Synthetic Observations Research Articles

Evaluation of sea surface temperature from ocean reanalysis products over the North Indian Ocean

Ocean and sea ice reanalyses (ORAs or ocean syntheses) are reconstructions of the ocean and sea ice states using an ocean model integration constrained by atmospheric surface forcing and ocean observations via a data assimilation method. Ocean reanalyses are a valuable tool for monitoring and understanding long-term ocean variability at depth, mainly because this part of the ocean is still largely unobserved. Sea surface temperature (SST) is the key variable that drives the air–sea interaction process on different time scales. Despite improvements in model and reanalysis schemes, ocean reanalyses show errors when evaluated with independent observations. The independent evaluation studies of SST from ocean reanalysis over the Indian Ocean are limited. In this study, we evaluated the SST from 10 reanalysis products (ECCO, BRAN, SODA, NCEP-GODAS, GODAS-MOM4p1, ORAS5, CGLORS, GLORYS2V4, GLOSEA, and GREP) and five synthetic observation products (COBE, ERSST, OISST, OSTIA, and HadISST) and from the pure observation-based product AMSR2 for 2012–2017 with 12 in-situ buoy observations (OMNI) over the Arabian Sea and Bay of Bengal. Even though the reanalysis and observational products perform very well in the open ocean, the performance is poorer near the coast and islands. The reanalysis products perform comparatively better than most of the observational products. COBE and OISST perform better among the synthetic observational products in the northern Indian Ocean. GODAS-MOM4p1 and GREP performs best among the reanalysis products, often surpassing the observational products. ECCO shows poorer performance and higher bias in the Bay of Bengal. Comparing the BRAN daily and monthly SST, the monthly SST performance of reanalysis is better than the daily time scale.

Assimilation of synthetic and real SWOT observations for the North Atlantic Ocean and Canadian east coast using the regional ice ocean prediction system

The Surface Water Ocean Topography (SWOT) mission significantly improves on the capabilities of current nadir altimeters by enabling two-dimensional mapping. Assimilating this advanced data into high-resolution models poses challenges. To address this, Observing System Simulation Experiments (OSSEs) were conducted to evaluate the effects of both simulated and actual SWOT data on the Regional Ice Ocean Prediction System (RIOPS). This study examines the OSSEs’ design, focusing on the simulated observations and assimilation systems used. The validity of the OSSE designs is confirmed by ensuring the deviations between the assimilation system and the Nature Run (NR) align with discrepancies observed between actual oceanic data and OSSE simulations. The study measures the impact of assimilating SWOT and two nadir altimeters by calculating root mean square forecast error for sea surface height (SSH), temperature, and velocities, along with performing wave-number spectra and coherence analyses of SSH errors. The inclusion of SWOT data is found to reduce RMS SSH errors by 16% and RMS velocity errors by 6% in OSSEs. The SSH error spectrum shows that the most notable improvements are for scales associated with the largest errors in the range of 200-400 km, with a 33% reduction compared to traditional data assimilation. Additionally, spectral coherence analysis shows that the limit of constrained scales is reduced from 280 km for conventional observations to 195 km when SWOT is assimilated as well. This study also represents our first attempt at assimilating early-release SWOT data. A set of Observing System (data denial) experiments using early-release SWOT measurements shows similar (but smaller) responses to OSSE experiments in a two nadir-altimeter context. In a six-altimeter constellation setup, a positive impact of SWOT is also noted, but of significantly diminished amplitude. These findings robustly advocate for the integration of SWOT observations into RIOPS and similar ocean analysis and forecasting frameworks.

Detectability of biosignatures in warm, water-rich atmospheres

Warm rocky exoplanets within the habitable zone of Sun-like stars are favoured targets for current and future missions. Theory indicates these planets could be wet at formation and remain habitable long enough for life to develop. However, it is unclear to what extent an early ocean on such worlds could influence the response of potential biosignatures. In this work we test the climate-chemistry response, maintenance, and detectability of biosignatures in warm, water-rich atmospheres with Earth biomass fluxes within the framework of the planned LIFE mission. We used the coupled climate-chemistry column model 1D-TERRA to simulate the composition of planetary atmospheres at different distances from the Sun, assuming Earth's planetary parameters and evolution. We increased the incoming instellation by up to 50 percent in steps of 10 percent, corresponding to orbits of 1.00 to 0.82 AU. Simulations were performed with and without modern Earth’s biomass fluxes at the surface. Theoretical emission spectra of all simulations were produced using the GARLIC radiative transfer model. LIFEsim was then used to add noise to and simulate observations of these spectra to assess how biotic and abiotic atmospheres of Earth-like planets can be distinguished. Increasing instellation leads to surface water vapour pressures rising from 0.01 bar (1.31<!PCT!>, S = 1.0) to 0.61 bar (34.72<!PCT!>, S = 1.5). In the biotic scenarios, the ozone layer survives because hydrogen oxide reactions with nitrogen oxides prevent the net ozone chemical sink from increasing. Methane is strongly reduced for instellations that are 20<!PCT!> higher than that of the Earth due to the increased hydrogen oxide abundances and UV fluxes. Synthetic observations with LIFEsim, assuming a 2.0 m aperture and resolving power of a R = 50, show that ozone signatures at 9.6 mu m reliably point to Earth-like biosphere surface fluxes of O$_2$ only for systems within 10 parsecs. The differences in atmospheric temperature structures due to differing H$_2$O profiles also enable observations at 15.0 mu m to reliably identify planets with a CH$_4$ surface flux equal to that of Earth's biosphere. Increasing the aperture to 3.5 m and increasing instrument throughput to 15<!PCT!> increases this range to 22.5 pc.

Mind the Kinematics Simulation of Planet–Disk Interactions: Time Evolution and Numerical Resolution

Planet–disk interactions can produce kinematic signatures in protoplanetary disks. While recent observations have detected non-Keplerian gas motions in disks, their origins are still being debated. To explore this, we conduct 3D hydrodynamic simulations using the code FARGO3D to study nonaxisymmetric kinematic perturbations at two scale heights induced by Jovian planets in protoplanetary disks, followed by examinations of detectable signals in synthetic CO emission line observations at millimeter wavelengths. We advocate for using residual velocity or channel maps, generated by subtracting an azimuthally averaged background of the disk, to identify planet-induced kinematic perturbations. We investigate the effects of two basic simulation parameters, simulation duration and numerical resolution, on the simulation results. Our findings suggest that a short simulation (e.g., 100 orbits) is insufficient to establish a steady velocity pattern given our chosen viscosity (α = 10−3) and displays plenty of fluctuations on an orbital timescale. Such transient features could be detected in observations. By contrast, a long simulation (e.g., 1000 orbits) is required to reach steady state in kinematic structures. At 1000 orbits, the strongest detectable velocity structures are found in the spiral wakes close to the planet. Through numerical convergence tests, we find hydrodynamics results converge in spiral regions at a resolution of 14 cells per disk scale height or higher. Meanwhile, synthetic observations produced from hydrodynamic simulations at different resolutions are indistinguishable with 0.″1 angular resolution and 10 hr of integration time on Atacama Large Millimeter/submillimeter Array.

Exploring the intermittency of magnetohydrodynamic turbulence by synchrotron polarization radiation

Context. Magnetohydrodynamic (MHD) turbulence plays a critical role in many key astrophysical processes, such as star formation, acceleration of cosmic rays, and heat conduction. However, its properties are still poorly understood. Aims. In this work, we explore how to extract the intermittency of compressible MHD turbulence from synthetic and real observations. Methods. We used three statistical methods, namely the probability distribution function, kurtosis, and scaling exponent of the multi-order structure function, to reveal the intermittency of MHD turbulence. Results. Our numerical results demonstrate that: (1) the synchrotron polarization intensity statistics can be used to probe the intermittency of magnetic turbulence, by which we can distinguish different turbulence regimes; (2) the intermittency of MHD turbulence is dominated by the slow mode in the sub-Alfvénic turbulence regime; and (3) the Galactic interstellar medium (ISM) in the low latitude region corresponds to the sub-Alfvénic and supersonic turbulence regime. Conclusions.We have successfully measured the intermittency of the Galactic ISM from synthetic and realistic observations.

Physical properties of extreme emission-line galaxies at z ∼ 4–9 from the JWST CEERS survey

Context. Extreme emission line galaxies (EELGs) are typically characterized by high equivalent widths (EWs) which are driven by elevated specific star formation rates (sSFRs) in low-mass galaxies with subsolar metallicities and little dust. Such extreme systems are exceedingly rare in the local universe, but the number density of EELGs increases with increasing redshift. Such starburst galaxies are currently strongly presumed to be the main drivers of hydrogen reionization over 5.5 < z < 15, which serves to motivate many of the searches for high-z EELGs. Aims. We aim to characterize the physical properties of a sample of ∼730 EELGs at 4 ≲ z < 9 photometrically selected from the CEERS survey using JWST/NIRCam. We validate our method and demonstrate the main physical properties of a subset of EELGs using NIRSpec spectra. Methods. We create synthetic NIRCam observations of EELGs using empirical templates based on ∼2000 local metal-poor starbursts to select EELGs based on color-color criteria. We study their properties based on SED fitting and flux excess from emission lines in the photometric filters. Results. Our sample of EELGs has a mean stellar mass of 107.84 M⊙ with high sSFRs from SED fitting with a mean value of 10−7.03 yr−1. We consider a delayed-τ model for the star formation history and find our sample of EELGs are young with a mean value of the time after the onset of star formation of 45 Myr. We find that they have similar line ratios to local metal-poor starburst galaxies with high log([OIII]/Hβ) ≳ 0.4−1 which indicates that star formation may be the dominant source of ionization in these galaxies. Based on the photometric fluxes and morphologies, we find an increase of EW([OIII]+Hβ) with sSFR and ΣSFR, and a decrease with age and stellar mass. The sample of EELGs can reach ΣSFR > 10 M⊙ yr−1 kpc−2 which indicate they are strong candidates of LyC leakers. Another indirect indicator is the high values of O32 > 5 that can be reached for some galaxies in the sample. This indicates that they may have the conditions to facilitate the escape of ionizing photons.

Combining low- and high-frequency microwave radiometer measurements from the MOSAiC expedition for enhanced water vapour products

Abstract. In the central Arctic, high-quality water vapour observations are sparse due to the low density of meteorological stations and uncertainties in satellite remote sensing. Different reanalyses also disagree on the amount of water vapour in the central Arctic. The Multidisciplinary drifting Observatory for the Study of the Arctic Climate (MOSAiC) expedition provides comprehensive observations that are suitable for evaluating satellite products and reanalyses. Radiosonde observations provide high-quality water vapour estimates with a high vertical but a low temporal resolution. Observations from the microwave radiometers (MWRs) on board the research vessel Polarstern complement these observations through high temporal resolution. In this study, we demonstrate the high accuracy of the combination of the two MWRs HATPRO (Humidity and Temperature Profiler) and MiRAC-P (Microwave Radiometer for Arctic Clouds – Passive). For this purpose, we developed new retrievals of integrated water vapour (IWV) and profiles of specific humidity and temperature using a neural network approach, including observations from both HATPRO and MiRAC-P to utilize their different water vapour sensitivity. The retrievals were trained with the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis version 5 (ERA5) and synthetic MWR observations simulated with the Passive and Active Microwave radiative TRAnsfer tool (PAMTRA). We applied the retrievals to synthetic and real observations and evaluated them with ERA5 and radiosondes launched during MOSAiC, respectively. To assess the benefit of the combination of HATPRO and MiRAC-P compared to single MWR retrievals, we compared the errors with respect to MOSAiC radiosondes and computed the vertical information content of the specific humidity profiles. The root mean squared error (RMSE) of IWV was reduced by up to 15 %. Specific humidity biases and RMSE were reduced by up to 75 % and 50 %, respectively. The vertical information content of specific humidity could be increased from 1.7 to 2.4 degrees of freedom. We also computed relative humidity from the retrieved temperature and specific humidity profiles and found that RMSE was reduced from 45 % to 15 %. Finally, we show a case study demonstrating the enhanced humidity profiling capabilities compared to the standard HATPRO-based retrievals. The vertical resolution of the retrieved specific humidity profiles is still low compared to radiosondes, but the case study revealed the potential to resolve major humidity inversions. To what degree the MWR combination detects humidity inversions, also compared to satellites and reanalyses, will be part of future work.

Reconstructing the Ocean State Using Argo Data and a Data-Driven Method

Abstract Over the past two decades, Argo profiles have provided unprecedented insight into the global patterns of space and time variability of ocean temperature and salinity, significantly reducing associated uncertainties. However, analyzing such assessments during the pre-Argo period remains challenging due to the scarcity of observations in many regions. From the Argo period, a set of dominant three-dimensional patterns can be estimated using Empirical Orthogonal Function (EOF) analysis, which helps to fill in observational gaps. From the associated principal components, temporal fluctuations can be observed, aiming to build a catalog of possible ocean state trajectories. To map pre-Argo observations, EOFs are used in a data assimilation framework that uses this catalog to feed an analog prediction and provide reanalysis. In this study, a new data-driven interpolation method called RedAnDA (Reduced-space Analog Data Assimilation) was tested in the tropical Pacific Ocean. RedAnDA was first validated through an Observing System Simulation Experiment (OSSE) approach, using synthetic observations extracted from a model simulation. It was subsequently applied to a real historical dataset and compared with other available reanalysis products. Overall, the reconstructed temperature field showed variability consistent with the OSSE true field and other reanalysis products in the real data application. Further improvements are needed to optimally estimate uncertainty, but RedAnDA already combines valuable information about state predictability, observational sampling, and unresolved scale issues.

Global-scale gravity wave analysis methodology for the ESA Earth Explorer 11 candidate CAIRT

Abstract. In the past, satellite climatologies of gravity waves (GWs) have initiated progress in their representation in global models. However, these could not provide the phase speed and direction distributions needed for a better understanding of the interaction between GWs and the large-scale winds directly. The ESA Earth Explorer 11 candidate CAIRT could provide such observations. CAIRT would use a limb-imaging Michelson interferometer resolving a wide spectral range, allowing temperature and trace gas mixing ratio measurements. With the proposed instrument design, a vertical resolution of 1 km, along-track sampling of 50 km, and across-track sampling of 25 km in a 400 km wide swath will be achieved. In particular, this allows for the observation of three-dimensional (3D), GW-resolving temperature fields throughout the middle atmosphere. In this work, we present the methodology for the GW analysis of CAIRT observations using a limited-volume 3D sinusoidal fit (S3D) wave analysis technique. We assess the capability of CAIRT to provide high-quality GW fields by the generation of synthetic satellite observations from high-resolution model data and comparison of the synthetic observations to the original model fields. For the assessment, wavelength spectra, phase speed spectra, horizontal distributions, and zonal means of GW momentum flux (GWMF) are considered. The atmospheric events we use to exemplify the capabilities of CAIRT are the 2006 sudden stratospheric warming (SSW) event, the quasi-biennial oscillation (QBO) in the tropics, and the mesospheric preconditioning phase of the 2019 SSW event. Our findings indicate that CAIRT would provide highly reliable observations not only of global-scale GW distributions and drag patterns but also of specific wave events and their associated wave parameters. Even under worse-than-expected noise levels of the instrument, the resulting GW measurements are highly consistent with the original model data. Furthermore, we demonstrate that the estimated GW parameters can be used for ray tracing, which physically extends the horizontal coverage of the observations beyond the orbit tracks.

Assimilation of Sentinel‐1 Backscatter to Update AquaCrop Estimates of Soil Moisture and Crop Biomass

AbstractThis study assesses the potential of regional microwave backscatter data assimilation (DA) in AquaCrop for the first time, using NASA's Land Information System. The objective is to assess whether the assimilation setup can improve surface soil moisture (SSM) and crop biomass estimates. SSM and crop biomass simulations from AquaCrop were updated using Sentinel‐1 synthetic aperture radar observations, over three regions in Europe in two separate DA experiments. The first experiment concerned updating SSM using VV‐polarized backscatter and the corrections were propagated via the model to the biomass. In the second experiment, the DA setup was extended by also updating the biomass with VH‐polarized backscatter. SSM was evaluated with local in situ data and with downscaled Soil Moisture Active Passive (SMAP) retrievals for all cropland grid cells, whereas crop biomass was compared to SMAP vegetation optical depth and the Copernicus dry matter productivity. The assimilation showed mixed results for root mean square error and Pearson's correlation, with slight overall improvements in the (anomaly) correlations of updated SSM relative to independent in situ and satellite data. By contrast, the biomass estimates obtained with backscatter DA did not agree better with reference data sets. Overall, the SSM evaluation showed that there is potential in using Sentinel‐1 backscatter for assimilation in AquaCrop, but the present setup was not able to improve crop biomass estimates. Our study reveals how the complex interaction between SSM, crop biomass and backscatter affect the impact and performance of DA, offering insight into ways to optimize DA for crop growth estimation.

Nonparametric Bayesian reconstruction of Galactic magnetic fields using information field theory

Context. Ultrahigh-energy cosmic rays (UHECRs) are charged particles with energies surpassing 1018 eV. Their sources remain elusive because they are obscured by deflections caused by the Galactic magnetic field (GMF). This challenge is further complicated by our limited understanding of the 3D structure of the GMF because current GMF observations primarily consist of quantities that are integrated along the line of sight (LOS). Nevertheless, data from upcoming stellar polarization surveys along with Gaia stellar parallax data are expected to yield local GMF measurements. Aims. This study is the second entry in our exploration of a Bayesian inference approach to the local GMF that uses synthetic local GMF observations that emulate forthcoming local GMF measurements, and attempts to use them to reconstruct its 3D structure. The ultimate aim is to trace back observed UHECRs and thereby update our knowledge about their possible origin. Methods. In this proof-of-concept work, we assumed as ground truth a magnetic field produced by a dynamo simulation of the Galactic ISM. We employed methods of Bayesian statistical inference in order to sample the posterior distribution of the GMF within part of the Galaxy. By assuming a known rigidity and arrival direction of an UHECR, we traced its trajectory back through various GMF configurations drawn from the posterior distribution. Our objective was to rigorously evaluate the performance of our algorithm in scenarios that closely mirror the setting of expected future applications. In pursuit of this, we conditioned the posterior to synthetically integrated LOS measurements of the GMF, in addition to synthetic local plane of sky-component measurements. Results. Our results demonstrate that for all locations of the observed arrival direction on the plane of sky, our algorithm is able to substantially update our knowledge on the original arrival direction of UHECRs with a rigidity of E/Z = 5 × 1019 eV, even without any LOS information. When the integrated data are included in the inference, the regions of the celestial sphere in which the maximum error occurs are greatly reduced. The maximum error is diminished by a factor of about 3 even in these regions in the specific setting we studied. Additionally, we are able to identify the regions in which the largest error is expected to occur.

Impact of the horizontal resolution of GEOS-Chem on land‒ocean and tropic‒extratropic partitioning and seasonal cycle in CO2 inversion

Abstract Credible regional carbon budget estimates from atmospheric CO2 measurements rely on the accuracy of atmospheric transport models (ATMs). However, the atmospheric transport in ATMs is usually simplified and spatiotemporally averaged, leading to systematic biases in simulating the atmospheric CO2 and estimating surface CO2 fluxes. We show that forward simulations of global CO2 using an ATM, GEOS-Chem, at a native resolution of 0.5°×0.625° and a coarse resolution of 4°×5° differ significantly near the surface in the Northern Hemisphere and the polar vortex, mainly because of advection in GEOS-Chem. Comparing observing system simulation experiments that assimilate synthetic observations sampled from the forward simulations, we separate the impact of coarse-resolution GEOS-Chem on regional flux estimates. The results suggest that a significant amount of annual carbon uptake from the ocean and tropics is improperly redistributed to the land and northern and southern extratropics, respectively. In addition, these errors lead to an underestimated seasonal amplitude in the northern extratropical land and a reversed sign of the seasonal cycle in the northern extratropical ocean. The reversed sign of the seasonal cycle has also been observed in a real data assimilation experiment and several state-of-the-art inversions, suggesting that reasonable ocean flux estimates depend strongly on the accuracy of the ATM.

Kinematics of the 2023 Kahramanmaraş Earthquake Doublet: Biased Near-Fault Data and Shallow Slip Deficit

Abstract Accurate estimate of the shallow slip deficit (SSD) for large strike-slip events is highly dependent on near-fault data. Previous studies have estimated the SSD of the 2023 Kahramanmaraş earthquake doublet, which may vary from a few percent to about 50%. Whether this reduced shallow slip is real or artificial is crucial for understanding the seismic hazards during and following the earthquake doublet. In this study, we inverted for the kinematic slip of this earthquake doublet with refined near-fault Interferometric Synthetic Aperture Radar observations and compared the results with the source model without further data processing. The model that excludes nonphysical data produced only ∼6% and ∼22% SSD for the M7.8 and 7.6 events, respectively, compared to ∼44% and ∼53% SSD of the model using original data. The increased data gap generally leads to overestimated SSD, but when the data coverage is almost complete, our result shows that the SSD is very sensitive to biased near-fault data which may induce significant artificial SSD. Our results suggest that overestimated SSD may be accompanied by slip migration from shallow toward deeper depths, to meet the total moment or energy release constrained by surface displacement data. The M7.8 event increased static Coulomb stress at where the second M7.6 event nucleated and thus may have triggered the following event.

Advancing student outcome predictions through generative adversarial networks

Predicting student outcomes is essential in educational analytics for creating personalised learning experiences. The effectiveness of these predictive models relies on having access to sufficient and accurate data. However, privacy concerns and the lack of student consent often restrict data collection, limiting the applicability of predictive models. To tackle this obstacle, we employ Generative Adversarial Networks, a type of Generative AI, to generate tabular data replicating and enlarging the dimensions of two distinct publicly available student datasets. The ‘Math dataset’ has 395 observations and 33 features, whereas the ‘Exam dataset’ has 1000 observations and 8 features. Using advanced Python libraries, Conditional Tabular Generative Adversarial Networks and Copula Generative Adversarial Networks, our methodology consists of two phases. First, a mirroring approach where we produce synthetic data matching the volume of the real datasets, focusing on privacy and evaluating predictive accuracy. Second, augmenting the real datasets with newly created synthetic observations to fill gaps in datasets that lack student data. We validate the synthetic data before employing these approaches using Correlation Analysis, Density Analysis, Correlation Heatmaps, and Principal Component Analysis. We then compare the predictive accuracy of whether students will pass or fail their exams across original, synthetic, and augmented datasets. Employing Feedforward Neural Networks, Convolutional Neural Networks, and Gradient-boosted Neural Networks, and using Bayesian optimisation for hyperparameter tuning, this research methodically examines the impact of synthetic data on prediction accuracy. We implement and optimize these models using Python. Our mirroring approach aims to achieve accuracy rates that closely align with the original data. Meanwhile, our augmenting approach seeks to reach a slightly higher accuracy level than when solely learning from the original data. Our findings provide actionable insights into leveraging advanced Generative AI techniques to enhance educational outcomes and meet our objectives successfully.

Assimilation of ground-based GNSS data using a local ensemble Kalman filter

Tropical cyclones become increasingly nonlinear and dynamically unstable in high-resolution models. The initial conditions are typically sub-optimal, leaving scope to improve the accuracy of forecasts with improved data assimilation. Simultaneously, the lack of real ground-based GNSS observations over the ocean poses significant challenges when evaluating the assimilation results in oceanic regions. In this study, an Observation System Simulation Experiment is carried out based on a tropical cyclone case. Assimilation experiments using the WRF-PDAF framework are conducted. Conventional and GNSS observation operators are implemented. A diverse array of synthetic observations, encompassing temperature (T), wind components (U and V), precipitable water (PW), and zenith total delay (ZTD), are assimilated utilizing the Local Error-Subspace Transform Kalman filter (LESTKF). The findings highlight the improvement in forecast accuracy achieved through the assimilation process over the ocean. Multiple observation types further improve the forecast accuracy. The study underscores the crucial role of GNSS data assimilation techniques. The assimilation of GNSS data presents potential for advancing weather forecasting capabilities. Thus, the construction of ground-based GNSS observation stations over the ocean is promising.

Validating full-spectrum fitting with a synthetic integral-field spectroscopic observation of the milky way

Abstract Ongoing deep IFS observations of disk galaxies provide opportunities for comparison with the Milky Way (MW) to understand galaxy evolution. However, such comparisons are marred by many challenges such as selection effects, differences in observations and methodology, and proper validation of full-spectrum fitting methods. In this study, we present a novel code GalCraft to address these challenges by generating mock IFS data cubes of the MW using simple stellar population models and a mock MW stellar catalog derived from E-Galaxia. We use the widely adopted full-spectrum fitting code pPXF to investigate the ability to recover kinematics and stellar populations for an edge-on mock MW IFS observation. We confirm that differences in kinematics, mean age, [M/H], and [α/Fe] between thin and thick disks can be distinguished. However, the age distribution is overestimated in the ranges between 2 − 4 and 12 − 14 Gyr compared to the expected values. This is likely due to the age spacing and degeneracy of SSP templates. We find systematic offsets in the recovered kinematics due to insufficient spectral resolution and the variation of line-of-sight velocity distribution with age and [M/H]. With future higher resolution and multi-[α/Fe] SSP templates, GalCraft will be useful to validate key signatures such as [α/Fe]-[M/H] distribution at different R and |z| and potentially infer radial migration and kinematic heating efficiency to study detailed chemodynamical evolution of MW-like galaxies.

Using Shortened Spin‐Ups to Speed Up Ocean Biogeochemical Model Optimization

AbstractThe performance of global ocean biogeochemical models can be quantified as the misfit between modeled tracer distributions and observations, which is sought to be minimized during parameter optimization. These models are computationally expensive due to the long spin‐up time required to reach equilibrium, and therefore optimization is often laborious. To reduce the required computational time, we investigate whether optimization of a biogeochemical model with shorter spin‐ups provides the same optimized parameters as one with a full‐length, equilibrated spin‐up over several millennia. We use the global ocean biogeochemical model MOPS with a range of lengths of model spin‐up and calibrate the model against synthetic observations derived from previous model runs using a derivative‐free optimization algorithm (DFO‐LS). When initiating the biogeochemical model with tracer distributions that differ from the synthetic observations used for calibration, a minimum spin‐up length of 2,000 years was required for successful optimization due to certain parameters which influence the transport of matter from the surface to the deeper ocean, where timescales are longer. However, preliminary results indicate that successful optimization may occur with an even shorter spin‐up by a judicious choice of initial condition, here the synthetic observations used for calibration, suggesting a fruitful avenue for future research.

X-ray polarisation in AGN circumnuclear media

Context. Cold gas and dust reprocess the central X-ray emission of active galactic nuclei (AGN), producing characteristic spectro-polarimetric features in the X-ray band. The recent launch of IXPE allows for observations of this X-ray polarisation signal, which encodes unique information on the parsec-scale circumnuclear medium of obscured AGN. However, the models for interpreting these polarimetric data are under-explored and do not reach the same level of sophistication as the corresponding spectral models. Aims. We aim at closing the gap between the spectral and spectro-polarimetric modelling of AGN circumnuclear media in the X-ray band by providing the tools for simulating X-ray polarisation in complex geometries of cold gas alongside X-ray spectra. Methods. We lay out the framework for X-ray polarisation in 3D radiative transfer simulations and provide an implementation to the 3D radiative transfer code SKIRT, focussing on (de)polarisation due to scattering and fluorescent re-emission. As an application, we explored the spectro-polarimetric properties of a 2D toroidal reprocessor of cold gas, modelling the circumnuclear medium of AGN. Results. For the 2D torus model, we find a complex behaviour of the polarisation angle with photon energy, which we interpret as a balance between the reprocessed photon flux originating from different sky regions, with a direct link to the torus geometry. We calculated a large grid of AGN torus models and demonstrated how spatially resolved X-ray polarisation maps could form a useful tool for interpreting the geometrical information that is encoded in IXPE observations. With this work, we release high-resolution AGN torus templates that simultaneously describe X-ray spectra and spectro-polarimetry for observational data fitting with XSPEC. Conclusions. The SKIRT code can now model X-ray polarisation simultaneously with X-ray spectra and provide synthetic spectro-polarimetric observations for complex 3D circumnuclear media, with all features of the established SKIRT framework available.

Effects of Radiative Transfer on the Observed Anisotropy in Magnetohydrodynamic Turbulent Molecular Simulations

We study the anisotropy of centroid and integrated intensity maps with synthetic observations. We perform postprocess radiative transfer including the optically thick regime that was not covered in Hernández-Padilla et al. We consider the emission in various CO molecular lines that range from optically thin to optically thick (12CO, 13CO, C18O, and C17O). The results for the velocity centroids are similar to those in the optically thin case. For instance, the anisotropy observed can be attributed to the Alfvén mode, which dominates over the slow and fast modes when the line of sight is at a high inclination with respect to the mean magnetic field. A few differences arise in the models with higher opacity, where some dependence on the sonic Mach number becomes evident. In contrast to the optically thin case, maps of integrated intensity become more anisotropic in optically thick lines. In this situation the scales probed are restricted, due to absorption, to smaller scales, which are known to be more anisotropic. We discuss how the sonic Mach number can affect the latter results, with highly supersonic cases exhibiting a lower degree of anisotropy.

Radio U-Net: a convolutional neural network to detect diffuse radio sources in galaxy clusters and beyond

ABSTRACT The forthcoming generation of radio telescope arrays promises significant advancements in sensitivity and resolution, enabling the identification and characterization of many new faint and diffuse radio sources. Conventional manual cataloguing methodologies are anticipated to be insufficient to exploit the capabilities of new radio surveys. Radio interferometric images of diffuse sources present a challenge for image segmentation tasks due to noise, artifacts, and embedded radio sources. In response to these challenges, we introduce Radio U-Net, a fully convolutional neural network based on the U-Net architecture. Radio U-Net is designed to detect faint and extended sources in radio surveys, such as radio haloes, relics, and cosmic web filaments. Radio U-Net was trained on synthetic radio observations built upon cosmological simulations and then tested on a sample of galaxy clusters, where the detection of cluster diffuse radio sources relied on customized data reduction and visual inspection of Low-Frequency Array Two metre Sky Survey (LoTSS) data. The 83 per cent of clusters exhibiting diffuse radio emission were accurately identified, and the segmentation successfully recovered the morphology of the sources even in low-quality images. In a test sample comprising 246 galaxy clusters, we achieved a 73 per cent accuracy rate in distinguishing between clusters with and without diffuse radio emission. Our results establish the applicability of Radio U-Net to extensive radio survey data sets, probing its efficiency on cutting-edge high-performance computing systems. This approach represents an advancement in optimizing the exploitation of forthcoming large radio surveys for scientific exploration.