Search

In this study, the results obtained from the statistical models of flares observed with ground-based telescopes and TESS observations for the UV Ceti-type stars AD Leo and V1005 Ori are presented. The distribution of flare equivalent duration versus total flare duration for the flares of both stars, observed with different telescopes, has been modelled using the one-phase exponential association (OPEA) model. Although no bifurcation was observed in the models derived with the flare data of V1005 Ori, the OPEA model exhibited bifurcation in almost all observation data of AD Leo. While many similar studies have shown that such models derived for a star do not vary over time and exhibit a single distribution in the same observation season, the stratification in AD Leo's distributions and consequently in its model is an unexpected result. [ABSTRACT FROM AUTHOR]

Studies have shown that the morphologies of galaxies are substantially transformed following coalescence after a merger, but post-mergers are notoriously difficult to identify, especially in imaging that is shallow or low resolution. We train convolutional neural networks (CNNs) to identify simulated post-merger galaxies in a range of image qualities, modelled after five real surveys: the Sloan Digital Sky Survey (SDSS), the Dark Energy Camera Legacy Survey (DECaLS), the Canada–France Imaging Survey (CFIS), the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP), and the Legacy Survey of Space and Time (LSST). Holding constant all variables other than imaging quality, we present the performance of the CNNs on reserved test set data for each image quality. The success of CNNs on a given data set is found to be sensitive to both imaging depth and resolution. We find that post-merger recovery generally increases with depth, but that limiting 5 |$\sigma$| point-source depths in excess of |$\sim 25$| mag, similar to what is achieved in CFIS, are only marginally beneficial. Finally, we present the results of a cross-survey inference experiment, and find that CNNs trained on a given image quality can sometimes be applied to different imaging data to good effect. The work presented here therefore represents a useful reference for the application of CNNs for merger searches in both current and future imaging surveys. [ABSTRACT FROM AUTHOR]

We present a new method to distinguish between different states (e.g. high and low, quiescent and flaring) in astronomical sources with count data. The method models the underlying physical process as latent variables following a continuous-space Markov chain that determines the expected Poisson counts in observed light curves in multiple passbands. For the underlying state process, we consider several autoregressive processes, yielding continuous-space hidden Markov models of varying complexity. Under these models, we can infer the state that the object is in at any given time. The continuous state predictions from these models are then dichotomized with the help of a finite mixture model to produce state classifications. We apply these techniques to X-ray data from the active dMe flare star EV Lac, splitting the data into quiescent and flaring states. We find that a first-order vector autoregressive process efficiently separates flaring from quiescence: flaring occurs over 30 per cent–40 per cent of the observation durations, a well-defined persistent quiescent state can be identified, and the flaring state is characterized by higher plasma temperatures and emission measures. [ABSTRACT FROM AUTHOR]

We assess the usefulness of gradient-based samplers, such as the no-U-turn sampler (NUTS), by comparison with traditional Metropolis–Hastings (MH) algorithms, in tomographic |$3\times 2$| point analyses. Specifically, we use the Dark Energy Survey (DES) Year 1 data and a simulated dataset for the Large Synoptic Survey Telescope (LSST) survey as representative examples of these studies, containing a significant number of nuisance parameters (20 and 32, respectively) that affect the performance of rejection-based samplers. To do so, we implement a differentiable forward model using jax-cosmo , and we use it to derive parameter constraints from both data sets using the nuts algorithm implemented in numpyro , and the Metropolis–Hastings algorithm as implemented in cobaya. When quantified in terms of the number of effective number of samples taken per likelihood evaluation, we find a relative efficiency gain of |$\mathcal {O}(10)$| in favour of NUTS. However, this efficiency is reduced to a factor |$\sim 2$| when quantified in terms of computational time, since we find the cost of the gradient computation (needed by nuts) relative to the likelihood to be |$\sim 4.5$| times larger for both experiments. We validate these results making use of analytical multivariate distributions (a multivariate Gaussian and a Rosenbrock distribution) with increasing dimensionality. Based on these results, we conclude that gradient-based samplers such as NUTS can be leveraged to sample high-dimensional parameter spaces in Cosmology, although the efficiency improvement is relatively mild for moderate (⁠|$\mathcal {O}(50)$|⁠) dimension numbers, typical of tomographic large-scale structure analyses. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

We present a detailed analysis of a new iterative density reconstruction algorithm. This algorithm uses a decreasing smoothing scale to better reconstruct the density field in Lagrangian space. We implement this algorithm to run on the quijote simulations, and extend it to (a) include a smoothing kernel that smoothly goes from anisotropic to isotropic, and (b) a variant that does not correct for redshift space distortions. We compare the performance of this algorithm with the standard reconstruction method. Our examinations of the methods include cross-correlation of the reconstructed density field with the linear density field, reconstructed two-point functions, and BAO parameter fitting. We also examine the impact of various parameters, such as smoothing scale, anisotropic smoothing, tracer type/bias, and the inclusion of second order perturbation theory. We find that the two reconstruction algorithms are comparable in most of the areas we examine. In particular, both algorithms give consistent fittings of BAO parameters. The fits are robust over a range of smoothing scales. We find the iterative algorithm is significantly better at removing redshift space distortions. The new algorithm will be a promising method to be employed in the ongoing and future large-scale structure surveys. [ABSTRACT FROM AUTHOR]

Direct imaging of exoplanets is particularly challenging due to the high contrast between the planet and the star luminosities, and their small angular separation. In addition to tailored instrumental facilities implementing adaptive optics and coronagraphy, post-processing methods combining several images recorded in pupil tracking mode are needed to attenuate the nuisances corrupting the signals of interest. Most of these post-processing methods build a model of the nuisances from the target observations themselves, resulting in strongly limited detection sensitivity at short angular separations due to the lack of angular diversity. To address this issue, we propose to build the nuisance model from an archive of multiple observations by leveraging supervised deep learning techniques. The proposed approach casts the detection problem as a reconstruction task and captures the structure of the nuisance from two complementary representations of the data. Unlike methods inspired by reference differential imaging, the proposed model is highly non-linear and does not resort to explicit image-to-image similarity measurements and subtractions. The proposed approach also encompasses statistical modelling of learnable spatial features. The latter is beneficial to improve both the detection sensitivity and the robustness against heterogeneous data. We apply the proposed algorithm to several data sets from the VLT/SPHERE instrument, and demonstrate a superior precision-recall trade-off compared to the PACO algorithm. Interestingly, the gain is especially important when the diversity induced by ADI is the most limited, thus supporting the ability of the proposed approach to learn information across multiple observations. [ABSTRACT FROM AUTHOR]

Tidal features are a key observable prediction of the hierarchical model of galaxy formation and contain a wealth of information about the properties and history of a galaxy. Modern wide-field surveys such as LSST and Euclid will revolutionize the study of tidal features. However, the volume of data will prohibit visual inspection to identify features, thereby motivating a need to develop automated detection methods. This paper presents a visual classification of ∼2000 galaxies from the DECaLS survey into different tidal feature categories: arms, streams, shells , and diffuse. We trained a convolutional neural network (CNN) to reproduce the assigned visual classifications using these labels. Evaluated on a testing set where galaxies with tidal features were outnumbered |$\sim 1:10$|⁠ , our network performed very well and retrieved a median |$98.7\pm 0.3$|⁠ , |$99.1\pm 0.5$|⁠ , |$97.0\pm 0.8$|⁠ , and |$99.4^{+0.2}_{-0.6}$|  per  cent of the actual instances of arm, stream, shell , and diffuse features respectively for just 20 per cent contamination. A modified version that identified galaxies with any feature against those without achieved scores of |$0.981^{+0.001}_{-0.003}$|⁠ , |$0.834^{+0.014}_{-0.026}$|⁠ , |$0.974^{+0.008}_{-0.004}$|⁠ , and |$0.900^{+0.073}_{-0.015}$| for the accuracy, precision, recall, and F1 metrics, respectively. We used a gradient-weighted class activation mapping analysis to highlight important regions on images for a given classification to verify the network was classifying the galaxies correctly. This is the first demonstration of using CNNs to classify tidal features into sub-categories, and it will pave the way for the identification of different categories of tidal features in the vast samples of galaxies that forthcoming wide-field surveys will deliver. [ABSTRACT FROM AUTHOR]

Weak gravitational lensing simulations serve as indispensable tools for obtaining precise cosmological constraints. In particular, it is crucial to address the systematic uncertainties in theoretical predictions, given the rapid increase in galaxy numbers and the reduction in observational noise. Both on-the-fly and post-processing methods for constructing lensing light-cones encounter limitations due to the finite simulated volume, necessitating the replication of the simulation box to encompass the volume to high redshifts. To address this issue, our primary focus lies on investigating and quantifying the impact of box replication on the convergence power spectrum and higher order moments of lensing fields. Subsequently, a univariate model is utilized to estimate the amplitude parameter A by fitting four statistics measured from partial sky light-cones along specific angles, to the averaged result from random directions. The investigation demonstrates that the systematic bias stemming from the box replication phenomenon falls within the bounds of statistical errors for the majority of cases. However, caution should be exercised when considering high-order statistics on a small sky coverage (⁠|${\lesssim} 25~\mathrm{deg^2}$|⁠). For this case, we have developed a code that facilitates the identification of optimal viewing angles for the light-cone construction. This code has been made publicly accessible at https://github.com/czymh/losf. [ABSTRACT FROM AUTHOR]

We show that convolution neural networks (CNNs) trained to find strong gravitational lens systems are biased towards systems with larger Einstein radii and large concentrated sources. This selection function is key to fully realizing the potential of the large samples of strong gravitational lens systems that will be found in upcoming wide-field surveys. In this paper, we use a CNN and three training data sets to quantify the network selection function and its implication for the many scientific applications of strong gravitational lensing. We use CNNs with similar architecture as is commonly found in the literature. The networks preferentially select systems with larger Einstein radii and larger sources with more concentrated source-light distributions. Increasing the detection significance threshold to 12 |$\sigma$| from 8 |$\sigma$| results in 50 per cent of the selected strong lens systems having Einstein radii |$\theta _\mathrm{E}$| |$\ge$| 1.04 arcsec from |$\theta _\mathrm{E}$| |$\ge$| 0.879 arcsec, source radii |$R_S$| |$\ge$| 0.194 arcsec from |$R_S$| |$\ge$| 0.178 arcsec, and source Sérsic indices |$n_{\mathrm{Sc}}^{\mathrm{S}}$| |$\ge$| 2.62 from |$n_{\mathrm{Sc}}^{\mathrm{S}}$| |$\ge$| 2.55. The model trained to find lensed quasars shows a stronger preference for higher lens ellipticities than those trained to find lensed galaxies. The selection function is independent of the slope of the power law of the mass profiles, hence measurements of this quantity will be unaffected. The lens finder selection function reinforces that of the lensing cross-section, and thus we expect our findings to be a general result for all galaxy–galaxy and galaxy–quasar lens finding neural networks. [ABSTRACT FROM AUTHOR]

Data from the JWST have revealed an intriguing population of bright galaxies at high redshifts. In this work, we use extreme-value statistics to calculate the distribution [in ultraviolet (UV) magnitude] of the brightest galaxies in the redshift range |$9 \lesssim z \lesssim 16$|⁠. We combine the generalized extreme-value approach with modelling of the galaxy luminosity function. We obtain predictions of the brightest galaxies for a suite of luminosity functions, including the Schechter and double power-law functions, as well as a model parametrized by the stellar formation efficiency |$f_*$|⁠. We find that the JWST data are broadly consistent with |$f_*$| of |$5\!\!-\!\!10{{\ \rm per\, cent}}$|⁠ , and that the brightest galaxy at |$z\sim 16$| will have |$M_{\rm UV}\approx -23.5^{0.8}_{0.4}$|⁠. If |$f_*$| is dependent on halo mass, we predict |$M_{\rm UV}\approx -22.5^{0.5}_{1.5}$| for such an object. We show that extreme-value statistics not only predict the magnitude of the brightest galaxies at high redshifts, but may also be able to distinguish between models of star formation in high-redshift galaxies. [ABSTRACT FROM AUTHOR]

The north-west side of the disk of M 31 is known as the near side because of the differential reddening of globular clusters found from their photographic photometry. This paper reports a simple geometric model to evaluate the visibility of the effect and its application to published CCD photometry on globular cluster systems of three spiral galaxies, M 31, M 33, and NGC 253. The color difference of globular cluster systems due to differential reddening was confirmed for M 31 and NGC 253; however, the data for M 33 were insufficient. The analysis reaffirms the currently adopted interpretation that the side on the minor axis of the galactic disk, where more conspicuous dust features and interstellar reddening are visible, is the nearer side to us and provides an additional basis for using spiral galaxies with identified spiral windings, S-wise or Z-wise, to study the large-scale spin distribution of galaxies in the universe. [ABSTRACT FROM AUTHOR]

We present a novel code, named SIESTA (Statistical matchIng between rEal and Synthetic sTellar popuLations), designed for performing statistical isochrone fitting to colour–magnitude diagrams (CMDs) of single stellar populations by leveraging comparisons between the observed stellar distribution and predictions from synthetic populations, simulated on top of a grid of isochrones. These synthetic populations encompass determinant factors such as the cluster's initial mass function (IMF), the presence of non-resolved binaries, as well as the expected photometric errors, and observational completeness (or the observed luminosity function). Employing Markov Chain Monte Carlo within a Bayesian framework, SIESTA allows for the determination of a cluster's age, metallicity, distance, colour excess, and binary fraction (with masses exceeding a certain ratio). In this study, we rigorously benchmark the SIESTA code utilizing synthetic populations and evaluate its performance against observations from the VISCACHA Survey in the Small Magellanic Cloud, focusing on five star clusters: Lindsay 114, NGC 152, Lindsay 91, Lindsay 113, and NGC 121. These clusters were chosen for their diverse age range, spanning from 0.04 to 10 Gyr. Our findings demonstrate the capability of the SIESTA code to accurately represent the observed CMDs of these clusters. Furthermore, we compare the results obtained with SIESTA to previous characterizations of these clusters, highlighting the consistency between the derived metallicity and spectroscopic determinations from various sources. [ABSTRACT FROM AUTHOR]

We measure the three-dimensional power spectrum (P3D) of the transmitted flux in the Lyman- |$\alpha$| (Ly  |$\alpha$|⁠) forest using the complete extended Baryon Oscillation Spectroscopic Survey data release 16 (eBOSS DR16). This sample consists of |$\sim$| 205 000 quasar spectra in the redshift range |$2\le z \le 4$| at an effective redshift |$z=2.334$|⁠. We propose a pair-count spectral estimator in configuration space, weighting each pair by |$\exp (i\mathbf {k}\cdot \mathbf {r})$|⁠ , for wave vector |$\mathbf {k}$| and pixel pair separation |$\mathbf {r}$|⁠ , effectively measuring the anisotropic power spectrum without the need for fast Fourier transforms. This accounts for the window matrix in a tractable way, avoiding artefacts found in Fourier-transform based power spectrum estimators due to the sparse sampling transverse to the line of sight of Ly  |$\alpha$| skewers. We extensively test our pipeline on two sets of mocks: (i) idealized Gaussian random fields with a sparse sampling of Ly  |$\alpha$| skewers, and (ii) log-normal LyaCoLoRe mocks including realistic noise levels, the eBOSS survey geometry and contaminants. On eBOSS DR16 data, the Kaiser formula with a non-linear correction term obtained from hydrodynamic simulations yields a good fit to the power spectrum data in the range |$(0.02 \le k \le 0.35)\, h\, {\rm Mpc}^{-1}\,$| at the 1–2σ level with a covariance matrix derived from LyaCoLoRe mocks. We demonstrate a promising new approach for full-shape cosmological analyses of Ly  |$\alpha$| forest data from cosmological surveys such as eBOSS, the currently observing Dark Energy Spectroscopic Instrument and future surveys such as the Prime Focus Spectrograph, WEAVE-QSO, and 4MOST. [ABSTRACT FROM AUTHOR]

Basilisk is a novel Bayesian hierarchical method for inferring the galaxy–halo connection, including its scatter, using the kinematics of satellite galaxies extracted from a redshift survey. In this paper, we introduce crucial improvements, such as updated central and satellite selection, advanced modelling of impurities and interlopers, extending the kinematic modelling to fourth order by including the kurtosis of the line-of-sight velocity distribution, and utilizing satellite abundance as additional constraint. This drastically enhances Basilisk 's performance, resulting in an unbiased recovery of the full conditional luminosity function (central and satellite) and with unprecedented precision. After validating Basilisk 's performance using realistic mock data, we apply it to the SDSS-DR7 data. The resulting inferences on the galaxy–halo connection are consistent with, but significantly tighter than, previous constraints from galaxy group catalogues, galaxy clustering, and galaxy–galaxy lensing. Using full projected phase-space information, Basilisk breaks the mass-anisotropy degeneracy, thus providing precise global constraint on the average orbital velocity anisotropy of satellite galaxies across a wide range of halo masses. Satellite orbits are found to be mildly radially anisotropic, in good agreement with the mean anisotropy for subhaloes in dark matter-only simulations. Thus, we establish Basilisk as a powerful tool that is not only more constraining than other methods on similar volumes of data, but crucially, is also insensitive to halo assembly bias which plagues the commonly used techniques like galaxy clustering and galaxy–galaxy lensing. [ABSTRACT FROM AUTHOR]

We measure the clustering of low-redshift Sloan Digital Sky Survey (SDSS) galaxies as a function of stellar mass (⁠|$10.0\lt \log (M_*/\mathrm{M}_\odot)\lt 11.5$|⁠) and specific star formation rate (sSFR) and compare the results to models of the galaxy–halo connection. We find that the autocorrelation functions of central galaxies exhibit little dependence on sSFR, with the well-known stronger clustering of quiescent galaxies mainly attributable to satellites. Because halo assembly history is known to affect distinct halo clustering, this result implies that there is little net correlation between halo assembly history and central galaxy sSFR. However, cross-correlations with satellites are stronger for quiescent centrals than star-forming centrals, consistent with quiescent centrals having more satellites in their haloes at fixed |$M_*$|⁠ , as found in SDSS group catalogues. We model the galaxy–halo connection in an N -body simulation by assigning sSFRs to central galaxies in three different ways. Two of the models depend on halo assembly history (being based on halo accretion rate or concentration), while the third is independent of halo assembly history (being based on peak halo circular velocity, |$V_\text{peak}$|⁠ , a proxy for halo mass). All three models replicate the observed autocorrelations of central galaxies, while only the |$V_\text{peak}$| model reproduces the observed cross-correlations with satellites. This further suggests that the effects of halo assembly history may not be easily seen in autocorrelations of centrals and implies that a more complete understanding of central galaxy clustering may require more than autocorrelations of centrals alone. Additionally, the good agreement with the |$V_\text{peak}$| model supports the idea that quiescent central galaxies reside in more massive haloes than star-forming central galaxies at fixed |$M_*$|⁠. [ABSTRACT FROM AUTHOR]

We reanalyse the recent compilation of distance measurements to M87 by collecting the data from published literature. Different from the traditional statistical methods, based on the principle of minimum information loss, we use a robust most frequent value (MFV) procedure to estimate the distance to M87, irrespective of the Gaussian or non-Gaussian distributions. The MFV-based robust estimate for the M87 distance modulus is given by |$31.09^{+0.04}_{-0.03}$| (statistical) |$^{+0.05}_{-0.07}$| (systematic) mag corresponding to a 68.27 per cent confidence interval, whereas the result of combining the two uncertainties in quadrature is |$31.09^{+0.06}_{-0.08}$|  mag. We also construct the error distributions of M87 distance moduli values according to the weighted mean, median, and MFV, which is non-Gaussian. This demonstrates that the MFV method offers a more accurate and robust estimate of the distance to M87 compared to methods that depend on the unfulfilled assumption of Gaussianity. [ABSTRACT FROM AUTHOR]

Major mergers between galaxies are predicted to fuel their central supermassive black holes (SMBHs), particularly after coalescence. However, determining the prevalence of active galactic nuclei (AGNs) in mergers remains a challenge, because AGN diagnostics are sensitive to details of the central structure (e.g. nuclear gas clouds, geometry, and orientation of a dusty torus) that are partly decoupled from SMBH accretion. X-rays, expected to be ubiquitous among accreting systems, are detectable through non-Compton-thick screens of obscuring material, and thus offer the potential for a more complete assessment of AGNs in mergers. But extant statistical X-ray studies of AGNs in mergers have been limited by either sparse, heterogeneous, or shallow on-sky coverage. We use new X-ray observations from the first SRG/eROSITA all-sky data release to characterize the incidence, luminosity, and observability of AGNs in mergers. Combining machine learning and visual classification, we identify 923 post-mergers in Dark Energy Camera Legacy Survey (DECaLS) imaging and select 4565 interacting galaxy pairs (with separations <120 kpc and mass ratios within 1:10) from the Sloan Digital Sky Survey. We find that galaxies with X-ray AGNs are 2.0 |$\pm$| 0.24 times as likely to be identified as post-mergers compared to non-AGN controls, and that post-mergers are 1.8 |$\pm$| 0.1 times as likely to host an X-ray AGN as non-interacting controls. A multiwavelength census of X-ray, optical, and mid-IR-selected AGNs suggests a picture wherein the underlying AGN fraction increases during pair-phase interactions, that galaxy pairs within ~20 kpc become heavily obscured, and that the obscuration often clears post-coalescence. [ABSTRACT FROM AUTHOR]

We introduce the parity-odd power (POP) spectra, a novel set of observables for probing parity violation in cosmological N -point statistics. POP spectra are derived from composite fields obtained by applying non-linear transformations, involving also gradients, curls, and filtering functions, to a scalar field. This compresses the parity-odd trispectrum into a power spectrum. These new statistics offer several advantages: they are computationally fast to construct, estimating their covariance is less demanding compared to estimating that of the full parity-odd trispectrum, and they are simple to model theoretically. We measure the POP spectra on simulations of a scalar field with a specific parity-odd trispectrum shape. We compare these measurements to semi-analytic theoretical calculations and find agreement. We also explore extensions and generalizations of these parity-odd observables. [ABSTRACT FROM AUTHOR]

The future of time-domain optical astronomy relies on the development of techniques and software capable of handling a rising amount of data and gradually complementing, or replacing if necessary, real observations. Next-generation surveys, like the Large Synoptic Survey Telescope, will open the door to the new era of optical astrophysics, creating, at the same time, a deficiency in spectroscopic data necessary to confirm the nature of each event and to fully recover the parametric space. In this framework, we developed Core collApse Supernovae parameTers estimatOR (CASTOR), a novel software for data analysis. CASTOR combines Gaussian process and other machine-learning techniques to build time-series templates of synthetic spectra and to estimate parameters of core-collapse supernovae (CCSNe) for which only multiband photometry is available. Techniques to build templates are fully data driven and non-parametric through empirical and robust models, and rely on the direct comparison with a training set of 111 CCSNe from the literature. Furthermore, CASTOR employees the real photometric data and the reconstructed synthetic spectra of an event to estimate parameters that belong to the supernova ejecta, to the stellar progenitor and to the event itself, in a rapid and user-friendly framework. In this work, we provide a demonstration of how CASTOR works, studying available data from SN 2015ap and comparing our results with those available in literature. [ABSTRACT FROM AUTHOR]

Context. When retrieving exoplanet atmospheric characteristics from spectroscopic observations, parameter estimation results strongly depend on the chosen forward model. In the era of the James Webb Space Telescope (JWST) and other next-generation facilities, the increased signal-to-noise ratio (S/N), wavelength coverage, and spectral resolution of observations warrant closer investigations into factors that could inadvertently bias the results of these retrievals. Aims. We aim to investigate the impact of utilising multi-point pressure–temperature (p–T) profiles of varying complexity on the retrieval of synthetically generated hot-Jupiter transmission spectra modelled after state-of-the-art observations of the hot Jupiter WASP-39 b with JWST. Methods. We performed homogenised atmospheric retrievals with the TauREx retrieval framework on a sample of synthetically generated transmission spectra, accounting for varying cases of underlying p–T profiles, cloud-top pressures, and expected noise levels. These retrievals are performed using a fixed-pressure multi-point p–T prescription with increasing complexity, ranging from isothermal to an eleven-point profile. We evaluated the performance of the retrievals based on the Bayesian model evidence, and the accuracy of the retrievals was compared to the known input parameters. Results. We find that performing atmospheric retrievals using an isothermal prescription for the pressure–temperature profile consistently results in wrongly retrieved atmospheric parameters when compared to the known input parameters. For an underlying p–T profile with a fully positive lapse rate, we find that a two-point profile is sufficient to retrieve the known atmospheric parameters, while under the presence of an atmospheric temperature inversion, we find that a more complex profile is necessary. Conclusions. Our investigation shows that, for a data quality scenario mirroring state-of-the-art observations of a hot Jupiter with JWST, an isothermal p–T prescription is insufficient to correctly retrieve the known atmospheric parameters. We find a model complexity preference dependent on the underlying pressure–temperature structure, but we argue that a p–T prescription on the complexity level of a four-point profile should be preferred. This represents the overlap between the lowest number of free parameters and the highest model preference in the cases investigated in this work. [ABSTRACT FROM AUTHOR]

Context. Inferring spectral parameters from X-ray data is one of the cornerstones of high-energy astrophysics, and is achieved using software stacks that have been developed over the last 20 years and more. However, as models get more complex and spectra are obtained with higher resolutions, these established software solutions become more feature-heavy, difficult to maintain and less efficient. Aims. We present jaxspec, a Python package for performing this task quickly and robustly in a fully Bayesian framework. Based on the JAX ecosystem, jaxspec allows the generation of differentiable likelihood functions compilable on core or graphical process units (GPUs), enabling the use of robust algorithms for Bayesian inference. Methods. We demonstrate the effectiveness of jaxspec samplers, in particular the no U-turn sampler, using a composite model and comparing what we obtain with the existing frameworks. We also demonstrate its ability to process high-resolution spectroscopy data using original methods by reproducing the results of the Hitomi collaboration on the Perseus cluster, while solving the inference problem using variational inference on a GPU. Results. We obtain identical results when compared to other software and approaches, meaning that jaxspec provides reliable results while being ~10 times faster than existing alternatives. In addition, we show that variational inference can produce convincing results even on high-resolution data in less than 10 minutes on a GPU. Conclusions. With this package, we aim to pursue the goal of opening up X-ray spectroscopy to the existing ecosystem of machine learning and Bayesian inference, enabling researchers to apply new methods to solve increasingly complex problems in the best possible way. Our long-term ambition is the scientific exploitation of the data from the newAthena X-ray Integral Field Unit (X-IFU). [ABSTRACT FROM AUTHOR]

Aims. By adopting the recently empirically derived dependence of α-elements on [α/Fe] instead of the conventionally applied uniform one, we tested the agreement between stellar model predictions and observations for red giant branch (RGB) stars in the APO-K2 catalogue. We particularly focused on the biases in effective temperature scales and on the robustness of age estimations. Methods. We computed a grid of stellar models relying on the empirical scaling of α-elements, investigating the offset in effective temperature ΔT between these models and observations, using univariate analyses for both metallicity [Fe/H] and [α/Fe]. To account for potential confounding factors, we then employed a multivariate generalised additive model to study the dependence of ΔT on [Fe/H], [α/Fe], log g, and stellar mass. Results. The initial analysis revealed a negligible trend of ΔT with [Fe/H], in contrast with previous works in the literature, which adopt a uniform relation between the various α-elements and [α/Fe]. A slight ΔT difference of 25 K was detected between stars with high and low α-enhancement. Our multivariate analysis reveals a dependence of ΔT on both [Fe/H] and [α/Fe], and highlights a significant dependence on stellar mass. This suggests a discrepancy in how effective temperature scales with stellar mass in the models compared to observations. Despite differences in assumed chemical composition, our analysis, through a fortunate cancellation effect, yields ages that are largely consistent with recent studies of the same sample. Notably, our analysis identifies a 6% fraction of stars younger than 4 Ga within the high-α population. However, our analysis of the [C/N] ratio supports the possible origin of these stars as a result of mergers or mass transfer events. [ABSTRACT FROM AUTHOR]

Aims. We compared stellar radii derived from asteroseismic scaling relations with those estimated using two independent surface brightness-colour relations (SBCRs) combined with Gaia DR3 parallaxes. Methods. We cross-matched asteroseismic and astrometric data for over 6400 red giant branch (RGB) and red clump (RC) stars from the APO-K2 catalogue with the TESS Input Catalogue v8.2 to obtain precise V band magnitudes and E(B − V) colour excesses. We then adopted two different SBCRs from the literature to derive stellar radius estimates, denoted as Ra and Rb, respectively. We analysed the ratio of these SBCR-derived radii to the asteroseismic radius estimates, R, provided in the APO-K2 catalogue. Results. Both SBCRs exhibited good agreement with asteroseismic radius estimates. On average, Ra was overestimated by 1.2% with respect to R, while Rb was underestimated by 2.5%. For stars larger than 20 R⊙, SBCR radii are systematically lower than asteroseismic ones. The dispersion in the radius ratio was similar for the two methods (around 10%). The agreement with asteroseismic radii shows a strong dependence on the parallax. The dispersion is halved for stars with a parallax greater than 2.5 mas. In this subsample, Rb showed perfect agreement with R, while Ra remained slightly overestimated, by 3%. A trend with [Fe/H] was found at a level of 4% to 6% per dex. Additionally, a clear trend with asteroseismic mass is found. For stars less massive than about 0.95 M⊙, SBCR radii were significantly higher than asteroseismic ones, by about 6%. This overestimation correlated with the presence of extended helium cores in these stars' structures relative to their envelopes. Furthermore, radius ratios showed a dichotomous behaviour at higher masses, mainly due to the presence of several RC stars with SBCR radii significantly lower with respect to asteroseismology. This behaviour originates from a different response of asteroseismic scaling relations and SBCR to [α/Fe] abundance ratios for massive stars, both in RGB and RC phases, which is reported here for the first time. [ABSTRACT FROM AUTHOR]

Context. At the dawn of the gamma–ray burst (GRB) afterglow era, a Cepheid-like correlation was discovered between the time variability V and the isotropic-equivalent peak luminosity Liso of the prompt emission of about a dozen long GRBs with measured redshift available at that time. Soon afterwards, the correlation was confirmed in a sample of about 30 GRBs, even though it was affected by significant scatter. Unlike the minimum variability timescale (MVT), V measures the relative power of short-to-intermediate timescales. Aims. We aim to test the correlation using about 200 long GRBs with spectroscopically measured redshift, detected by Swift, Fermi, and Konus/WIND, for which both observables can be accurately estimated. Methods. The variability for all selected GRBs was calculated according to the original definition using the 64 ms background-subtracted light curves of Swift/BAT (Fermi/GBM) in the 15–150 (8–900) keV energy passband. Peak luminosities were either taken from the literature or derived from modelling broad-band spectra acquired with either Konus/WIND or Fermi/GBM. Results.The statistical significance of the correlation has weakened to ≲2%, mostly due to the appearance of a number of smooth and luminous GRBs that are characterised by a relatively small V. At odds with most long GRBs, three out of four long-duration merger candidates have high V and low Liso. Conclusions. The luminosity is more tightly connected with shortest timescales measured by MVT than the short to intermediate timescales measured by V. We discuss the implications for internal dissipation models and the role of the e± photosphere. We identified a few smooth GRBs with a single broad pulse and low V that might have an external shock origin, in contrast with most GRBs. The combination of high variability (V ≳ 0.1), low luminosity Liso ≲ 1051 erg s−1, and short MVT (≲0.1 s) could be a good indicator for a compact binary merger origin. [ABSTRACT FROM AUTHOR]

Context. Accurate mock halo catalogues are indispensable data products for developing and validating cosmological inference pipelines. A major challenge in generating mock catalogues is modelling the halo or galaxy bias, which is the mapping from matter density to dark matter halos or observable galaxies. To this end, N-body codes produce state-of-the-art catalogues. However, generating large numbers of these N-body simulations for big volumes, especially if magnetohydrodynamics are included, requires significant computational time. Aims. We introduce and benchmark a differentiable and physics-informed neural network that can generate mock halo catalogues of comparable quality to those obtained from full N-body codes. The model design is computationally efficient for the training procedure and the production of large mock catalogue suites. Methods. We present a neural network, relying only on 18 to 34 trainable parameters, that produces halo catalogues from dark matter overdensity fields. The reduction in network weights was realised through incorporating symmetries motivated by first principles into our model architecture. We trained our model using dark-matter-only N-body simulations across different resolutions, redshifts, and mass bins. We validated the final mock catalogues by comparing them to N-body halo catalogues using different N-point correlation functions. Results. Our model produces mock halo catalogues consistent with the reference simulations, showing that this novel network is a promising way to generate mock data for upcoming wide-field surveys due to its computational efficiency. Moreover, we find that the network can be trained on approximate overdensity fields to reduce the computational cost further. We also present how the trained network parameters can be interpreted to give insights into the physics of structure formation. Finally, we discuss the current limitations of our model as well as more general requirements and pitfalls of approximate halo mock generation that became evident from this study. [ABSTRACT FROM AUTHOR]

We present Gravity.jl, a new proprietary software for the modeling of gravitational lens systems. Gravity.jl is written in the Julia programming language, and is designed to be fast, accurate, and flexible. It can be used to model gravitational lens systems composed of multiple lensing planes, and to perform Bayesian inference on the lens model parameters. In this paper we present the theoretical and statistical ideas behind the code, and we describe its main features. In this first paper of the series, we focus on the modeling of point-like and small extended sources, for which we can linearize the lens equation. We show a practical use of Gravity.jl on a galaxy-scale lens, and we compare the results with those obtained with other codes. We also show how Gravity.jl can be used to perform Bayesian inference on cosmological parameters. [ABSTRACT FROM AUTHOR]

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 (σNMAD) 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 FROM AUTHOR]

While the appeal of their extraordinary radio luminosity to our curiosity is undiminished, the nature of fast radio bursts (FRBs) has remained unclear. The challenge has been due in part to small sample sizes and limited understanding of telescope selection effects. We here present the first inclusion of the entire set of one-off FRBs from CHIME/FRB Catalog 1 in frbpoppy. Where previous work had to curate this data set, and fit for few model parameters, we have developed full multi-dimensional Markov chain Monte Carlo (MCMC) capabilities for frbpoppy – the comprehensive, open-science FRB population synthesis code – that allow us to include all one-off CHIME bursts. Through the combination of these two advances we now find the best description of the real, underlying FRB population, with higher confidence than before. We show that 4 ± 3 × 103 one-off FRBs go off every second between Earth and z = 1; and we provide a mock catalog based on our best model, for straightforward inclusion in other studies. We investigate CHIME side-lobe detection fractions, and FRB luminosity characteristics, to show that some bright, local FRBs are still being missed. We find strong evidence that FRB birth rates evolve with the star formation rate of the Universe, even with a hint of a short (0.1−1 Gyr) delay time. The preferred contribution of the hosts to the FRB dispersion agrees with a progenitor birth location in the host disk. This population-based evidence solidly aligns with magnetar-like burst sources, and we conclude FRBs are emitted by neutron stars. [ABSTRACT FROM AUTHOR]

The formation of massive objects via gravitational collapse is relevant both for explaining the origin of the first supermassive black holes and in the context of massive star formation. Here, we analyze simulations of the formation of massive objects pursued by different groups and in various environments, concerning the formation of supermassive black holes, primordial stars, as well as present-day massive stars. We focus here particularly on the regime of small virial parameters, that is, low ratios of the initial kinetic to gravitational energy, low to moderate Mach numbers, and the phase before feedback is very efficient. We compare the outcomes of collapse under different conditions using dimensionless parameters, particularly the star formation efficiency є*, the fraction ƒ* of mass in the most massive object relative to the total stellar mass, and the fraction ƒtot of mass of the most massive object as a function of the total mass. We find that in all simulations analyzed here, ƒtot increases as a function of є*, although the steepness of the increase depends on the environment. The relation between ƒ* and є* is found to be more complex and also strongly depends on the number of protostars present at the beginning of the simulations. We show that a collision parameter, estimated as the ratio of the system size divided by the typical collision length, allows us to approximately characterize whether collisions are important. A high collision parameter implies a steeper increase in the relation between ƒtot and є*. We analyze the statistical correlation between the dimensionless quantities using the Spearman coefficient and further confirm via a machine learning analysis that good predictions of ƒ* can be obtained from є* together with a rough estimate of the collision parameter. This suggests that a good estimate of the mass of the most massive object can be obtained once the maximum efficiency for a given environment is known and an estimate for the collision parameter has been determined. [ABSTRACT FROM AUTHOR]

Third-generation (3G) gravitational-wave detectors such as the Einstein Telescope (ET) will observe binary black hole (BBH) mergers at redshifts up to z ∼ 100. However, an unequivocal determination of the origin of high-redshift sources will remain uncertain because of the low signal-to-noise ratio (S/N) and poor estimate of their luminosity distance. This study proposes a machine-learning approach to infer the origins of high-redshift BBHs. We specifically differentiate those arising from Population III (Pop. III) stars, which probably are the first progenitors of star-born BBH mergers in the Universe, and those originated from Population I-II (Pop. I–II) stars. We considered a wide range of models that encompass the current uncertainties on Pop. III BBH mergers. We then estimated the parameter errors of the detected sources with ET using the Fisher information-matrix formalism, followed by a classification using XGBOOST, which is a machine-learning algorithm based on decision trees. For a set of mock observed BBHs, we provide the probability that they belong to the Pop. III class while considering the parameter errors of each source. In our fiducial model, we accurately identify ≳10% of the detected BBHs that originate from Pop. III stars with a precision > 90%. Our study demonstrates that machine-learning enables us to achieve some pivotal aspects of the ET science case by exploring the origin of individual high-redshift GW observations. We set the basis for further studies, which will integrate additional simulated populations and account for further uncertainties in the population modeling. [ABSTRACT FROM AUTHOR]

The properties of satellite dwarf galaxies provide important empirical insights for verifying cosmological models on galaxy scales. Their phase-space correlations, in particular, offer interesting insights into a broad range of models, since they are dominated by gravity and are largely independent of the specific dark matter flavor or baryonic processes that are considered. Next to the much-debated planes-of-satellites phenomenon, the lopsided distribution of satellites relative to their host galaxy has been studied observationally and in cosmological simulations. The degree to which observed lopsidedness is consistent with expectations from simulations is still unclear. We quantified the level of lopsidedness in isolated observed satellite systems under six different metrics. We studied 47 systems from the MATLAS survey beyond the local volume (LV) as well as 21 LV satellite systems from the ELVES survey. The satellite systems are complete to an estimated absolute magnitude of M ∼ −9. We find that the so-called wedge metric, counting the number of dwarfs in wedges with varying opening angles, is best suited to capture a system's overall lopsidedness. Under this metric, our analysis reveals that ∼16 percent of the tested satellite systems exhibit a statistically significant degree of lopsidedness when compared to systems with randomly generated satellite position angles. This presents a notable excess over the expected 5% (2σ level) of significantly lopsided systems in a sample with no overall inherent lopsidedness. To gain the most rounded picture, however, a combination of metrics that are sensitive to different features of lopsidedness should be used. Combining all tested metrics, the number of significantly lopsided systems increases to ∼21 percent. Contrary to recent results from the literature, we find more lopsided systems among the red early-type galaxies in the MATLAS survey compared to the mostly blue late-type hosts in ELVES. We further find that satellite galaxies at larger distances from the host, potentially recently accreted, are likely the primary contributors to the reported excess of lopsidedness. Our results set the groundwork that allows a comparison with similar systems in cosmological simulations to assess the consistency with the standard model. [ABSTRACT FROM AUTHOR]

Context. Self-calibration methods with the CLEAN algorithm have been widely employed in very long baseline interferometry (VLBI) data processing in order to correct antenna-based amplitude and phase corruptions present in the data. However, human interaction during the conventional CLEAN self-calibration process can impose a strong effective prior, which in turn may produce artifacts within the final image and hinder the reproducibility of final results. Aims. In this work, we aim to demonstrate a combined self-calibration and imaging method for VLBI data in a Bayesian inference framework. The method corrects for amplitude and phase gains for each antenna and polarization mode by inferring the temporal correlation of the gain solutions. Methods. We use Stokes I data of M87 taken with the Very Long Baseline Array (VLBA) at43 GHz, pre-calibrated using the rPICARD CASA-based pipeline. For antenna-based gain calibration and imaging, we use the Bayesian imaging software resolve. To estimate gain and image uncertainties, we use a variational inference method. Results. We obtain a high-resolution M87 Stokes I image at 43 GHz in conjunction with antenna-based gain solutions using our Bayesian self-calibration and imaging method. The core with counter-jet structure is better resolved, and extended jet emission is better described compared to the CLEAN reconstruction. Furthermore, uncertainty estimation of the image and antenna-based gains allows us to quantify the reliability of the result. Conclusions. Our Bayesian self-calibration and imaging method is able to reconstruct robust and reproducible Stokes I images and gain solutions with uncertainty estimation by taking into account the uncertainty information in the data. [ABSTRACT FROM AUTHOR]

Context. The upcoming Square Kilometer Array (SKA) will set a new standard regarding data volume generated by an astronomical instrument, which is likely to challenge widely adopted data-analysis tools that scale inadequately with the data size. Aims. The aim of this study is to develop a new source detection and characterization method for massive radio astronomical datasets based on modern deep-learning object detection techniques. For this, we seek to identify the specific strengths and weaknesses of this type of approach when applied to astronomical data. Methods. We introduce YOLO-CIANNA, a highly customized deep-learning object detector designed specifically for astronomical datasets. In this paper, we present the method and describe all the elements introduced to address the specific challenges of radio astronomical images. We then demonstrate the capabilities of this method by applying it to simulated 2D continuum images from the SKA observatory Science Data Challenge 1 (SDC1) dataset. Results. Using the SDC1 metric, we improve the challenge-winning score by +139% and the score of the only other post-challenge participation by +61%. Our catalog has a detection purity of 94% while detecting 40–60% more sources than previous top-score results, and exhibits strong characterization accuracy. The trained model can also be forced to reach 99% purity in post-process and still detect 10–30% more sources than the other top-score methods. It is also computationally efficient, with a peak prediction speed of 500 images of 512×512 pixels per second on a single GPU. Conclusions. YOLO-CIANNA achieves state-of-the-art detection and characterization results on the simulated SDC1 dataset and is expected to transfer well to observational data from SKA precursors. [ABSTRACT FROM AUTHOR]

Context. The ages of star clusters and co-moving stellar groups contain essential information about the Milky Way. Their special properties and placement throughout the galactic disk make them excellent tracers of galactic structure and key components to unlocking its star formation history. Yet, even though the importance of stellar population ages has been widely recognized, their determination remains a challenging task often associated with highly model-dependent and uncertain results. Aims. We propose a new approach to this long-standing problem, which relies on empirical isochrones of known clusters extracted from high-quality observational data. These purely observation-based data products open up the possibility of relative age determination, free of stellar evolution model assumptions. Methods. For the derivation of the empirical isochrones, we used a combination of the statistical analysis tool principal component analysis for preprocessing and the supervised machine learning method support vector regression for curve extraction. To improve the statistical reliability of our result, we defined the empirical isochrone of a color-magnitude diagram (CMD) of a cluster as the median calculated from a set of nboot = 1000 curves derived from bootstrapped data. The algorithm requires no physical priors, is computationally fast, and can easily be generalized over a large range of CMD combinations and evolutionary stages of clusters. Results. We provide empirical isochrones in all Gaia DR2 and DR3 color combinations for 83 nearby clusters (d < 500 pc), which cover an estimated age range of 7 Myr to 3 Gyr. In doing so, we pave the way for a relative comparison between individual stellar populations based on an age-scaling ladder of empirical isochrones of known clusters. Furthermore, due to the exceptional precision of the available observational data, we report accurate lower main sequence empirical isochrones for many clusters in our sample, which are of special interest as this region is known to be especially complex to model. We validate our method and results by comparing the extracted empirical isochrones to cluster ages in the literature. We also investigate the added information that empirical isochrones covering the lower main sequence can provide on case studies of the IC 4665 cluster and the Meingast 1 stream. Conclusions. The archive of empirical isochrones offers a novel approach to validating age estimates and can be used as an age-scaling ladder or age brackets for new populations and serve as calibration data for further constraining stellar evolution models. [ABSTRACT FROM AUTHOR]

Context. The estimation of rotation periods of stars is a key challenge in stellar astrophysics. Given the large amount of data available from ground-based and space-based telescopes, there is a growing interest in finding reliable methods to quickly and automatically estimate stellar rotation periods with a high level of accuracy and precision. Aims. This work aims to develop a computationally inexpensive approach, based on machine learning techniques, to accurately predict thousands of stellar rotation periods. Methods. The innovation in our approach is the use of the XGBoost algorithm to predict the rotation periods of Kepler targets by means of regression analysis. Therefore, we focused on building a robust supervised machine learning model to predict surface stellar rotation periods from structured data sets built from the Kepler catalogue of K and M stars. We analysed the set of independent variables extracted from Kepler light curves and investigated the relationships between them and the ground truth. Results. Using the extreme gradient boosting (GB) method, we obtained a minimal set of variables that can be used to build machine learning models for predicting stellar rotation periods. Our models have been validated by predicting the rotation periods of about 2900 stars. The results are compatible with those obtained by classical techniques and comparable to those obtained by other recent machine learning approaches, with the advantage of using fewer predictors. When restricting the analysis to stars with rotation periods of less than 45 d, our models are on average wrong less than 5% of the time. Conclusions. We have developed an innovative approach based on a machine learning method to accurately fit the rotation periods of stars. Based on the results of this study, we conclude that the best models generated by the proposed methodology can compete with the latest state-of-the-art approaches, while offering the advantage of being computationally cheaper, easy to train, and reliant only on small sets of predictors. [ABSTRACT FROM AUTHOR]

The most recent SH0ES measurement of the Hubble constant employs corrections of type Ia supernova magnitudes due to extinction in their host galaxies. These corrections are estimated using a probabilistic model which is trained on Hubble flow (⁠|$z\gt 0.03$|⁠) supernovae and extrapolated to the calibration galaxies (those with observed Cepheids), despite the fact that the latter are selected based on criteria favouring discy and dust-rich systems. We show that this standard approach underestimates the brightness of reddened supernovae in the high stellar-mass (⁠|$M_{\star }\gt 10^{10}\, \mathrm{M}_{\odot }$|⁠) calibration galaxies. This can be traced back to the fact that for these galaxies, a low total-to-selective extinction coefficient (⁠|$R_{\rm B}\sim 3$|⁠) is assumed, while for the low stellar-mass analogues a more standard |$R_{\rm B}\sim 4$| is adopted. We propose a minimalistic modification of the extinction model in the calibration galaxies in order to alleviate this systematic effect. The modification is twofold and it involves: (i) the same, Milky Way-like distribution of |$R_{\rm B}$| (with mean |$R_{\rm B}$| of 4.3 – consistent with the extinction curve used for colour corrections of the Cepheids – and scatter 0.4) and (ii) a modified shape of the |$E(B-V)$| reddening distribution while keeping the same effective slope of the supernova peak magnitude–colour relation and the same mean |$E(B-V)$| reddening as measured for supernovae in the Hubble flow. We show that this new approach yields a significantly better fit (⁠|$\Delta \mathrm{ BIC}=-11$|⁠) to the calibration data and results in a lower value of |$H_{0}$|⁠. Our result is |$H_{0}=70.5\pm 1$|  km s |$^{-1}$|  Mpc |$^{-1}$| implying a reduction of the Hubble constant tension from |$5.2\sigma$| to |$2.8\sigma$|⁠. [ABSTRACT FROM AUTHOR]

We present the High- z Evolution of Large and Luminous Objects (HELLO) project, a set of |$\sim \!30$| high-resolution cosmological simulations aimed to study Milky Way analogues (⁠|$M_\star \sim 10^{10-11}$|   |${\mathrm{M}}_{\odot }$|⁠) at high redshift (⁠|$z\sim [2-4]$|⁠). Based on the numerical investigation of a hundred astrophysical objects, HELLO features an updated scheme for chemical enrichment and the addition of local photoionization feedback. Independently of redshift and mass, our galaxies exhibit a smooth progression along the star formation main sequence until |$M_\star \sim \!10^{10.5}$|⁠ , around which our sample at |$z \sim 4$| remains mostly unperturbed while the most massive galaxies at |$z \sim 2$| reach their peak star formation rate (SFR) and its subsequent decline, due to a mix of gas consumption and stellar feedback. While active galactic nucleus feedback remains subdominant with respect to stellar feedback for energy deposition, its localized nature likely adds to the physical processes leading to declining SFRs. The phase in which a galaxy in our mass range can be found at a given redshift is set by its gas reservoir and assembly history. Finally, our galaxies are in excellent agreement with various scaling relations observed with the Hubble Space Telescope and the JWST , and hence can be used to provide the theoretical framework to interpret current and future observations from these facilities and shed light on the transition from star-forming to quiescent galaxies. [ABSTRACT FROM AUTHOR]

Nested sampling parameter estimation differs from evidence estimation, in that it incurs an additional source of uncertainty. This uncertainty affects estimates of parameter means and credible intervals in gravitational wave analyses and beyond, and yet, it is typically not accounted for in standard uncertainty estimation methods. In this paper, we present two novel methods to quantify this uncertainty more accurately for any chain based nested sampler, using the additional likelihood calls made at run time in producing independent samples. Using injected signals of black hole binary coalescences as an example, we first show concretely that the usual uncertainty estimation method is insufficient to capture the true error bar on parameter estimates. We then demonstrate how the extra points in the chains of chain based samplers may be carefully utilized to estimate this uncertainty correctly, and provide a way to check the accuracy of the resulting error bars. Finally, we discuss how this uncertainty affects p – p plots and coverage assessments. [ABSTRACT FROM AUTHOR]

With the release of a large amount of astronomical data, an increasing number of close-in hot Jupiters have been discovered. Calculating their evolutionary curves using star–planet interaction models presents a challenge. To expedite the generation of evolutionary curves for these close-in hot Jupiter systems, we utilized tidal interaction models established on mesa to create 15 745 samples of star–planet systems and 7500 samples of stars. Additionally, we employed a neural network (Multilayer Perceptron – MLP) to predict the evolutionary curves of the systems, including stellar effective temperature, radius, stellar rotation period, and planetary orbital period. The median relative errors of the predicted evolutionary curves were found to be 0.15 per cent, 0.43 per cent, 2.61 per cent, and 0.57 per cent, respectively. Furthermore, the speed at which we generate evolutionary curves exceeds that of model-generated curves by more than four orders of magnitude. We also extracted features of planetary migration states and utilized lightgbm to classify the samples into six categories for prediction. We found that by combining three types that undergo long-term double synchronization into one label, the classifier effectively recognized these features. Apart from systems experiencing long-term double synchronization, the median relative errors of the predicted evolutionary curves were all below 4 per cent. Our work provides an efficient method to save significant computational resources and time with minimal loss in accuracy. This research also lays the foundation for analysing the evolutionary characteristics of systems under different migration states, aiding in the understanding of the underlying physical mechanisms of such systems. Finally, to a large extent, our approach could replace the calculations of theoretical models. [ABSTRACT FROM AUTHOR]

The most recent SH0ES measurement of the Hubble constant employs corrections of type Ia supernova magnitudes due to extinction in their host galaxies. These corrections are estimated using a probabilistic model which is trained on Hubble flow (⁠|$z\gt 0.03$|⁠) supernovae and extrapolated to the calibration galaxies (those with observed Cepheids), despite the fact that the latter are selected based on criteria favouring discy and dust-rich systems. We show that this standard approach underestimates the brightness of reddened supernovae in the high stellar-mass (⁠|$M_{\star }\gt 10^{10}\, \mathrm{M}_{\odot }$|⁠) calibration galaxies. This can be traced back to the fact that for these galaxies, a low total-to-selective extinction coefficient (⁠|$R_{\rm B}\sim 3$|⁠) is assumed, while for the low stellar-mass analogues a more standard |$R_{\rm B}\sim 4$| is adopted. We propose a minimalistic modification of the extinction model in the calibration galaxies in order to alleviate this systematic effect. The modification is twofold and it involves: (i) the same, Milky Way-like distribution of |$R_{\rm B}$| (with mean |$R_{\rm B}$| of 4.3 – consistent with the extinction curve used for colour corrections of the Cepheids – and scatter 0.4) and (ii) a modified shape of the |$E(B-V)$| reddening distribution while keeping the same effective slope of the supernova peak magnitude–colour relation and the same mean |$E(B-V)$| reddening as measured for supernovae in the Hubble flow. We show that this new approach yields a significantly better fit (⁠|$\Delta \mathrm{ BIC}=-11$|⁠) to the calibration data and results in a lower value of |$H_{0}$|⁠. Our result is |$H_{0}=70.5\pm 1$|  km s |$^{-1}$|  Mpc |$^{-1}$| implying a reduction of the Hubble constant tension from |$5.2\sigma$| to |$2.8\sigma$|⁠. [ABSTRACT FROM AUTHOR]

Low-magnitude (FD(%) ≥ − 3 % ) Forbush decreases (FDs) and their space weather linkages are recently becoming popular in the literature. Accurate timing and correct magnitude measurement of weak FDs are a desideratum. This demands disentangling the effects of the ever present cosmic ray (CR) diurnal wave that exerts undesirable influence on CR data. An enhanced version of a recently developed algorithm has been deployed to decompose raw CR data at Apatity (APTY) and Moscow (MOSC) neutron monitor (NM) stations into low and high frequency signals. A subroutine in the Fast Fourier transform software simultaneously calculates the magnitude and the epoch time of the events from the transformed data. The software selected 335 and 359 low magnitude FDs respectively for APTY and MOSC observatories. The large catalogues of low-amplitude FDs selected compared to those in literature may be attributed to the efficiency of the present algorithm. We carried out a regression analysis on the magnitude of these events and the corresponding solar wind disturbance agents. The analysis shows that for the APTY NM station, the correlation coefficient results r for FD-interplanetary magnetic field (IMF) and FD-planetary geomagnetic activity index (ap) relations are statistically significant at 95% confidence level. At the MOSC station, we find r ∼ −0.41 statistically significant at 95% confidence level for FD-solar wind speed (SWS) relation (after removing events due to co-rotating interaction regions). Analyzing all (low- and high-amplitude) FDs, r is relatively strong and statistically significant at 95% confidence level. The implications of these findings are discussed. [ABSTRACT FROM AUTHOR]

We present a statistical analysis of the ages and metallicities of triple stellar systems that are known to host exoplanets. With controversial cases disregarded, so far 27 of those systems have been identified. Our analysis, based on an exploratory approach, shows that those systems are on average notably younger than stars situated in the solar neighborhood. Though the statistical significance of this result is not fully established, the most plausible explanation is a possible double selection effect due to the relatively high mass of planet-hosting stars of those systems (which spend less time on the main-sequence than low-mass stars) and that planets in triple stellar systems may be long-term orbitally unstable. The stellar metallicities are on average solar-like; however, owing to the limited number of data, this result is not inconsistent with the previous finding that stars with planets tend to be metal-rich as the deduced metallicity distribution is relatively broad. [ABSTRACT FROM AUTHOR]

Understanding physical mechanisms relies on the accurate determination of fundamental constants, although inherent limitations in experimental techniques introduce uncertainties into these measurements. This paper explores the uncertainties associated with measuring the fine-structure constant (⁠|$\alpha$|⁠) and the proton-to-electron mass ratio (⁠|$\mu$|⁠) using observed fast radio bursts (FRBs). We select 50 localized FRBs to quantify the effects of varying this fundamental coupling on the relation between dispersion measure and redshift. By leveraging independent measurements of dispersion measures and redshifts of these FRBs, we constrain the uncertainties in |$\alpha$| and |$\mu$| approximately to |$\Delta \alpha /\alpha =1.99\times 10^{-5}$| and |$\Delta \mu /\mu =-1.00\times 10^{-5}$| within the standard |$\Lambda$| CDM cosmological framework. Remarkably, these constraints improve nearly an order-of-magnitude when considering a dynamical dark energy model. This investigation not only yields one of the most stringent constraints on |$\alpha$| and |$\mu$| to date but also emphasizes the criticality of accounting for the energy scale of the system when formulating constraints on fundamental parameters. [ABSTRACT FROM AUTHOR]

In the context of the cosmological and constrained Exploring the Local Universe with the reConstructed Initial Density field (ELUCID) simulation, this study explores the statistical characteristics of filaments within the cosmic web, focussing on aspects such as the distribution of filament lengths and their radial density profiles. Using the classification of the cosmic web environment through the Hessian matrix of the density field, our primary focus is on how cosmic structures react to the two variables |$R_{\rm s}$| and |$\lambda _{\rm th}$|⁠. The findings show that the volume fractions of knots, filaments, sheets, and voids are highly influenced by the threshold parameter |$\lambda _{\rm th}$|⁠ , with only a slight influence from the smoothing length |$R_{\rm s}$|⁠. The central axis of the cylindrical filament is pinpointed using the medial-axis thinning algorithm of the COsmic Web Skeleton (COWS) method. It is observed that median filament lengths tend to increase as the smoothing lengths increase. Analysis of filament length functions at different values of |$R_{\rm s}$| indicates a reduction in shorter filaments and an increase in longer filaments as |$R_{\rm s}$| increases, peaking around |$2.5R_{\rm s}$|⁠. The study also shows that the radial density profiles of filaments are markedly affected by the parameters |$R_{\rm s}$| and |$\lambda _{\rm th}$|⁠ , showing a valley at approximately |$2R_{\rm s}$|⁠ , with increases in the threshold leading to higher amplitudes of the density profile. Moreover, shorter filaments tend to have denser profiles than their longer counterparts. [ABSTRACT FROM AUTHOR]

The statistical properties of energy and waiting time carry essential information about the source of repeating fast radio bursts (FRBs). In this paper, we investigate the randomness of energy and waiting time using four data samples from three extremely active repeating FRBs observed by the Five-hundred-meter Aperture Spherical radio Telescope (FAST). We report the deviation from complete randomness of the burst activity using three statistics, i.e. Hurst exponent, Pincus index, and non-Gaussian probability density distribution of fluctuations. First, the Hurst exponent greater than 0.5 reveals that there is long-term memory in the time series of energy and waiting time. Second, the deviation of the Pincus index from 1.0 manifests that the time series is not completely random. Finally, the fluctuations of energy and waiting time follow the scale-invariant q -Gaussian distribution. All these statistical properties imply that, although the time series of repeating FRBs seems to be irregular, they are not completely random, similar to the features of self-organized criticality. [ABSTRACT FROM AUTHOR]

Binary star systems are expected to follow Newtonian dynamics similarly to planetary systems. However, reports have been made of wide binary systems with separations around 0.01 pc and larger, showing potential deviations from standard Newtonian motion. This phenomenon, suggestive of the flattening of galactic rotation curves, calls for closer inspection. This study presents an analysis of wide binary stars using data from Gaia Data Release 3 (DR3), a space-based astrometry mission funded by the European Space Agency. The study compares different choices of selection criteria to examine the nature of these apparent anomalous kinematics within the solar neighbourhood. The Gaia data set furnishes detailed parameters, including radial velocity, mass, age, and binary probability in addition to standard parameters. A custom Python tool named bynary facilitated both data processing and analysis. This report reveals that the signs of any anomalous signals systematically diminish as the initial selection criteria are relaxed for degrouping while subsequent filtering remains stringent, leading to the complete elimination of any apparent non-Newtonian motion for binary separations within 0.5 pc. The study shows that any observed anomalous behaviour in solar neighbourhood wide binaries within 130 pc must be produced either by faint companion stars orbiting primary or secondary stars, or by flyby stars. The findings emphasize the importance of the choice of selection criteria in disentangling genuine binary dynamics from external influences. The conclusions align with the predictions of Newtonian mechanics and general relativity, though they do not exclude other phenomena at larger scales. [ABSTRACT FROM AUTHOR]

Galaxy–galaxy lensing (GGL) and clustering measurements from the Dark Energy Spectroscopic Instrument Year 1 (DESI Y1) data set promise to yield unprecedented combined-probe tests of cosmology and the galaxy–halo connection. In such analyses, it is essential to identify and characterize all relevant statistical and systematic errors. We forecast the covariances of DESI Y1 GGL + clustering measurements and the systematic bias due to redshift evolution in the lens samples. Focusing on the projected clustering and GGL correlations, we compute a Gaussian analytical covariance, using a suite of N -body and lognormal simulations to characterize the effect of the survey footprint. Using the DESI one percent survey data, we measure the evolution of galaxy bias parameters for the DESI luminous red galaxy (LRG) and bright galaxy survey (BGS) samples. We find mild evolution in the LRGs in |$0.4 < z < 0.8$|⁠ , subdominant to the expected statistical errors. For BGS, we find less evolution for brighter absolute magnitude cuts, at the cost of reduced sample size. We find that for a redshift bin width |$\Delta z = 0.1$|⁠ , evolution effects on DESI Y1 GGL is negligible across all scales, all fiducial selection cuts, all fiducial redshift bins. Galaxy clustering is more sensitive to evolution due to the bias squared scaling. Nevertheless the redshift evolution effect is insignificant for clustering above the 1-halo scale of |$0.1h^{-1}$|  Mpc. For studies that wish to reliably access smaller scales, additional treatment of redshift evolution is likely needed. This study serves as a reference for GGL and clustering studies using the DESI Y1 sample. [ABSTRACT FROM AUTHOR]

Intensity mapping experiments will soon have surveyed large swathes of the sky, providing information about the underlying matter distribution of our early Universe. The resulting maps can be used to recover statistical information, such as the power spectrum, about the measured spectral lines (for example, H  i , [C  ii ], and [O  iii ]). However precise power spectrum measurements, such as the 21 cm autocorrelation, continue to be challenged by the presence of bright foregrounds and non-trivial systematics. By cross-correlating different data sets, it may be possible to mitigate the effects of both foreground uncertainty and uncorrelated instrumental systematics. Beyond their own merit, cross-correlations could also be used to recover autocorrelation information. Such a technique has been proposed in the literature for recovering the 21 cm power spectrum. Generalizing this result, we develop a statistical framework for combining multiple cross-correlation signals in order to infer information about the corresponding autocorrelations. We do this first within the least squares estimator framework, and show how one can derive their estimator, along with several alternative estimators. We also investigate the posterior distribution of recovered autocorrelation and associated model parameters. We find that for certain noise regimes and cosmological signal modelling assumptions this procedure is effective at recovering autospectra from a set of cross-correlations. Finally, we showcase our framework in the context of several near-future line intensity mapping experiments. [ABSTRACT FROM AUTHOR]

We perform SubHalo Abundance Matching (SHAM) studies on UNIT simulations with {σ, Vceil, vsmear}-SHAM and {σ, Vceil, fsat}-SHAM. They are designed to reproduce the clustering on 5-30 of luminous red galaxies (LRGs), emission-line galaxies (ELGs), and quasi-stellar objects (QSOs) at 0.4 < z < 3.5 from DESI (Dark Energy Spectroscopic Instrument) One Percent Survey. Vceil is the incompleteness of the massive host (sub)haloes and is the key to the generalized SHAM. vsmear models the clustering effect of redshift uncertainties, providing measurements consistent with those from repeat observations. A free satellite fraction fsat is necessary to reproduce the clustering of ELGs. We find ELGs present a more complex galaxy-halo mass relation than LRGs reflected in their weak constraints on σ. LRGs, QSOs, and ELGs show increasing Vceil values, corresponding to the massive galaxy incompleteness of LRGs, the quenched star formation of ELGs and the quenched black hole accretion of QSOs. For LRGs, a Gaussian vsmear presents a better profile for subsamples at redshift bins than a Lorentzian profile used for other tracers. The impact of the statistical redshift uncertainty on ELG clustering is negligible. The best-fitting satellite fraction for DESI ELGs is around 4 per cent, lower than previous estimations for ELGs. The mean halo mass log10((Mvir)) in for LRGs, ELGs, and QSOs are 13.16 ± 0.01, 11.90 ± 0.06, and 12.66 ± 0.45, respectively. Our generalized SHAM algorithms facilitate the production of multitracer galaxy mocks for cosmological tests.