Search

We computed the evidence in favour of two models, one based on field-aligned thermal conduction alone and another that includes thermal misbalance as well, to explain the damping of slow magneto-acoustic waves in hot coronal loops. Our analysis is based on the computation of the marginal likelihood and the Bayes factor for the two damping models. We quantified their merit to explain the apparent relationship between slow mode periods and damping times, measured with SOHO/SUMER in a set of hot coronal loops. The results indicate evidence in favour of the model with thermal misbalance in the majority of the sample, with a small population of loops for which thermal conduction alone is more plausible. The apparent possibility of two different regimes of slow-wave damping, if due to differences between the loops of host active regions and/or the photospheric dynamics, may help to reveal the coronal heating mechanism. [ABSTRACT FROM AUTHOR]

Understanding the evolution of galaxies provides crucial insights into a broad range of aspects in astrophysics, including structure formation and growth, the nature of dark energy and dark matter, baryonic physics, and more. It is, however, infeasible to track the evolutionary processes of individual galaxies in real time given their long timescales. As a result, galaxy evolution analyses have been mostly based on ensembles of galaxies that are supposed to be from the same population according to usually basic and crude observational criteria. We propose a new strategy of evaluating the evolution of an individual galaxy by identifying its descendant galaxies as guided by cosmological simulations. As a proof of concept, we examined the evolution of the total mass distribution of a target strong lensing galaxy at z = 0.884 using the proposed strategy. We selected 158 galaxies from the TNG300 simulation that we identified as analogs of the target galaxy. We followed their descendants and found 11 observed strong lensing galaxies that match in stellar mass and size with the descendants at their redshifts. The observed and simulated results are discussed, although no conclusive assessment is made given the low statistical significance due to the small sample size. Nevertheless, the test confirms that our proposed strategy is already feasible with existing data and simulations. We expect it to play an even more important role in studying galaxy evolution as more strong lens systems and larger simulations become available with the advent of next-generation survey programs and cosmological simulations. [ABSTRACT FROM AUTHOR]

Context. Understanding the intricacies behind the presence and absence of sources in an astronomical catalogue is crucial for the accurate interpretation of astronomical data. In particular, for the multi-dimensional Gaia data, filters and cuts on different parameters or measurements introduce a selection function that may unintentionally alter scientific conclusions in subtle ways. Aims. We aim to develop a methodology to estimate the selection function for different subsamples of stars in the Gaia catalogue. Methods. Comparing the number of stars in a given subsample to that in the overall Gaia catalogue provides an estimate of the subsample membership probability as a function of sky position, magnitude, and colour. The method used to make this estimate must differentiate the stochastic absence of subsample stars from selection effects. When multiplied with the overall Gaia catalogue selection function, this provides the total selection function of the subsample. Results. We present our new method for estimating the selection function by applying it to the sources in Gaia DR3 with heliocentric radial velocity measurements. We also compute the selection function for the stars in the Gaia-Sausage/Enceladus sample, confirming that the apparent asymmetry of its debris across the sky is merely caused by selection effects. Conclusions. The method we have developed estimates the selection function of the stars present in a subsample of Gaia data, given that the subsample is completely contained in the Gaia parent catalogue (for which the selection function is known). This tool is made available in a GaiaUnlimited Python package. [ABSTRACT FROM AUTHOR]

The exoplanet population characterized by relatively short orbital periods (P < 100 d) around solar-type stars is dominated by super-Earths and sub-Neptunes. However, these planets are missing in our Solar System and the reason behind this absence is still unknown. Two theoretical scenarios invoke the role of Jupiter as the possible culprit: Jupiter may have acted as a dynamical barrier to the inward migration of sub-Neptunes from beyond the water iceline; alternatively, Jupiter may have considerably reduced the inward flux of material (pebbles) required to form super-Earths inside that iceline. Both scenarios predict an anti-correlation between the presence of small planets and that of cold Jupiters in exoplanetary systems. To test that prediction, we homogeneously analyzed the radial-velocity measurements of 38 Kepler and K2 transiting small planet systems gathered over nearly ten years with the HARPS-N spectrograph, as well as publicly available radial velocities collected with other facilities. We used Bayesian differential evolution Markov chain Monte Carlo techniques, which in some cases were coupled with Gaussian process regression to model non-stationary variations due to stellar magnetic activity phenomena. We detected five cold Jupiters in three systems: two in Kepler-68, two in Kepler-454, and a very eccentric one in K2-312. We also found linear trends caused by bound companions in Kepler-93, Kepler-454, and K2-12, with slopes that are still compatible with a planetary mass for outer bodies in the Kepler-454 and K2-12 systems. By using binomial statistics and accounting for the survey completeness, we derived an occurrence rate of 9.3−2.9+7.7% for cold Jupiters with 0.3–13 MJup and 1–10 AU, which is lower but still compatible at 1.3σ with the value measured from radial-velocity surveys for solar-type stars, regardless of the presence or absence of small planets. The sample is not large enough to draw a firm conclusion about the predicted anti-correlation between small planets and cold Jupiters; nevertheless, we found no evidence of previous claims of an excess of cold Jupiters in small planet systems. As an important byproduct of our analyses, we homogeneously determined the masses of 64 Kepler and K2 small planets, reaching a precision better than 5, 7.5, and 10σ for 25, 13, and 8 planets, respectively. Finally, we release the 3661 HARPS-N radial velocities used in this work to the scientific community. These radial-velocity measurements mainly benefit from an improved data reduction software that corrects for subtle prior systematic effects. [ABSTRACT FROM AUTHOR]

Context. Large-scale astronomical surveys such as the Zwicky Transient Facility (ZTF) opened a new window of opportunity in the search for rare astrophysical phenomena. Community brokers, such as FINK, have the task of identifying interesting candidates and redistributing them to the community. For the specific case of fast transients, this identification should be done early, based on a limited number of observed photometric epochs, thus allowing it to trigger further observations. Aims. We describe the fast transient classification algorithm in the centre of the kilonova (KN) science module currently implemented in the FINK broker, and we report classification results based on simulated catalogues and real data from the ZTF alert stream. Methods. We used noiseless, homogeneously sampled simulations to construct a basis of principal components. All light curves from more realistic ZTF simulations were written as a linear combination of this basis. The corresponding coefficients were used as features in training a random forest classifier. The same method was applied to two different datasets, illustrating possible representations of ZTF light curves. The latter aimed to simulate the data situation found within the ZTF alert stream. Results. Classification based on simulations mimicking ZTF alerts resulted in 69.30% precision and 69.74% recall when applied to a simulated test sample, thus confirming the robustness of precision results when limited to 30 days of observations. Dwarf flares and point Type Ia supernovae were the most frequent contaminants. The final trained model was integrated into the FINK broker and has been distributing fast transients, tagged as KN_candidates, to the astronomical community, especially through the GRANDMA collaboration. Conclusions. We show that features specifically designed to grasp different light-curve behaviours provide enough information to separate fast (KN-like) from slow (non-KN-like) evolving events. This module represents one crucial link in an intricate chain of infrastructure elements for multi-messenger astronomy, which is currently being put in place by the FINK broker team in preparation for the arrival of data from the Vera Rubin Observatory Legacy Survey of Space and Time. [ABSTRACT FROM AUTHOR]

Context. The PLATO mission is scheduled for launch in 2026. It will monitor more than 245 000 FGK stars of magnitude 13 or brighter for planet transit events. Among the key scientific goals are the detection of Earth-Sun analogs; the detailed characterization of stars and planets in terms of mass, radius, and ages; the detection of planetary systems with longer orbital periods than are detected in current surveys; and to advance our understanding of planet formation and evolution processes. Aims. This study aims to estimate the number of exoplanets that PLATO can detect as a function of planetary size and period, stellar brightness, and observing strategy options. Deviations from these estimates will be informative of the true occurrence rates of planets, which helps constraining planet formation models. Methods. For this purpose, we developed the Planet Yield for PLATO estimator (PYPE), which adopts a statistical approach. We apply given occurrence rates from planet formation models and from different search and vetting pipelines for the Kepler data. We estimate the stellar sample to be observed by PLATO using a fraction of the all-sky PLATO stellar input catalog (PIC). PLATO detection efficiencies are calculated under different assumptions that are presented in detail in the text. Results. The results presented here primarily consider the current baseline observing duration of 4 yr. We find that the expected PLATO planet yield increases rapidly over the first year and begins to saturate after 2 yr. A nominal (2+2) 2-yr mission could yield about several thousand to several tens of thousands of planets, depending on the assumed planet occurrence rates. We estimate a minimum of 500 Earth-size (0.8−1.25 RE) planets, about a dozen of which would reside in a 250–500 days period bin around G stars. We find that one-third of the detected planets are around stars bright enough (V ≤11) for RV-follow-up observations. We find that a 3-yr-long observation followed by 6 two-month short observations (3+1 yr) yield roughly twice as many planets as two long observations of 2 yr (2+2 yr). The former strategy is dominated by short-period planets, while the latter is more beneficial for detecting earths in the habitable zone. Conclusions. Of the many sources of uncertainties for the PLATO planet yield, the real occurrence rates matters most. Knowing the latter is crucial for using PLATO observations to constrain planet formation models by comparing their statistical yields. [ABSTRACT FROM AUTHOR]

Context. Modern-day time-domain photometric surveys collect a lot of observations of various astronomical objects and the coming era of large-scale surveys will provide even more information on their properties. Spectroscopic follow-ups are especially crucial for transients such as supernovae and most of these objects have not been subject to such studies. Aims. Flux time series are actively used as an affordable alternative for photometric classification and characterization, for instance, peak identifications and luminosity decline estimations. However, the collected time series are multidimensional and irregularly sampled, while also containing outliers and without any well-defined systematic uncertainties. This paper presents a search for the best-performing methods to approximate the observed light curves over time and wavelength for the purpose of generating time series with regular time steps in each passband. Methods. We examined several light curve approximation methods based on neural networks such as multilayer perceptrons, Bayesian neural networks, and normalizing flows to approximate observations of a single light curve. Test datasets include simulated PLAsTiCC and real Zwicky Transient Facility Bright Transient Survey light curves of transients. Results. The tests demonstrate that even just a few observations are enough to fit the networks and improve the quality of approximation, compared to state-of-the-art models. The methods described in this work have a low computational complexity and are significantly faster than Gaussian processes. Additionally, we analyzed the performance of the approximation techniques from the perspective of further peak identification and transients classification. The study results have been released in an open and user-friendly Fulu Python library available on GitHub for the scientific community. [ABSTRACT FROM AUTHOR]

Context. Lunar swirls are bright albedo features only found on the Moon that are still not entirely understood. It is commonly accepted that reduced space weathering plays a role in explaining the origins of lunar swirls because the local magnetic fields that are typically associated with these albedo anomalies are effective in reducing the solar wind influx. However, additional processes are required to fully explain the spectral, photometric, and polarimetric properties of the swirls. Aims. In this study, we compare the photometric properties of the Chang'e-5 landing site to those of the Reiner Gamma swirl. Because the physical effects of a landing rocket jet on the lunar regolith are relatively well known, these observations can provide important insights into the physical properties of lunar swirls. Methods. We determined the single scattering albedo, opposition effect strength, and surface roughness of the Reiner Gamma swirl and the Chang'e-5 landing site with their respective statistical uncertainties based on the Hapke model and Bayesian inference sampling. Results. The Chang'e-5 landing site and the Reiner Gamma swirl exhibit similar photometric properties, in particular: an increased albedo and a reduced opposition effect strength. Additionally, the landing site is about 20% less rough compared to the surrounding area. Conclusions. These findings suggest that the swirl surface is less porous compared to the surrounding surface, similarly to a landing site where the top layer of the regolith has been blown away effectively so that the compactness was increased. We conclude that external mechanisms that are able to compress the uppermost regolith layer are involved in lunar swirl formation, such as interactions with the gaseous hull of a passing comet. [ABSTRACT FROM AUTHOR]

Context. The study of the multiplicity of massive stars gives hints on their formation processes and their evolutionary paths, which are still not fully understood. Large separation binaries (>50 milliseconds of arc, mas) can be probed by adaptive-optics-assisted direct imaging and sparse aperture masking, while close binaries can be resolved by photometry and spectroscopy. However, optical long baseline interferometry is mandatory to establish the multiplicity of Galactic massive stars at the separation gap between 1 and 50 mas. Aims. In this paper, we aim to demonstrate the capability of the new interferometric instrument MIRC-X, located at the CHARA Array, to study the multiplicity of O-type stars and therefore probe the full range of separation for more than 120 massive stars (H < 7.5 mag). Methods. We initiated a pilot survey of bright O-type stars (H < 6.5 mag) observable with MIRC-X. We observed 29 O-type stars, including two systems in average atmospheric conditions around a magnitude of H = 7.5 mag. We systematically reduced the obtained data with the public reduction pipeline of the instrument. We analyzed the reduced data using the dedicated python software CANDID to detect companions. Results. Out of these 29 systems, we resolved 19 companions in 17 different systems with angular separations between ~0.5 and 50 mas. This results in a multiplicity fraction fm = 17/29 = 0.59 ± 0.09, and an average number of companions fc = 19/29 = 0.66 ± 0.13. Those results are in agreement with the results of the SMASH+ survey in the Southern Hemisphere. Thirteen of these companions have been resolved for the first time, including the companion responsible for the nonthermal emission in Cyg OB2-5 A and the confirmation of the candidate companion of HD 47129 suggested by SMASH+. Conclusions. A large survey on more than 120 northern O-type stars (H < 7.5) is possible with MIRC-X and will be fruitful. [ABSTRACT FROM AUTHOR]

As a consequence of galaxy clustering, close galaxies observed on the plane of the sky should be spatially correlated with a probability that is inversely proportional to their angular separation. In principle, this information can be used to improve photometric redshift estimates when spectroscopic redshifts are available for some of the neighbouring objects. Depending on the depth of the survey, however, this angular correlation is reduced by chance projections. In this work, we implement a deep-learning model to distinguish between apparent and real angular neighbours by solving a classification task. We adopted a graph neural network architecture to tie together photometry, spectroscopy, and the spatial information between neighbouring galaxies. We trained and validated the algorithm on the data of the VIPERS galaxy survey, for which photometric redshifts based on spectral energy distribution are also available. The model yields a confidence level for a pair of galaxies to be real angular neighbours, enabling us to disentangle chance superpositions in a probabilistic way. When objects for which no physical companion can be identified are excluded, all photometric redshift quality metrics improve significantly, confirming that their estimates were of lower quality. For our typical test configuration, the algorithm identifies a subset containing ~75% high-quality photometric redshifts, for which the dispersion is reduced by as much as 50% (from 0.08 to 0.04), while the fraction of outliers reduces from 3% to 0.8%. Moreover, we show that the spectroscopic redshift of the angular neighbour with the highest detection probability provides an excellent estimate of the redshift of the target galaxy, comparable to or even better than the corresponding template-fitting estimate. [ABSTRACT FROM AUTHOR]

Context. Pressure profiles are sensitive probes of the thermodynamic conditions and the internal structure of galaxy clusters. The intra-cluster gas resides in hydrostatic equilibrium within the dark-matter gravitational potential. However, this equilibrium may be perturbed; for example, as a consequence of thermal energy losses, feedback, and non-thermal pressure supports. Accurate measures of the gas pressure over cosmic time are crucial for constraining cluster evolution as well as the contributions from astrophysical processes. Aims. In this work we present a novel algorithm for deriving the pressure profiles of galaxy clusters from the Sunyaev-Zeldovich (SZ) signal measured on a combination of Planck and South Pole Telescope (SPT) observations. The synergy of the two instruments makes it possible to track the profiles on a wide range of spatial scales. We exploited the sensitivity of the Planck High-Frequency Instrument to the larger scales in order to observe the faint peripheries, and took advantage of the higher spatial resolution of SPT to solve the innermost regions. Methods. We developed a two-step pipeline to take advantage of the specifications of each instrument. We first performed a component separation on the two data sets separately in order to remove the background (CMB) and foreground (Galactic emission) contaminants. We then jointly fitted a parametric pressure profile model on a combination of Planck and SPT data. Results. We validated our technique on a sample of six CHEX-MATE clusters detected by SPT. We compare the results of the SZ analysis with profiles derived from X-ray observations with XMM-Newton. We find excellent agreement between these two independent probes of the gas pressure structure. [ABSTRACT FROM AUTHOR]

Context. Barium (Ba) stars are characterised by an abundance of heavy elements made by the slow neutron capture process (s-process). This peculiar observed signature is due to the mass transfer from a stellar companion, bound in a binary stellar system, to the Ba star observed today. The signature is created when the stellar companion is an asymptotic giant branch (AGB) star. Aims. We aim to analyse the abundance pattern of 169 Ba stars using machine learning techniques and the AGB final surface abundances predicted by the FRUITY and Monash stellar models. Methods. We developed machine learning algorithms that use the abundance pattern of Ba stars as input to classify the initial mass and metallicity of each Ba star's companion star using stellar model predictions. We used two algorithms. The first exploits neural networks to recognise patterns, and the second is a nearest-neighbour algorithm that focuses on finding the AGB model that predicts the final surface abundances closest to the observed Ba star values. In the second algorithm, we included the error bars and observational uncertainties in order to find the best-fit model. The classification process was based on the abundances of Fe, Rb, Sr, Zr, Ru, Nd, Ce, Sm, and Eu. We selected these elements by systematically removing s-process elements from our AGB model abundance distributions and identifying the elements whose removal had the biggest positive effect on the classification. We excluded Nb, Y, Mo, and La. Our final classification combined the output of both algorithms to identify an initial mass and metallicity range for each Ba star companion. Results. With our analysis tools, we identified the main properties for 166 of the 169 Ba stars in the stellar sample. The classifications based on both stellar sets of AGB final abundances show similar distributions, with an average initial mass of M = 2.23 Mand 2.34 M and an average [Fe/H] = -0.21 and -0.11, respectively. We investigated why the removal of Nb, Y, Mo, and La improves our classification and identified 43 stars for which the exclusion had the biggest effect. We found that these stars have statistically significant and different abundances for these elements compared to the other Ba stars in our sample. We discuss the possible reasons for these differences in the abundance patterns. [ABSTRACT FROM AUTHOR]

We applied the newly developed rose diagram overlay method to detect the layered structure of 88 nearby open clusters (=500 pc) on the three projections after the distance correction of their member stars, based on the catalog in literature. The results show that with the rose diagram overlay method, a total of 74 clusters in our sample have a layered structure, while the remaining clusters are without a clear layered structure. We for the first time defined the layered structure parameters for the sample clusters. Meanwhile, we found that the layered circle core area (s) has a strong positive correlation with the number of cluster members, while the kernel instability index (1) has a strong negative correlation with the number of cluster members. Our study provides a novel perspective for the detection of the layered structure of open clusters. [ABSTRACT FROM AUTHOR]

Context. Weak lensing mass-mapping is a useful tool for accessing the full distribution of dark matter on the sky, but because of intrinsic galaxy ellipticies, finite fields, and missing data, the recovery of dark matter maps constitutes a challenging, ill-posed inverse problem. Aims. We introduce a novel methodology that enables the effcient sampling of the high-dimensional Bayesian posterior of the weak lensing mass-mapping problem, relying on simulations to define a fully non-Gaussian prior. We aim to demonstrate the accuracy of the method to simulated fields, and then proceed to apply it to the mass reconstruction of the HST/ACS COSMOS field. Methods. The proposed methodology combines elements of Bayesian statistics, analytic theory, and a recent class of deep generative models based on neural score matching. This approach allows us to make full use of analytic cosmological theory to constrain the 2pt statistics of the solution, to understand any differences between this analytic prior and full simulations from cosmological simulations, and to obtain samples from the full Bayesian posterior of the problem for robust uncertainty quantification. Results. We demonstrate the method in the TNG simulations and find that the posterior mean significantly outperfoms previous methods (Kaiser-Squires, Wiener filter, Sparsity priors) both for the root-mean-square error and in terms of the Pearson correlation. We further illustrate the interpretability of the recovered posterior by establishing a close correlation between posterior convergence values and the S/N of the clusters artificially introduced into a field. Finally, we apply the method to the reconstruction of the HST/ACS COSMOS field, which yields the highest-quality convergence map of this field to date. Conclusions. We find the proposed approach to be superior to previous algorithms, scalable, providing uncertainties, and using a fully non-Gaussian prior. [ABSTRACT FROM AUTHOR]

Super-sample covariance (SSC) is an important effect for cosmological analyses that use the deep structure of the cosmic web; it may, however, be nontrivial to include it practically in a pipeline. We solve this difficulty by presenting a formula for the precision (inverse covariance) matrix and show applications to update likelihood or Fisher forecast pipelines. The formula has several advantages in terms of speed, reliability, stability, and ease of implementation. We present an analytical application to show the formal equivalence between three approaches to SSC: (i) at the usual covariance level, (ii) at the likelihood level, and (iii) with a quadratic estimator. We then present an application of this computationally efficient framework for studying the impact of inaccurate modelling of SSC responses for cosmological constraints from stage IV surveys. We find that a weak-lensing-only analysis is very sensitive to inaccurate modelling of the scale dependence of the response, which needs to be calibrated at the ∼15% level. The sensitivity to this scale dependence is less severe for the joint weak-lensing and galaxy clustering analysis (also known as 3×2pt). Nevertheless, we find that both the amplitude and scale-dependence of the responses have to be calibrated at better than 30%. [ABSTRACT FROM AUTHOR]

Context. The future deployment of the Square Kilometer Array (SKA) will lead to a massive influx of astronomical data and the automatic detection and characterization of sources will therefore prove crucial in utilizing its full potential. Aims. We examine how existing astronomical knowledge and tools can be utilized in a machine learning-based pipeline to find 3D spectral line sources. Methods. We present a source-finding pipeline designed to detect 21-cm emission from galaxies that provides the second-best submission of SKA Science Data Challenge 2. The first pipeline step was galaxy segmentation, which consisted of a convolutional neural network (CNN) that took an H I cube as input and output a binary mask to separate galaxy and background voxels. The CNN was trained to output a target mask algorithmically constructed from the underlying source catalog of the simulation. For each source in the catalog, its listed properties were used to mask the voxels in its neighborhood that capture plausible signal distributions of the galaxy. To make the training more efficient, regions containing galaxies were oversampled compared to the background regions. In the subsequent source characterization step, the final source catalog was generated by the merging and dilation modules of the existing source-finding software SOFIA, and some complementary calculations, with the CNN-generated mask as input. To cope with the large size of H I cubes while also allowing for deployment on various computational resources, the pipeline was implemented with flexible and configurable memory usage. Results. We show that once the segmentation CNN has been trained, the performance can be fine-tuned by adjusting the parameters involved in producing the catalog from the mask. Using different sets of parameter values offers a trade-off between completeness and reliability. [ABSTRACT FROM AUTHOR]

Context. Star-forming regions are excellent benchmarks for testing and validating theories of star formation and stellar evolution. The Perseus star-forming region, being one of the youngest (< 10 Myr), closest (280−320 pc), and most studied in the literature, is a fundamental benchmark. Aims. We aim to study the membership, phase-space structure, mass, and energy (kinetic plus potential) distribution of the Perseus star-forming region using public catalogues (Gaia, APOGEE, 2MASS, and Pan-STARRS). Methods. We used Bayesian methodologies that account for extinction to identify the Perseus physical groups in the phase-space, retrieve their candidate members, derive their properties (age, mass, 3D positions, 3D velocities, and energy), and attempt to reconstruct their origin. Results. We identify 1052 candidate members in seven physical groups (one of them new) with ages between 3 and 10 Myr, dynamical super-virial states, and large fractions of energetically unbounded stars. Their mass distributions are broadly compatible with that of Chabrier for masses ≳0.1 M⊙ and do not show hints of over-abundance of low-mass stars in NGC 1333 with respect to IC 348. These groups' ages, spatial structure, and kinematics are compatible with at least three generations of stars. Future work is still needed to clarify if the formation of the youngest was triggered by the oldest. Conclusions. The exquisite Gaia data complemented with public archives and mined with comprehensive Bayesian methodologies allow us to identify 31% more members than previous studies, discover a new physical group (Gorgophone: 7 Myr, 191 members, and 145 M⊙), and confirm that the spatial, kinematic, and energy distributions of these groups support the hierarchical star formation scenario. [ABSTRACT FROM AUTHOR]

With the aim of bringing substantial insight to the fundamental question of how galaxies acquire their material for star formation, we present the first comprehensive characterisation of the galaxy connectivity (i.e. the number of small-scale filamentary streams connected to a galaxy) in relation to the cosmic environment, and a statistical exploration of the impact of connectivity on the star formation rate (SFR) at z = 2. We detected kiloparsec-scale filaments directly connected to galaxies by applying the DisPerSE filament finder to the dark matter density around 2942 central galaxies (M* > 108 M⊙/h) of the TNG50-1 simulation. Our results demonstrate that galaxy connectivity spans a broad range (from 0 to 9), with more than half of the galaxies connected to two or three streams. We examined a variety of factors that might influence the connectivity and found that it increases with mass, decreases with local density for low-mass galaxies, and does not depend on local environment, estimated by the Delaunay tessellation, for high-mass galaxies. Beyond mass and local density, we further classified galaxies according to their location in different cosmic web environments, and we highlight the influence of the large-scale structure on the number of connected streams. Our results reflect the different strengths of the cosmic tides, which can prevent the formation of coherent streams feeding the galaxies or even disconnect the galaxy from its local web. Finally, we show that at fixed local density, the SFR of low-mass galaxies is up to 5.9σ higher as a result of connectivity. This SFR boost is even higher (6.3σ) for galaxies that are embedded in cosmic filaments, where the available matter reservoirs are large. A milder impact is found for high-mass galaxies, which indicates different relative efficiencies of matter inflow via small-scale streams in galaxies of different masses. [ABSTRACT FROM AUTHOR]

The prediction of solar flares is of practical and scientific interest; however, many machine learning methods used for this prediction task do not provide the physical explanations behind a model's performance. We made use of two recently developed explainable artificial intelligence techniques called gradient-weighted class activation mapping (Grad-CAM) and expected gradients (EG) to reveal the decision-making process behind a high-performance neural network that has been trained to distinguish between Mg II spectra derived from flaring and nonflaring active regions, a fact that can be applied to the task of short timescale flare forecasting. The two techniques generate visual explanations (heatmaps) that can be projected back onto the spectra, allowing for the identification of features that are strongly associated with precursory flare activity. We automated the search for explainable interpretations on the level of individual wavelengths, and provide multiple examples of flare prediction using IRIS spectral data, finding that prediction scores in general increase before flare onset. Large IRIS rasters that cover a significant portion of the active region and coincide with small preflare brightenings both in IRIS and SDO/AIA images tend to lead to better forecasts. The models reveal that Mg II triplet emission, flows, as well as broad and highly asymmetric spectra are all important for the task of flare prediction. Additionally, we find that intensity is only weakly correlated to a spectrum's prediction score, meaning that low intensity spectra can still be of great importance for the flare prediction task, and that 78% of the time, the position of the model's maximum attention along the slit during the preflare phase is predictive of the location of the flare's maximum UV emission. [ABSTRACT FROM AUTHOR]

Context. The lack of sub-Jovian planets on orbits of Porb < 3 days is a puzzling aspect of galaxy formation with regard to the distribu-tion of exoplanets whose origins are currently unresolved. Aims. The possible explanations behind the formation of the sub-Jovian or Neptunian desert include several scenarios that can lead to different shapes for the boundary, predicting various dependencies between the position of the boundary and the stellar parameters. Methods. We explored the exoplanet distribution in various 2D and 3D projections, revealing the stellar-dependent substructures in the Porb–MP and the Porb–RP parameter plane. Results. We demonstrate that the upper boundary includes a range of planets, namely, inflated hot Jupiters and normal hot Jupiters, in the two parameter planes, respectively. We confirm the dependence of the boundary on several stellar parameters and, based on a fuzzy clustering analysis, we provide quantitative formulae for the dependencies in groups of smaller and larger planets. The overall period-radius distribution shows chemical substructures as well, with the boundary being dependent on volatiles and alpha-elements, alongside marginal (to none) dependence found for refractory elements. Conclusions. These findings confirm multiple plausible causes for the formation of the desert, particularly preferring those scenarios related to the irradiation-driven loss of the atmospheres of moderately massive planets as the predominant process in shaping planetary distributions. [ABSTRACT FROM AUTHOR]

Context. The morphological classification of galaxies is considered a relevant issue and can be approached from different points of view. The increasing growth in the size and accuracy of astronomical data sets brings with it the need for the use of automatic methods to perform these classifications. Aims. The aim of this work is to propose and evaluate a method for the automatic unsupervised classification of kinematic morphologies of galaxies that yields a meaningful clustering and captures the variations of the fundamental properties of galaxies. Methods.We obtained kinematic maps for a sample of 2064 galaxies from the largest simulation of the EAGLE project that mimics integral field spectroscopy images. These maps are the input of a dimensionality reduction algorithm followed by a clustering algorithm. We analysed the variation of physical and observational parameters among the clusters obtained from the application of this procedure to different inputs. The inputs studied in this paper are (a) line-of-sight velocity maps for the whole sample of galaxies observed at fixed inclinations; (b) line-of-sight velocity, dispersion, and flux maps together for the whole sample of galaxies observed at fixed inclinations; (c) line-of-sight velocity, dispersion, and flux maps together for two separate subsamples of edge-on galaxies with similar amount of rotation; and (d) line-of-sight velocity, dispersion, and flux maps together for galaxies from different observation angles mixed. Results. The application of the method to solely line-of-sight velocity maps achieves a clear division between slow rotators (SRs) and fast rotators (FRs) and can differentiate rotation orientation. By adding the dispersion and flux information at the input, low-rotation edge-on galaxies are separated according to their shapes and, at lower inclinations, the clustering using the three types of maps maintains the overall information obtained using only the line-of-sight velocity maps. This method still produces meaningful groups when applied to SRs and FRs separately, but in the first case the division into clusters is less clear than when the input includes a variety of morphologies. When applying the method to a mixture of galaxies observed from different inclinations, we obtain results that are similar to those in our previous experiments with the advantage that in this case the input is more realistic. In addition, our method has proven to be robust: it consistently classifies the same galaxies viewed from different inclinations. [ABSTRACT FROM AUTHOR]

Context. NOTT (formerly Hi-5) is a new high-contrast L′ band (3.5–4.0 µm) beam combiner for the VLTI designed with an ambitious aim to be sensitive to young giant exoplanets down to 5 mas separation around nearby stars. The performance of nulling interferometers in these wavelengths is affected both by fundamental noise from the background and contributions of instrumental noise. This motivates the development of end-to-end simulations to optimize these instruments. Aims. The aim of this study is to enable a performance evaluation of NOTT and inform the design of such instruments with current and future infrastructures in mind, taking into account the different sources of noise and their correlation. Methods. SCIFYsim is an end-to-end simulator for single-mode-filtered beam combiners, with an emphasis on nulling interferometers. We use it to compute a covariance matrix of the errors. We then use statistical detection tests based on likelihood ratios to compute compound detection limits for the instrument. Results. With the current assumptions as to the performance of the wavefront correction systems, the errors are dominated by correlated instrumental errors down to stars of magnitude 6–7 in the L band, beyond which thermal background from the telescopes and relay system becomes dominant. Conclusions. SCIFYsim is suited to anticipating some of the challenges of design, tuning, operation, and signal processing for integrated-optics beam combiners. The detection limits found for this early version of NOTT simulation with the unit telescopes are compatible with detections at contrasts up to 105 in the L band at separations of 5–80 mas around bright stars. [ABSTRACT FROM AUTHOR]

The Euclid Space Telescope will provide deep imaging at optical and near-infrared wavelengths, along with slitless near-infrared spectroscopy, across ~15 000deg2 of the sky. Euclid is expected to detect ~12 billion astronomical sources, facilitating new insights into cosmology, galaxy evolution, and various other topics. In order to optimally exploit the expected very large dataset, appropriate methods and software tools need to be developed. Here we present a novel machine-learning-based methodology for the selection of quiescent galaxies using broadband Euclid IE, YE, JE, and HE photometry, in combination with multi-wavelength photometry from other large surveys (e.g. the Rubin LSST). The ARIADNE pipeline uses meta-learning to fuse decision-tree ensembles, nearest-neighbours, and deep-learning methods into a single classifier that yields significantly higher accuracy than any of the individual learning methods separately. The pipeline has been designed to have 'sparsity awareness', such that missing photometry values are informative for the classification. In addition, our pipeline is able to derive photometric redshifts for galaxies selected as quiescent, aided by the 'pseudo-labelling' semi-supervised method, and using an outlier detection algorithm to identify and reject likely catastrophic outliers. After the application of the outlier filter, our pipeline achieves a normalised mean absolute deviation of ≲0.03 and a fraction of catastrophic outliers of ≲0.02 when measured against the COSMOS2015 photometric redshifts. We apply our classification pipeline to mock galaxy photometry catalogues corresponding to three main scenarios: (i) Euclid Deep Survey photometry with ancillary ugriz, WISE, and radio data; (ii) Euclid Wide Survey photometry with ancillary ugriz, WISE, and radio data; and (iii) Euclid Wide Survey photometry only, with no foreknowledge of galaxy redshifts. In a like-for-like comparison, our classification pipeline outperforms UVJ selection, in addition to the Euclid IE – YE, JE – HE and u – IE, IE – JE colour-colour methods, with improvements in completeness and the F1-score (the harmonic mean of precision and recall) of up to a factor of 2. [ABSTRACT FROM AUTHOR]

Context. Stellar age determinations for field stars are crucial for studying the evolutionary history of the Galaxy. The vast majority of the research in this area has so far been focused on stars with typical disk characteristics. Aims. Nowadays, the availability of high-quality asteroseismic data for stars with typical halo characteristics makes it possible to extend such investigations. The aim of this paper is to study the precision and theoretical biases in the age determinations of halo stars adopting both asteroseismic and classic observational constraints. Methods. We adopt the well-tested SCEPtER pipeline, covering evolutionary phases up to the red giant branch (RGB). The fitting grids contain stars with mass in the range of [0.7; 1.0] M⊙ and metallicity [Fe/H] from −2.5 to −0.5, which are typical ranges seen in the halo population. We investigate several scenarios characterised by different adopted observational uncertainties. We also assess the impact of systematic discrepancies between the recovery grid models and target stars by computing several synthetic grids of stellar models with perturbed input physics. Results. We achieve more precise asteroseismic age estimates for old metal-poor stars than for more metallic stars. In our reference scenario (errors in Δν and νmax of 2.5% and 5% respectively), we recover ages for stars in the main sequence (MS) or subgiant branch (SGB) with a typical 10%−20% precision, while we recover those of RGB stars with a precision of about 60%. However, recent observations allow tighter constraints on asteroseismic parameters by about a factor of 3. With this assumption, the age precision in RGB improved to 20%, while few modifications occur in the other analysed evolutionary phases. Our investigation of the relevance of systematic discrepancies between grid models and target stars shows that a mismatch in the mixing-length parameter value between grids and targets (from 1.9 to 1.74) leads to significant bias in the age estimations for MS stars (about 10%), but this bias is smaller for SGB and RGB stars. Neglecting the microscopic diffusion effect in the recovery grid leads to a typical 40% bias in age estimates for stars on the MS. Finally, we applied the age estimation technique to stars in globular clusters, adopting typical observational uncertainties from the literature. We find a precision in age estimates of around 20% for MS stars and up to 40% for RGB stars. These uncertainties are greater than those obtained with classical methods, which are therefore still to be preferred. We also applied the SCEPtER pipeline to the age determination of the stars of the cluster M4, relying on asteroseismic data for seven RGB stars from the literature. We obtain a cluster age of 11.9 ± 1.5 Gyr and a mass at the turn-of off 0.86 ± 0.04 M⊙, which are in good agreement with literature results. [ABSTRACT FROM AUTHOR]

The 21 cm emission from atomic hydrogen (H I) is one of the most important tracers of the structure and dynamics of the interstellar medium. Thanks to Galactic rotation, the line is Doppler shifted and, assuming a model for the velocity field, data from gas line surveys can be deprojected along the line of sight. However, given our vantage point in the Galaxy, such a reconstruction suffers from a number of ambiguities. Here, we argue that those can be cured by exploiting the spatial coherence of the gas density that is implied by the physical processes shaping it. We have adopted a Bayesian inference framework that allows reconstructing the three-dimensional map of H I and quantifying its uncertainty. We employ data from the HI4PI compilation to produce three-dimensional maps of Galactic H I. The reconstructed density shows structure on a variety of scales. In particular, some spurs and spiral arms can be identified with ease. We discuss the morphology of the surface mass density and the radial and vertical profiles. [ABSTRACT FROM AUTHOR]

Aims. For decades now, researchers have been looking for a way to tie the kinematic and dynamic reference frames. Certain worldwide organizations have looked to using co-location in space, combining various techniques. Given the long list of possible applications of the Global Navigation Satellite System (GNSS), it is worthwhile investigating the connection between the most accurate and stable International Celestial Reference Frame (ICRF) and the Earth-centered Celestial Inertial reference frame (ECI) used in GNSS data processing. Methods. We simulated phase-referencing observations of GNSS satellites and nearby radio source calibrators to realize the connection between the two celestial reference frames. We designed two schemes for observation plans. One scheme is to select the satellite target when it can be observed by the greatest number of stations in order to obtain high-precision positioning. During each scan, we employ four regional networks to simultaneously track four chosen satellites. The alternative scheme is to observe satellite orbits of as many satellites as possible on different daily observations. In addition, to test the two schemes, we used Monte Carlo methods to generate 1000 groups of random errors in the simulation. Results. Finally, we estimate the right ascension and declination offsets (∆α, ∆δ) of GNSS satellites in the ICRF, and then derive frame tie parameters based on those results: three global rotation angles (A1, A2, A3). The celestial angular offset results assessed from the former scheme show that this scheme leads to high precision of namely 1 mas, while the parameters of the frame tie determined from the second scheme can achieve an improved precision of better than 1.3 µas. [ABSTRACT FROM AUTHOR]

We present the first Bayesian method for tomographic decomposition of the plane-of-sky orientation of the magnetic field with the use of stellar polarimetry and distance. This standalone tomographic inversion method presents an important step forward in reconstructing the magnetized interstellar medium (ISM) in three dimensions within dusty regions. We develop a model in which the polarization signal from the magnetized and dusty ISM is described by thin layers at various distances, a working assumption which should be satisfied in small-angular circular apertures. Our modeling makes it possible to infer the mean polarization (amplitude and orientation) induced by individual dusty clouds and to account for the turbulence-induced scatter in a generic way. We present a likelihood function that explicitly accounts for uncertainties in polarization and parallax. We develop a framework for reconstructing the magnetized ISM through the maximization of the log-likelihood using a nested sampling method. We test our Bayesian inversion method on mock data, representative of the high Galactic latitude sky, taking into account realistic uncertainties from Gaia and as expected for the optical polarization survey PASIPHAE according to the currently planned observing strategy. We demonstrate that our method is effective at recovering the cloud properties as soon as the polarization induced by a cloud to its background stars is higher than ~0.1% for the adopted survey exposure time and level of systematic uncertainty. The larger the induced polarization is, the better the method's performance, and the lower the number of required stars. Our method makes it possible to recover not only the mean polarization properties but also to characterize the intrinsic scatter, thus creating new ways to characterize ISM turbulence and the magnetic field strength. Finally, we apply our method to an existing data set of starlight polarization with known line-of-sight decomposition, demonstrating agreement with previous results and an improved quantification of uncertainties in cloud properties. [ABSTRACT FROM AUTHOR]

Interpreting and modelling astronomical catalogues requires an understanding of the catalogues' completeness or selection function: what properties determine an object's probability of being including in the catalogue? Here we set out to empirically quantify the completeness of the overall catalogue of Gaia's third data release (DR3). This task is not straightforward because Gaia is the all-sky optical survey with the highest angular resolution to date and no consistent ground truth exists to allow direct comparisons. However, well-characterised deeper imaging enables an empirical assessment of Gaia's G-band completeness across parts of the sky. On this basis, we devised a simple analytical completeness model of Gaia as a function of the observed G magnitude and position over the sky, which accounts for both the effects of crowding and the complex Gaia scanning law. Our model only depends on a single quantity: the median magnitude M10 in a patch of the sky of catalogued sources with astrometric_matched_transits ≤10. We note that M10 reflects elementary completeness decisions in the Gaia pipeline and is computable from the Gaia DR3 catalogue itself and therefore applicable across the whole sky. We calibrated our model using the Dark Energy Camera Plane Survey (DECaPS) and tested its predictions against Hubble Space Telescope observations of globular clusters. We found that our model predicts Gaia's completeness values to a few per cent (RMS) across the sky. We make the model available as a part of the gaiaunlimited Python package built and maintained by the GaiaUnlimited project. [ABSTRACT FROM AUTHOR]

Context. As modern-day precision cosmology aims for statistical uncertainties of the percent level or lower, it becomes increasingly important to reconsider estimator assumptions at each step of the process, along with their consequences on the statistical variability of the scientific results. Aims. We compare L1 regression statistics to the weighted mean, the canonical L2 method based on Gaussian assumptions, to infer the weak gravitational shear signal from a catalog of background ellipticity measurements around a sample of clusters, which has been a standard step in the processes of many recent analyses. Methods. We use the shape measurements of background sources around 6925 AMICO clusters detected in the KiDS third data release. We investigate the robustness of our results and the dependence of uncertainties on the signal-to-noise ratios of the background source detections. Using a halo model approach, we derive lensing masses from the estimated excess surface density profiles. Results. The highly significant shear signal allows us to study the scaling relation between the r-band cluster luminosity, L200, and the derived lensing mass, M200. We show the results of the scaling relations derived in 13 bins in L200, with a tightly constrained power-law slope of ∼1.24 ± 0.08. We observe a small, but significant, relative bias of a few percent in the recovered excess surface density profiles between the two regression methods, which translates to a 1σ difference in M200. The efficiency of L1 is at least that of the weighted mean and increases with higher signal-to-noise shape measurements. Concluions. Our results indicate the relevance of optimizing the estimator for inferring the gravitational shear from a distribution of background ellipticities. The interpretation of measured relative biases can be gauged by deeper observations, and the increased computation times remain feasible. [ABSTRACT FROM AUTHOR]

We present a set of maps classifying regions of the sky according to their information gain potential as quantified by Fisher information. These maps can guide the optimal retrieval of relevant physical information with targeted cosmological searches. Specifically, we calculated the response of observed cosmic structures to perturbative changes in the cosmological model and we charted their respective contributions to Fisher information. Our physical forward-modeling machinery transcends the limitations of contemporary analyses based on statistical summaries to yield detailed characterizations of individual 3D structures. We demonstrate this advantage using galaxy counts data and we showcase the potential of our approach by studying the information gain of the Coma cluster. We find that regions in the vicinity of the filaments and cluster core, where mass accretion ensues from gravitational infall, are the most informative with regard to our physical model of structure formation in the Universe. Hence, collecting data in those regions would be most optimal for testing our model predictions. The results presented in this work are the first of their kind to elucidate the inhomogeneous distribution of cosmological information in the Universe. This study paves a new way forward for the performance of efficient targeted searches for the fundamental physics of the Universe, where search strategies are progressively refined with new cosmological data sets within an active learning framework. [ABSTRACT FROM AUTHOR]

Context. Serendipitous X-ray surveys have proven to be an efficient way to find rare objects, for example tidal disruption events, changing-look active galactic nuclei (AGN), binary quasars, ultraluminous X-ray sources, and intermediate mass black holes. With the advent of very large X-ray surveys, an automated classification of X-ray sources becomes increasingly valuable. Aims. This work proposes a revisited naive Bayes classification of the X-ray sources in the Swift-XRT and XMM-Newton catalogs into four classes – AGN, stars, X-ray binaries (XRBs), and cataclysmic variables (CVs) – based on their spatial, spectral, and timing properties and their multiwavelength counterparts. An outlier measure is used to identify objects of other natures. The classifier is optimized to maximize the classification performance of a chosen class (here XRBs), and it is adapted to data mining purposes. Methods. We augmented the X-ray catalogs with multiwavelength data, source class, and variability properties. We then built a reference sample of about 25 000 X-ray sources of known nature. From this sample, the distribution of each property was carefully estimated and taken as reference to assign probabilities of belonging to each class. The classification was then performed on the whole catalog, combining the information from each property. Results. Using the algorithm on the Swift reference sample, we retrieved 99%, 98%, 92%, and 34% of AGN, stars, XRBs, and CVs, respectively, and the false positive rates are 3%, 1%, 9%, and 15%. Similar results are obtained on XMM sources. When applied to a carefully selected test sample, representing 55% of the X-ray catalog, the classification gives consistent results in terms of distributions of source properties. A substantial fraction of sources not belonging to any class is efficiently retrieved using the outlier measure, as well as AGN and stars with properties deviating from the bulk of their class. Our algorithm is then compared to a random forest method; the two showed similar performances, but the algorithm presented in this paper improved insight into the grounds of each classification. Conclusions. This robust classification method can be tailored to include additional or different source classes and can be applied to other X-ray catalogs. The transparency of the classification compared to other methods makes it a useful tool in the search for homogeneous populations or rare source types, including multi-messenger events. Such a tool will be increasingly valuable with the development of surveys of unprecedented size, such as LSST, SKA, and Athena, and the search for counterparts of multi-messenger events. [ABSTRACT FROM AUTHOR]

Owing to a lack of multiple components of prompt γ-ray emissions in short gamma-ray bursts (sGRBs), how these distinct components are correlated remains unclear. In this paper we investigate the spectral and temporal properties of precursors, main peaks, and extended emissions in 26 sGRBs including GRB 170817A. It has been found that peak energies (Ep) in each pulse are uncorrelated with the pulse duration (tdur). Meanwhile, we find that there is no obvious correlation between peak energy and energy fluence. Interestingly, there is no obvious spectral evolution from earlier precursors to later extended emissions in view of the correlations of tdur with either the Ep or the low-energy spectrum index, α. A power-law correlation between the average flux (Fp) and the energy fluence (Sγ), log Fp = (0.62 ± 0.07) log Sγ + (0.27 ± 0.07), is found to exist in the individual segments instead of mean peaks. Furthermore, we also find that the main peaks are on average brighter than the precursors or the extend emissions by about one order of magnitude. On the basis of all the above analyses, we can conclude that three emissive components could share the same radiation mechanisms, but that they might be dominated by diverse physical processes. [ABSTRACT FROM AUTHOR]

We tested whether refracted gravity, a theory of modified gravity that describes the dynamics of galaxies without the aid of dark matter, can model the dynamics of the three massive elliptical galaxies, NGC 1407, NGC 4486, and NGC 5846, out to ∼10Re, where the baryonic mass component fades out and dark matter is required in Newtonian gravity. We probed these outer regions with the kinematics of the globular clusters provided by the SLUGGS survey. Refracted gravity mimics dark matter with the gravitational permittivity, a monotonic function of the local mass density depending on three parameters, ϵ0, ρc, and Q, which are expected to be universal. Refracted gravity satisfactorily reproduces the velocity dispersion profiles of the stars and red and blue globular clusters, with stellar mass-to-light ratios in agreement with stellar population synthesis models, and orbital anisotropy parameters consistent with previous results obtained in Newtonian gravity with dark matter. The sets of the three parameters of the gravitational permittivity found for each galaxy are consistent with each other within ∼2σ. We compare the mean {ϵ0,Q,log10(ρc[g cm−3])} = {0.089−0.035+0.038, 0.47−0.21+0.29, −24.25−0.20+0.28} { ϵ 0 , Q , log 10 ρ c g cm − 3 } = { 0. 089 − 0.035 + 0.038 , 0. 47 − 0.21 + 0.29 , − 24. 25 − 0.20 + 0.28 } $ \{\epsilon_0,Q,\log_{10}\left(\rho_{\mathrm{c}}\left[\mathrm{g}\,\mathrm{cm}^{-3}\right]\right)\} = \{0.089^{+0.038}_{-0.035}, 0.47^{+0.29}_{-0.21}, -24.25^{+0.28}_{-0.20}\} $ found here with the means of the parameters required to model the rotation curves and vertical velocity dispersion profiles of 30 disk galaxies from the DiskMass Survey (DMS): ρc and Q agree within 1σ with the DMS values, whereas ϵ0 agrees within 3σ. This agreement suggests that ellipticals and disk galaxies allow for common values of the parameters of the permittivity and supports the universality of the permittivity function. [ABSTRACT FROM AUTHOR]

Context. Computational techniques are essential for mining large databases produced in modern surveys with value-added products. Aims. This paper presents a machine learning procedure to carry out a galaxy morphological classification and photometric redshift estimates simultaneously. Currently, only a spectral energy distribution (SED) fitting has been used to obtain these results all at once. Methods. We used the ancillary data gathered in the OTELO catalog and designed a nonsequential neural network that accepts optical and near-infrared photometry as input. The network transfers the results of the morphological classification task to the redshift fitting process to ensure consistency between both procedures. Results. The results successfully recover the morphological classification and the redshifts of the test sample, reducing catastrophic redshift outliers produced by an SED fitting and avoiding possible discrepancies between independent classification and redshift estimates. Our technique may be adapted to include galaxy images to improve the classification. [ABSTRACT FROM AUTHOR]

The long-term variations in the orbit of the Earth govern the insolation on its surface and hence its climate. The use of the astronomical signal, whose imprint has been recovered in the geological records, has revolutionized the determination of the geological timescales. However, the orbital variations beyond 60 Myr cannot be reliably predicted because of the chaotic dynamics of the planetary orbits in the Solar System. Taking this dynamical uncertainty to account is necessary for a complete astronomical calibration of geological records. Our work addresses this problem with a statistical analysis of 120 000 orbital solutions of the secular model of the Solar System ranging from 500 Myr to 5 Gyr. We obtain the marginal probability density functions of the fundamental secular frequencies using kernel density estimation. The uncertainty of the density estimation is also obtained here in the form of confidence intervals determined by the moving block bootstrap method. The results of the secular model are shown to be in good agreement with those of the direct integrations of a comprehensive model of the Solar System. Application of our work is illustrated on two geological data sets: the Newark-Hartford records and the Libsack core. [ABSTRACT FROM AUTHOR]

Intensity mapping of the 21 cm signal of neutral hydrogen will yield exciting insights into the Epoch of Reionisation and the nature of the first galaxies. However, the large amount of data that will be generated by the next generation of radio telescopes, such as the Square Kilometre Array, as well as the numerous observational obstacles to overcome, require analysis techniques tuned to extract the reionisation history and morphology. In this context, we introduce a one-point statistic, which we refer to as the local variance, σloc, that describes the distribution of the mean differential 21 cm brightness temperatures measured in two-dimensional maps along the frequency direction of a light cone. The local variance takes advantage of what is usually considered an observational bias, the sample variance. We find the redshift-evolution of the local variance to not only probe the reionisation history of the observed patches of the sky, but also trace the ionisation morphology. This estimator provides a promising tool to constrain the midpoint of reionisation as well as gain insight into the ionising properties of early galaxies. [ABSTRACT FROM AUTHOR]

Context. Current models of galaxy evolution are constrained by the analysis of catalogs containing the flux and size of galaxies extracted from multiband deep fields. However, these catalogs contain inevitable observational and extraction-related biases that can be highly correlated. In practice, taking all of these effects simultaneously into account is difficult, and therefore the derived models are inevitably biased as well. Aims. To address this issue, we use robust likelihood-free methods to infer luminosity function parameters, which is made possible by the massive compression of multiband images using artificial neural networks. This technique makes the use of catalogs unnecessary when observed and simulated multiband deep fields are compared and model parameters are constrained. Because of the efficient data compression, the method is not affected by the required binning of the observables inherent to the use of catalogs. Methods. A forward-modeling approach generates galaxies of multiple types depending on luminosity function parameters rendered on photometric multiband deep fields that include instrumental and observational characteristics. The simulated and the observed images present the same selection effects and can therefore be properly compared. We trained a fully convolutional neural network to extract the most model-parameter-sensitive summary statistics out of these realistic simulations, shrinking the dimensionality of the summary space to the number of parameters in the model. Finally, using the trained network to compress both observed and simulated deep fields, the model parameter values were constrained through population Monte Carlo likelihood-free inference. Results. Using synthetic photometric multiband deep fields similar to previously reported CFHTLS and WIRDS D1/D2 deep fields and massively compressing them through the convolutional neural network, we demonstrate the robustness, accuracy, and consistency of this new catalog-free inference method. We are able to constrain the parameters of luminosity functions of different types of galaxies, and our results are fully compatible with the classic catalog-extraction approaches. [ABSTRACT FROM AUTHOR]

Context. The JAXA asteroid sample return mission Hayabusa2 acquired a huge quantity of data from the asteroid (162173) Ryugu during its 1.5 years in asteroid proximity orbit. On December 5, 2020 (Japan time), Hayabusa brought back to Earth a 5.4 g sample from Ryugu's surface. Aims. We analyzed the near-infrared spectra of Ryugu, in particular the band at 2.72 μm, with the aim to investigate the hydrogen content of the H2O and OH− groups in hydrated phyllosilicates on Ryugu's surface. Aims. We applied two different methods, normalized optical path length (NOPL) and effective single-particle absorption thickness (ESPAT), to the 3 μm region absorption band, and we compared the obtained spectral parameters with those obtained from carbonaceous chondrite meteorites whose H content was determined in the laboratory. Methods. We derived an exponential correlation between the selected meteorite H content and its respective ESPAT and NOPL parameters. The average value of the H content obtained on Ryugu's surface with its relative variations, combining the results obtained with the two methods, is 0.52−0.21+0.16 0. 52 − 0.21 + 0.16 $ 0.52 _{-0.21}^{+0.16} $ wt.%. These methods can be applied to other asteroids that exhibit a 3 μm region absorption band to estimate the mean average of H content. Results. The results of the ESPAT and NOPL methods used on the Ryugu spectral data present small variations across Ryugu's surface and do not show any evident relation with the surface geomorphological structures. Our estimation of the global average H content of Ryugu is in agreement with those of several aqueously altered carbonaceous chondrites measured in the laboratory and is most similar to the H content of heated CM. The study of phyllosilicate H2O and OH− group hydrogen content on Ryugu and the derived method may be applied to other observed primitive asteroids. The obtained results will allow Solar System evolution models to be constrained and will allow the formation and evolution of the Solar System to be better understood. [ABSTRACT FROM AUTHOR]

Aims. Long gamma-ray bursts (LGRBs) have been shown to be powerful probes of the Universe, in particular for studying the star formation rate up to very high redshift (z ∼ 9). Since LGRBs are produced by only a small fraction of massive stars, it is paramount to have a good understanding of their underlying intrinsic population in order to use them as cosmological probes without introducing any unwanted bias. The goal of this work is to constrain and characterise this intrinsic population. Methods. We developed a Monte Carlo model where each burst is described by its redshift and its properties at the peak of the light curve. We derived the best fit parameters by comparing our synthetic populations to carefully selected observational constraints based on the CGRO/BATSE, Fermi/GBM and Swift/BAT samples with appropriate flux thresholds. We explored different scenarios in terms of the cosmic evolution of the luminosity function and/or of the redshift distribution as well as including or not the presence of intrinsic spectral-energetics (Ep − L) correlations. Results. We find that the existence of an intrinsic Ep − L correlation is preferred but with a shallower slope than observed (αA ∼ 0.3) and a larger scatter (∼0.4 dex). We find a strong degeneracy between the cosmic evolution of the luminosity and of the LGRB rate, and show that a sample both larger and deeper than SHOALS by a factor of three is needed to lift this degeneracy. Conclusions. The observed Ep − L correlation cannot be explained only by selection effects although these do play a role in shaping the observed relation. The degeneracy between the cosmic evolution of the luminosity function and of the redshift distribution of LGRBs should be included in the uncertainties of star formation rate estimates; these amount to a factor of 10 at z = 6 and up to a factor of 50 at z = 9. [ABSTRACT FROM AUTHOR]

Context. Galaxies can be classified as passive ellipticals or star-forming discs. Ellipticals dominate at the high end of the mass range, and therefore there must be a mechanism responsible for the quenching of star-forming galaxies. This could either be due to the secular processes linked to the mass and star formation of galaxies or to external processes linked to the surrounding environment. However, the contribution from these smooth and stochastic processes to galaxy quenching has yet to be quantified. Aims. In this paper, we analytically model the processes that govern galaxy evolution and quantify their contribution. The key advantage of our method is that we do not assume the strength of the contribution from any of these processes beforehand, but instead aim to find their efficiencies. We have specifically studied the effects of mass quenching, gas stripping, and mergers on galaxy quenching. Methods. To achieve this, we first assumed a set of differential equations that describe the processes that shape galaxy evolution. We then modelled the parameters of these equations by maximising likelihood. These equations describe the evolution of galaxies individually, but the parameters of the equations are constrained by matching the extrapolated intermediate-redshift galaxies with the low-redshift galaxy population. In this study, we modelled the processes that change star formation and stellar mass in massive galaxies from the GAMA survey between z ≈ 0.4 and the present. Results. We identified and quantified the contributions from mass quenching, gas stripping, and mergers to galaxy quenching. By modelling mass quenching, we found that quenching begins for galaxies above a mass of ≈1010.2 M⊙, but is dependent on the gas accretion rate before quenching. The quenching timescale is on average 1.2 Gyr and a closer look reveals support for the slow-then-rapid quenching scenario. The major merging rate of galaxies is about once per 10 Gyr, while the rate of ram pressure stripping is significantly higher. In galaxies with decreasing star formation, we show that star formation is lost to fast quenching mechanisms such as ram pressure stripping and is countered by mergers, at a rate of about 41% Gyr−1 and to mass quenching 49% Gyr−1. Therefore, slow quenching mechanisms have a greater influence on galaxies in group or cluster environments than fast quenching mechanisms. [ABSTRACT FROM AUTHOR]

We performed an internal-consistency test of the KiDS+VIKING-450 (KV450) cosmic shear analysis with a colour-based split of source galaxies. Utilising the same measurements and calibrations for both sub-samples, we inspected the characteristics of the shear measurements and the performance of the calibration pipelines. On the modelling side, we examined the observational nuisance parameters, specifically those for the redshift calibration and intrinsic alignments, using a Bayesian analysis with dedicated test parameters. We verified that the current nuisance parameters are sufficient for the KV450 data to capture residual systematics, with slight deviations seen in the second and the third redshift tomographic bins. Our test also showcases the degeneracy between the inferred amplitude of intrinsic alignments and the redshift uncertainties in low redshift tomographic bins. The test is rather insensitive to the background cosmology and, therefore, can be implemented before any cosmological inference is made. [ABSTRACT FROM AUTHOR]