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Abstract Inferences from population genomic data provide valuable insights into the demographic history of a population. Likewise, in genomic epidemiology, pathogen genomic data provide key insights into epidemic dynamics and potential sources of transmission. Yet, predicting what information will be gained from genomic data about variables of interest and how different sampling strategies will impact the quality of downstream inferences remains challenging. As a result, population genomics and related fields such as phylodynamics and phylogeography largely lack theory to guide decisions on how best to sample individuals for genomic sequencing. By adopting a sequential decision making framework based on Markov decision processes, we model how sampling interacts with a population’s demographic history to shape the ancestral or genealogical relationships of sampled individuals. By probabilistically considering these ancestral relationships, we can use Markov decision processes to predict the expected value of sampling in terms of information gained about estimated variables. This in turn allows us to very efficiently explore and identify optimal sampling strategies even when the informational value of sampling depends on past or future sampling events. To illustrate our framework, we develop Markov decision processes for three common demographic and epidemiological inference problems: estimating population growth rates, minimizing the transmission distance between sampled individuals and estimating migration rates between subpopulations. In each case, the Markov decision process allows us to identify optimal sampling strategies that maximize the information gained from genomic data while minimizing the associated costs of sampling. 

ABSTRACT The Eos cloud, recently discovered in the far ultraviolet via H$$_2$$ fluorescence, is one of the nearest known dark molecular clouds to the Sun, with a distance spanning from $${\sim} 94\rm{\!-\!}136$$ pc. However, with a mass ($${\sim} 5.5\times 10^3$$ M$$_\odot$$) just under $$40\,$$ per cent that of star forming clouds like Taurus and evidence for net molecular dissociation, its evolutionary and star forming status is uncertain. We use Gaia data to investigate whether there is evidence for a young stellar population that may have formed from the Eos cloud. Comparing isochrones and pre-main sequence evolutionary models there is no clear young stellar population in the region. While there are a small number of $${<} 10$$ Myr stars, that population is statistically indistinguishable from those in similar search volumes at other Galactic latitudes. We also find no unusual spatial or kinematic clustering toward the Eos cloud over distances $$70\!-\!150$$ pc. Overall, we conclude that the Eos cloud has most likely not undergone any recent substantial star formation and further study of the dynamics of the cloud is required to determine whether it will do so in the future. 

Abstract We use a suite of 3D simulations of star-forming molecular clouds, with and without stellar feedback and magnetic fields, to investigate the effectiveness of different fitting methods for volume and column density probability distribution functions (PDFs). The first method fits a piecewise lognormal and power-law (PL) function to recover PDF parameters such as the PL slope and transition density. The second method fits a polynomial spline function and examines the first and second derivatives of the spline to determine the PL slope and the functional transition density. The first PL (set by the transition between lognormal and PL function) can also be visualized in the derivatives directly. In general, the two methods produce fits that agree reasonably well for volume density but vary for column density, likely due to the increased statistical noise in the column density PDFs as compared to the volume density PDFs. We test a well-known conversion for estimating volume density PL slopes from column density slopes and find that the spline method produces a better match (χ²of 3.34 versusχ²of 5.92), albeit with a significant scatter. Ultimately, we recommend the use of both fitting methods on column density data to mitigate the effects of noise. 

Abstract We study the effects of active galactic nuclei (AGN) feedback on the Lyαforest 1D flux power spectrum (P1D). Using theSimbacosmological-hydrodynamic simulations, we examine the impact that adding different AGN feedback modes has on the predicted P1D. We find that, forSimba, the impact of AGN feedback is most dramatic at lower redshifts (z < 1) and that AGN jet feedback plays the most significant role in altering the P1D. The effects of AGN feedback can be seen across a large range of wavenumbers (1.5 × 10⁻³ < k < 10⁻¹s km⁻¹) changing the ionization state of hydrogen in the IGM through heating. AGN feedback can also alter the thermal evolution of the IGM and thermally broaden individual Lyαabsorbers. For theSimbamodel, these effects become observable atz ≲ 1.0. At higher redshifts (z > 2.0), AGN feedback has a 2% effect on the P1D fork < 5 × 10⁻²s km⁻¹and an 8% effect fork > 5 × 10⁻²s km⁻¹. We show that the small-scale effect is reduced when normalizing the simulation to the observed mean flux. On large scales, the effect of AGN feedback appears via a change in the IGM temperature and is thus unlikely to bias cosmological parameters. The strong AGN jets in theSimbasimulation can reproduce thez > 2 Lyαforest. We stress that analyses comparing different AGN feedback models to future higher precision data will be necessary to determine the full extent of this effect. 

ABSTRACT We present an investigation of clustered stellar feedback in the form of superbubbles identified within 11 galaxies from the FIRE-2 (Feedback in Realistic Environments) cosmological zoom-in simulation suite, at both cosmic noon (1 < z < 3) and in the local universe. We study the spatially resolved multiphase outflows that these supernovae drive, comparing our findings with recent theory and observations. These simulations consist of five Large Magellanic Cloud–mass galaxies and six Milky Way-mass progenitors (with a minimum baryonic particle mass of $$m_{\rm b.min} = 7100\,{\rm M}_{\odot }$$). For all galaxies, we calculate the local and galaxy-averaged mass and energy-loading factors from the identified outflows. We also characterize the multiphase morphology and properties of the identified superbubbles, including the ‘shell’ of cool ($$T\lt 10^5$$ K) gas and break out of energetic hot ($$T\gt 10^5$$ K) gas when the shell bursts. We find that these simulations, regardless of redshift, have mass-loading factors and momentum fluxes in the cool gas that largely agree with recent observations. Lastly, we also investigate how methodological choices in measuring outflows can affect loading factors for galactic winds. 

Abstract Advancing our understanding of astrophysical turbulence is bottlenecked by the limited resolution of numerical simulations that may not fully sample scales in the inertial range. Machine-learning (ML) techniques have demonstrated promise in upscaling resolution in both image analysis and numerical simulations (i.e., superresolution). Here we employ and further develop a physics-constrained convolutional neural network ML model called “MeshFreeFlowNet” (MFFN) for superresolution studies of turbulent systems. The model is trained on both the simulation images and the evaluated partial differential equations (PDEs), making it sensitive to the underlying physics of a particular fluid system. We develop a framework for 2D turbulent Rayleigh–Bénard convection generated with theDedaluscode by modifying the MFFN architecture to include the full set of simulation PDEs and the boundary conditions. Our training set includes fully developed turbulence sampling Rayleigh numbers (Ra) ofRa= 10⁶–10¹⁰. We evaluate the success of the learned simulations by comparing the power spectra of the directDedalussimulation to the predicted model output and compare both ground-truth and predicted power spectral inertial range scalings to theoretical predictions. We find that the updated network performs well at allRastudied here in recovering large-scale information, including the inertial range slopes. The superresolution prediction is overly dissipative at smaller scales than that of the inertial range in all cases, but the smaller scales are better recovered in more turbulent than laminar regimes. This is likely because more turbulent systems have a rich variety of structures at many length scales compared to laminar flows. 

Abstract The observed rest-UV luminosity function at cosmic dawn (z∼ 8–14) measured by JWST revealed an excess of UV-luminous galaxies relative to many prelaunch theoretical predictions. A high star formation efficiency (SFE) and a top-heavy initial mass function (IMF) are among the mechanisms proposed for explaining this excess. Although a top-heavy IMF has been proposed for its ability to increase the light-to-mass ratio (Ψ_UV), the resulting enhanced radiative pressure from young stars could decrease the SFE, potentially driving galaxy luminosities back down. In this Letter, we use idealized radiation hydrodynamic simulations of star cluster formation to explore the effects of a top-heavy IMF on the SFE of clouds typical of the high-pressure conditions found at these redshifts. We find that the SFE in star clusters with solar-neighborhood-like dust abundance decreases with increasingly top-heavy IMFs—by ∼20% for an increase of a factor of 4 in Ψ_UVand by 50% for a factor of ∼10 in Ψ_UV. However, we find that an expected decrease in the dust-to-gas ratio (∼0.01 × solar) at these redshifts can completely compensate for the enhanced light output. This leads to a (cloud-scale; ∼10 pc) SFE that is ≳70% even for a factor of 10 increase in Ψ_UV, implying that highly efficient star formation is unavoidable for high surface density and low-metallicity conditions. Our results suggest that a top-heavy IMF, if present, likely coexists with efficient star formation in these galaxies.