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Abstract Bayesian multidimensional scaling (BMDS) is a probabilistic dimension reduction tool that allows one to model and visualize data consisting of dissimilarities between pairs of objects. Although BMDS has proven useful within, e.g., Bayesian phylogenetic inference, its likelihood and gradient calculations require burdensome$$\mathcal {O}(N^2)$$floating-point operations, whereNis the number of data points. Thus, BMDS becomes impractical asNgrows large. We propose and compare two sparse versions of BMDS (sBMDS) that apply log-likelihood and gradient computations to subsets of the observed dissimilarity matrix data. Landmark sBMDS (L-sBMDS) extracts columns, while banded sBMDS (B-sBMDS) extracts diagonals of the data. These sparse variants let one specify a time complexity between$$N^2$$andN. Under simplified settings, we prove posterior consistency for subsampled distance matrices. Through simulations, we examine the accuracy and computational efficiency across all models using both the Metropolis-Hastings and Hamiltonian Monte Carlo algorithms. We observe approximately 3-fold, 10-fold and 40-fold speedups with negligible loss of accuracy, when applying the sBMDS likelihoods and gradients to 500, 1000 and 5,000 data points with 50 bands (landmarks); these speedups only increase with the size of data considered. Finally, we apply the sBMDS variants to: (1) the phylogeographic modeling of multiple influenza subtypes to better understand how these strains spread through global air transportation networks and (2) the clustering of ArXiv manuscripts based on low-dimensional representations of article abstracts. In the first application, sBMDS contributes to holistic uncertainty quantification within a larger Bayesian hierarchical model. In the second, sBMDS approximates uncertainty quantification for a downstream modeling task. 

ABSTRACT The growth of ionized hydrogen bubbles in the intergalactic medium around early luminous objects is a fundamental process during the Epoch of Reionization (EoR). Observations using Ly $$\alpha$$ emission from high-redshift galaxies and forthcoming 21 cm maps are beginning to constrain the sizes of these ionized regions. In this study, we analyse bubble sizes and their evolution using the state-of-the-art thesan radiation-hydrodynamics simulation suite, which self-consistently models radiation transport and realistic galaxy formation throughout a large $$(95.5\, \text{cMpc})^3$$ volume of the universe. Analogous to the accretion and merger tree histories employed in galaxy formation simulations, we characterize the growth and merger rates of ionized bubbles by focusing on the spatially resolved redshift of reionization. By tracing the chronological expansion of bubbles, we partition the simulation volume and construct a natural ionization history. We identify three distinct stages of ionized growth: (1) initial slow expansion around the earliest ionizing sources, (2) accelerated growth through percolation, and (3) rapid expansion dominated by the largest bubble. Notably, we find that the largest bubble emerges by $$z \approx 9\!-\!10$$, well before the midpoint of reionization. This bubble becomes dominant during the second growth stage, and defines the third stage by rapidly expanding to encompass the remainder of the simulation volume. Additionally, we observe a sharp decline in the number of bubbles with radii around $$\sim 10$$ cMpc, indicating a characteristic scale in the final segmented size distribution. Overall, these chronologically sequenced spatial reconstructions offer crucial insights into the physical mechanisms driving ionized bubble growth during the EoR, providing a framework for interpreting reionization itself. 

Context.Feedback from stars in the form of radiation, stellar winds, and supernovae is crucial to regulating the star formation activity of galaxies. Dwarf galaxies are especially susceptible to these processes, making them an ideal test bed for studying the effects of stellar feedback in detail. Recent numerical models have aimed to resolve the interstellar medium (ISM) in dwarf galaxies with a very high resolution of several solar masses. However, when it comes to modeling the radiative feedback from stars, many models opt for simplified approaches instead of explicitly solving radiative transfer (RT) because of the computational complexity involved. Aims.We introduce the Realistic ISM modeling in Galaxy Evolution and Lifecycles (RIGEL) model, a novel framework to self-consistently model the effects of stellar feedback in the multiphase ISM of dwarf galaxies with explicit RT on a star-by-star basis. Methods.The RIGEL model integrates detailed implementations of feedback from individual massive stars into the state-of-the-art radiation-hydrodynamics code,AREPO-RT. It forms individual massive stars from the resolved multiphase ISM by sampling the initial mass function and tracks their evolution individually. The lifetimes, photon production rates, mass-loss rates, and wind velocities of these stars are determined by their initial masses and metallicities based on a library that incorporates a variety of stellar models. The RT equations are solved explicitly in seven spectral bins accounting for the infrared to He IIionizing bands, using a moment-base scheme with the M1 closure relation. The thermochemistry model tracks the nonequilibrium H, He chemistry as well as the equilibrium abundance of C I, C II, O I, O II, and CO in the irradiated ISM to capture the thermodynamics of all ISM phases, from cold molecular gas to hot ionized gas. Results.We evaluated the performance of the RIGEL model using 1 M_⊙resolution simulations of isolated dwarf galaxies. We found that the star formation rate (SFR) and interstellar radiation field (ISRF) show strong positive correlations with the metallicity of the galaxy. Photoionization and photoheating can reduce the SFR by an order of magnitude by removing the available cold, dense gas fuel for star formation. The presence of ISRF also significantly changes the thermal structure of the ISM. Radiative feedback occurs immediately after the birth of massive stars and rapidly disperses the molecular clouds within 1 Myr. As a consequence, radiative feedback reduces the age spread of star clusters to less than 2 Myr, prohibits the formation of massive star clusters, and shapes the cluster initial mass function to a steep power-law form with a slope of ∼ − 2. The mass-loading factor (measured atz = 1 kpc) of the fiducial galaxy has a median ofη_M ∼ 50, while turning off radiative feedback reduces this factor by an order of magnitude. Conclusions.We demonstrate that RIGEL effectively captures the nonlinear coupling of early radiative feedback and supernova feedback in the multiphase ISM of dwarf galaxies. This novel framework enables the utilization of a comprehensive stellar feedback and ISM model in cosmological simulations of dwarf galaxies and various galactic environments spanning a wide dynamic range in both space and time. 

Cyclical MCMC is a novel MCMC framework recently proposed by Zhang et al. (2019) to address the challenge posed by high-dimensional multimodal posterior distributions like those arising in deep learning. The algorithm works by generating a nonhomogeneous Markov chain that tracks -- cyclically in time -- tempered versions of the target distribution. We show in this work that cyclical MCMC converges to the desired limit in settings where the Markov kernels used are fast mixing, and sufficiently long cycles are employed. However in the far more common settings of slow mixing kernels, the algorithm may fail to converge to the correct limit. In particular, in a simple mixture example with unequal variance where powering is known to produce slow mixing kernels, we show by simulation that cyclical MCMC fails to converge to the desired limit. Finally, we show that cyclical MCMC typically estimates well the local shape of the target distribution around each mode, even when we do not have convergence to the target. 

ABSTRACT An important characteristic of cosmic hydrogen reionization is the growth of ionized gas bubbles surrounding early luminous objects. Ionized bubble sizes are beginning to be probed using Lyman α emission from high-redshift galaxies, and will also be probed by upcoming 21 cm maps. We present results from a study of bubble sizes using the state-of-the-art thesan radiation-hydrodynamics simulation suite, which self-consistently models radiation transport and realistic galaxy formation. We employ the mean free path method and track the evolution of the effective ionized bubble size at each point (Reff) throughout the Epoch of Reionization. We show that there is a slow growth period for regions ionized early, but a rapid ‘flash ionization’ process for regions ionized later as they immediately enter a large, pre-existing bubble. We also find that bright sources are preferentially in larger bubbles, and find consistency with recent observational constraints at z ≳ 9, but tension with idealized Lyman α damping-wing models at z ≈ 7. We find that high-overdensity regions have larger characteristic bubble sizes, but the correlation decreases as reionization progresses, likely due to runaway formation of large percolated bubbles. Finally, we compare the redshift at which a region transitions from neutral to ionized (zreion) with the time it takes to reach a given bubble size and conclude that zreion is a reasonable local probe of small-scale bubble size statistics ($$R_\text{eff} \lesssim 1\, \rm {cMpc}$$). However, for larger bubbles, the correspondence between zreion and size statistics weakens due to the time delay between the onset of reionization and the expansion of large bubbles, particularly at high redshifts. 

Abstract It has been claimed that traditional models struggle to explain the tentative detection of the 21 cm absorption trough centered atz∼ 17 measured by the EDGES collaboration. On the other hand, it has been shown that the EDGES results are consistent with an extrapolation of a declining UV luminosity density, following a simple power law of deep Hubble Space Telescope observations of 4 <z< 9 galaxies. We here explore the conditions by which the EDGES detection is consistent with current reionization and post-reionization observations, including the neutral hydrogen fraction atz∼ 6–8, Thomson-scattering optical depth, and ionizing emissivity atz∼ 5. By coupling a physically motivated source model derived from radiative transfer hydrodynamic simulations of reionization to a Markov Chain Monte Carlo sampler, we find that it is entirely possible to reconcile existing high-redshift (cosmic dawn) and low-redshift (reionization) constraints. In particular, we find that high contributions from low-mass halos along with high photon escape fractions are required to simultaneously reproduce cosmic dawn and reionization constraints. Our analysis further confirms that low-mass galaxies produce a flatter emissivity evolution, which leads to an earlier onset of reionization with a gradual and longer duration, resulting in a higher optical depth. While the models dominated by faint galaxies successfully reproduce the measured globally averaged quantities over the first one billion years, they underestimate the late redshift-instantaneous measurements in efficiently star-forming and massive systems. We show that our (simple) physically motivated semianalytical prescription produces results that are consistent with the (sophisticated) state-of-the-artTHESANradiation-magnetohydrodynamic simulation of the reionization. 

Abstract We investigate how feedback and environment shapes the X-ray scaling relations of early-type galaxies (ETGs), especially at the low-mass end. We select central-ETGs from the TNG100 box of IllustrisTNG that have stellar masses log10(M*/M⊙) ∈ [10.7, 11.9]. We derive mock X-ray luminosity (LX, 500) and spectroscopic-like temperature (Tsl, 500) of hot gas within R500 of the ETG haloes using the MOCK-X pipeline. The scaling between LX, 500 and the total mass within 5 effective radii ($$M_{5R_{\rm e}}$$) agrees well with observed ETGs from Chandra. IllustrisTNG reproduces the observed increase in scatter of LX, 500 towards lower masses, and we find that ETGs with $$\log _{10} (M_{5R_{\rm e}}/\mathrm{M_{\odot }}) \leqslant 11.5$$ with above-average LX, 500 experienced systematically lower cumulative kinetic AGN feedback energy historically (vice versa for below-average ETGs). This leads to larger gas mass fractions and younger stellar populations with stronger stellar feedback heating, concertedly resulting in the above-average LX, 500. The LX, 500–Tsl, 500 relation shows a similar slope to the observed ETGs but the simulation systematically underestimates the gas temperature. Three outliers that lie far below the LX–Tsl relation all interacted with larger galaxy clusters recently and demonstrate clear features of environmental heating. We propose that the distinct location of these backsplash ETGs in the LX–Tsl plane could provide a new way of identifying backsplash galaxies in future X-ray surveys. 

Abstract Our food system is complex, multifaceted, and in need of an upgrade. Population growth, climate change, and socioeconomic disparities are some of the challenges that create a systemic threat to its sustainability and capacity to address the needs of an evolving planet. The mission of the AI Institute of Next Generation Food Systems (AIFS) is to leverage the latest advances in AI to help create a more sustainable, efficient, nutritious, safe, and resilient food system. Instead of using AI in isolation, AIFS views it as the connective tissue that can bring together interconnected solutions from farm to fork. From guiding molecular breeding and building autonomous robots for precision agriculture, to predicting pathogen outbreaks and recommending personalized diets, AIFS projects aspire to pave the way for infrastructure and systems that empower practitioners to build the food system of the next generation. Workforce education, outreach, and ethical considerations related to the emergence of AI solutions in this sector are an integral part of AIFS with several collaborative activities aiming to foster an open dialogue and bringing closer students, trainees, teachers, producers, farmers, workers, policy makers, and other professionals.