We demonstrate that the Bayesian evidence can be used to find a good approximation of the ground truth likelihood function of a dataset, a goal of the likelihood-free inference (LFI) paradigm. As a concrete example, we use forward modelled sky-averaged 21-cm signal antenna temperature datasets where we artificially inject noise structures of various physically motivated forms. We find that the Gaussian likelihood performs poorly when the noise distribution deviates from the Gaussian case, for example, heteroscedastic radiometric or heavy-tailed noise. For these non-Gaussian noise structures, we show that the generalised normal likelihood is on a similar Bayesian evidence scale with comparable sky-averaged 21-cm signal recovery as the ground truth likelihood function of our injected noise. We therefore propose the generalised normal likelihood function as a good approximation of the true likelihood function if the noise structure is a priori unknown.

We demonstrate the effectiveness of a Bayesian evidence -based analysis for diagnosing and disentangling the sky-averaged 21-cm signal from instrumental systematic effects. As a case study, we consider a simulated REACH pipeline with an injected systematic. We demonstrate that very poor performance or erroneous signal recovery is achieved if the systematic remains unmodelled. These effects include sky-averaged 21-cm posterior estimates resembling a very deep or wide signal. However, when including parameterised models of the systematic, the signal recovery is dramatically improved in performance. Most importantly, a Bayesian evidence-based model comparison is capable of determining whether or not such a systematic model is needed as the true underlying generative model of an experimental dataset is in principle unknown. We, therefore, advocate a pipeline capable of testing a variety of potential systematic errors with the Bayesian evidence acting as the mechanism for detecting their presence.

Understanding the physical and evolutionary properties of Hot Stellar Systems (HSS) is a major challenge in astronomy. We studied the dataset on 13 456 HSS of Misgeld & Hilker (2011, MNRAS, 414, 3 699) that includes 12 763 candidate globular clusters using stellar mass ( $M_s$ ), effective radius ( $R_e$ ) and mass-to-luminosity ratio ( $M_s/L_\nu$ ), and found multi-layered homogeneous grouping among these stellar systems. Our methods elicited eight homogeneous ellipsoidal groups at the finest sub-group level. Some of these groups have high overlap and were merged through a multi-phased syncytial algorithm motivated from Almodóvar-Rivera & Maitra (2020, JMLR, 21, 1). Five groups were merged in the first phase, resulting in three complex-structured groups. Our algorithm determined further complex structure and permitted another merging phase, revealing two complex-structured groups at the highest level. A nonparametric bootstrap procedure was also used to estimate the confidence of each of our group assignments. These assignments generally had high confidence in classification, indicating great degree of certainty of the HSS assignments into our complex-structured groups. The physical and kinematic properties of the two groups were assessed in terms of $M_s$ , $R_e$ , surface density and $M_s/L_\nu$ . The first group consisted of older, smaller and less bright HSS while the second group consisted of brighter and younger HSS. Our analysis provides novel insight into the physical and evolutionary properties of HSS and also helps understand physical and evolutionary properties of candidate globular clusters. Further, the candidate globular clusters (GCs) are seen to have very high chance of really being GCs rather than dwarfs or dwarf ellipticals that are also indicated to be quite distinct from each other.

It is believed that the tilt in the bipolar magnetic regions (BMRs) is produced due to a torque induced by the Coriolis force, acting on the diverging flow from the apex of the rising flux tube of the toroidal field in the solar convection zone (SCZ).The BMRs with a strong magnetic field are expected to have reduced tilt as they rise very quickly in the SCZ. This effect can provide the required nonlinear quenching mechanism to suppress the growth of magnetic filed in the dynamo models. Here, we use the magnetograms of the Michelson Doppler Imager (1996–2011) and Helioseismic and Magnetic Imager (2010–2018) to automatically detect the BMRs and look for the signature of tilt quenching. Based on the Bayesian inference method, our results show that the posterior distribution of quenching parameters is Gaussian, and the mean of this distribution agrees with the earlier findings.

The VISTA Magellanic Clouds Survey (VMC) is a near-infrared survey of the Magellanic system. The VMC data has been exploited to detect and study statistically correlated young groups of stars — also known as “young stellar structures” — in the Large and Small Magellanic Clouds (LMC and SMC). We showcase the ∼ 3000 recently detected young stellar structures in the LMC and their similarity to the fractal interstellar medium. We discuss how their properties indicate their formation mechanisms and that there are no preferred scales of star formation in the LMC.

In optical and infrared long-baseline interferometry, data often display significant correlated errors because of uncertain multiplicative factors such as the instrumental transfer function or the pixel-to-visibility matrix. In the context of model fitting, this situation often leads to a significant bias in the model parameters. In the most severe cases, this can can result in a fit lying outside of the range of measurement values. This is known in nuclear physics as Peelle’s Pertinent Puzzle. I show how this arises in the context of interferometry and determine that the relative bias is of the order of the square root of the correlated component of the relative uncertainty times the number of measurements. It impacts preferentially large datasets, such as those obtained in medium to high spectral resolution. I then give a conceptually simple and computationally cheap way to avoid the issue: model the data without covariances, estimate the covariance matrix by error propagation using the modelled data instead of the actual data, and perform the model fitting using the covariance matrix. I also show that a more imprecise but also unbiased result can be obtained from ignoring correlations in the model fitting.

We present an overview of the SkyMapper optical follow-up programme for gravitational-wave event triggers from the LIGO/Virgo observatories, which aims at identifying early GW170817-like kilonovae out to $\sim200\,\mathrm{Mpc}$ distance. We describe our robotic facility for rapid transient follow-up, which can target most of the sky at $\delta<+10\deg $ to a depth of $i_\mathrm{AB}\approx 20\,\mathrm{mag}$ . We have implemented a new software pipeline to receive LIGO/Virgo alerts, schedule observations and examine the incoming real-time data stream for transient candidates. We adopt a real-bogus classifier using ensemble-based machine learning techniques, attaining high completeness ( $\sim98\%$ ) and purity ( $\sim91\%$ ) over our whole magnitude range. Applying further filtering to remove common image artefacts and known sources of transients, such as asteroids and variable stars, reduces the number of candidates by a factor of more than 10. We demonstrate the system performance with data obtained for GW190425, a binary neutron star merger detected during the LIGO/Virgo O3 observing campaign. In time for the LIGO/Virgo O4 run, we will have deeper reference images allowing transient detection to $i_\mathrm{AB}\approx 21\,\mathrm{mag}$ .

The remnant phase of a radio galaxy begins when the jets launched from an active galactic nucleus are switched off. To study the fraction of radio galaxies in a remnant phase, we take advantage of a $8.31$ deg$^2$ subregion of the GAMA 23 field which comprises of surveys covering the frequency range 0.1–9 GHz. We present a sample of 104 radio galaxies compiled from observations conducted by the Murchison Widefield Array (216 MHz), the Australia Square Kilometer Array Pathfinder (887 MHz), and the Australia Telescope Compact Array (5.5 GHz). We adopt an ‘absent radio core’ criterion to identify 10 radio galaxies showing no evidence for an active nucleus. We classify these as new candidate remnant radio galaxies. Seven of these objects still display compact emitting regions within the lobes at 5.5 GHz; at this frequency the emission is short-lived, implying a recent jet switch off. On the other hand, only three show evidence of aged lobe plasma by the presence of an ultra-steep-spectrum ($\alpha<-1.2$) and a diffuse, low surface brightness radio morphology. The predominant fraction of young remnants is consistent with a rapid fading during the remnant phase. Within our sample of radio galaxies, our observations constrain the remnant fraction to $4\%\lesssim f_{\mathrm{rem}} \lesssim 10\%$; the lower limit comes from the limiting case in which all remnant candidates with hotspots are simply active radio galaxies with faint, undetected radio cores. Finally, we model the synchrotron spectrum arising from a hotspot to show they can persist for 5–10 Myr at 5.5 GHz after the jets switch of—radio emission arising from such hotspots can therefore be expected in an appreciable fraction of genuine remnants.

Most stars form in clumpy and sub-structured clusters. These properties also emerge in hydro-dynamical simulations of star-forming clouds, which provide a way to generate realistic initial conditions for N-body runs of young stellar clusters. However, producing large sets of initial conditions by hydro-dynamical simulations is prohibitively expensive in terms of computational time. We introduce a novel technique for generating new initial conditions from a given sample of hydro-dynamical simulations, at a tiny computational cost. In particular, we apply a hierarchical clustering algorithm to learn a tree representation of the spatial and kinematic relations between stars, where the leaves represent the single stars and the nodes describe the structure of the cluster at larger and larger scales. This procedure can be used as a basis for the random generation of new sets of stars, by simply modifying the global structure of the stellar cluster, while leaving the small-scale properties unaltered.

The Bisous model is a tool that uses stochastic methods to detect the network of galactic filaments. This model is explicitly developed to detect the structure from observational data, using only galaxy positions as input. This paper shows that the Bisous model gives reliable results and including photometric data improves the resulting filamentary network. We used MultiDark-Galaxies catalogue to create a mock with photometric redshifts and samples with different galaxy number densities. We found that the filaments detected with the Bisous model are reliable; 85% of the detected filaments are unchanged compared to results with more complete input data. Adding photometric data improves the fraction of galaxies in filaments. Using the confusion matrix technique, we found the false discovery rate to always be below 5% when using photometric data.

We present a machine learning methodology to separate quasars from galaxies and stars using data from S-PLUS in the Stripe-82 region. In terms of quasar classification, we achieved 95.49% for precision and 95.26% for recall using a Random Forest algorithm. For photometric redshift estimation, we obtained a precision of 6% using k-Nearest Neighbour.

Modelling and interpreting the SEDs of galaxies has become one of the key tools at the disposal of extragalactic astronomers. Ideally, we could hope that, through a detailed study of its SED, we can infer the correct physical properties and the evolutionary history of a galaxy. In the past decade, panchromatic SED fitting, i.e. modelling the SED over the entire UV–submm wavelength regime, has seen an enormous advance. Several advanced new codes have been developed, nearly all based on Bayesian inference modelling. In this review, we briefly touch upon the different ingredients necessary for panchromatic SED modelling, and discuss the methodology and some important aspects of Bayesian SED modelling. The current uncertainties and limitations of panchromatic SED modelling are discussed, and we explore some avenues how the models and techniques can potentially be improved in the near future.

In this paper, I review several dust evolution studies based on the DustPedia nearby galaxy sample. I first present the dust spectral energy distribution model, implementing a hierarchical Bayesian method, that we have developed. I then discuss the dust evolution trends we have derived among (integrated) and within (resolved) galaxies. In particular, we show that the trend of dust-to-gas ratio with metallicity is clearly non-linear, indicating the need for grain growth in the interstellar medium. Our trend is closer to the one derived with damped Lyα systems than what was suggested by previous studies. We finally demonstrate the universal processing of small amorphous carbon grains by stellar photons.

Panchromatic modeling is one of the most powerful tools at our disposal to measure reliably the physical properties of galaxies across cosmic times. We present here an entirely new implementation in python of one such tool: CIGALE. Developed along three main design principles: simplicity, modularity, and efficiency, it has proven to be a versatile code that in addition to estimating the physical properties of galaxies (or regions within galaxies), can generate arbitrary sets of theoretical models or be used as a library to build other tools. Among its defining features, it is a truly panchromatic code ranging from the far-ultraviolet to the radio that takes into account numerous physical components (including active nuclei or synchrotron emission), that can fit non-photometric data, handle upper limits, determine photometric redshifts, and even build mock catalogs.

The Herschel Extragalactic Legacy Project (HELP) focuses to publish an astronomical multiwavelength catalogue of millions of objects over 1300 deg2 of the Herschel Space Observatory survey fields. Millions of galaxies with ultraviolet–far infrared photometry make HELP a perfect sample for testing spectral energy distribution fitting models, and to prepare tools for next-generation data. In the frame of HELP collaboration we estimated the main physical properties of all galaxies from the HELP database and we checked a new procedure to select peculiar galaxies from large galaxy sample and we investigated the influence of used modules for stellar mass estimation.

Optical properties of infrared-bright (IR-bright) dust-obscured galaxies (DOGs) are reported. DOGs are faint in optical but very bright in mid-IR, which are powered by active star formation (SF) or active galactic nucleus (AGN), or both. The DOGs is a candidate population that are evolving from a gas-rich merger to a quasar. By combining three catalogs of optical (Subaru Hyper Suprime-Cam), near-IR (VIKING), and mid-IR (ALLWISE), we have discovered 571 IR-bright DOGs. Using their spectral energy distributions, we classified the selected DOGs into the SF-dominated DOGs and the AGN-dominated DOGs. We found that the SF-dominated DOGs show a redder optical color than the AGN-dominated DOGs. Interestingly, some DOGs shows extremely blue color in optical (blue-excess DOGs: bluDOGs). A possible origin for this blue excess is either the leaked AGN light or stellar UV light from nuclear starbursts. The BluDOGs may be in the transition phase from obscured AGNs to unobscured AGNs.

The distribution of galaxies has been studied to show the difference between the blue cloud and red sequence and to define the green valley region. However, there are still many open questions regarding the importance of the green valley for understanding the morphological transformation and evolution of galaxies, how galaxies change from late-type to early-type and the role of AGN in galaxy formation and evolution scenario. The work focused on studying in more details the properties of green valley galaxies by testing the six most used selection criteria, differences between them, and how they may affect the main results and conclusions. The main findings are that, by selecting the green valley galaxies using different criteria, we are selecting different types of galaxies in terms of their stellar masses, sSFR, SFR, spectroscopic classification and morphological properties, where the difference was more significant for colour criteria than for sSFR and SFR vs. M* criteria.

By comparing two age indicators of high-mass white dwarfs (WDs) derived from Gaia data, two discoveries have been made recently: one is the existence of a cooling anomaly that produces the Q branch structure on the Hertzsprung–Russell diagram, and the other is the existence of high-mass WDs as double-WD merger products. The former poses a challenge for WD cooling models, and the latter has implications on binary evolution and type-Ia supernovae.

Active Galactic Nuclei (AGN) are ubiquitous variable sources. This trademark property allows the study of many aspects of AGN physics which are not possible by other means. In this review I summarize what has been learnt by the close monitoring of AGN flux variations with special emphasis in studies conducted in optical and near-infrared domain. I also highlight what knowledge is still missing from our picture of AGN phenomena, as well as possible developments expected in this new era of time-domain astronomy.

For the past 25 years, we have been considering the Stark effect for neutral helium lines in DB white dwarfs using the standard Stark broadening theory in both the impact regime (in the center of the lines) and the quasi-static regime (in the wings) for the electrons, while neglecting the effect of ions in motion. Although this is probably a good approximation based on previous theoretical work, the transition between the two regimes for the electrons and the contribution of the ions very near the core might be poorly represented. To better represent these particularities, we report here the results of a new series of simulations that treat the local dynamics and interactions of both electrons and ions around a neutral helium atom. From these simulations, we produce new improved line profiles, which we compare with our previous analytical results.