Future Surveys Research Articles

Assessing the prevalence of trachoma in the East, North, Far North and Adamaoua regions of Cameroon, 2016-2022.

Baseline prevalence surveys in Cameroon in 2010-2012 showed that trachoma was endemic primarily in the north of the country, with 23 evaluation units (EUs) requiring interventions against active (inflammatory) trachoma. This study presents data from prevalence surveys conducted in 2016-2022 following interventions against trachoma in the East, North, Far North and Adamaoua regions of Cameroon. EUs were created based on health district boundaries. Within each EU, clusters were selected using probability of selection proportional to population size. Participants were examined for trachomatous inflammation-follicular (TF) and trachomatous trichiasis (TT). A total of 151800 people were examined in 45 surveys across 35 EUs. Based on the most recent survey results, TF prevalence was greater than the 5% TF elimination threshold in two EUs. Ten EUs had TT prevalence estimates greater than the 0.2% elimination threshold. Trachoma remains a public health problem in Cameroon. Continued interventions are needed in EUs with prevalence estimates greater than elimination thresholds, including antibiotic mass drug administration and improved access to TT surgery. Future surveys will be needed to determine when national elimination of trachoma as a public health problem has been achieved.

PICZL : Image-based photometric redshifts for AGN

Computing reliable photometric redshifts (photo-z) for active galactic nuclei (AGN) is a challenging task, primarily due to the complex interplay between the unresolved relative emissions associated with the supermassive black hole and its host galaxy. Spectral energy distribution (SED) fitting methods, while effective for galaxies and AGN in pencil-beam surveys, face limitations in wide or all-sky surveys with fewer bands available, lacking the ability to accurately capture the AGN contribution to the SED, hindering reliable redshift estimation. This limitation is affecting the many tens of millions of AGN detected in existing datasets, such as those AGN clearly singled out and identified by SRG/eROSITA. Our goal is to enhance photometric redshift performance for AGN in all-sky surveys while simultaneously simplifying the approach by avoiding the need to merge multiple data sets. Instead, we employ readily available data products from the 10th Data Release of the Imaging Legacy Survey for the Dark Energy Spectroscopic Instrument, which covers > 20,000 deg of extragalactic sky with deep imaging and catalog-based photometry in the grizW1-W4 bands. We fully utilize the spatial flux distribution in the vicinity of each source to produce reliable photo-z. We introduce PICZL a machine-learning algorithm leveraging an ensemble of convolutional neural networks. Utilizing a cross-channel approach, the algorithm integrates distinct SED features from images with those obtained from catalog-level data. Full probability distributions are achieved via the integration of Gaussian mixture models. On a validation sample of 8098 AGN PICZL achieves an accuracy $ NMAD $ of 4.5<!PCT!> with an outlier fraction eta of 5.6<!PCT!>. These results significantly outperform previous attempts to compute accurate photo-z for AGN using machine learning. We highlight that the model's performance depends on many variables, predominantly the depth of the data and associated photometric error. A thorough evaluation of these dependencies is presented in the paper. Our streamlined methodology maintains consistent performance across the entire survey area, when accounting for differing data quality. The same approach can be adopted for future deep photometric surveys such as LSST and Euclid, showcasing its potential for wide-scale realization. With this paper, we release updated photo-z (including errors) for the XMM-SERVS W-CDF-S, ELAIS-S1 and LSS fields.

A summary of updated UNSCEAR evaluations on medical and occupational exposures to ionising radiation and call for participation in UNSCEAR Global Surveys on radiation exposure.

This paper summarises key findings of United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR) 2020/2021 Report on the evaluations of medical and occupational exposures to ionising radiation. Medical exposure remains by far the largest human-made source of radiation exposure of the population. In the period 2009-18, about 4.2 billion medical radiological examinations were performed annually, resulting in an effective dose per caput of 0.57mSv. The worldwide annual number of workers exposed to natural and human-made sources of ionising radiation is estimated to be ~24 million in the period 2010-14. About 52% of those were employed in the sectors that involve exposure to natural sources of radiation. The annual effective dose was estimated to be around 2.0mSv for workers exposed to natural sources (excluding radon exposure in workplaces other than mines) and 0.5mSv for workers exposed to human-made sources. It is important to motivate United Nations Member States to fully participate in UNSCEAR's global surveys on radiation exposure. Future surveys will be relevant and useful and adapted to changing data sources and uses of radiation across the world.

Classification of Variable Star Light Curves with Convolutional Neural Network

The classification of variable stars is essential for understanding stellar evolution and dynamics. With the growing volume of light curve data from extensive surveys, there is a need for automated and accurate classification methods. Traditional methods often rely on manual feature extraction and selection, which can be time-consuming and less adaptable to large datasets. In this work, we present an approach using a convolutional neural network (CNN) to classify variable stars using only raw light curve data and their known periods, without the need for manual feature extraction or hand-selected data preprocessing. Our method utilizes phase-folding to organize the light curves and directly learns the variability patterns crucial for classification. Trained and tested on the Optical Gravitational Lensing Experiment (OGLE) dataset, our model demonstrates an average accuracy of 88% and an F1 score of 0.89 across five well-known classes of variable stars. We also compared our classification model with the Random Forest (RF) classifier and showed that our model gives better results across all of the classification metrics. By leveraging CNN, our approach does not need manual feature extraction and can handle diverse light curve shapes and sampling cadences. This automated, data-driven method offers a powerful tool for classifying variable stars, enabling efficient processing of large datasets from current and future sky surveys.

Dropout in a Longitudinal Survey of Amazon Mechanical Turk Workers With Low Back Pain: Observational Study.

Surveys of internet panels such as Amazon's Mechanical Turk (MTurk) are common in health research. Nonresponse in longitudinal studies can limit inferences about change over time. This study aimed to (1) describe the patterns of survey responses and nonresponse among MTurk members with back pain, (2) identify factors associated with survey response over time, (3) assess the impact of nonresponse on sample characteristics, and (4) assess how well inverse probability weighting can account for differences in sample composition. We surveyed adult MTurk workers who identified as having back pain. We report participation trends over 3 survey waves and use stepwise logistic regression to identify factors related to survey participation in successive waves. A total of 1678 adults participated in wave 1. Of those, 983 (59%) participated in wave 2 and 703 (42%) in wave 3. Participants who did not drop out took less time to complete previous surveys (30 min vs 35 min in wave 1, P<.001; 24 min vs 26 min in wave 2, P=.02) and reported having fewer health conditions (5.88 vs 6.6, P<.001). In multivariate models predicting responding at wave 2, lower odds of participation were associated with more time to complete the baseline survey (odds ratio [OR] 0.98, 95% CI 0.97-0.99), being Hispanic (compared with non-Hispanic, OR 0.69, 95% CI 0.49-0.96), having a bachelor's degree as their terminal degree (compared with all other levels of education, OR 0.58, 95% CI 0.46-0.73), having more pain interference and intensity (OR 0.75, 95% CI 0.64-0.89), and having more health conditions. In contrast, older respondents (older than 45 years age compared with 18-24 years age) were more likely to respond to the wave 2 survey (OR 2.63 and 3.79, respectively) and those whose marital status was divorced (OR 1.81) and separated (OR 1.77) were also more likely to respond to the wave 2 survey. Weighted analysis showed slight differences in sample demographics and conditions and larger differences in pain assessments, particularly for those who responded to wave 2. Longitudinal studies on MTurk have large, differential dropouts between waves. This study provided information about the individuals more likely to drop out over time, which can help researchers prepare for future surveys.

Inferring stellar parameters and their uncertainties from high-resolution spectroscopy using invertible neural networks

New spectroscopic surveys will increase the number of astronomical objects in need of characterisation by more than an order of magnitude. Machine learning tools are required to address this data deluge in a fast and accurate fashion. Most machine learning algorithms cannot directly estimate error, making them unsuitable for reliable science. We aim to train a supervised deep-learning algorithm tailored for high-resolution observational stellar spectra. This algorithm accurately infers precise estimates while providing coherent estimates of uncertainties by leveraging information from both the neural network and the spectra. We trained a conditional invertible neural network (cINN) on observational spectroscopic data obtained from the GIRAFFE spectrograph (HR10 and HR21 setups) within the Gaia-ESO survey. A key feature of cINN is its ability to produce the Bayesian posterior distribution of parameters for each spectrum. By analysing this distribution, we inferred stellar parameters and their corresponding uncertainties. We carried out several tests to investigate how parameters are inferred and errors are estimated. We achieved an accuracy of 28K in $T_ eff $, 0.06 dex in $ g$, 0.03 dex in Fe/H $, and between 0.05 dex and 0.17 dex for the other abundances for high-quality spectra. Accuracy remains stable with low signal-to-noise ratio (between 5 and 25) spectra, with an accuracy of 39K in $T_ eff $, 0.08 dex in $ g$, and 0.05 dex in Fe/H $. The uncertainties obtained are well within the same order of magnitude. The network accurately reproduces astrophysical relationships both on the scale of the Milky Way and within smaller star clusters. We created a table containing the new parameters generated by our cINN. This neural network represents a compelling proposition for future astronomical surveys. These derived uncertainties are coherent and can therefore be reused in future works as Bayesian priors.

Real-world patterns in remote longitudinal study participation: A study of the Swiss Multiple Sclerosis Registry.

Remote longitudinal studies are on the rise and promise to increase reach and reduce participation barriers in chronic disease research. However, maintaining long-term retention in these studies remains challenging. Early identification of participants with different patterns of long-term retention offers the opportunity for tailored survey adaptations. Using data from the online arm of the Swiss Multiple Sclerosis Registry (SMSR), we assessed sociodemographic, health-related, and daily-life related baseline variables against measures of long-term retention in the follow-up surveys through multivariable logistic regressions and unsupervised clustering analyses. We further explored follow-up survey completion measures against survey requirements to inform future survey designs. Our analysis included data from 1,757 participants who completed a median of 4 (IQR 2-8) follow-up surveys after baseline with a maximum of 13 possible surveys. Survey start year, age, citizenship, MS type, symptom burden and independent driving were significant predictors of long-term retention at baseline. Three clusters of participants emerged, with no differences in long-term retention outcomes revealed across the clusters. Exploratory assessments of follow-up surveys suggest possible trends in increased survey complexity with lower rates of survey completion. Our findings offer insights into characteristics associated with long-term retention in remote longitudinal studies, yet they also highlight the possible influence of various unexplored factors on retention outcomes. Future studies should incorporate additional objective measures that reflect participants' individual contexts to understand their ability to remain engaged long-term and inform survey adaptations accordingly.

Sensitivity Analysis of Simulation-Based Inference for Galaxy Clustering

Abstract Simulation-based inference (SBI) is a promising approach to leverage high fidelity cosmological simulations and extract information from the non-Gaussian, non-linear scales that cannot be modeled analytically. However, scaling SBI to the next generation of cosmological surveys faces the computational challenge of requiring a large number of accurate simulations over a wide range of cosmologies, while simultaneously encompassing large cosmological volumes at high resolution. This challenge can potentially be mitigated by balancing the accuracy and computational cost for different components of the the forward model while ensuring robust inference. To guide our steps in this, we perform a sensitivity analysis of SBI for galaxy clustering on various components of the cosmological simulations: gravity model, halo-finder and the galaxy-halo distribution models (halo-occupation distribution, HOD). We infer the σ8 and Ωm using galaxy power spectrum multipoles and the bispectrum monopole assuming a galaxy number density expected from the luminous red galaxies observed using the Dark Energy Spectroscopy Instrument (DESI). We find that SBI is insensitive to changing gravity model between N-body simulations and particle mesh (PM) simulations. However, changing the halo-finder from friends-of-friends (FoF) to Rockstar can lead to biased estimate of σ8 based on the bispectrum. For galaxy models, training SBI on more complex HOD leads to consistent inference for less complex HOD models, but SBI trained on simpler HOD models fails when applied to analyze data from a more complex HOD model. Based on our results, we discuss the outlook on cosmological simulations with a focus on applying SBI approaches to future galaxy surveys.

Small Area Estimation using Multilevel Regression and Poststratification to Estimate Cannabis Use in the State of Montana

Background: Small area substance use prevalence estimates at the county, city, or congressional district level are generally unavailable. In this study, we design a cannabis use survey for the state of Montana and use multilevel regression and poststratification (MRP) to generate county-level population prevalence estimates for past year cannabis use. Methods: We developed a survey that asks questions about cannabis perceptions and use patterns. We analyzed the survey data specifically for the outcome variable of past year cannabis use using MRP to generate population level prevalence estimates at the county level for the state of Montana. Results: We received 1,958 responses from our survey. We generated county level estimates by age group for cannabis use over the past year and found that MRP estimates were consistent with prior estimations of cannabis use at the state level and provided the ability to use additional data and validated assumptions to refine and downscale estimations of cannabis use, particularly in counties with low response rates. Conclusion: Multi-modal survey dissemination was cost effective, but future surveys should intend to recruit a larger and more representative sample to minimize selection bias and improve estimation for demographic sub-groups. Overall, MRP provided a promising methodology for generating small-area cannabis use prevalence estimates, adjusting as much as possible for non-representativeness and non-response.

Forecasting the Population of Globular Cluster Streams in Milky Way–type Galaxies

Thin stellar streams originating from globular clusters (GCs) are among the most sensitive tracers of low-mass dark matter subhalos. Joint analysis of the entire population of stellar streams will place the most robust constraints on the dark matter subhalo mass function, and therefore the nature of dark matter. Here we use a hierarchical model of GC formation to forecast the total number, masses, and radial distribution of dissolved GC in Milky Way–like galaxies. Furthermore, we generate mock stellar streams from these progenitors’ orbital histories taking into account the clusters’ formation and accretion times, mass, and metallicity. Out of ∼10,000 clusters more massive than 104 M ⊙, ∼9000 dissolved in the central bulge and are fully phase mixed at the present, while the remaining ∼1000 survive as coherent stellar streams. This suggests that the current census of ∼80 GC streams in the Milky Way is severely incomplete. Beyond 15 kpc from the Galactic center we are missing ∼100 streams, of which the vast majority are from accreted GCs. Deep Rubin photometry (g ≲ 27.5) would be able to detect these streams, even the most distant ones beyond >75 kpc. We also find that M31 will have an abundance of streams at galactocentric radii of 30–100 kpc. We conclude that future surveys will find a multitude of stellar streams from GCs, which can be used for dark matter subhalo searches.

Assessing the reliability of species distribution models under changing environments: a case study on cetaceans in the North-East Atlantic

1.Species Distribution Models (SDMs) assume stable relationships between species and their environment from which predictions are made. These relationships are likely to vary with changing environments, and predictions might depend more on modelling choices than on empirical data. Reliability assessments of predictions are necessary to support policy-making.2.We identified environmental extrapolations among potential predictions of cetaceans’ distribution from 2005 to 2020 in the North-East Atlantic and calculated the percentages of calibration data with similar environments (nearby data), supporting these predictions. Thus, the assessment of reliability is generic, as evaluated before model fitting.3.Predictions on continental shelves were extensively supported by the calibration data and were more reliable throughout the year than predictions on continental slopes and abyssal plains, which were more supported in summer. Predictions off Portugal were particularly uncertain due to the lack of surveys in this region of deep, warmer waters with seamounts.4.The high effort between May and July led to a southern winter shift of nearby data, following the decrease in temperatures. A large part of the predictions between December and April was extrapolated due to the low coverage of the winter primary productivity drops, spring peaks and cold waters. They were based on data collected during other seasons and regions, and given the large spatial extent of the area, and the seasonality and regionality of the cetacean distributions, reliable winter predictions might be restricted to geographic areas where winter surveys took place. These predictions are more uncertain and warrant caution.5.Synthesis and applications: extrapolations and nearby data highlighted environmental gaps to predict cetacean distributions in the North-East Atlantic, which could be covered by future surveys. This informs model users of regions and periods when predictions reliability becomes uncertain. SDMs are invaluable tools for supporting conservation applications and, despite the warnings that have been issued, the degree of information available for predicting distribution is still rarely reported. We recommend adding this assessment as routine information on the reliability of predictions.

An annotated checklist of the bees of Washington state

Bees (Hymenoptera: Apoidea) are vital components of global ecosystems, yet knowledge of their distribution is limited in many regions. Washington state is located in an ecologically diverse part of North America and encompasses habitat types and plant communities known for high bee species richness. To establish a baseline for future studies on bee communities in the state, we used published and unpublished datasets to develop a preliminary annotated checklist of bees occurring in Washington state. We document, with high confidence, 565 species of bees in Washington and identify an additional 102 species likely to occur in the state. We anticipate future research survey efforts, such as the newly initiated Washington Bee Atlas, will discover several species that have the potential to occur in Washington and provide new data for 84 species which have not been recorded in more than 50 years.

Digging into the Ultraviolet Luminosity Functions of Galaxies at High Redshifts: Galaxies Evolution, Reionization, and Cosmological Parameters

Thanks to the successful performance of the James Webb Space Telescope, our understanding of the epoch of reionization of the Universe has been advanced. The ultraviolet luminosity functions (UV LFs) of galaxies span a wide range of redshifts, not only revealing the connection between galaxies and dark matter (DM) halos but also providing information during reionization. In this work, we develop a model connecting galaxy counts and apparent magnitude based on UV LFs, which incorporates redshift-dependent star formation efficiency (SFE) and corrections for dust attenuation. By synthesizing some observations across the redshift range of 4 ≤ z ≤ 10 from various galaxy surveys, we discern the evolving SFE with increasing redshift and DM halo mass through model fitting. Subsequent analyses indicate that the Thomson scattering optical depth was τe=0.054−0.003+0.001 and the epoch of reionization started (ended) at z=18.8−6.0+7.2 ( z=5.3−1.0+0.8 ), which is insensitive to the choice of the truncated magnitude of the UV LFs. Incorporating additional data sets and some reasonable constraints, the amplitude of matter perturbation is found to be σ 8 = 0.80 ± 0.05, which is consistent with the standard ΛCDM model. Future galaxy surveys and the dynamical simulations of galaxy evolution will break the degeneracy between SFE and cosmological parameters, improving the accuracy and the precision of the UV LF model further.

Machine learning-based identification of geomorphological units in Quintero Bay (32°S) and its implications for the search for early drowned archaeological sites on the western coast of South America

High-resolution predictive modeling of submerged landscapes has successfully allowed the detection of early archaeological sites that are presently underwater. These models have traditionally relied on geophysical techniques, which can be both time-consuming and expensive, especially for extensive survey areas. In contrast, geomorphological mapping using Machine Learning (ML) techniques has emerged as a rapid and accessible alternative with numerous advantages over conventional methods. In this work, we employ ML algorithms (Random Forest, Support Vector Machine, Partial Least Squares, and Principal Component Analysis) trained on land to analyze the seabed of Quintero Bay to identify relic landforms that characterize the paleolandscape within which the submerged early site GNLQ1 formed. The methodology also included a multicriteria analysis that integrated geological (geomorphological, tectonic, eustatic) and archaeological (attributes of non-submerged records in the region) approaches to delineate potential areas of archaeological interest. The findings of this work can guide and enhance future archaeological research. The results underscore the importance of possessing a comprehensive understanding of the study area and its associated variables to the successful application of ML techniques. This also applies to modeling drowned paleolandscapes. Nevertheless, despite these challenges, ML-based modeling of drowned paleolandscapes can provide an overview of the distribution of geoforms comprising the paleolandscape, which in turn can help identify future geophysical survey areas to focus on in the search for archaeological evidence, thereby improving our understanding of the relationship between early human groups and these landscapes.

Extragalactic stellar tidal streams in the Dark Energy Survey

Context. Stellar tidal streams are a key tracer of galaxy evolution and have the potential to provide an indirect means for tracing dark matter. For the Local Group, many diffuse substructures have been identified and their link to galaxy evolution has been traced. However, the Local Group does not offer a statistically significant sample of stellar tidal streams. Thus, an analysis of a larger sample beyond the Local Group is required to better probe the frequency and characteristics of these streams to verify whether these properties are in agreement with the predictions of the ΛCDM model and its implementation in cosmological simulations, taking into account the impact of the baryonic physics modelling. Aims. The main scope of the Stellar Stream Legacy Survey is to obtain a statistically significant sample of stellar streams in the local Universe to be able to trace and study minor mergers and their contribution to galaxy evolution with respect to the ΛCDM theory. For that purpose, we are carrying out the first systematic survey of faint stellar debris from tidally disrupted dwarf satellites around nearby galaxies up to a distance of 100 Mpc. Methods. In this paper, we present a catalogue with the results of the first harvest of stellar tidal streams found by visual inspection in deep images of ∼700 galaxies from the Dark Energy Survey (DES). We also include, for the first time, a photometric characterisation of the streams obtained by measuring their surface brightnesses and colours. Results. We found a total of 63 streams in our sample at distances between 40 and 100 Mpc, including 58 that were not previously reported. We measured their average surface brightness for the g band, the r band and the z band, to be 28.35 ± 0.20, 27.81 ± 0.13, and 27.62 ± 0.09 mag arcsec−2, respectively. By applying a statistical analysis to our findings, we obtained a stream detection frequency of 9.1% ± 1.1% for the given surface brightness limit of the DES image sample, in agreement with previous studies. We identified stream progenitors in 5–14% of our stream sample, depending on the confidence level. Conclusions. The first catalogue of streams in the local Universe presented here will be complemented by future stream surveys within the Stellar Stream Legacy Survey and can be exploited in studies pertaining to galaxy evolution and cosmological models. In this work, we have learnt that the faintest measured stream surface brightness can be significantly brighter than the surface brightness limit of an image measured at the pixel level (in our case up to ∼1 mag arcsec−2 for the r band) mainly due to correlated noise present in the images.

How Accurate are Transient Spectral Classification Tools?— A Study Using 4646 SEDMachine Spectra

Accurate classification of transients obtained from spectroscopic data are important to understand their nature and discover new classes of astronomical objects. For supernovae (SNe), SNID, NGSF (a Python version of SUPERFIT), and DASH are widely used in the community. Each tool provides its own metric to help determine classification, such as rlap of SNID, chi2/dof of NGSF, and Probability of DASH. However, we do not know how accurate these tools are, and they have not been tested with a large homogeneous data set. Thus, in this work, we study the accuracy of these spectral classification tools using 4646 SEDMachine spectra, which have accurate classifications obtained from the Zwicky Transient Facility Bright Transient Survey (BTS). Comparing our classifications with those from BTS, we have tested the classification accuracy in various ways. We find that NGSF has the best performance (overall Accuracy 87.6% when samples are split into SNe Ia and Non-Ia types), while SNID and DASH have similar performance with overall Accuracy of 79.3% and 76.2%, respectively. Specifically for SNe Ia, SNID can accurately classify them when rlap > 15 without contamination from other types, such as Ibc, II, SLSN, and other objects that are not SNe (Purity > 98%). For other types, determining their classification is often uncertain. We conclude that it is difficult to obtain an accurate classification from these tools alone. This results in additional human visual inspection effort being required in order to confirm the classification. To reduce this human visual inspection and to support the classification process for future large-scale surveys, this work provides supporting information, such as the accuracy of each tool as a function of its metric.

The SRG/eROSITA All-Sky Survey

Context. Galaxy groups lying between galaxies and galaxy clusters in the mass spectrum of dark matter halos play a crucial role in the evolution and formation of the large-scale structure. Their shallower potential wells compared to clusters of galaxies make them excellent sources to constrain non-gravitational processes such as feedback from the central active galactic nuclei (AGN). Aims. We investigate the impact of feedback, particularly from AGN, on the entropy and characteristic temperature measurements of galaxy groups detected in the SRG/eROSITA’s first All-Sky Survey (eRASS1) to shed light on the characteristics of the feedback mechanisms and help guide future AGN feedback implementations in numerical simulations. Methods. We analyzed the deeper eROSITA observations of 1178 galaxy groups detected in the eRASS1. We divided the sample into 271 subsamples based on their physical and statistical properties and extracted average thermodynamic properties, including the electron number density, temperature, and entropy, at three characteristic radii from cores to outskirts along with the integrated temperature by jointly analyzing X-ray images and spectra following a Bayesian approach. Results. We present the tightest constraints with unprecedented statistical precision on the impact of AGN feedback through our average entropy and characteristic temperature measurements of the largest group sample used in X-ray studies, incorporating major systematics in our analysis. We find that entropy shows an increasing trend with temperature in the form of a power-law-like relation at the higher intra-group medium (IGrM) temperatures, while for the low-mass groups with cooler (T &lt; 1.44 keV) IGrM temperatures, a slight flattening is observed on the average entropy. Overall, the observed entropy measurements agree well with the earlier measurements in the literature. Additionally, comparisons with the state-of-the-art cosmological hydrodynamic simulations (MillenniumTNG, Magneticum, OWL) after applying the selection function calibrated for our galaxy groups reveal that observed entropy profiles in the cores are below the predictions of simulations. At the mid-region, the entropy measurements agree well with the Magneticum simulations, whereas the predictions of MillenniumTNG and OWL simulations fall below observations. At the outskirts, the overall agreement between the observations and simulations improves, with Magneticum simulations reproducing the observations the best. Conclusions. These measurements will pave the way for achieving more realistic AGN feedback implementations in numerical simulations. The future eROSITA Surveys will enable the extension of the entropy measurements in even cooler IGrM temperatures below 0. 5 keV, allowing for the testing of the AGN feedback models in this regime.

Confusion of extragalactic sources in the far-infrared: A baseline assessment of the performance of PRIMAger in intensity and polarization

Because of their limited angular resolution, far-infrared telescopes are usually affected by the confusion phenomenon. Since several galaxies can be located in the same instrumental beam, only the brightest objects emerge from the fluctuations caused by fainter sources. The PRobe far-Infrared Mission for Astrophysics imager (PRIMAger) will observe the mid- and far-infrared (25--235\,mu m) sky both in intensity and polarization. We aim to provide predictions of the confusion level and its consequences for future surveys. We produced simulated PRIMAger maps affected only by the confusion noise using the simulated infrared extragalactic sky (SIDES) semi-empirical simulation. We then estimated the confusion limit in these maps and extracted the sources using a basic blind extractor. By comparing the input galaxy catalog and the extracted source catalog, we derived various performance metrics as completeness, purity, and the accuracy of various measurements (e.g., the flux density in intensity and polarization or the polarization angle). In intensity maps, we predict that the confusion limit increases rapidly with increasing wavelength (from 21\,mu Jy at 25\,mu m to 46\,mJy at 235\,mu m). The confusion limit in polarization maps is more than two orders of magnitude lower (from 0.03\,mJy at 96\,mu m to 0.25\,mJy at 235\,mu m). Both in intensity and polarization maps, the measured (polarized) flux density is dominated by the brightest galaxy in the beam, but other objects also contribute in intensity maps at longer wavelengths (sim 30\,&lt;!PCT!&gt; at 235\,mu m). We also show that galaxy clustering has a mild impact on confusion in intensity maps (up to 25\,&lt;!PCT!&gt;), while it is negligible in polarization maps. In intensity maps, a basic blind extraction will be sufficient to detect galaxies at the knee of the luminosity function up to zsim 3 and 1011\,M$_ main-sequence galaxies up to zsim 5. In polarization for the most conservative sensitivity forecast (payload requirements), sim 200 galaxies can be detected up to z=1.5 in two 1\,500\,h surveys covering 1\,deg$^2$ and 10\,deg$^2$. For a conservative sensitivity estimate, we expect sim 8\,000 detections up to z=2.5, opening a totally new window on the high-z dust polarization. Finally, we show that intensity surveys at short wavelengths and polarization surveys at long wavelengths tend to reach confusion at similar depth. There is thus a strong synergy between them.

How Long Will the Quasar UV/Optical Flickering Be Damped? II. The Observational Test

The characteristic timescale at which the variability of active galactic nuclei (AGNs) turns from red noise to white noise can probe the accretion physics around supermassive black holes (SMBHs). A number of works have studied the characteristic timescale of quasars and obtained quite different scaling relations between the timescale and quasar physical properties. One possible reason for the discrepancies is that the characteristic timescale can be easily underestimated if the light curves are not long enough. In this work, we construct well-defined AGN samples to observationally test the relationships between the characteristic timescale and AGN properties obtained by previous works. Our samples eliminate the effects of insufficient light-curve lengths. We confirm that the timescale predictions of the Corona Heated Accretion disk Reprocessing model are consistent with our timescale measurements. The timescale predictions by empirical relations are systematically smaller than our measured ones. Our results provide further evidence that AGN variability is driven by thermal fluctuations in SMBH accretion disks. Future flagship time-domain surveys can critically test our conclusions and reveal the physical nature of AGN variability.

Running with the bulls

Context. Stars and planets form in regions of enhanced stellar density, subjecting protoplanetary discs to gravitational perturbations from neighbouring stars. Observations in the Taurus star-forming region have uncovered evidence of at least three recent, star-disc encounters that have truncated discs (HV/DO Tau, RW Aurigae, and UX Tau), raising questions about the frequency of such events. Aims. We aim to assess the probability of observing truncating star-disc encounters in Taurus. Methods. We generated a physically motivated dynamical model including binaries and a spatial-kinematic substructure to follow the historical dynamical evolution of the Taurus star-forming region. We used this model to track star-disc encounters and the resulting outer disc truncation over the lifetime of Taurus. Results. A quarter of discs are truncated below 30 au by dynamical encounters, but this truncation mostly occurs in binaries over the course of a few orbital periods, on a timescale ≲0.1 Myr. Nonetheless, some truncating encounters still occur up to the present age of Taurus. Strongly truncating encounters (ejecting ≳10 percent of the disc mass) occur at a rate ∼10 Myr−1, sufficient to explain the encounter between HV and DO Tau ∼0.1 Myr ago. If encounters that eject only ∼1 percent of the disc mass are responsible for RW Aurigae and UX Tau, then they are also expected with encounter rate Γenc ∼ 100–200 Myr−1. However, the observed sample of recent encounters is probably incomplete, since these examples occurred in systems that are not consistent with a random drawing from the mass function. One more observed example would statistically imply additional physics, such as replenishment of the outer disc material. Conclusions. The marginal consistency of the frequency of observed recent star-disc encounters with theoretical expectations underlines the value of future large surveys searching for external structures associated with recent encounters. The outcome of such a survey offers a highly constraining, novel probe of protoplanetary disc physics.