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The effect of angular momentum on galaxy formation and evolution has been studied for several decades. Our recent two papers using IllustrisTNG-100 simulation have revealed the acquisition path of the angular momentum from large-scale environment (satellites within hundreds of kpc) through the circumgalactic medium (CGM) to the stellar discs, putting forward the co-rotation scenario across the three distance scales. In real observations, although the rotation signature for the CGM and environmental three-dimensional angular momentum are difficult to obtain, line-of-sight kinematics of group member galaxies and stellar disc kinematics of central galaxies are available utilizing existing group catalogue data and integral field unit (IFU) data. In this paper, we use (1) the group catalogue of SDSS DR7 and MaNGA IFU stellar kinematic maps and (2) the group catalogue of GAMA DR4 data and SAMI IFU stellar kinematic maps, to test if the prediction above can be seen in real data. We found the co-rotation pattern between stellar discs and satellites can be concluded with 99.7 per cent confidence level (∼3σ) when combining the two data sets. And the random tests show that the signal can be scarcely drawn from random distribution. [ABSTRACT FROM AUTHOR]

In a companion paper, we introduced a publicly available pipeline to characterize exoplanet atmospheres through high-resolution spectroscopy. In this paper, we use this pipeline to study the biases and degeneracies that arise in atmospheric characterization of exoplanets in near-infrared ground-based transmission spectroscopy. We inject synthetic planetary transits into sequences of SPIRou spectra of the well known M dwarf star Gl 15 A, and study the effects of different assumptions on the retrieval. We focus on (i) mass and radius uncertainties, (ii) non-isothermal vertical profiles, and (iii) identification and retrieval of multiple species. We show that the uncertainties on mass and radius should be accounted for in retrievals and that depth-dependent temperature information can be derived from high-resolution transmission spectroscopy data. Finally, we discuss the impact of selecting wavelength orders in the retrieval and the issues that arise when trying to identify a single species in a multispecies atmospheric model. This analysis allows us to understand better the results obtained through transmission spectroscopy and their limitations in preparation to the analysis of actual SPIRou data. [ABSTRACT FROM AUTHOR]

Faraday tomography through broad-band polarimetry can provide crucial information on magnetized astronomical objects, such as quasars, galaxies, or galaxy clusters. However, the limited wavelength coverage of the instruments requires that we solve an ill-posed inverse problem when we want to obtain the Faraday dispersion function (FDF), a tomographic distribution of the magneto-ionic media along the line of sight. This paper explores the use of wavelet transforms and the sparsity of the transformed FDFs in the form of wavelet shrinkage (WS) for finding better solutions to the inverse problem. We recently proposed the Constraining and Restoring iterative Algorithm for Faraday Tomography (CRAFT; Cooray et al. 2021, MNRAS, 500, 5129), a new flexible algorithm that showed significant improvements over the popular methods such as Rotation Measure Synthesis. In this work, we introduce CRAFT + WS, a new version of CRAFT incorporating the ideas of wavelets and sparsity. CRAFT + WS exhibits significant improvements over the original CRAFT when tested for a complex FDF of a realistic Galactic model. Reconstructions of FDFs demonstrate super-resolution in Faraday depth, uncovering previously unseen Faraday complexities in observations. The proposed approach will be necessary for effective cosmic magnetism studies using the Square Kilometre Array and its precursors. The code is made publicly available at 〈 https://github.com/suchethac/craft 〉. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach to generate samples from target distributions that are difficult to sample from using Markov chain Monte Carlo (MCMC) methods. Traditional MCMC algorithms often face slow convergence due to the difficulty in finding proposals that suit the problem at hand. To address this issue, the paper introduces the approximate posterior ensemble sampler (APES) algorithm, which employs kernel density estimation and radial basis interpolation to create an adaptive proposal, leading to fast convergence of the chains. The APES algorithm's scalability to higher dimensions makes it a practical solution for complex problems. The proposed method generates an approximate posterior probability that closely approximates the desired distribution and is easy to sample from, resulting in smaller autocorrelation times and a higher probability of acceptance by the chain. We compare the performance of the APES algorithm with the affine invariance ensemble sampler with the stretch move in various contexts, demonstrating the efficiency of the proposed method. For instance, on the Rosenbrock function, the APES presented an autocorrelation time 140 times smaller than the affine invariance ensemble sampler. The comparison showcases the effectiveness of the APES algorithm in generating samples from challenging distributions. This paper presents a practical solution to generating samples from complex distributions while addressing the challenge of finding suitable proposals. With new cosmological surveys set to deal with many new systematics, this method offers a practical solution for the upcoming era of cosmological analyses. The algorithms presented in this paper are available at https://github.com/NumCosmo/NumCosmo. [ABSTRACT FROM AUTHOR]

This paper presents analytical results on the anatomy of nested sampling, from which a technique is developed to estimate the run-time of the algorithm that works for any nested sampling implementation. We test these methods on both toy models and true cosmological nested sampling runs. The method gives an order-of-magnitude prediction of the endpoint at all times, forecasting the true endpoint within standard error around the halfway point. [ABSTRACT FROM AUTHOR]

In this paper, a semisupervised machining learning technique had been utilized to analyse low-resolution stellar spectra from Large sky Area Multi-Object fiber Spectroscopic Telescope (LAMOST) Data Release 9 (DR9). We identified approximately 111 000 potential very metal-poor stars. Estimation of their stellar parameters (⁠|$T_{\rm eff}$|⁠ , |${\rm log}\, \rm {g}$|⁠ , [Fe/H]) indicate that over 99 per cent are metal-poor ([Fe/H] < −1.0), comprising 32 631 very metal-poor ([Fe/H] < −2.0), 702 extremely metal-poor ([Fe/H] < −3.0) and 30 ultra metal-poor ([Fe/H] < −4.0) stars. Based on kinematic characteristics, stars were categorized into thick disc-like, thin disc-like, and halo-like groups. We analysed their metallicity distributions (MDs) with respect to vertical height (⁠|$|Z|$|⁠) and orbital eccentricity (e). Thick disc-like stars in current sample show a clear trend of decreasing metallicty with increasing |$|Z|$| or e for the ranges −3.0 |$\lt $| [Fe/H] |$\lt $| −1.2 and |$|Z|$| |$\lt $| 3 kpc. Conversely, thin disc-like stars in current sample exhibit a slight increase in the fraction of more metal-poor stars with |$|Z|$| for the ranges −3.0 |$\lt $| [Fe/H] |$\lt $| −1.2 and |$|Z|$| |$\lt$| 1 kpc, but no obvious correlation with e. Additionally, we confirmed the presence of two prominent substructures among halo-like stars. One exhibits a high eccentricity (⁠|$e\ \gt\ 0.8$|⁠) orbit and higher metallicity, while the other follows a retrograde orbit with moderate eccentricity (⁠|$e\sim 0.6$|⁠) and lower metallicity. We believe they are related to the merger events known as Gaia Sausage and Sequoia, respectively. Furthermore, our observations indicate that the Sequoia has lower eccentricity and metallicity compared to the Gaia Sausage. [ABSTRACT FROM AUTHOR]

Association analysis is an important task that aims to investigate correlations in astronomical spectral data and mine relationships between different data features. With the rapid development of various sky survey projects, multiple association analysis methods have been applied to efficiently investigate the correlation between spectral data. However, due to the different focuses of analysing the relationships within spectral data, the performance and applicability scenarios of association analysis methods vary. We present the third article in the series to provide a comprehensive review of algorithms for astronomical spectral association analysis. First, this paper outlines the ideas and applications of association analysis algorithms for astronomical spectra in the current literature. Secondly, experiments are conducted on a unified A-type stellar spectral data set constructed based on three different signal-to-noise ratios and data volumes to examine the performance of different algorithms in analysing the correlation between data features. The results indicate that association rule algorithms can more comprehensively and effectively uncover the correlations among different spectral features, while regression analysis algorithms offer a simpler and more intuitive approach to analysing relationships between features. The spectral data used in the experiments are obtained from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope. Finally, the source code of association analysis algorithms and manuals for usage are provided on GitHub. [ABSTRACT FROM AUTHOR]

Low surface brightness substructures around galaxies, known as tidal features, are a valuable tool in the detection of past or ongoing galaxy mergers, and their properties can answer questions about the progenitor galaxies involved in the interactions. The assembly of current tidal feature samples is primarily achieved using visual classification, making it difficult to construct large samples and draw accurate and statistically robust conclusions about the galaxy evolution process. With upcoming large optical imaging surveys such as the Vera C. Rubin Observatory's Legacy Survey of Space and Time, predicted to observe billions of galaxies, it is imperative that we refine our methods of detecting and classifying samples of merging galaxies. This paper presents promising results from a self-supervised machine learning model, trained on data from the Ultradeep layer of the Hyper Suprime-Cam Subaru Strategic Program optical imaging survey, designed to automate the detection of tidal features. We find that self-supervised models are capable of detecting tidal features, and that our model outperforms previous automated tidal feature detection methods, including a fully supervised model. An earlier method applied to real galaxy images achieved 76 per cent completeness for 22 per cent contamination, while our model achieves considerably higher (96 per cent) completeness for the same level of contamination. We emphasize a number of advantages of self-supervised models over fully supervised models including maintaining excellent performance when using only 50 labelled examples for training, and the ability to perform similarity searches using a single example of a galaxy with tidal features. [ABSTRACT FROM AUTHOR]

As the hunt for an Earth-like exoplanets has intensified in recent years, so has the effort to characterize and model the stellar signals that can hide or mimic small planetary signals. Stellar variability arises from a number of sources, including granulation, supergranulation, oscillations, and activity, all of which result in quasi-periodic or stochastic behaviour in photometric and/or radial velocity observations. Traditionally, the characterization of these signals has mostly been done in the frequency domain. However, the recent development of scalable Gaussian process regression methods makes direct time-domain modelling of stochastic processes a feasible and arguably preferable alternative, obviating the need to estimate the power spectral density of the data before modelling it. In this paper, we compare the two approaches using a series of experiments on simulated data. We show that frequency-domain modelling can lead to inaccurate results, especially when the time-sampling is irregular. By contrast, Gaussian process regression results are often more precise, and systematically more accurate, in both the regular and irregular time-sampling regimes. While this work was motivated by the analysis of radial velocity and photometry observations of main-sequence stars in the context of planet searches, we note that our results may also have applications for the study of other types of astrophysical variability such as quasi-periodic oscillations in X-ray binaries and active galactic nuclei variability. [ABSTRACT FROM AUTHOR]

It is crucial for a deeper understanding of the formation and evolution of galaxies in the Universe to study stellar mass (M *) and star formation rate (SFR). Traditionally, astronomers infer the properties of galaxies from spectra, which are highly informative, but expensive and hard to be obtained. Fortunately, modern sky surveys obtained a vast amount of high-spatial-resolution photometric images. The photometric images are obtained relatively economically than spectra, and it is very helpful for related studies if M * and SFR can be estimated from photometric images. Therefore, this paper conducted some preliminary researches and explorations on this regard. We constructed a deep learning model named Galaxy Efficient Network (GalEffNet) for estimating integrated M * and specific star formation rate (sSFR) from Dark Energy Spectroscopic Instrument galaxy images. The GalEffNet primarily consists of a general feature extraction module and a parameter feature extractor. The research results indicate that the proposed GalEffNet exhibits good performance in estimating M * and sSFR, with σ reaching 0.218 and 0.410 dex. To further assess the robustness of the network, prediction uncertainty was performed. The results show that our model maintains good consistency within a reasonable bias range. We also compared the performance of various network architectures and further tested the proposed scheme using image sets with various resolutions and wavelength bands. Furthermore, we conducted applicability analysis on galaxies of various sizes, redshifts, and morphological types. The results indicate that our model performs well across galaxies with various characteristics and indicate its potentials of broad applicability. [ABSTRACT FROM AUTHOR]

Galaxy clusters are the most massive gravitationally bound systems consisting of dark matter, hot baryonic gas, and stars. They play an important role in observational cosmology and galaxy evolution studies. We develop a deep learning model for segmentation of Sunyaev–Zeldovich (SZ) signal on Atacama Cosmology Telescope (ACT) +  Planck intensity maps and construct a pipeline for microwave cluster detection in the ACT footprint. The proposed model allows us to identify previously unknown galaxy clusters, i.e. it is capable of detecting SZ sources below the detection threshold adopted in the published galaxy clusters catalogues [such as ACT DR5 and Planck Sunyaev–Zeldovich 2 (PSZ2)]. In this paper, we use the derived SZ signal map to considerably improve a cluster purity in the extended catalogue of Sunyaev–Zeldovich objects from Planck data (SZcat) in the ACT footprint. From SZcat, we create a new microwave galaxy cluster catalogue (ComPACT), which includes 2962 SZ objects with cluster purity conservatively estimated as ≳74–84 per cent. We categorize objects in the catalogue into three categories, based on their cluster reliability. Within the ComPACT catalogue, there are ≳977 new clusters with respect to the ACT DR5 and PSZ2 catalogues. [ABSTRACT FROM AUTHOR]

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

This paper presents a new method to estimate systematic errors in the maximum-likelihood regression of count data. The method is applicable in particular to X-ray spectra in situations where the Poisson log-likelihood, or the Cash goodness-of-fit statistic, indicate a poor fit that is attributable to overdispersion of the data. Overdispersion in Poisson data is treated as an intrinsic model variance that can be estimated from the best-fit model, using the maximum-likelihood C min statistic. The paper also studies the effects of such systematic errors on the Δ C likelihood-ratio statistic, which can be used to test for the presence of a nested model component in the regression of Poisson count data. The paper introduces an overdispersed χ2 distribution that results from the convolution of a χ2 distribution that models the usual Δ C statistic, and a zero-mean Gaussian that models the overdispersion in the data. This is proposed as the distribution of choice for the Δ C statistic in the presence of systematic errors. The methods presented in this paper are applied to XMM–Newton data of the quasar 1ES 1553+113 that were used to detect absorption lines from an intervening warm-hot intergalactic medium (WHIM). This case study illustrates how systematic errors can be estimated from the data, and their effect on the detection of a nested component, such as an absorption line, with the Δ C statistic. [ABSTRACT FROM AUTHOR]

Unsupervised learning, a branch of machine learning that can operate on unlabelled data, has proven to be a powerful tool for data exploration and discovery in astronomy. As large surveys and new telescopes drive a rapid increase in data size and richness, these techniques offer the promise of discovering new classes of objects and of efficient sorting of data into similar types. However, unsupervised learning techniques generally require feature extraction to derive simple but informative representations of images. In this paper, we explore the use of self-supervised deep learning as a method of automated representation learning. We apply the algorithm Bootstrap Your Own Latent to Galaxy Zoo DECaLS images to obtain a lower dimensional representation of each galaxy, known as features. We briefly validate these features using a small supervised classification problem. We then move on to apply an automated clustering algorithm, demonstrating that this fully unsupervised approach is able to successfully group together galaxies with similar morphology. The same features prove useful for anomaly detection, where we use the framework astronomaly to search for merger candidates. While the focus of this work is on optical images, we also explore the versatility of this technique by applying the exact same approach to a small radio galaxy data set. This work aims to demonstrate that applying deep representation learning is key to unlocking the potential of unsupervised discovery in future data sets from telescopes such as the Vera C. Rubin Observatory and the Square Kilometre Array. [ABSTRACT FROM AUTHOR]

The environmental dependence of Type Ia supernova (SN Ia) luminosities is well established, and efforts are being made to find its origin. Previous studies typically use the currently observed status of the host galaxy. However, given the delay time between the birth of the progenitor star and the SN Ia explosion, the currently observed status may differ from the birth environment of the SN Ia progenitor star. In this paper, employing the chemical evolution and accurately determined stellar population properties of 44 early-type host galaxies, we, for the first time, estimate the SN Ia progenitor star birth environment, specifically [Fe/H]Birth and [α/Fe]Birth. We show that [α/Fe]Birth has a |$30.4^{\text{+10.6}}_{-10.1}{{\ \rm per\ cent}}$| wider range than the currently observed [α/Fe]Current, while the range of [Fe/H]Birth is not statistically different (⁠|$17.9^{\text{+26.0}}_{-27.1}{{\ \rm per\ cent}}$|⁠) to that of [Fe/H]Current. The birth and current environments of [Fe/H] and [α/Fe] are sampled from different populations (p -values of the Kolmogorov–Smirnov test <0.01). We find that light-curve fit parameters are insensitive to [Fe/H]Birth (<0.9σ for the non-zero slope), while a linear trend is observed with Hubble residuals (HRs) at the 2.4σ significance level. With [α/Fe]Birth, no linear trends (<1.1σ) are observed. Interestingly, we find that [α/Fe]Birth clearly splits the SN Ia sample into two groups: SN Ia exploded in [α/Fe]Birth-rich or [α/Fe]Birth-poor environments. SNe Ia exploded in different [α/Fe]Birth groups have different weighted-means of light-curve shape parameters: 0.81 ± 0.33 (2.5σ). They are thought to be drawn from different populations (p -value = 0.01). Regarding SN Ia colour and HRs, there is no difference (<1.0σ) in the weighted-means and distribution (p -value > 0.27) of each [α/Fe]Birth group. [ABSTRACT FROM AUTHOR]

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

We have performed targeted searches of known extragalactic transient events at millimetre wavelengths using nine seasons (2013–2021) of 98, 150, and 229 GHz Atacama Cosmology Telescope (ACT) observations that mapped ∼40 per cent of the sky for most of the data volume. Our data cover 88 gamma-ray bursts (GRBs), 12 tidal disruption events (TDEs), and 203 other transients, including supernovae (SNe). We stack our ACT observations to increase the signal-to-noise ratio of the maps. In all cases but one, we do not detect these transients in the ACT data. The single candidate detection (event AT2019ppm), seen at ∼5σ significance in our data, appears to be due to active galactic nuclei activity in the host galaxy coincident with a transient alert. For each source in our search we provide flux upper limits. For example, the medians for the 95 per cent confidence upper limits at 98 GHz are 15, 18, and 16 mJy for GRBs, SNe, and TDEs, respectively, in the first month after discovery. The projected sensitivity of future wide-area cosmic microwave background surveys should be sufficient to detect many of these events using the methods described in this paper. [ABSTRACT FROM AUTHOR]

The study of the orbital period variation of short-period binary systems has been important to understand several physical phenomena, such as the emission of gravitational waves, angular momentum loss via magnetic braking, matter transfer between the components, apsidal motion, quadrupole moment variation, and presence of circumbinary bodies. With the advent of large space missions, e.g. Kepler and Transiting Exoplanet Survey Satellite (TESS), an enormous amount of high-precision photometric data with temporal coverage from years to decades has become available. Thus, in this work, we propose to study the orbital period variation of a sample of 253 binary that was observed by both Kepler and TESS and therefore with a temporal coverage of more than 10 yr. The main goal of this paper is the search and characterization of third bodies. Based on the periodicity analysis of the observed minus calculated (O − C) diagram of the sample, 75 of them showed periodic variation and therefore were classified as binary systems with third-body candidates, while the remaining 178 did not show periodic variations. This result is a two-fold increase in tertiary candidates around binary systems compared to the study carried out with only Kepler data. Although our estimated rate of third-body candidates (∼30 per cent) is higher than that obtained only with Kepler data (∼20 per cent), it should be taken as a lower limit. [ABSTRACT FROM AUTHOR]

Black hole X-ray binaries (BHBs) offer insights into extreme gravitational environments and the testing of general relativity. The X-ray spectrum collected by NICER offers valuable information on the properties and behaviour of BHBs through spectral fitting. However, traditional spectral fitting methods are slow and scale poorly with model complexity. This paper presents a new semisupervised autoencoder neural network for parameter prediction and spectral reconstruction of BHBs, showing an improvement of up to a factor of 2700 in speed while maintaining comparable accuracy. The approach maps the spectral features from the numerous outbursts catalogued by NICER and generalizes them to new systems for efficient and accurate spectral fitting. The effectiveness of this approach is demonstrated in the spectral fitting of BHBs and holds promise for use in other areas of astronomy and physics for categorizing large data sets. The code is available via https://github.com/EthanTreg/Spectrum-Machine-Learning. [ABSTRACT FROM AUTHOR]

Global 21-cm experiments are built to study the evolution of the Universe between the cosmic dawn and the epoch of reionization. FlexKnot is a function parametrized by freely moving knots stringed together by splines. Adopting the FlexKnot function as the signal model has the potential to separate the global 21-cm signal from the foregrounds and systematics while being capable of recovering the crucial features given by theoretical predictions. In this paper, we implement the FlexKnot method by integrating twice over a function of freely moving knots interpolated linearly. The function is also constrained at the lower frequencies corresponding to the dark ages by theoretical values. The FlexKnot model is tested in the framework of the realistic data analysis pipeline of the REACH global signal experiment using simulated antenna temperature data. We demonstrate that the FlexKnot model performs better than existing signal models, e.g. the Gaussian signal model, at reconstructing the shape of the true signals present in the simulated REACH data, especially for injected signals with complex structures. The capabilities of the FlexKnot signal model is also tested by introducing various systematics and simulated global signals of different types. These tests show that four to five knots are sufficient to recover the general shape of most realistic injected signals, with or without sinusoidal systematics. We show that true signals whose absorption trough is of amplitude between 120 and 450 mK can be well recovered with systematics up to about 50 mK. [ABSTRACT FROM AUTHOR]

Over the past several decades, conventional spectral analysis techniques of polluted white dwarfs have become powerful tools to learn about the geology and chemistry of extrasolar bodies. Despite their proven capabilities and extensive legacy of scientific discoveries, these techniques are, however, still limited by their manual, time-intensive, and iterative nature. As a result, they are susceptible to human errors and are difficult to scale up to population-wide studies of metal pollution. This paper seeks to address this problem by presenting cecilia , the first machine learning (ML)-powered spectral modelling code designed to measure the metal abundances of intermediate-temperature (10 000 ≤ T eff ≤ 20 000 K), Helium-rich polluted white dwarfs. Trained with more than 22 000 randomly drawn atmosphere models and stellar parameters, our pipeline aims to overcome the limitations of classical methods by replacing the generation of synthetic spectra from computationally expensive codes and uniformly spaced model grids, with a fast, automated, and efficient neural-network-based interpolator. More specifically, cecilia combines state-of-the-art atmosphere models, powerful artificial intelligence tools, and robust statistical techniques to rapidly generate synthetic spectra of polluted white dwarfs in high-dimensional space, and enable accurate (≲0.1 dex) and simultaneous measurements of 14 stellar parameters – including 11 elemental abundances – from real spectroscopic observations. As massively multiplexed astronomical surveys begin scientific operations, cecilia 's performance has the potential to unlock large-scale studies of extrasolar geochemistry and propel the field of white dwarf science into the era of Big Data. In doing so, we aspire to uncover new statistical insights that were previously impractical with traditional white dwarf characterization techniques. [ABSTRACT FROM AUTHOR]

Pulsar time has different physical mechanisms from atomic time, and these two time-scales are complementary in terms of stability performance. The joint timekeeping of pulsar and atomic time is currently an important application direction. Our research utilizes a wavelet analysis method to perform multiscale analysis of time signals in both the time and frequency domains, and assigns corresponding weights to different frequency bands for synthesis, which solves the problem of balancing long- and short-term stability in unified weighted synthesis. In this paper, a joint time-scale APT (atomic pulsar time) is obtained by a fusion of pulsar ensemble time (EPT) and atomic time TA(NTSC) (AT) based on wavelet analysis. The results show that APT effectively integrates the stability advantages of both EPT and AT, and the short-term stability of APT at 0.4 yr is consistent with that of AT, reaching 5.6 × 10−15 as evaluated by σ z variance. The stability of APT remains consistent with EPT after 3.2 yr, and the long-term stability at 13.1 yr is close to 1.3 × 10−16, perfectly retaining the excellent long-term stability characteristics of pulsar time. The root mean square of APT's clock difference relative to TT(BIPM) is much lower than that of TA(NTSC). Moreover, we propose ways to improve the stability of APT through experimental comparisons. The results indicate that APT has the potential to serve as a reference for steering UTC(NTSC) in the future, improving the long-term timekeeping capability of local atomic time in China, thereby comprehensively improving the stability and reliability of China's time reference. [ABSTRACT FROM AUTHOR]

Modern cosmological hydrodynamical galaxy simulations provide tens of thousands of reasonably realistic synthetic galaxies across cosmic time. However, quantitatively assessing the level of realism of simulated universes in comparison to the real one is difficult. In this paper of the Extracting Reality from Galaxy Observables with Machine Learning series, we utilize contrastive learning to directly compare a large sample of simulated and observed galaxies based on their stellar-light images. This eliminates the need to specify summary statistics and allows to exploit the whole information content of the observations. We produce survey-realistic galaxy mock data sets resembling real Hyper Suprime-Cam (HSC) observations using the cosmological simulations TNG50 and TNG100. Our focus is on galaxies with stellar masses between 109 and 1012 M⊙ at z  = 0.1–0.4. This allows us to evaluate the realism of the simulated TNG galaxies in comparison to actual HSC observations. We apply the self-supervised contrastive learning method Nearest Neighbour Contrastive Learning to the images from both simulated and observed data sets (g -, r -, i -bands). This results in a 256-dimensional representation space, encoding all relevant observable galaxy properties. First, this allows us to identify simulated galaxies that closely resemble real ones by seeking similar images in this multidimensional space. Even more powerful, we quantify the alignment between the representations of these two image sets, finding that the majority (≳ 70 per cent) of the TNG galaxies align well with observed HSC images. However, a subset of simulated galaxies with larger sizes, steeper Sérsic profiles, smaller Sérsic ellipticities, and larger asymmetries appears unrealistic. We also demonstrate the utility of our derived image representations by inferring properties of real HSC galaxies using simulated TNG galaxies as the ground truth. [ABSTRACT FROM AUTHOR]

Young stellar clusters are predominantly the hub of star formation and hence, ideal to perform comprehensive studies over the least explored substellar regime. Various unanswered questions like the mass distribution in brown dwarf regime and the effect of diverse cluster environment on brown dwarf formation efficiency still plague the scientific community. The nearby young cluster, IC 1396 with its feedback-driven environment, is ideal to conduct such study. In this paper, we adopt a multiwavelength approach, using deep Subaru HSC along with other data sets and machine learning techniques to identify the cluster members complete down to ∼ 0.03 M⊙ in the central 22 arcmin area of IC 1396. We identify 458 cluster members including 62 brown dwarfs which are used to determine mass distribution in the region. We obtain a star-to-brown dwarf ratio of ∼ 6 for a stellar mass range 0.03–1 M⊙ in the studied cluster. The brown dwarf fraction is observed to increase across the cluster as radial distance from the central OB-stars increases. This study also compiles 15 young stellar clusters to check the variation of star-to-brown dwarf ratio relative to stellar density and ultraviolet (UV) flux ranging within 4–2500 stars pc−2 and 0.7–7.3  G 0, respectively. The brown dwarf fraction is observed to increase with stellar density but the results about the influence of incident UV flux are inconclusive within this range. This is the deepest study of IC 1396 as of yet and it will pave the way to understand various aspects of brown dwarfs using spectroscopic observations in future. [ABSTRACT FROM AUTHOR]

Estimators for n -point clustering statistics in Fourier-space demand that modern surveys of large-scale structure be transformed to Cartesian coordinates to perform Fast Fourier Transforms (FFTs). In this work, we explore this transformation in the context of pixelized line intensity maps (LIM), highlighting potential biasing effects on power-spectrum measurements. Current analyses often avoid a complete resampling of the data by approximating survey geometry as rectangular in Cartesian space, an increasingly inaccurate assumption for modern wide-sky surveys. Our simulations of a |$20\, {\times }\, 20\, \text{deg}^2$| 21 cm LIM survey at |$0.34\, {\lt }\, z\, {\lt }\, 0.54$| show this assumption biases power-spectrum measurements by |${\gt }\, 20~{{\ \rm per\ cent}}$| across all scales. We therefore present a more robust framework for regridding the voxel intensities on to a 3D FFT field by coordinate transforming large numbers of Monte-Carlo sampling particles. Whilst this unbiases power-spectrum measurements on large scales, smaller scale discrepancies remain, caused by structure smoothing and aliasing from separations unresolved by the grid. To correct these effects, we introduce modelling techniques, higher order particle assignments, and interlaced FFT grids to suppress the aliased power. Using a piecewise cubic spline (PCS) particle assignment and an interlaced FFT field, we achieve sub-per cent accuracy up to 80 per cent of the Nyquist frequency for our 21 cm LIM simulations. We find a more subtle hierarchical improvement in results for higher order assignment schemes, relative to the gains made for galaxy surveys, which we attribute to the extra complexity in LIM from additional discretizing steps. python code accompanying this paper is available at github.com/stevecunnington/gridimp. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the software package propane , written for the r data analysis language. propane combines the full range of wcslib projections with the C++ image manipulation routines provided by the cimg library. propane offers routines for image warping and combining (including stacking), and various related tasks such as image alignment tweaking and pixel masking. It can stack an effectively unlimited number of target frames using multiple parallel cores, and offers threading for many lower level routines. It has been used for a number of current and upcoming large surveys, and we present a range of its capabilities and features. propane is already available under a permissive open-source LGPL-3 license at github.com/asgr/ProPane (DOI: 10.5281/zenodo.10057053). [ABSTRACT FROM AUTHOR]

This paper presents GPFC, a novel Graphics Processing Unit (GPU) Phase Folding and Convolutional Neural Network (CNN) system to detect exoplanets using the transit method. We devise a fast-folding algorithm parallelized on a GPU to amplify low signal-to-noise ratio transit signals, allowing a search at high precision and speed. A CNN trained on two million synthetic light curves reports a score indicating the likelihood of a planetary signal at each period. While the GPFC method has broad applicability across period ranges, this research specifically focuses on detecting ultrashort-period planets with orbital periods less than one day. GPFC improves on speed by three orders of magnitude over the predominant Box-fitting Least Squares (BLS) method. Our simulation results show GPFC achieves 97 per cent training accuracy, higher true positive rate at the same false positive rate of detection, and higher precision at the same recall rate when compared to BLS. GPFC recovers 100 per cent of known ultrashort-period planets in Kepler light curves from a blind search. These results highlight the promise of GPFC as an alternative approach to the traditional BLS algorithm for finding new transiting exoplanets in data taken with Kepler and other space transit missions such as K 2, TESS , and future PLATO and Earth 2.0. [ABSTRACT FROM AUTHOR]

The 1D power spectrum P 1D of the Ly α forest provides important information about cosmological and astrophysical parameters, including constraints on warm dark matter models, the sum of the masses of the three neutrino species, and the thermal state of the intergalactic medium. We present the first measurement of P 1D with the quadratic maximum likelihood estimator (QMLE) from the Dark Energy Spectroscopic Instrument (DESI) survey early data sample. This early sample of 54 600 quasars is already comparable in size to the largest previous studies, and we conduct a thorough investigation of numerous instrumental and analysis systematic errors to evaluate their impact on DESI data with QMLE. We demonstrate the excellent performance of the spectroscopic pipeline noise estimation and the impressive accuracy of the spectrograph resolution matrix with 2D image simulations of raw DESI images that we processed with the DESI spectroscopic pipeline. We also study metal line contamination and noise calibration systematics with quasar spectra on the red side of the Ly α emission line. In a companion paper, we present a similar analysis based on the Fast Fourier Transform estimate of the power spectrum. We conclude with a comparison of these two approaches and discuss the key sources of systematic error that we need to address with the upcoming DESI Year 1 analysis. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) and deep learning techniques are playing an increasing role in astronomy to deal with the data avalanche. Here we describe an application for finding resolved planetary nebulae (PNe) in crowded, wide-field, narrow-band Hα survey imagery in the Galactic plane, to test and facilitate more objective, reproducible, efficient and reliable trawls for them. PNe are important for studying the late-stage stellar evolution of low-mass to intermediate-mass stars. However, the confirmed ∼3800 Galactic PNe fall far short of the numbers expected. Traditional visual searching for resolved PNe is time-consuming because of the large data size and areal coverage of modern astronomical surveys. The training and validation data set of our algorithm was built with the INT Photometric Hα Survey (IPHAS) and true PNe from the Hong Kong/AAO/Strasbourg Hα (HASH) data base. Our algorithm correctly identified 444 PNe in the validation set of 454 PNe, with only 16 explicable 'false' positives, achieving a precision rate of 96.5 per cent and a recall rate of 97.8 per cent. After transfer learning, it was then applied to the VST Photometric Hα Survey of the Southern Galactic plane and bulge (VPHAS+), examining 979 out of 2284 survey fields, each covering 1° × 1°. It returned ∼20 000 detections, including 2637 known PNe and other kinds of catalogued non-PNe. A total of 815 new high-quality PNe candidates were found, 31 of which were selected as top-quality targets for optical spectroscopic follow-up. We found that 74 per cent of them are true, likely, and possible PNe. Representative preliminary confirmatory spectroscopy results are presented here to demonstrate the effectiveness of our techniques, with full details to be given in our forthcoming paper. [ABSTRACT FROM AUTHOR]

There is a wide demand for high-precision and high-stability time-scales, which are mainly established by atomic clocks. The short-term stability of atomic clocks is good, while the long-term stability is poor. Meanwhile, millisecond pulsars have good long-term stability. It is expected to produce time-scales with high-stability performance by combining pulsars and atomic clocks to keep time. In this paper, a novel joint timekeeping algorithm based on Vondrak–Cepek filtering is proposed. The filter makes a compromise between the fidelity of the observation data and their first derivatives and the smoothness of the filtering results. Also, it can give full play to the long-term stability of pulsars and the short-term stability of atomic clocks. Experiments and results show that the joint timekeeping algorithm improved σ z of 7.58 × 10−13 on 6.5-d time interval with respect to that obtained from the pulsar ensemble time-scale algorithm. Also, the joint timekeeping algorithm improved σ z of 6.94 × 10−15 on 9.1-yr time interval with respect to that obtained from the atomic ensemble time-scale algorithm. The algorithm proposed in this paper can be applied to scenes requiring high-stability time-scales. [ABSTRACT FROM AUTHOR]

The high resolution spectrograph ESPRESSO on the VLT allows measurements of fundamental constants at unprecedented precision and hence enables tests for space–time variations predicted by some theories. In a series of recent papers, we developed optimal analysis procedures that expose and eliminate the subjectivity and bias in previous quasar absorption system measurements. In this paper, we analyse the ESPRESSO spectrum of the absorption system at zabs  = 1.15 towards the quasar HE 0515-4414. Our goal here is not to provide a new unbiased measurement of Δα/α in this system (that will be done separately). Rather, it is to carefully examine the impact of blinding procedures applied in many previous measurements of the fine structure constant in quasar absorption systems. To do this, we emulate previous procedures, using supercomputer Monte Carlo AI calculations to generate a large number of independently constructed models of the absorption complex. Each model is obtained using ai-vpfit , with Δα/α fixed until a 'final' model for the absorption system is obtained, at which point Δα/α is then released as a free parameter for one final optimization. The results show that the value of Δα/α obtained in this way is systematically biased towards the initially fixed value i.e. this process produces measurements that are unrelated to the true value of Δα/α. The implication is straightforward: to avoid bias, all future measurements must include Δα/α as a free parameter from the beginning of the modelling process. [ABSTRACT FROM AUTHOR]

In this series of papers, we employ several machine learning (ML) methods to classify the point-like sources from the miniJPAS catalogue, and identify quasar candidates. Since no representative sample of spectroscopically confirmed sources exists at present to train these ML algorithms, we rely on mock catalogues. In this first paper, we develop a pipeline to compute synthetic photometry of quasars, galaxies, and stars using spectra of objects targeted as quasars in the Sloan Digital Sky Survey. To match the same depths and signal-to-noise ratio distributions in all bands expected for miniJPAS point sources in the range 17.5 ≤ r < 24, we augment our sample of available spectra by shifting the original r -band magnitude distributions towards the faint end, ensure that the relative incidence rates of the different objects are distributed according to their respective luminosity functions, and perform a thorough modelling of the noise distribution in each filter, by sampling the flux variance either from Gaussian realizations with given widths, or from combinations of Gaussian functions. Finally, we also add in the mocks the patterns of non-detections which are present in all real observations. Although the mock catalogues presented in this work are a first step towards simulated data sets that match the properties of the miniJPAS observations, these mocks can be adapted to serve the purposes of other photometric surveys. [ABSTRACT FROM AUTHOR]

This paper is devoted to an interdisciplinary method modelling the internal structure of differentiated asteroids via a data-driven approach called invertible neural networks (INNs). The model estimation of the internal structure can be generalized as an inverse problem of estimating internal parameters from a set of observations. Previous works (e.g. Park et al. 2014 ; Takahashi and Scheeres 2014) used the full gravity field data measures to derive the heterogeneous mass distribution. However, in our method, only the flight state of the spacecraft is adopted as the observation data. Since the internal parameters may not be uniquely determined, typical feedforward neural networks cannot simply be applied to such an inverse problem. The INNs adopted in this paper can 'read' the interior information from a flight trajectory of the spacecraft directly. The INNs are employed to establish the two-directional mapping between the group of physical parameters and the set of flight state observations of position and velocity. The INNs are trained in a bi-directional way using four losses. Finally, the performances of the trained networks are shown in both overfit and underfit situations where the internal structure of asteroids can be estimated by this INNs-based method accurately and effectively. The results also show that even when the degeneracy occurs, the true solution still falls inside the estimation distribution. [ABSTRACT FROM AUTHOR]

Observationally, the mysterious fast radio bursts (FRBs) are classified as repeating ones and apparently non-repeating ones. While repeating FRBs cannot be classified into the non-repeating group, it is unknown whether the apparently non-repeating FRBs are actually repeating FRBs whose repetitions are yet to be discovered, or whether they belong to another physically distinct type from the repeating ones. In a series of two papers, we attempt to disentangle this mystery with machine learning methods. In this first paper, we focus on an array of supervised machine learning methods. We train the machine learning algorithms with a fraction of the observed FRBs in the first CHIME/FRB catalogue, telling them which ones are apparently non-repeating and which ones are repeating. We then let the trained models predict the repetitiveness of the rest of the FRB data with the observed parameters, and we compare the predictions with the observed repetitiveness. We find that the models can predict most FRBs correctly, hinting towards distinct mechanisms behind repeating and non-repeating FRBs. We also find that the two most important distinguishing factors between non-repeating and repeating FRBs are brightness temperature and rest-frame frequency bandwidth. By applying the trained models back to the entire first CHIME catalogue, we further identify some potentially repeating FRBs currently reported as non-repeating. We recommend a list of these bursts as targets for future observing campaigns to search for repeated bursts in a combination with the results presented in Paper II using unsupervised machine learning methods. [ABSTRACT FROM AUTHOR]

In this paper, we construct the circular velocity curve of the Milky Way out to ∼30 kpc, providing an updated model of the dark matter density profile. We derive precise parallaxes for 120 309 stars with a data-driven model, using APOGEE DR17 spectra combined with Gaia DR3, 2MASS , and WISE photometry. At outer galactic radii up to 30 kpc, we find a significantly faster decline in the circular velocity curve compared to the inner parts. This decline is better fit with a cored Einasto profile with a slope parameter |$0.91^{+0.04}_{-0.05}$| than a generalized Navarro–Frenk–White (NFW) profile. The virial mass of the best-fitting dark matter halo profile is only |$1.81^{+0.06}_{-0.05}\times 10^{11}$| M⊙, significantly lower than what a generalized NFW profile delivers. We present a study of the potential systematics, affecting mainly large radii. Such a low mass for the Galaxy is driven by the functional forms tested, given that it probes beyond our measurements. It is found to be in tension with mass measurements from globular clusters, dwarf satellites, and streams. Our best-fitting profile also lowers the expected dark matter annihilation signal flux from the galactic centre by more than an order of magnitude, compared to an NFW profile-fit. In future work, we will explore profiles with more flexible functional forms to more fully leverage the circular velocity curve and observationally constrain the properties of the Milky Way's dark matter halo. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a novel data augmentation methodology based on Conditional Progressive Generative Adversarial Networks (CPGAN) to generate diverse black hole (BH) images, accounting for variations in spin and electron temperature prescriptions. These generated images are valuable resources for training deep learning algorithms to accurately estimate black hole parameters from observational data. Our model can generate BH images for any spin value within the range of [−1, 1], given an electron temperature distribution. To validate the effectiveness of our approach, we employ a convolutional neural network to predict the BH spin using both the GRMHD images and the images generated by our proposed model. Our results demonstrate a significant performance improvement when training is conducted with the augmented data set while testing is performed using GRMHD simulated data, as indicated by the high R 2 score. Consequently, we propose that GANs can be employed as cost-effective models for black hole image generation and reliably augment training data sets for other parametrization algorithms. [ABSTRACT FROM AUTHOR]

Gamma-ray burst (GRB) afterglow light curves and spectra provide information about the density of the environment, the energy of the explosion, the properties of the particle acceleration process, and the structure of the decelerating jet. Due to the large number of parameters involved, the model can present a certain degree of parameter degeneracy. In this paper, we generated synthetic photometric data points using a standard GRB afterglow model and fit them using the Markov chain Monte Carlo (MCMC) method. This method has emerged as the preferred approach for analysing and interpreting data in astronomy. We show that, depending on the choice of priors, the parameter degeneracy can go unnoticed by the MCMC method. Furthermore, we apply the MCMC method to analyse the GRB 170817A afterglow. We find that there is a complete degeneracy between the energy of the explosion E , the density of the environment n , and the microphysical parameters describing the particle acceleration process (e.g. ϵ e and ϵ B ), which cannot be determined by the afterglow light curve alone. Our results emphasize the importance of gaining a deep understanding of the degeneracy properties which can be present in GRB afterglows models, as well as the limitations of the MCMC method. In the case of GRB 170817, we get the following values for the physical parameters: E  = 8 × 1050–1 × 1053 erg, n  = 7 × 10−5–9 × 10−3, ϵ e  = 10−3–0.3, ϵ B  = 10−10–0.3. [ABSTRACT FROM AUTHOR]

A key challenge in the search for primordial B -modes is the presence of polarized Galactic foregrounds, especially thermal dust emission. Power-spectrum-based analysis methods generally assume the foregrounds to be Gaussian random fields when constructing a likelihood and computing the covariance matrix. In this paper, we investigate how non-Gaussianity in the dust field instead affects CMB and foreground parameter inference in the context of inflationary B -mode searches, capturing this effect via modifications to the dust power-spectrum covariance matrix. For upcoming experiments such as the Simons Observatory, we find no dependence of the tensor-to-scalar ratio uncertainty |$\sigma (r)$| on the degree of dust non-Gaussianity or the nature of the dust covariance matrix. We provide an explanation of this result, noting that when frequency decorrelation is negligible, dust in mid-frequency channels is cleaned using high-frequency data in a way that is independent of the spatial statistics of dust. We show that our results hold also for non-zero levels of frequency decorrelation that are compatible with existing data. We find, however, that neglecting the impact of dust non-Gaussianity in the covariance matrix can lead to inaccuracies in goodness-of-fit metrics. Care must thus be taken when using such metrics to test B -mode spectra and models, although we show that any such problems can be mitigated by using only cleaned spectrum combinations when computing goodness-of-fit statistics. [ABSTRACT FROM AUTHOR]

The long-term prediction of the solar cycle is of great significance for aerospace, communication, and space missions. For a long time, many studies have used relatively primitive deep learning methods to predict the solar cycle, and most of them do not perform well in the long-term prediction. In this paper, we proposed XG-SN ensemble model. The model used extreme gradient boosting (XGBoost) ensemble learning method, combined with sample convolution and interaction net (SCINet), and neural basis expansion analysis for the interpretable time series (N-BEATS) to make predictions for known solar cycles. 13 months of smoothed monthly total sunspot numbers were selected as the data set. The model performance was evaluated by mean absolute error (MAE), root-mean-square error (RMSE), and mean absolute time lag (MATL) between the predicted and actual values. The first two evaluation metrics measured the prediction deviation from the numerical dimension, and the last one measured the prediction deviation from the temporal dimension. The results show that the model achieves the MAE, RMSE, and MATL values of 13.19, 17.13, and 0.08, respectively, in Solar Cycle 13 to 24. Our model is able to better predict in most cycles, ensuring accurate prediction of peaks with little time lag. [ABSTRACT FROM AUTHOR]

The recent serendipitous discovery of a new population of short duration X-ray transients, thought to be associated with collisions of compact objects or stellar explosions in distant galaxies, has motivated efforts to build up statistical samples by mining X-ray telescope archives. Most searches to date, however, do not fully exploit recent developments in the signal and imaging processing research domains to optimize searches for short X-ray flashes. This paper addresses this issue by presenting a new source detection pipeline, STATiX (Space and Time Algorithm for Transients in X-rays), which directly operates on three-dimensional X-ray data cubes consisting of two spatial and one temporal dimension. The algorithm leverages wavelet transforms and the principles of sparsity to denoise X-ray observations and then detect source candidates on the denoised data cubes. The light curves of the detected sources are then characterized using the Bayesian blocks algorithm to identify flaring periods. We describe the implementation of STATiX in the case of XMM–Newton data, present extensive validation and performance verification tests based on simulations and also apply the pipeline to a small subset of seven XMM–Newton observations, which are known to contain transients sources. In addition to known flares in the selected fields, we report a previously unknown short duration transient found by our algorithm that is likely associated with a flaring Galactic star. This discovery demonstrates the potential of applying STATiX to the full XMM–Newton archive. [ABSTRACT FROM AUTHOR]

Direct imaging is an active research topic in astronomy for the detection and the characterization of young substellar objects. The very high contrast between the host star and its companions makes the observations particularly challenging. In this context, post-processing methods combining several images recorded with the pupil tracking mode of telescope are needed. In previous works, we have presented a data-driven algorithm, PACO , capturing locally the spatial correlations of the data with a multivariate Gaussian model. PACO delivers better detection sensitivity and confidence than the standard post-processing methods of the field. However, there is room for improvement due to the approximate fidelity of the PACO statistical model to the time evolving observations. In this paper, we propose to combine the statistical model of PACO with supervised deep learning. The data are first pre-processed with the PACO framework to improve the stationarity and the contrast. A convolutional neural network (CNN) is then trained in a supervised fashion to detect the residual signature of synthetic sources. Finally, the trained network delivers a detection map. The photometry of detected sources is estimated by a second CNN. We apply the proposed approach to several data sets from the VLT/SPHERE instrument. Our results show that its detection stage performs significantly better than baseline methods (cADI and PCA), and leads to a contrast improvement up to half a magnitude compared to PACO. The characterization stage of the proposed method performs on average on par with or better than the comparative algorithms (PCA and PACO) for angular separation above 0.5 arcsec. [ABSTRACT FROM AUTHOR]

Currently, astrometric microlensing is the only tool that can directly measure the mass of a single star; it can also help us to detect compact objects such as isolated neutron stars and black holes. The number of microlensing events that are being predicted and reported is increasing. In this paper, potential lens stars are selected from three types of stars: high-proper-motion stars, nearby stars, and high-mass stars. For each potential lens star, we select a larger search scope to find possible matching sources and to avoid missing events as much as possible. Using data from Gaia Data Release 3, we predict 4500 astrometric microlensing events with δθ+ > 0.1 mas, which occur between J2010.0 and J2070.0, where 1664 events are different from those found previously. There are 293 lens stars that can cause two or more events, where five lens stars can cause more than 50 events. We find that 116 events have the distance of background stars from the proper motion path of lens stars more than 8 arcsec in the reference epoch, where the maximum distance is 16 |${_{.}^{\prime\prime}}$| 6. Thus, the cone search method of expanding the search range of sources for each potential lens star can reduce the possibility of missing events. [ABSTRACT FROM AUTHOR]

The point-spread function (PSF) is a fundamental property of any astronomical instrument. In interferometers, differing array configurations combined with their uv coverage, and various weighting schemes can produce an irregular but deterministic PSF. As a result, the PSF is often deconvolved using CLEAN-style algorithms to improve image fidelity. In this paper, we revisit a significant effect that causes the flux densities measured with any interferometer to be systematically offset from the true values. Using a suite of carefully controlled simulations, we show that the systematic offset originates from a mismatch in the units of the image produced by these CLEAN-style algorithms. We illustrate that this systematic error can be significant, ranging from a few to tens of per cent. Accounting for this effect is important for current and future interferometric arrays, such as MeerKAT, LOFAR, and the SKA, whose core-dominated configuration naturally causes an irregular PSF. We show that this offset is independent of other systematics, and can worsen due to some factors such as the goodness of the fit to the PSF, the deconvolution depth, and the signal-to-noise ratio of the source. Finally, we present several methods that can reduce this effect to just a few per cent. [ABSTRACT FROM AUTHOR]

Atmospheric characterization of exoplanets from the ground is an actively growing field of research. In this context, we have created the ATMOSPHERIX consortium: a research project aimed at characterizing exoplanets atmospheres using ground-based high-resolution spectroscopy. This paper presents the publicly available data analysis pipeline and demonstrates the robustness of the recovered planetary parameters from synthetic data. Simulating planetary transits using synthetic transmission spectra of a hot Jupiter that were injected into real SPIRou observations of the non-transiting system Gl 15 A, we show that our pipeline is successful at recovering the planetary signal and input atmospheric parameters. We also introduce a deep learning algorithm to optimize data reduction which proves to be a reliable, alternative tool to the commonly used principal component analysis. We estimate the level of uncertainties and possible biases when retrieving parameters such as temperature and composition and hence the level of confidence in the case of retrieval from real data. Finally, we apply our pipeline onto two real transits of HD 189733 b observed with SPIRou and obtain similar results than in the literature. In summary, we have developed a publicly available and robust pipeline for the forthcoming studies of the targets to be observed in the framework of the ATMOSPHERIX consortium, which can easily be adapted to other high resolution instruments than SPIRou (e.g. VLT-CRIRES, MAROON-X, ELT-ANDES). [ABSTRACT FROM AUTHOR]

Sky subtraction is a crucial step in the data reduction process for LAMOST, including dark, bright, and grey nights. During the pilot survey, on bright nights, atmospheric scattering of moonlight can introduce gradients in the sky background. In observations during bright moonlit nights, the sky component is significant, and sometimes, variations in colour can be observed in the sky spectra. This phenomenon is not universally present during observations on bright moonlit nights. Taking this into consideration, this paper proposes a weighted trend-surface method to reconstruct the sky component within the science target fibre, aiming to achieve the subtraction of the sky component. We constructed a sky model using a trend surface, utilizing data from all sky fibre spectra on the same spectrograph to predict the sky component for each fibre spectrum. Subsequently, the reconstructed sky spectrum data were compared with the actually observed sky spectrum data and the 'super sky' from LAMOST's pipeline. The results indicate that our method is closer to the observed real sky spectrum than the 'super sky', showing smaller residuals and variance, with an average closer to zero. This method serves as a viable solution, particularly when dealing with colour variations observed during bright moonlit nights. [ABSTRACT FROM AUTHOR]

In recent years, methods for Bayesian inference have been widely used in many different problems in physics where detection and characterization are necessary. Data analysis in gravitational-wave astronomy is a prime example of such a case. Bayesian inference has been very successful because this technique provides a representation of the parameters as a posterior probability distribution, with uncertainties informed by the precision of the experimental measurements. During the last couple of decades, many specific advances have been proposed and employed in order to solve a large variety of different problems. In this work, we present a Markov Chain Monte Carlo (MCMC) algorithm that integrates many of those concepts into a single MCMC package. For this purpose, we have built Eryn , a user-friendly and multipurpose toolbox for Bayesian inference, which can be utilized for solving parameter estimation and model selection problems, ranging from simple inference questions, to those with large-scale model variation requiring trans-dimensional MCMC methods, like the Laser Interferometer Space Antenna Global Fit problem. In this paper, we describe this sampler package and illustrate its capabilities on a variety of use cases. [ABSTRACT FROM AUTHOR]

Recently, the scalar–tensor representation of f (R, T) gravity was used to explore gravitationally induced particle production/annihilation. Using the framework of irreversible thermodynamics of open systems in the presence of matter creation/annihilation, the physical and cosmological consequences of this setup were investigated in detail. In this paper, we test observationally the scalar–tensor representation of f (R, T) gravity in the context of the aforementioned framework, using the Hubble and Pantheon + measurements. The best fit parameters are obtained by solving numerically the modified Friedmann equations of two distinct cosmological models in scalar–tensor f (R, T) gravity, corresponding to two different choices of the potential, and by performing a Markov Chain Monte Carlo analysis. The best parameters are used to compute the cosmographic parameters, that is, the deceleration, the jerk, and the snap parameters. Using the output resulting from the Markov Chain Monte Carlo analysis, the cosmological evolution of the creation pressure and of the matter creation rates are presented for both models. To figure out the statistical significance of the studied scalar–tensor f (R, T) gravity, the Bayesian and the corrected Akaike information criteria are used. The latter indicates that the first considered model in scalar–tensor f (R, T) gravity is statistically better than ΛCDM, that is, it is more favoured by observations. Besides, a continuous particle creation process is present in Model 1. Alternatively, for large redshifts, in Model 2 the particle creation rate may become negative, thus indicating the presence of particle annihilation processes. However, both models lead to an accelerating expansion of the universe at late times, with a deceleration parameter equivalent to that of the ΛCDM model. [ABSTRACT FROM AUTHOR]

Bayesian analysis has become an indispensable tool across many different cosmological fields, including the study of gravitational waves, the cosmic microwave background, and the 21-cm signal from the Cosmic Dawn, among other phenomena. The method provides a way to fit complex models to data describing key cosmological and astrophysical signals and a whole host of contaminating signals and instrumental effects modelled with 'nuisance parameters'. In this paper, we summarize a method that uses masked autoregressive flows and kernel density estimators to learn marginal posterior densities corresponding to core science parameters. We find that the marginal or 'nuisance-free' posteriors and the associated likelihoods have an abundance of applications, including the calculation of previously intractable marginal Kullback–Leibler divergences and marginal Bayesian model dimensionalities, likelihood emulation, and prior emulation. We demonstrate each application using toy examples, examples from the field of 21-cm cosmology, and samples from the Dark Energy Survey. We discuss how marginal summary statistics like the Kullback–Leibler divergences and Bayesian model dimensionalities can be used to examine the constraining power of different experiments and how we can perform efficient joint analysis by taking advantage of marginal prior and likelihood emulators. We package our multipurpose code up in the pip-installable code margarine for use in the wider scientific community. [ABSTRACT FROM AUTHOR]

Ground-based, high precision observations of the light curves of objects such as transiting exoplanets rely on the application of differential photometry. The flux of the target object is measured relative to a comparison star in the same field, allowing correction for systematic trends in the light curve, mainly due to atmospheric effects including the variation of extinction with airmass. However, the precision of the light curve is then limited by the random noise for the measurements of both the target object and the comparison star. For time-resolved photometry using short exposure times of up to a few tens of seconds, the time-scale of the systematic variations due to atmospheric (or other) effects can be much longer than the cadence of the observations. In this case, the overall signal-to-noise ratio of the observation may be improved significantly by applying some temporal binning to the measurements of the comparison star, before comparison with the target object, without reducing the cadence of the overall light curve. In this paper, we will describe a data reduction pipeline for implementing this method which optimizes the number of frames to be binned for the comparison star, and we present example results for time-resolved photometric data. An example of applying the technique on an exoplanet transit light curve of WASP-166b is presented using four comparison stars of different magnitudes. [ABSTRACT FROM AUTHOR]

Citizen science is gaining popularity as a valuable tool for labelling large collections of astronomical images by the general public. This is often achieved at the cost of poorer quality classifications made by amateur participants, which are usually verified by employing smaller data sets labelled by professional astronomers. Despite its success, citizen science alone will not be able to handle the classification of current and upcoming surveys. To alleviate this issue, citizen science projects have been coupled with machine learning techniques in pursuit of a more robust automated classification. However, existing approaches have neglected the fact that, apart from the data labelled by amateurs, (limited) expert knowledge of the problem is also available along with vast amounts of unlabelled data that have not yet been exploited within a unified learning framework. This paper presents an innovative learning methodology for citizen science capable of taking advantage of expert- and amateur-labelled data, featuring a transfer of labels between experts and amateurs. The proposed approach first learns from unlabelled data with a convolutional auto-encoder and then exploits amateur and expert labels via the pre-training and fine-tuning of a convolutional neural network, respectively. We focus on the classification of galaxy images from the Galaxy Zoo project, from which we test binary, multiclass, and imbalanced classification scenarios. The results demonstrate that our solution is able to improve classification performance compared to a set of baseline approaches, deploying a promising methodology for learning from different confidence levels in data labelling. [ABSTRACT FROM AUTHOR]