data, and, for the first time, higher redshift data, namely the number counts of H  |$\alpha$| emitters. We discover tensions when exploring fits for the observational data when applying a heuristic weighting scheme in the MCMC framework. We find improved fits to the H  |$\alpha$| number counts while maintaining appropriate predictions for the local universe luminosity function. For a flux limited Euclid -like survey to a depth of |$2\times 10^{-16}~\textrm {erg}^{-1}~\textrm {s}^{-1}~\textrm {cm}^{-2}$| for sources in the redshift range |$0.9< z< 1.8$|⁠ , we estimate 2962–4331 H  |$\alpha$| emission-line sources deg |$^{-2}$|⁠. For a Nancy Grace Roman survey, with a flux limit of |$1\times 10^{-16}~\textrm {erg}^{-1}~\textrm {s}^{-1}~\textrm {cm}^{-2}$| and a redshift range |$1.0< z< 2.0$|⁠ , we predict 6786–10 322 H  |$\alpha$| emission-line sources deg |$^{-2}$|⁠. [ABSTRACT FROM AUTHOR]

Cosmological parameter inference has been dominated by the Bayesian approach for the past two decades, primarily due to its computational efficiency. However, the Bayesian approach involves integration of the posterior probability and therefore depends on both the choice of model parametrization and the choice of prior on the model parameter space. In some cases, this can lead to conclusions that are driven by choice of parametrization and priors rather than by data. The profile likelihood method provides a complementary frequentist tool that can be used to investigate this effect. In this paper, we present the code prospect for computing profile likelihoods in cosmology. We showcase the code using a phenomenological model for converting dark matter into dark radiation that suffers from large volume effects and prior dependence. prospect is compatible with both cobaya and montepython , and is publicly available at https://github.com/AarhusCosmology/prospect_public. [ABSTRACT FROM AUTHOR]

Radio interferometers targeting the 21cm brightness temperature fluctuations at high redshift are subject to systematic effects that operate over a range of different time-scales. These can be isolated by designing appropriate Fourier filters that operate in fringe-rate (FR) space, the Fourier pair of local sidereal time. Applications of FR filtering include separating effects that are correlated with the rotating sky versus those relative to the ground, down-weighting emission in the primary beam sidelobes, and suppressing noise. FR filtering causes the noise contributions to the visibility data to become correlated in time, however, making interpretation of subsequent averaging and error estimation steps more subtle. In this paper, we describe fringe-rate filters that are implemented using discrete prolate spheroidal sequences, and designed for two different purposes–beam sidelobe/horizon suppression (the 'mainlobe' filter), and ground-locked systematics removal (the 'notch' filter). We apply these to simulated data, and study how their properties affect visibilities and power spectra generated from the simulations. Included is an introduction to fringe-rate filtering and a demonstration of fringe-rate filters applied to simple situations to aid understanding. [ABSTRACT FROM AUTHOR]

Recent observations by the Event Horizon Telescope (EHT) of supermassive black holes M87* and Sgr A* offer valuable insights into their space–time properties and astrophysical conditions. Utilizing a library of model images (⁠|$\sim 2$|  million for Sgr A*) generated from general-relativistic magnetohydrodynamic (GRMHD) simulations, limited and coarse insights on key parameters such as black hole spin, magnetic flux, inclination angle, and electron temperature were gained. The image orientation and black hole mass estimates were obtained via a scoring and an approximate rescaling procedure. Lifting such approximations, probing the space of parameters continuously, and extending the parameter space of theoretical models is both desirable and computationally prohibitive with existing methods. To address this, we introduce a new Bayesian scheme that adaptively explores the parameter space of ray-traced, GRMHD models. The general relativistic radiative transfer code IPOLE is integrated with the EHT parameter estimation tool THEMIS. The pipeline produces a ray-traced model image from GRMHD data, computes predictions for very long baseline interferometric (VLBI) observables from the image for a specific VLBI array configuration and compares to data, thereby sampling the likelihood surface via a Markov chain Monte Carlo scheme. At this stage we focus on four parameters: accretion rate, electron thermodynamics, inclination, and source position angle. Our scheme faithfully recovers parameters from simulated VLBI data and accommodates time-variability via an inflated error budget. We highlight the impact of intrinsic variability on model fitting approaches. This work facilitates more informed inferences from GRMHD simulations and enables expansion of the model parameter space in a statistically robust and computationally efficient manner. [ABSTRACT FROM AUTHOR]

As the catalogue of gravitational-wave transients grows, several entries appear 'exceptional' within the population. Tipping the scales with a total mass of |$\sim 150 \,{\rm M}_\odot$|⁠ , GW190521 likely contained black holes in the pair-instability mass gap. The event GW190814, meanwhile, is unusual for its extreme mass ratio and the mass of its secondary component. A growing model-building industry has emerged to provide explanations for such exceptional events, and Bayesian model selection is frequently used to determine the most informative model. However, Bayesian methods can only take us so far. They provide no answer to the question: does our model provide an adequate explanation for exceptional events in the data? If none of the models we are testing provide an adequate explanation, then it is not enough to simply rank our existing models – we need new ones. In this paper, we introduce a method to answer this question with a frequentist p -value. We apply the method to different models that have been suggested to explain the unusually massive event GW190521: hierarchical mergers in active galactic nuclei and globular clusters. We show that some (but not all) of these models provide adequate explanations for exceptionally massive events like GW190521. [ABSTRACT FROM AUTHOR]

We investigate theoretical systematics caused by the application of the halo occupation distribution (HOD) to the study of galaxy clustering at non-linear scales. To do this, we repeat recent cosmological analyses using extended HOD models based on both the Aemulus and Aemulus |$\nu$| simulation suites, allowing for variations in the dark matter halo shape, incompleteness, baryonic effects, and position bias of central galaxies. We fit to the galaxy correlation function including the projected correlation function, redshift-space monopole and quadrupole, and consider how the changes in HOD affect the retrieval of cosmological information. These extensions can be understood as an evaluation of the impact of the secondary bias in the clustering analysis. In the application of BOSS (Baryon Oscillation Spectroscopic Survey) galaxies, these changes do not have a significant impact on the measured linear growth rate. However, we do find weak to mild evidence for some of the effects modelled by the empirical parametrizations adopted. The modelling is able to make the HOD approach more complete in terms of cosmological constraints. We anticipate that the future and better data can provide tighter constraints on the new prescriptions of the HOD model. [ABSTRACT FROM AUTHOR]

Type Ia supernovae (SNe Ia) are standarizable candles whose observed light curves can be used to infer their distances, which can in turn be used in cosmological analyses. As the quantity of observed SNe Ia grows with current and upcoming surveys, increasingly scalable analyses are necessary to take full advantage of these new data sets for precise estimation of cosmological parameters. Bayesian inference methods enable fitting SN Ia light curves with robust uncertainty quantification, but traditional posterior sampling using Markov Chain Monte Carlo (MCMC) is computationally expensive. We present an implementation of variational inference (VI) to accelerate the fitting of SN Ia light curves using the BayeSN hierarchical Bayesian model for time-varying SN Ia spectral energy distributions. We demonstrate and evaluate its performance on both simulated light curves and data from the Foundation Supernova Survey with two different forms of surrogate posterior–a multivariate normal and a custom multivariate zero-lower-truncated normal distribution–and compare them with the Laplace Approximation and full MCMC analysis. To validate of our variational approximation, we calculate the Pareto-smoothed importance sampling diagnostic, and perform variational simulation-based calibration. The VI approximation achieves similar results to MCMC but with an order-of-magnitude speed-up for the inference of the photometric distance moduli. Overall, we show that VI is a promising method for scalable parameter inference that enables analysis of larger data sets for precision cosmology. [ABSTRACT FROM AUTHOR]

Context. The PLAnetary Transits and Oscillations of stars (PLATO) mission will observe the same area of the sky continuously for at least two years in an effort to detect transit signals of an Earth-like planet orbiting a solar-like star. Aims. We aim to study how short-term solar-like variability caused by oscillations and granulation would affect PLATO's ability to detect and size Earth if PLATO were to observe the Solar System itself. We also compare different approaches to mitigate noise caused by short-term solar-like variability and perform realistic transit fitting of transit signals in PLATO-like light curves. Methods. We injected Earth-like transit signals onto real solar data taken by the Helioseismic and Magnetic Imager (HMI) instrument on board the Solar Dynamics Observatory (SDO). We isolated short-term stellar variability in the HMI observations by removing any variability with characteristic timescales longer than five hours using a smooth Savitzky-Golay filter. We then added a noise model for a variety of different stellar magnitudes computed by PlatoSim assuming an observation by all 24 normal cameras. We first compared four different commonly used treatments of correlated noise in the time domain by employing them in a transit fitting scheme. We then tried to recover pairs of transit signals using an algorithm similar to the transit least squares algorithm. Finally, we performed transit fits using realistic priors on planetary and stellar parameters and assessed how accurately the pair of two injected transits was recovered. Results. We find that short-term solar-like variability affects the correct retrieval of Earth-like transit signals in PLATO data. Variability models accounting for variations with typical timescales at the order of one hour are sufficient to mitigate these effects. We find that when the limb-darkening coefficients of the host star are properly constrained, the impact parameter does not negatively affect the detectability of a transit signal or the retrieved transit parameters, except for high values (b > 0.8). For bright targets (8.5–10.5 mag), the transit signal of an Earth analogue can reliably be detected in PLATO data. For faint targets a detection is still likely, though the results of transit search algorithms have to be verified by transit-fitting algorithms to avoid false positive detections being flagged. For bright targets (V-mag ≤ 9.5), the radius of an Earth-like planet orbiting a solar-like star can be correctly determined at a precision of 3% or less, assuming that at least two transit events are observed and the characteristics of the host star are well understood. [ABSTRACT FROM AUTHOR]

In this study, we re-evaluate the baryonic Tully–Fisher relation (BTFR) by analyzing the correlations between maximum rotational velocity and various mass components, including stellar mass, atomic hydrogen (H  i) mass, baryonic mass, and dark matter mass in a sample of 141 disk galaxies from the SPARC (Spitzer Photometry and Accurate Rotation Curves) database, augmented by recent data on stellar and dark matter masses. We apply multiple statistical methods, including Monte Carlo orthogonal distance regression (MCODR), Monte Carlo least-squares (MCLS), and traditional least-squares (LS), to assess the impact of different fitting techniques on the derived scaling relations between the mass components (stellar, H  i , and dark matter) and maximum rotational velocities of these galaxies. We find that the selection of statistical methods significantly influences the derived slopes and intercepts the relation between maximum rotational velocity and mass components. The MCODR method that accounts for errors in both variables consistently produces steeper slopes, suggesting a stronger correlation between stellar mass and rotational dynamics compared to other methods. In contrast, the MCLS method tends to yield flatter slopes, highlighting the sensitivity of this approach to outliers. Despite the variations in slope and intercept across different methods, the fundamental relation between baryonic mass and rotational velocity remains robust. We have also compared dark matter mass derived from different halo models [NFW (Navarro–Frenk–White) versus combined NFW + Dekel–Zhao profiles] and noted that the slope from the NFW profile is slightly steeper than that from the combined profile, highlighting the sensitivity of scaling relations to the selection of halo model. Overall, this study reinforces the robustness of the BTFR across different mass components in disk galaxies while emphasizing the critical role of statistical methods and dark matter profiles in analyzing galactic dynamics. [ABSTRACT FROM AUTHOR]

This paper investigates the spatial structure and tidal tails of the nearby (⁠|$\sim$| 1100 pc) old open cluster NGC 2112 (⁠|$\sim$| 1.2 Gyr) based on reliable cluster members from Gaia-DR3. A robust clustering method is used to calculate membership probabilities for sample stars within |$2.^{\!\!\!\circ }5$| (⁠|$\sim$| 48 pc) of the cluster center. More than 1200 cluster members are identified based on the membership probabilities. We find that the cluster members can be naturally segregated into two distinct components (core and halo) using the Gaussian mixture model. The major and minor axes of the core and halo are determined using principal component analysis (PCA). The lengths of the semi-major axes of the core and halo are estimated to be |$\sim\! 22^{\prime }$| (⁠|$\sim$| 7 pc) and |$\sim 86^{\prime }$| (⁠|$\sim$| 28 pc), respectively. We find that the core and halo have minor to major axis ratios of |$e\sim 0.92$| and 0.75 respectively, indicating that the halo is strongly influenced by the external tidal field. Moreover, we find two |$\sim$| 36 pc long tidal tails around the cluster, nearly aligned with the orbit of the cluster. The tidal tails contain various types of stars, including main-sequence stars, main-sequence turn-off (MSTO) stars, blue straggler (BS) stars, and red giant branch stars. The high-mass tail stars (e.g. MSTO stars, BS stars) could not have formed only via star evaporation driven by two-body relaxation. We estimate that the observed tidal tails could have formed within the last |$\sim$| 30 Myr. [ABSTRACT FROM AUTHOR]

The Core Accretion model is widely accepted as the primary mechanism for forming planets up to a few Jupiter masses. However, the formation of super-massive planets remains a subject of debate, as their formation via the Core Accretion model requires super-solar metallicities. Assuming stellar atmospheric abundances reflect the composition of protoplanetary disks, and that disk mass scales linearly with stellar mass, we calculated the total amount of metals in planet-building materials that could contribute to the formation of massive planets. In this work, we studied a sample of 172 Jupiter-mass planets and 93 planets with masses exceeding 4 M ♃ . Our results consistently demonstrate that planets with masses above 4 M ♃ form in disks with at least as much metal content as those hosting planets with masses between 1 and 4 M ♃ , often with slightly higher metallicity, typically exceeding that of the proto-solar disk. We interpret this as strong evidence that the formation of very massive Jupiters is feasible through Core Accretion and encourage planet formation modelers to test our observational conclusions. [ABSTRACT FROM AUTHOR]

Variations in scaling behaviour in the flux and emissions of gravitational lensed quasars can provide valuable information about the dynamics within the sources and their cosmological evolution with time. Here, we study the multifractal behaviour of the light curves (LCs) of 14 lensed quasars with multiple images in the r band, with redshift ranging from 0.657 to 2.730, in the search for potential differences in non-linearity between the signals of the quasar multiple images. Among these lensed systems, nine present two images, two present three images, and three present four images. To this end, we apply the wavelet transform-based multifractal analysis formalism called wavelet transform modulus maxima. We identify strong multifractal signatures in the LCs of the images of all analysed lensed quasar systems, independently of the number of images, with a significant difference between the degree of multifractality of all the images and combinations. We have also searched for a possible connection between the degree of multifractality and the characteristic parameters related to the quasar source and the lensing galaxy. These parameters include the Einstein ring radius and the accretion disc size and the characteristic time-scales related to microlensing variability. The analysis reveals some apparent trends, pointing to a decrease in the degree of multifractality with the increase of the quasar's source size and time-scale. Using a larger sample and following a similar approach, this study confirms a previous finding for the quasar Q0957 + 561. [ABSTRACT FROM AUTHOR]

Accurate estimation of photometric redshifts (photo- z s) is crucial for cosmological surveys. Various methods have been developed for this purpose, such as template fitting methods and machine learning techniques, each with its own applications, advantages, and limitations. In this study, we propose a new approach that utilizes a deep learning model based on recurrent neural networks with long short-term memory (LSTM) to predict photo- z. Unlike many existing machine learning models, our method requires only flux measurements from different observed filters as input. The model can automatically learn the complex relationships between the flux data across different wavelengths, eliminating the need for manually extracted or derived input features, thereby providing precise photo- z estimates. The effectiveness of our proposed model is evaluated using simulated data from the Chinese Space Station Telescope (CSST) sourced from the Hubble Space Telescope Advanced Camera for Surveys (HST-ACS) and the COSMOS catalogue, considering anticipated instrument effects of the future CSST. Results from experiments demonstrate that our LSTM model, compared to commonly used template fitting and machine learning approaches, requires minimal input parameters and achieves high precision in photo- z estimation. For instance, when trained on the same data set and provided only with photometric fluxes as input features, the proposed LSTM model yields one-third of the outliers |$f_{\mathrm{out}}$| observed with a multi-layer perceptron neural network (MLP) model, while the normalized median absolute deviation |$\rm \sigma _{NMAD}$| is only two-thirds that of the MLP model. This study presents a novel approach to accurately estimate photo- z s of galaxies using photometric data from large-scale survey projects. [ABSTRACT FROM AUTHOR]

Analysing next-generation cosmological data requires balancing accurate modelling of non-linear gravitational structure formation and computational demands. We propose a solution by introducing a machine learning-based field-level emulator, within the Hamiltonian Monte Carlo-based Bayesian Origin Reconstruction from Galaxies (BORG) inference algorithm. Built on a V-net neural network architecture, the emulator enhances the predictions by first-order Lagrangian perturbation theory to be accurately aligned with full N -body simulations while significantly reducing evaluation time. We test its incorporation in BORG for sampling cosmic initial conditions using mock data based on non-linear large-scale structures from N -body simulations and Gaussian noise. The method efficiently and accurately explores the high-dimensional parameter space of initial conditions, fully extracting the cross-correlation information of the data field binned at a resolution of |$1.95\,h^{-1}$|  Mpc. Percent-level agreement with the ground truth in the power spectrum and bispectrum is achieved up to the Nyquist frequency |$k_\mathrm{N} \approx 2.79h \,\, \mathrm{Mpc}^{-1}$|⁠. Posterior resimulations – using the inferred initial conditions for N -body simulations – show that the recovery of information in the initial conditions is sufficient to accurately reproduce halo properties. In particular, we show highly accurate |$M_{200\mathrm{c}}$| halo mass function and stacked density profiles of haloes in different mass bins |$[0.853,16]\times 10^{14}\,{\rm M}_{\odot }\,h^{-1}$|⁠. As all available cross-correlation information is extracted, we acknowledge that limitations in recovering the initial conditions stem from the noise level and data grid resolution. This is promising as it underscores the significance of accurate non-linear modelling, indicating the potential for extracting additional information at smaller scales. [ABSTRACT FROM AUTHOR]

Stellar active regions like spots and faculae can distort the shapes of spectral lines, inducing variations in the radial velocities that are often orders of magnitude larger than the signals from Earth-like planets. Efforts to mitigate these activity signals have hitherto focused on either the time or the velocity (wavelength) domains. We present a physics-driven Gaussian process (GP) framework to model activity signals directly in time series of line profiles or cross-correlation functions (CCFs). Unlike existing methods that correct activity signals in line profile time series, our approach exploits the time correlation between velocity (wavelength) bins in the line profile variations, and is based on a simplified but physically motivated model for the origin of these variations. When tested on both synthetic and real data sets with signal-to-noise ratios down to ∼100, our method was able to separate the planetary signal from the activity signal, even when their periods were identical. We also conducted injection/recovery tests using two years of realistically sampled HARPS-N solar data, demonstrating the ability of the method to accurately recover a signal induced by a 1.5-Earth mass planet with a semi-amplitude of 0.3 m s−1 and a period of 33 d during high solar activity. [ABSTRACT FROM AUTHOR]

In this paper, we examine how stellar flare activity varies with evolution stage, rotation period, and spectral type. To do this, we examine the distributions of the flare equivalent duration on a logarithmic scale, which we consider as an indicator of the maximum energy level that a star can reach in white-light flares. We conduct these analyses using two distinct statistical models of data obtained from white-light flare patrols of 33 stars. These models are the one-phase exponential association (OPEA) and cumulative flare frequency models. The results show that the value of the OPEA model parameter Plateau has a linear relationship with the stellar B – V colour index and the rotation period. In addition, it is shown that flare time-scales, and therefore the maximum magnetic loop height that stars can have, vary according to the evolutionary stage of the star. Finally, it is concluded that the cumulative flare frequency parameters of the stars are not as effective as the OPEA parameters at exhibiting these variations. [ABSTRACT FROM AUTHOR]

The study of binary stars in the Galactic halo provides crucial insights into the dynamical history and formation processes of the Milky Way. In this work, we aim to investigate the binary fraction in a sample of accreted and in situ halo stars, focusing on short-period binaries. Utilizing data from Gaia Data Release 3 (DR3), we analysed the radial velocity uncertainty |$\sigma _{\mathrm{RV}}$| distribution of a sample of main-sequence stars. We used a novel Bayesian framework to model the dependence in |$\sigma _{\mathrm{RV}}$| of single and binary systems allowing us to estimate binary fractions F in a sample of bright (⁠|$G_{\mathrm{RVS}}$| < 12) Gaia sources. We selected the samples of in situ and accreted halo stars based on estimating the 6D phase space information and affiliating the stars to the different samples on an action angle versus energy (⁠|$L_{\mathrm{z}}{\!-\!}E$|⁠) diagram. Our results indicate a higher, though not significant, binary fraction in accreted stars compared to the in situ halo sample. We further explore binary fractions using cuts in E and |$L_z$|⁠ , and find a higher binary fraction in both high-energy and prograde orbits that might be explained by differences in metallicity. By cross-matching our Gaia sample with APOGEE DR17 catalogue, we confirm the results of previous studies on higher binary fractions in metal-poor stars and find the fractions of accreted and in situ halo stars consistent with this trend. Our finding provides new insights into binary stars' formation processes and dynamical evolution in the primordial Milky Way Galaxy and its accreted dwarf Galaxies. [ABSTRACT FROM AUTHOR]

Angular and spectral differential imaging is an observational technique of choice to investigate the immediate vicinity of stars. By leveraging the relative angular motion and spectral scaling between on-axis and off-axis sources, post-processing techniques can separate residual star light from light emitted by surrounding objects such as circumstellar discs or point-like objects. This paper introduces a new algorithm that jointly unmixes these components and deconvolves disc images. The proposed algorithm is based on a statistical model of the residual star light, accounting for its spatial and spectral correlations. These correlations are crucial yet remain inadequately modelled by existing reconstruction algorithms. We employ dedicated shrinkage techniques to estimate the large number of parameters of our correlation model in a data-driven fashion. We show that the resulting separable model of the spatial and spectral covariances captures very accurately the star light, enabling its efficient suppression. We apply our method to data sets from the Very Large Telescope/Spectro-Polarimetry High-contrast Exoplanet REsearch instrument and compare its performance with standard algorithms (median subtraction, PCA, PACO). We demonstrate that considering the multiple correlations within the data significantly improves reconstruction quality, resulting in better preservation of both disc morphology and photometry. With its unique joint spectral modelling, the proposed algorithm can reconstruct discs with circular symmetry (e.g. rings, spirals) at intensities one million times fainter than the star, without needing additional reference data sets free from off-axis objects. [ABSTRACT FROM AUTHOR]

Gamma-ray bursts (GRBs) can be studied with their linearly dependent parameters alongside the standard |$T_{90}$| distribution. The generalized linear model (GLM) identifies the number of linear dependences in a two-parameter space. Classically, GRBs are classified into two classes by the presence of bimodality in the histogram of |$T_{90}$|⁠. However, additional classes and different features of GRBs are fascinating topics to explore. In this work, we investigate GRB features in the |$T_{90} {-}T_{50}$| plane using the GLM for three major catalogues: Swift , the Fermi Gamma-ray Burst Monitor (GBM), and the Burst And Transient Source Experiment (BATSE). This study shows five linear features for the Fermi GBM catalogue and four linear features for the BATSE catalogue, directing us towards the possibility of non-Gaussianity in the light curves of GRBs. [ABSTRACT FROM AUTHOR]

Power spectrum estimators are an important tool in efforts to detect the 21 cm brightness temperature fluctuations from neutral hydrogen at early times. An initial detection will likely be statistical in nature, meaning that it will not be possible to make a coherent map of the brightness temperature fluctuations; instead, only their variance will be measured against a background of noise and residual systematic effects. Optimal quadratic estimator-based methods often apply an inverse covariance weighting to the data. However, inaccurate covariance modelling can lead to reduced sensitivity and, in some cases, severe signal loss. We recently proposed a Bayesian method to jointly estimate the 21 cm fluctuations, their power spectrum, and foreground emission. Instead of requiring a fixed a priori estimate of the covariance, we estimate the covariance as part of the inference. Choices of parametrization, particularly of the foregrounds, are subject to model errors and could lead to biases and other ill effects if not properly controlled. In this paper, we investigate the effects of inaccurate foreground models on 21 cm power spectrum recovery. Using simulated visibilities, we find that, even in the most extreme scenarios tested, our approach is capable of recovering 21 cm delay power spectrum estimates consistent with a known input signal for delays |$\gtrsim 300$| ns (⁠|$\sim$| 88 per cent of the available Fourier modes). This is true even when using foreground models derived from modified foreground catalogues containing spatial and spectral perturbations at the quoted level of uncertainty on our foreground catalogues. [ABSTRACT FROM AUTHOR]

Exploiting the Persistent Homology technique and its complementary representations, we examine the footprint of summed neutrino mass (⁠|$M_{\nu }$|⁠) in the various density fields simulated by the publicly available Quijote suite. The evolution of topological features by utilizing the superlevel filtration on three-dimensional density fields at zero redshift, reveals a remarkable benchmark for constraining the cosmological parameters, particularly |$M_{\nu }$| and |$\sigma _8$|⁠. The abundance of independent closed surfaces (voids) compared to the connected components (clusters) and independent loops (filaments), is more sensitive to the presence of |$M_{\nu }$| for |$R=5$| Mpc  |$h^{-1}$| irrespective of whether using the total matter density field (m) or cold dark matter + baryons field (⁠|$\mathrm{ \mathrm{cb}}$|⁠). Reducing the degeneracy between |$M_{\nu }$| and |$\sigma _8$| is achieved via Persistent Homology for the m field but not for the |$\mathrm{cb}$| field. The uncertainty of |$M_{\nu }$| at |$1\sigma$| confidenc interval from the joint analysis of Persistent Homology vectorization for the m and |$\mathrm{cb}$| fields smoothed by |$R=5$| Mpc  |$h^{-1}$| at |$z=0$| reaches 0.0152 and 0.1242 eV, respectively. Noticing the use of the three-dimensional underlying density field at |$z=0$|⁠ , the mentioned uncertainties can be treated as the theoretical lower limits. [ABSTRACT FROM AUTHOR]

We present a statistical, photometric, and spectral energy distribution (SED) analysis to characterize the blue straggler stars (BSSs) populations in the Galactic old open cluster Berkeley 39. Berkeley 39 is a 6.16 Gyr old open cluster located at a distance of 3.99 Kpc. Gaia DR3 astrometry data have been used to estimate the membership probabilities using ensemble-based unsupervised machine learning techniques. We identified 21 BSS candidates on the colour–magnitude diagram, with 19 of them being detected in the Swift /UVOT UVW2 filter. We analysed the radial surface density profile and examined the cluster dynamical states and mass segregation effect. The SEDs of 19 BSSs are constructed using multiwavelength data covering UV to IR wavelengths. A single-component SED is fitted successfully for 14 BSS candidates. We discovered hot companions in five BSS candidates. These hot companions have temperatures of approximately 14 000 to 23 000 K, radii ranging from 0.04 to 0.13 R |$_{\odot }$|⁠ , and luminosities ranging from 0.16 to 2.91 L |$_{\odot }$|⁠. Among these, three are most likely extremely low-mass white dwarfs (WDs) with masses around 0.17 to 0.18 M |$_{\odot }$|⁠ , and two are low-mass WDs with masses around 0.18 to 0.39 M |$_{\odot }$|⁠. This confirms that they are post-mass transfer (Case A or Case B) systems. We also investigated the variable characteristics of BSSs by analysing their light curves using data from TESS. Our analysis confirms that two BSSs identified as eclipsing binaries in Gaia DR3 are indeed eclipsing binaries. Additionally, one of the two eclipsing binary BSSs shows evidence of having hot companions, as indicated by the multiwavelength SEDs. [ABSTRACT FROM AUTHOR]