Search

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

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

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

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

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

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

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

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

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

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

Aims. We introduce a new deep-learning approach for the reconstruction of 3D dust density and temperature distributions from multi-wavelength dust emission observations on the scale of individual star-forming cloud cores (<0.2 pc). Methods. We constructed a training data set by processing cloud cores from the Cloud Factory simulations with the POLARIS radiative transfer code to produce synthetic dust emission observations at 23 wavelengths between 12 and 1300 µm. We simplified the task by reconstructing the cloud structure along individual lines of sight (LoSs) and trained a conditional invertible neural network (cINN) for this purpose. The cINN belongs to the group of normalising flow methods and it is able to predict full posterior distributions for the target dust properties. We tested different cINN setups, ranging from a scenario that includes all 23 wavelengths down to a more realistically limited case with observations at only seven wavelengths. We evaluated the predictive performance of these models on synthetic test data. Results. We report an excellent reconstruction performance for the 23-wavelength cINN model, achieving median absolute relative errors of about 1.8% in log(n/m−3) and 1% in log(Tdust/K), respectively. We identify trends towards an overestimation at the low end of the density range and towards an underestimation at the high end of both the density and temperature values, which may be related to a bias in the training data. After limiting our coverage to a combination of only seven wavelengths, we still find a satisfactory performance with average absolute relative errors of about 2.8% and 1.7% in log(n/m−3) and log(Tdust/K). Conclusions. This proof-of-concept study shows that the cINN-based approach for 3D reconstruction of dust density and temperature is very promising and it is even compatible with a more realistically constrained wavelength coverage. [ABSTRACT FROM AUTHOR]

Context. Solar wind ions backscattering is a fundamental plasma-surface interaction process that may occur on all celestial bodies exposed to the solar wind and lacking a significant atmosphere or magnetosphere. Yet, observations have been limited to the regolith-covered Moon and Phobos, one of the Martian moons. Aims. We aim to expand our knowledge of the process to include comets by investigating the backscattering of solar wind protons from the surface of comet 67P/Churyumov-Gerasimenko. Methods. We used one of the ion spectrometers on board ESA's Rosetta spacecraft to search for evidence of backscattered solar wind protons from the cometary surface. The signal of interest was expected to be very weak and several statistical treatments of the data were essential to eliminate any influence from background noise and instrumental effects. Due to limited knowledge of the signal location within the observed parameter space, we conducted a statistical analysis to identify the most probable conditions for detecting the signal. Results. No significant solar wind backscattered protons were ever observed by the instrument. The statement applies to the large spectrum of observation conditions. An upper limit of the backscattered proton flux is given, as well as an upper limit of the backscattering efficiency of 9 × 10−4. Conclusions. The surface of comet 67P/Churyumov-Gerasimenko distinguishes itself as a notably weak reflector of solar wind protons, with its backscattering efficiency, at most, as large as the lowest observed backscattering efficiency from the lunar regolith. [ABSTRACT FROM AUTHOR]

Direct imaging searches for exoplanets around stars detect many spurious candidates that are in fact background field stars. To help distinguish these from genuine companions, multi-epoch astrometry can be used to identify a common proper motion with the host star. Although this is frequently done, many approaches lack an appropriate model for the motions of the background population, or do not use a statistical framework to properly quantify the results. For this study we used Gαìα astrometry combined with 2MASS photometry to model the parallax and proper motion distributions of field stars around exoplanet host stars as a function of candidate magnitude. We developed a likelihood-based method that compares the positions of a candidate at multiple epochs with the positions expected under both this field star model and a co-moving companion model. Our method propagates the covariances in the Gαìα astrometry and the candidate positions. True companions are assumed to have long periods compared to the observational baseline, so we currently neglect orbital motion. We applied our method to a sample of 23 host stars with 263 candidates identified in the B-Star Exoplanet Abundance Study (BEAST) survey on VLT/SPHERE. We identified seven candidates in which the odds ratio favours the co-moving companion model by a factor of 100 or more. Most of these detections are based on only two or three epochs separated by less than three years, so further epochs should be obtained to reassess the companion probabilities. Our method is publicly available as an open-source python package from GitHub to use with any data. [ABSTRACT FROM AUTHOR]

In a population of multiple protostellar systems with discs, the sub-population of circumbinary discs whose orbital plane is highly misaligned with respect to the binary's orbital plane constrains the initial distribution of orbital parameters of the whole population. We show that by measuring the polar disc fraction and the average orbital eccentricity in the polar discs, one can constrain the distributions of initial eccentricity and mutual inclination in multiple stellar systems at birth. [ABSTRACT FROM AUTHOR]

Aims. We are testing the Brans–Dicke class of scalar tensor theories with planetary ephemerides. Methods. In this work, we apply our recently proposed Bayesian methodology to the Brans–Dicke case, with an emphasis on the issue of the strong equivalence principle (SEP). Results. We use an MCMC approach coupled to full, consistent planetary ephemeris construction (from point-mass body integration to observational fit) and compare the posterior distributions obtained with and without the introduction of potential violations of the SEP. Conclusions. We observe a shift in the confidence levels of the posteriors obtained. We interpret this shift as marginal evidence that the effect of violation of the SEP can no longer be assumed to be negligible in planetary ephemerides with the current data. We also notably report that the constraint on the Brans–Dicke parameter with planetary ephemerides is getting closer to the figure reported from the Cassini spacecraft alone, and also to the constraints from pulsars. We anticipate that data from future spacecraft missions, such as BepiColombo, will significantly enhance the constraints based on planetary ephemerides. [ABSTRACT FROM AUTHOR]

We present a free-form lens model for the multiply lensed quasar in the galaxy cluster SDSS J1004+4112. Our lens model draws minimal assumptions on the distribution of mass in the lens plane. We have paid particular attention to the model uncertainties on the predicted time delay originating from the particular configuration of model variables. Taking into account this uncertainty, we obtained a value of the Hubble constant of H0 = 74−13+9 km s−1 Mpc−1, which is consistent with recent independent estimates. The predicted time delay between the central image E and image C (the first to arrive) is ΔTE−C = 3200 ± 200 days for the estimated Hubble constant. Future measurements of ΔTE−C will allow for a tighter constraint to be imposed on H0 in this cluster-QSO system. [ABSTRACT FROM AUTHOR]

Context. The measurement of the Yarkovsky effect on near-Earth asteroids (NEAs) is common practice in orbit determination today, and the number of detections will increase with the developments of new and more accurate telescopic surveys. However, the process of finding new detections and identifying spurious ones is not yet automated, and it often relies on personal judgment. Aims. We aim to introduce a more automated procedure that can search for NEA candidates to measure the Yarkovsky effect, and that can identify spurious detections. Methods. The expected semi-major axis drift on an NEA caused by the Yarkovsky effect was computed with a Monte Carlo method on a statistical model of the physical parameters of the asteroid that relies on the most recent NEA population models and data. The expected drift was used to select candidates in which the Yarkovsky effect might be detected, according to the current knowledge of their orbit and the length of their observational arc. Then, a nongravitational acceleration along the transverse direction was estimated through orbit determination for each candidate. If the detected acceleration was statistically significant, we performed a statistical test to determine whether it was compatible with the Yarkovsky effect model. Finally, we determined the dependence on an isolated tracklet. Results. Among the known NEAs, our procedure automatically found 348 detections of the Yarkovsky effect that were accepted. The results are overall compatible with the predicted trend with the inverse of the diameter, and the procedure appears to be efficient in identifying and rejecting spurious detections. This algorithm is now adopted by the ESA NEO Coordination Centre to periodically update the catalogue of NEAs with a measurable Yarkovsky effect, and the results are automatically posted on the web portal. [ABSTRACT FROM AUTHOR]

We investigate the main tensions within the current standard model of cosmology from the perspective of the main statistics of cosmic voids, using the final BOSS DR12 data set. For this purpose, we present the first estimate of the S8 ≡ σ8 Ωm/0.3 and H0 parameters obtained from void number counts and shape distortions. To analyze void counts we relied on an extension of the popular volume-conserving model for the void size function, tailored to the application on data, including geometric and dynamic distortions. We calibrated the two nuisance parameters of this model with the official BOSS Collaboration mock catalogs and propagated their uncertainty through the statistical analysis of the BOSS void number counts. The constraints from void shapes come from the study of the geometric distortions of the stacked void-galaxy cross-correlation function. In this work we focus our analysis on the Ωm − σ8 and Ωm − H0 parameter planes and derive the marginalized constraints S8 = 0.813−0.068+0.093 and H0 = 67.3−9.1+10.0 km s−1 Mpc−1, which are fully compatible with constraints from the literature. These results are expected to notably improve in precision when analyzed jointly with independent probes and will open a new viewing angle on the rising cosmological tensions in the near future. [ABSTRACT FROM AUTHOR]

Globular clusters (GCs) have been proposed as promising sites for discovering intermediate-mass black holes (IMBHs), offering the possibility to gain crucial insights into the formation and evolution of these elusive objects. The Galactic GC 47 Tucanae (also known as NGC 104) has been suggested as a potential IMBH host, yet previous studies have yielded conflicting results. Therefore, we present a set of self-consistent dynamical models based on distribution functions (DFs) that depend on action integrals to assess the presence (or absence) of an IMBH in 47 Tucanae. Leveraging the state-of-the-art Multi Unit Spectroscopic Explorer (MUSE) and Hubble Space Telescope (HST) data, we analyzed the three-dimensional (3D) kinematics of the cluster's central regions, fitting individual star velocities down to the sub-arcsec scale (approximately 10−2 pc). According to our analysis, the inner kinematics of 47 Tucanae is incompatible with a central BH more massive than 578 M⊙ (at 3σ). This is the most stringent upper limit placed thus far on the mass of a putative IMBH in 47 Tucanae via a dynamical study. [ABSTRACT FROM AUTHOR]

Context. Recently, a primordial family of moderate-albedo asteroid fragments was discovered in the inner main belt. Its age was estimated to be 4.4 ± 1.7 Gyr. However, there is a lack of compositional characterization, which is important to the study of the earliest collisions in the main belt. Aims. In addition to the previously identified members and the parameters that define the family's borders (V shape), we expanded the list of family members to include asteroids located within the central region of the V shape. These additional potential members were selected based on their diameter (larger than 7 km) and their geometric visible albedo (greater than or equal to 12%). Subsequently, we conducted a spectroscopic survey to determine the dominant taxonomy and composition of this family. This allowed us to further refine the list of family members by removing interlopers. Methods. From an initial list of 263 asteroids that are considered to be potential members of the aforementioned primordial family, we retrieved their spectra in the visible and near-infrared range from the literature and from the Gala DR3 spectral catalog of Solar System objects. For asteroids with no or poor signal-to-noise ratio spectra in the literature, we carried out new ground-based observations. We obtained new spectra for 33 members of the family using the 1.82 m Asiago Telescope for the visible spectroscopy, while for near-infrared spectroscopy, we used the 3.58 m Telescopio Nazionale Galileo (TNG) and the 4.30 m Lowell Discovery Telescope (LDT). Results. In total, we collected spectra for 261 potential members of the primordial S-type family out of 263. We determined their spectral taxonomy and properties, such as spectral slopes and absorption band parameters, when existing. Using the taxonomical characterization and the orbital space parameters, we identified and removed 71 interlopers from the potential members list. The final list of the primordial S-type family members includes 190 asteroids. The family is dominated by S-complex (~71%) asteroids with a mineralogy similar to ordinary chondrites and pyroxene-rich minerals. The family also contains members classified as L-types and V-types. (~15% and ~9%, respectively). Conclusions. The mean albedo of the family is ~23%, and its largest probable remnant is the asteroid (30) Urania. The estimated size of the family parent body ranges between 110 and 210 km. This size range is compatible with the progenitor of H and L chondrites. [ABSTRACT FROM AUTHOR]

Context. Validating the accelerating expansion of the universe is an important aspect in improving our understanding of the evolution of the universe. By constraining the cosmic acceleration parameter XH, we can discriminate between the cosmological constant plus cold dark matter (ΛCDM) model and the Lemaître–Tolman–Bondi (LTB) model. Aims. In this paper, we explore the possibility of constraining the cosmic acceleration parameter with the inspiral gravitational waveform of neutron star binaries (NSBs) in the frequency range of 0.1 Hz–10 Hz, which can be detected by the second-generation space-based gravitational wave detector DECIGO. Methods. We used a convolutional neural network (CNN) and a long short-term memory (LSTM) network combined with a gated recurrent unit (GRU), along with a Fisher information matrix to derive constraints on the cosmic acceleration parameter, XH. Results. We assumed that our networks estimate the cosmic acceleration parameter without biases (the expected value of the estimation is equal to the true value). Under this assumption, based on the simulated gravitational wave data with a time duration of one month, we conclude that CNN can limit the relative error to 15.71%, while LSTM network combined with GRU can limit the relative error to 14.14%. Additionally, using a Fisher information matrix for gravitational wave data with a five-year observation can limit the relative error to 32.94%. Conclusions. Under the assumption of an unbiased estimation, the neural networks can offer a high-precision estimation of the cosmic acceleration parameter at different redshifts. Therefore, DECIGO is expected to provide direct measurements of the acceleration of the universe by observing the chirp signals of coalescing binary neutron stars. [ABSTRACT FROM AUTHOR]

We present ICAROGW 2.0, a pure python code developed to infer the astrophysical and cosmological population properties of noisy, heterogeneous, and incomplete observations. The code has mainly been developed for compact binary coalescence (CBC) population inference with gravitational wave (GW) observations. It contains several models for the masses, spins, and redshift of CBC distributions and it is able to infer population distributions, as well as the cosmological parameters and possible general relativity deviations at cosmological scales. Here, we present the theoretical and computational foundations of ICAROGW 2.0 and describe how the code can be employed for population and cosmological inference using (i) only GWs, (ii) GWs and galaxy surveys, and (iii) GWs with electromagnetic counterparts. We discuss the code performance on GPUs, finding a gain in computation time of about two orders of magnitude when more than 100 GW events are involved in the analysis. We have validated the code by re-analyzing GW population and cosmological studies, finding very good agreement with previous results. [ABSTRACT FROM AUTHOR]

Context. The reliability of one-dimensional disequilibrium chemistry models in hot exoplanet atmospheres depends on the chemical network used. To develop robust networks, we can rely on combustion studies that provide C/H/O/N chemical networks validated by vast amount of experimental data generated by the extensive research that has been done on hydrocarbon combustion and NOx formation in the last decades. Aims. We aimed to build a new and updated C0–C2 chemical network to study the C/H/O/N disequilibrium chemistry of warm and hot exoplanet atmospheres that relies on extensively validated and recent state-of-the-art combustion networks. The reliability range of this network was aimed for conditions between 500–2500 K and 100–10−6 bar, with cautious extrapolation at lower temperature values. Methods. We compared the predictions of seven networks over a large set of experiments, covering a wide range of conditions (pressures, temperatures, and initial compositions). To examine the consequences of this new chemical network on exoplanets atmospheric studies, we generated abundances profiles for GJ 436 b, GJ 1214 b, HD 189733 b, and HD 209458 b, using the 1D kinetic model FRECKLL and calculated the corresponding transmission spectra using TauREx 3.1. These spectra and abundance profiles have been compared with results obtained with our previous chemical network. Results. Our new kinetic network is composed of 145 species and 1313 reactions mostly reversible. This network proves to be more accurate than our previous one for the tested experimental conditions. The nitrogen chemistry update is found to be very impactful on the abundance profiles, particularly for HCN, with differences up to four orders of magnitude. The CO2 profiles are also significantly affected, with important repercussions on the transmission spectrum of GJ 436 b. Conclusions. These effects highlight the importance of using extensively validated chemical networks to gain confidence in our models predictions. As shown with CH2NH, the coupling between carbon and nitrogen chemistry combined with radicals produced by photolysis can have huge effects impacting the transmission spectra. This should be kept in mind when adding new elements like sulfur, as only adding a sub-mechanism neglects these coupling effects. [ABSTRACT FROM AUTHOR]

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]

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. 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]

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]

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. 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]

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]

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]

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 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]

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]

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]

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]