We present three cosmological radiation-hydrodynamic zoom simulations of the progenitor of a Milky Way-mass galaxy from the MEGATRON suite. The simulations combine on-the-fly radiative transfer with a detailed non-equilibrium thermochemical network (81 ions and molecules), resolving the cold and warm gas in the circumgalactic medium (CGM) on spatial scales down to 20 pc and on average 200 pc at cosmic noon. Comparing our full non-equilibrium calculation with local radiation to traditional post-processed photoionization equilibrium (PIE) models assuming a uniform UV background (UVB), we find that non-equilibrium physics and local radiation fields fundamentally impact the thermochemistry of the CGM. Recombination lags and local radiation anisotropy shift ions away from their PIE+UVB values and modify covering fractions (for example, HI damped Ly$\alpha$ absorbers differ by up to 40%). In addition, a resolution study with cooling-length refinement allows us to double the resolution in the cold and warm CGM gas, reaching 120 pc on average. When refining on cooling length, the mass of the lightest cold clumps decreases tenfold to $\approx 10^4\,M_\odot$, their boundary layers develop sharper ion stratification, and the warm gas is better resolved, boosting the abundance of warm gas tracers such as CIV and OIII. Together, these results demonstrate that non-equilibrium thermochemistry coupled to radiative transfer, combined with physically motivated resolution criteria, is essential to predict circumgalactic absorption and emission signatures and to guide the design of targeted observations with existing and upcoming facilities.

We present the COLIBRE galaxy formation model and the COLIBRE suite of cosmological hydrodynamical simulations. COLIBRE includes new models for radiative cooling, dust grains, star formation, stellar mass loss, turbulent diffusion, pre-supernova stellar feedback, supernova feedback, supermassive black holes and active galactic nucleus (AGN) feedback. The multiphase interstellar medium is explicitly modelled without a pressure floor. Hydrogen and helium are tracked in non-equilibrium, with their contributions to the free electron density included in metal-line cooling calculations. The chemical network is coupled to a dust model that tracks three grain species and two grain sizes. In addition to the fiducial thermally-driven AGN feedback, a subset of simulations uses black hole spin-dependent hybrid jet/thermal AGN feedback. To suppress spurious transfer of energy from dark matter to stars, dark matter is supersampled by a factor 4, yielding similar dark matter and baryonic particle masses. The subgrid feedback model is calibrated to match the observed $z \approx 0$ galaxy stellar mass function, galaxy sizes, and black hole masses in massive galaxies. The COLIBRE suite includes three resolutions, with particle masses of $\sim 10^5$, $10^6$, and $10^7\,\text{M}_\odot$ in cubic volumes of up to 50, 200, and 400 cMpc on a side, respectively. The two largest runs use 136 billion ($5 \times 3008^3$) particles. We describe the model, assess its strengths and limitations, and present both visual impressions and quantitative results. Comparisons with various low-redshift galaxy observations generally show very good numerical convergence and excellent agreement with the data.

We present the MEGATRON suite of cosmological radiation hydrodynamics simulations following the formation of Milky Way-mass galaxies from the earliest cosmic epochs when Population III stars form to Cosmic Noon. The suite represents the first set of cosmological simulations that couples a vast non-equilibrium thermochemistry network of primordial species, metals, and molecules to multifrequency, on-the-fly radiation transport, allowing us to directly predict the spectral properties of early galaxies. By initializing the simulations at zero metallicity, resolving haloes well below the atomic cooling threshold, reaching parsec-scale resolution, and modeling a Milky Way-mass environment, we aim to address four key science themes: 1) Star formation at cosmic dawn, 2) Galaxy formation and the interstellar medium in the epoch of reionization, 3) The circumgalactic medium towards cosmic noon, and 4) Reionization in a local volume environment and near-field cosmology. In this introductory work, we present an overview of the physical characteristics of high-redshift MEGATRON galaxies and their environment at $z>8$. We present a library of $>175,000$ simulated galaxy spectra and demonstrate how the diversity of galaxy spectra seen by JWST is naturally reproduced in the context of a $\Lambda$CDM cosmology. This project represents a step towards making more direct comparisons between simulations and observations and will enable future work to both optimize methods for inferring galaxy properties from observations and to elucidate the physics that governs galaxy formation in the early Universe.

We study the stellar mass-iron metallicity relation of dwarf galaxies in the new high-resolution MEGATRON cosmological radiation-hydrodynamics simulations. These simulations model galaxy formation up to $z\approx8$ in a region that will collapse into a Milky-Way-like galaxy at $z=0$, while self-consistently tracking Population III and II (Pop.~III, Pop.~II) star formation, feedback and chemical enrichment. MEGATRON dwarf galaxies are in excellent agreement with the observed stellar mass-metallicity relation at $z=0$, including an over-abundance of dwarfs along a flat plateau in metallicity ($\langle [\rm{Fe}/\rm{H}] \rangle \approx -2.5$) at low stellar masses ($M_{\star} \leq 10^5 \, \rm{M}_{\odot}$). We tie this feature to the chemical enrichment of dwarf galaxies by Pop.~III pair-instability supernova (PISN) explosions. The strong Lyman-Werner background (LW) from the protogalaxy ensures that PISNe occur in haloes massive enough ($\approx 10^7\, \rm{M}_{\odot}$) to retain their ejecta. We also predict a tail of $\approx 20\%$ of iron-deficient ($\langle [\rm{Fe}/\rm{H}] \rangle \leq - 3$) dwarf galaxies. We show that both plateau and tail (i) are robust to large variations in Pop.~II feedback assumptions, and (ii) survive in bound satellites surrounding the central galaxy at $z=0$.

URIEL is a knowledge base offering geographical, phylogenetic, and typological vector representations for 7970 languages. It includes distance measures between these vectors for 4005 languages, which are accessible via the lang2vec tool. Despite being frequently cited, URIEL is limited in terms of linguistic inclusion and overall usability. To tackle these challenges, we introduce URIEL+, an enhanced version of URIEL and lang2vec that addresses these limitations. In addition to expanding typological feature coverage for 2898 languages, URIEL+ improves the user experience with robust, customizable distance calculations to better suit the needs of users. These upgrades also offer competitive performance on downstream tasks and provide distances that better align with linguistic distance studies.

We present radial profiles of surface brightness in UV and IR bands, estimate stellar mass surface density ($\Sigma_\star$) and star formation rate surface density ($\Sigma_\mathrm{SFR}$), and predict the CO-to-H$_2$ conversion factor ($\alpha_\mathrm{CO}$) for over 5,000 local galaxies with stellar mass $M_\star\,{\geq}\,10^{9.3}\rm\,M_\odot$. We build these profiles and measure galaxy half-light radii using GALEX and WISE images from the $z$0MGS program, with special care given to highly inclined galaxies. From the UV and IR surface brightness profiles, we estimate $\Sigma_\star$ and $\Sigma_\mathrm{SFR}$ and use them to predict $\alpha_\mathrm{CO}$ with state-of-the-art empirical prescriptions. We validate our (kpc-scale) $\alpha_\mathrm{CO}$ predictions against observational estimates, finding the best agreement when accounting for CO-dark gas as well as CO emissivity and excitation effects. The CO-dark correction plays a primary role in lower-mass galaxies, whereas CO emissivity and excitation effects become more important in higher-mass and more actively star-forming galaxies, respectively. We compare our estimated $\alpha_\mathrm{CO}$ to observed galaxy-integrated SFR to CO luminosity ratio as a function of $M_\star$. A large compilation of literature data suggests that star-forming galaxies with $M_\star = 10^{9.5{-}11}\,M_\odot$ show strong anti-correlations of SFR/$L^\prime_\mathrm{CO(1{-}0)} \propto M_\star^{-0.29}$ and SFR/$L^\prime_\mathrm{CO(2{-}1)} \propto M_\star^{-0.40}$. The estimated $\alpha_\mathrm{CO}$ trends, when combined with a constant molecular gas depletion time $t_\mathrm{dep}$, can only explain ${\approx}1/3$ of these SFR/$L^\prime_\mathrm{CO}$ trends. This suggests that $t_\mathrm{dep}$ being systematically shorter in lower-mass star-forming galaxies is the main cause of the observed SFR/$L^\prime_\mathrm{CO}$ variations. (Abridged)

Now detected out to redshifts of $z\sim 14.5$, the rest-frame ultraviolet and optical spectra of galaxies encode numerous physical properties of the interstellar medium (ISM). Accurately extracting these properties from spectra remains a key challenge that numerical simulations are uniquely suited to address. We present a study of the observed ISM of galaxies in MEGATRON: a suite of cosmological radiation hydrodynamics simulations coupled to on-the-fly non-equilibrium thermochemistry, with multiple prescriptions for star formation/feedback and parsec-scale resolution; capable of directly predicting spectroscopic properties of early galaxies. We find that irrespective of feedback physics used, the ISM of high-redshift galaxies is denser, less metal enriched, and subject to higher ionization parameters and radiation fields compared to similar mass galaxies in the local Universe -- in agreement with interpretations of JWST observations. Using common observational techniques to infer bulk galaxy properties, we find that ISM gas density controls the slope of the mass-metallicity relation. Similarly, at the densities reached in some high-redshift galaxies, O32 becomes a density tracer rather than one of ionization parameter. This motivates the use of other line ratios like C43 and N43 to infer the ionization state of the gas. Finally, various feedback models populate different regions of strong-line diagnostic diagrams as the line ratios are sensitive to the feedback-modulated density-temperature structure of the ISM. Therefore, observed strong-line diagnostics can provide a strong constraint on the underlying physics of star formation and feedback in the high-redshift Universe.

We derive the Tully-Fisher (TFR, $M_\ast-V_{\rm circ, f}$) and Fall (FR, $j_\ast-M_\ast$) relations at redshift $z = 0.9$ using a sample of 43 main-sequence disc galaxies with H$\alpha$ IFU data and JWST/HST imaging. The strength of our analysis lies in the use of state-of-the-art 3D kinematic models to infer galaxy rotation curves, the inclusion and morphological modelling of NIR bands, and the use of SED modelling applied to our photometry measurements to estimate stellar masses. After correcting the inferred H$\alpha$ velocities for asymmetric drift, we find a TFR of the form $\log(M_\ast / M_\odot) = a \log(V_{\rm circ,f} / 150~\mathrm{km\,s^{-1}}) + b$, with $a=3.82^{+0.55}_{-0.40}$ and $b=10.27^{+0.06}_{-0.07}$, as well as a FR of the form $\log(j_\ast / \mathrm{kpc\,km\,s^{-1}}) = a \log(M_\ast / 10^{10.5} M_\odot) + b$, with $a=0.44^{+0.06}_{-0.06}$ and $b=2.86^{+0.02}_{-0.02}$. When compared to their $z=0$ counterparts, we find moderate evolution in the TFR and strong evolution in the FR over the past $8$ Gyr. We interpret our findings in the context of the galaxy-to-halo scaling parameters $f_{\rm M}=M_\ast/M_{\rm vir}$ and $f_{\rm j}=j_\ast/j_{\rm vir}$. We infer that at $z=0.9$ both $f_{\rm M}$ and $f_{\rm j}$ are higher and less mass-dependent than at $z=0$. We speculate that the evolution of $f_{\rm j}$ can be driven by more efficient and centrally concentrated stellar feedback at $z=0.9$, or by an appreciable dry merger history. We also show that assuming the galaxies populating our $z=0.9$ relations evolve into those populating the $z=0$ relations leads to an apparent discrepancy with the hierarchical growth of dark matter halos. To solve this issue, one needs to evoke a progenitor bias scenario, unknown systematics affecting our and previous measurements, or consider the possibility that H$\alpha$ kinematics is not a reliable dynamical tracer.

H2 is the most abundant molecule in the interstellar medium and is a useful tool to study photodissociation regions, where radiative feedback from massive stars on molecular clouds is dominant. The James Webb Space Telescope, with its high spatial resolution, sensitivity, and wavelength coverage provides unique access to the detection of most of H2 lines and the analysis of its spatial morphology. Our goal is to use H2 line emission detected with the JWST in the Horsehead nebula to constrain the physical parameters (e.g., extinction, gas temperature, thermal pressure) throughout the PDR and its geometry. The study of H2 morphology reveals that FUV-pumped lines peak closer to the edge of the PDR than thermalized lines. From H2 lines, we estimate the value of extinction throughout the PDR. We find that AV is increasing from the edge of the PDR to the second and third H2 filaments. Then, we study the H2 excitation in different regions across the PDR. The temperature profile shows that the observed gas temperature is quite constant throughout the PDR, with a slight decline in each of the dissociation fronts. This study also reveals that the OPR is far from equilibrium. We observe a spatial separation of para and ortho rovibrational levels, indicating that efficient ortho-para conversion and preferential ortho self-shielding are driving the spatial variations of the OPR. Finally, we derive a thermal pressure in the first filament around P > 6x10$^6$ K cm$^{-3}$, about ten times higher than that of the ionized gas. We highlight that template stationary 1D PDR models cannot account for the intrinsic 2D structure and the very high temperature observed in the Horsehead nebula. We argue the highly excited, over-pressurized H2 gas at the edge of the PDR interface could originate from the mixing between the cold and hot phase induced by the photo-evaporation of the cloud.

We present measurements of the masses associated with $\sim18,000$ HII regions across 19 nearby star-forming galaxies by combining data from JWST, HST, MUSE, ALMA, VLA, and MeerKAT from the multi-wavelength PHANGS survey. We report 10 pc-scale measurements of the mass of young stars, ionized gas, and older disk stars coincident with each HII region, as well as the initial and current mass of molecular gas, atomic gas, and swept-up shell material, estimated from lower resolution data. We find that the mass of older stars dominates over young stars at $\gtrsim10\rm\,pc$ scales, and ionized gas exceeds the stellar mass in most optically bright HII regions. Combining our mass measurements for a statistically large sample of HII regions, we derive 10 pc scale star-formation efficiencies $\approx6{-}17\%$ for individual HII regions. Comparing each region's self-gravity with the ambient ISM pressure and total pressure from pre-supernova stellar feedback, we show that most optically bright HII regions are over-pressured relative to their own self-gravity and the ambient ISM pressure, and that they are hence likely expanding into their surroundings. Larger HII regions in galaxy centers approach dynamical equilibrium. The self-gravity of regions is expected to dominate over pre-supernova stellar feedback pressure at $\gtrsim130\rm\,pc$ and $60\rm\,pc$ scales in galaxy disks and centers, respectively, but is always sub-dominant to the ambient ISM pressure on HII region scales. Our measurements have direct implications for the dynamical evolution of star-forming regions and the efficiency of stellar feedback in ionizing and clearing cold gas.

Nigeria is a multilingual country with 500+ languages. Naija is a Nigerian Pidgin spoken by approximately 120M speakers and it is a mixed language (e.g., English, Portuguese, Yoruba, Hausa and Igbo). Although it has mainly been a spoken language until recently, there are some online platforms (e.g., Wikipedia), publishing in written Naija as well. West African Pidgin English (WAPE) is also spoken in Nigeria and it is used by BBC to broadcast news on the internet to a wider audience not only in Nigeria but also in other West African countries (e.g., Cameroon and Ghana). Through statistical analyses and Machine Translation experiments, our paper shows that these two pidgin varieties do not represent each other (i.e., there are linguistic differences in word order and vocabulary) and Generative AI operates only based on WAPE. In other words, Naija is underrepresented in Generative AI, and it is hard to teach LLMs with few examples. In addition to the statistical analyses, we also provide historical information on both pidgins as well as insights from the interviews conducted with volunteer Wikipedia contributors in Naija.

The latest GWTC-4 release from the LIGO-Virgo-KAGRA (LVK) collaboration nearly doubles the known population of double compact object mergers and reveals a new trimodal structure in the chirp-mass distribution of merging binary black holes (BBHs) below 30 Msun. Recent detailed stellar evolution models show that features in the pre-collapse cores of massive stars produce a bimodal black hole (BH) mass distribution, which naturally extends to a trimodal BBH chirp-mass distribution. Both distributions depend only weakly on metallicity, implying universal structural features which can be tested with LVK observations. Using a new compact-remnant mass prescription derived from these models, we perform rapid population synthesis simulations to test the robustness of the predicted chirp-mass structure against uncertainties in binary evolution and cosmic star formation history, and compare these results with the current observational data. The trimodal chirp-mass distribution emerges as a robust outcome of the new remnant-mass model, persisting across variations in binary and cosmic physics. In contrast, traditional BH formation models lacking a bimodal BH mass spectrum fail to reproduce the observed trimodality. The updated models also predict lower BBH merger rates by a factor of a few, in closer agreement with LVK constraints. Intriguingly, the central chirp-mass peak, dominated by unequal-mass BBHs, originates from a previously underappreciated formation pathway in which strong luminous blue variable winds suppress binary interaction before the first BH forms. If isolated binary evolution dominates BBH formation below 30 Msun, the relative heights of the three chirp-mass peaks offer powerful observational constraints on core collapse, BH formation, binary evolution, and cosmic star formation. These universal structural features may also serve as standard sirens for precision cosmology.

Accurate and efficient methods to evaluate cosmological distances are an important tool in modern precision cosmology. In a flat $\Lambda$CDM cosmology, the luminosity distance can be expressed in terms of elliptic integrals. We derive an alternative and simple expression for the luminosity distance in a flat $\Lambda$CDM based on hypergeometric functions. Using a timing experiment we compare the computation time for the numerical evaluation of the various exact formulae, as well as for two approximate fitting formulae available in the literature. We find that our novel expression is the most efficient exact expression in the redshift range $z\gtrsim1$. Ideally, it can be combined with the expression based on Carlson's elliptic integrals in the range $z\lesssim1$ for high precision cosmology distance calculations over the entire redshift range. On the other hand, for practical work where relative errors of about 0.1% are acceptable, the analytical approximation proposed by Adachi & Kasai (2012) is a suitable alternative.

We present new frontiers in the modelling of the spectral energy distributions (SED) of active galaxies by introducing the radio-to-X-ray fitting capabilities of the publicly available Bayesian code AGNfitter. The new code release, called AGNfitter-rx, models the broad-band photometry covering the radio, infrared (IR), optical, ultraviolet (UV) and X-ray bands consistently, using a combination of theoretical and semi-empirical models of the AGN and host galaxy emission. This framework enables the detailed characterization of four physical components of the active nuclei: the accretion disk, the hot dusty torus, the relativistic jets/core radio emission, and the hot corona; alongside modeling three components within the host galaxy: stellar populations, cold dust, and the radio emission from the star-forming regions. Applying AGNfitter-rx to a diverse sample of 36 AGN SEDs at z<0.7 from the AGN SED ATLAS, we investigate and compare the performance of state-of-the-art torus and accretion disk emission models on fit quality and inferred physical parameters. We find that clumpy torus models that include polar winds and semi-empirical accretion disk templates including emission line features significantly increase the fit quality in 67% of the sources, by effectively reducing by $2\sigma$ fit residuals in the $1.5-5 \mu \rm m$ and $0.7 \mu \rm m$ regimes.We demonstrate that, by applying AGNfitter-rx on photometric data, we are able to estimate inclination and opening angles of the torus, consistent with spectroscopic classifications within the AGN unified model, as well as black hole mass estimates in agreement with virial estimates based on H$\alpha$. The wavelength coverage and the flexibility for the inclusion of state-of-the-art theoretical models make AGNfitter-rx a unique tool for the further development of SED modelling for AGNs in present and future radio-to-X-ray galaxy surveys.

Dust attenuation in galaxies has often been used as a proxy for the extinction of point sources, such as supernovae, even though this approach ignores fundamental differences between the two cases. We present an analysis of the impact of geometric effects and scattering within dusty media on recovered galaxy dust properties. We use SKIRT, a radiative transfer code, to simulate observations of point sources embedded in dust clouds, as well as spiral and elliptical galaxies. We examine various galaxy morphologies, inclinations, and instrument apertures. We find that in galaxies the scattering of light into the line of sight and the presence of sources at different depths within the galaxy make attenuation fundamentally different from extinction. For a medium with intrinsic extinction slope Rv=3.068, we recover effective attenuation slopes Rv_e ranging from 0.5 to 7, showing that the two quantities are not analogous, even for local resolved observations. We find that Rv_e greatly depends on dust density, galaxy morphology, and inclination, the latter being the most significant. A single simulated galaxy, viewed from different angles, can reproduce the well-known relation between attenuation strength Av_e and Rv_e observed for star-forming galaxy samples. An increase in dust density leads to higher Rv_e across all inclinations, which, assuming a correlation between stellar mass and dust density, explains the increase in Rv_e with mass observed in star-forming galaxies. However, we are unable to explain the differences in Rv_e between star-forming and quiescent high-mass galaxies. We conclude that highly attenuated regions of simulated face-on galaxies yield Rv_e within 10% of the intrinsic extinction slope of the medium, allowing for the distinction of different dust types. For edge-on spirals, however, the median Rv_e for low Av_e regions appears to better approximate the extinction slope.

Existing linguistic knowledge bases such as URIEL+ provide valuable geographic, genetic and typological distances for cross-lingual transfer but suffer from two key limitations. One, their one-size-fits-all vector representations are ill-suited to the diverse structures of linguistic data, and two, they lack a principled method for aggregating these signals into a single, comprehensive score. In this paper, we address these gaps by introducing a framework for type-matched language distances. We propose novel, structure-aware representations for each distance type: speaker-weighted distributions for geography, hyperbolic embeddings for genealogy, and a latent variables model for typology. We unify these signals into a robust, task-agnostic composite distance. In selecting transfer languages, our representations and composite distances consistently improve performance across a wide range of NLP tasks, providing a more principled and effective toolkit for multilingual research.

Large Language Models (LLMs) are increasingly being integrated into various medical fields, including mental health support systems. However, there is a gap in research regarding the effectiveness of LLMs in non-English mental health support applications. To address this problem, we present a novel multilingual adaptation of widely-used mental health datasets, translated from English into six languages (e.g., Greek, Turkish, French, Portuguese, German, and Finnish). This dataset enables a comprehensive evaluation of LLM performance in detecting mental health conditions and assessing their severity across multiple languages. By experimenting with GPT and Llama, we observe considerable variability in performance across languages, despite being evaluated on the same translated dataset. This inconsistency underscores the complexities inherent in multilingual mental health support, where language-specific nuances and mental health data coverage can affect the accuracy of the models. Through comprehensive error analysis, we emphasize the risks of relying exclusively on LLMs in medical settings (e.g., their potential to contribute to misdiagnoses). Moreover, our proposed approach offers significant cost savings for multilingual tasks, presenting a major advantage for broad-scale implementation.

Optimizing language models for use in conversational agents requires large quantities of example dialogues. Increasingly, these dialogues are synthetically generated by using powerful large language models (LLMs), especially in domains with challenges to obtain authentic human data. One such domain is human resources (HR). In this context, we compare two LLM-based dialogue generation methods for the use case of generating HR job interviews, and assess whether one method generates higher-quality dialogues that are more challenging to distinguish from genuine human discourse. The first method uses a single prompt to generate the complete interview dialog. The second method uses two agents that converse with each other. To evaluate dialogue quality under each method, we ask a judge LLM to determine whether AI was used for interview generation, using pairwise interview comparisons. We demonstrate that despite a sixfold increase in token cost, interviews generated with the dual-prompt method achieve a win rate up to ten times higher than those generated with the single-prompt method. This difference remains consistent regardless of whether GPT-4o or Llama 3.3 70B is used for either interview generation or judging quality.