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Machine learning based surrogate models offer researchers powerful tools for accelerating simulation-based workflows. However, as standard datasets in this space often cover small classes of physical behavior, it can be difficult to evaluate the efficacy of new approaches. To address this gap, we introduce the Well: a large-scale collection of datasets containing numerical simulations of a wide variety of spatiotemporal physical systems. The Well draws from domain experts and numerical software developers to provide 15TB of data across 16 datasets covering diverse domains such as biological systems, fluid dynamics, acoustic scattering, as well as magneto-hydrodynamic simulations of extra-galactic fluids or supernova explosions. These datasets can be used individually or as part of a broader benchmark suite. To facilitate usage of the Well, we provide a unified PyTorch interface for training and evaluating models. We demonstrate the function of this library by introducing example baselines that highlight the new challenges posed by the complex dynamics of the Well. The code and data is available at https://github.com/PolymathicAI/the_well., Comment: 38th Conference on Neural Information Processing Systems (NeurIPS 2024) Track on Datasets and Benchmarks

The characterization of magnetic fields within molecular clouds is fundamental to understanding star formation processes. Accurately gauging the three-dimensional structure of these fields presents a challenge, as observational techniques such as dust polarization and the Zeeman effect each provide only partial information on the orientation and line-of-sight strength, respectively. By analyzing a suite of AREPO simulations, this paper investigates how observables can relate to underlying physical properties to derive a more comprehensive picture of the magnetic field's inclination angle and strength, specifically in regions where both dust polarization and Zeeman data are available. To demonstrate the method, we produce synthetic observations of the polarization angle dispersion and line-of-sight Alfv\'en Mach Number and explore the behavior of the inclination angle, $\gamma$, and strength of the magnetic field in regions where both Zeeman and dust polarization data are available. We find that dust polarization data can be used to determine the inclination angle if the cloud is known to be trans-Alfv\'enic or sub-Alfv\'enic. The strength of the magnetic field relative to turbulence can be estimated by comparing polarization observations to Zeeman observations. Comparing the dispersion of the polarization angle to the estimated line-of-sight Alfv\'en Mach Number provides clues about the strength of the magnetic field and, consequently, the orientation of the magnetic field., Comment: 16 pages, 12 figures, 1 table

We study the effects of AGN feedback on the Lyman-$\alpha$ forest 1D flux power spectrum (P1D). Using the Simba cosmological-hydrodynamic simulations, we examine the impact that adding different AGN feedback modes has on the predicted P1D. We find that, for Simba, 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 ($1.5\times10^{-3}2.0$), AGN feedback has a $2\%$ effect on the P1D for $k<5\times10^{-2}$ s/km and an $8\%$ effect for $k>5\times10^{-2}$ s/km. 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 the Simba simulation can reproduce the $z>2$ Lyman-$\alpha$ forest. 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., Comment: 17 pages, 5 figures, submitted to ApJ

To date, JWST has detected the earliest known star clusters in our Universe (Adamo et al. 2024, Messa et al. 2024, Vanzella et al. 2024, Mowla et al. 2024). They appear to be relatively compact (~few pc, Adamo et al. 2024) and had only recently formed their stars. It was speculated that these clusters may be the earliest progenitors of globular clusters ever detected. Globular clusters are a relic of the initial stages of star formation in the Universe. However, because they contain little to no dark matter (e.g., Heggie & Hut 1996, Bradford et al. 2011, Conroy et al. 2011, Ibata et al. 2013), their formation mechanism poses a significant theoretical challenge. A recent suggestion pointed out that the relative velocity between the gas and the dark matter (Tseliakhovich & Hirata 2010) in the early Universe could naturally form potentially star-forming regions outside of dark matter halos. Here, for the first time, we follow the star formation process of these early Universe objects using high-resolution hydrodynamical simulations, including mechanical feedback. Our results suggest that the first dark matter-less star clusters are top-heavy, with a higher abundance of massive stars compared to today's clusters and extremely high stellar mass surface densities compared to the local Universe., Comment: 10 pages, 5 figures

The escape of LyC photons emitted by massive stars from the dense interstellar medium of galaxies is one of the most significant bottlenecks for cosmological reionization. The escape fraction shows significant scatter between galaxies, and anisotropic, spatial variation within them, motivating further study of the underlying physical factors responsible for these trends. We perform numerical radiation hydrodynamic simulations of idealized clouds with different gas surface densities (compactness) $\Sigma \sim 10^2$--$10^5 \, M_{\odot} \rm{pc}^{-2}$, meant to emulate star cluster-forming clumps ranging from conditions typical of the local Universe to the high ISM-pressure conditions more frequently encountered at high redshift. Our results indicate that dense compact star clusters with $\Sigma \gtrsim 10^4 \, M_{\odot} \rm{pc}^{-2}$ efficiently leak LyC photons, with cloud-scale luminosity-weighted average escape fractions $\gtrsim 80\%$ as opposed to $\lesssim 10\%$ for $\Sigma \sim 100 \, M_{\odot} \rm{pc}^{-2}$. This occurs due to higher star formation efficiencies and shorter dynamical timescales at higher $\Sigma$; the former results in higher intrinsic LyC emission, and the latter implies rapid evolution, with a burst of star formation followed by rapid gas dispersal, permitting high LyC escape well before the intrinsic LyC emission of stellar populations drop ($\sim 4 \, \mathrm{Myr}$). LyC escape in dense clouds is primarily facilitated by highly ionized outflows driven by radiation pressure on dust with velocities $ \sim 3$ times the cloud escape velocity. We also vary the (assumed) dust abundances ($Z_{\rm{d}}$) and find a very mild increase ($\sim 10%$) in the escape fraction for $\sim 100$ lower $Z_{\mathrm{d}}$. Our results suggest a scenario in which localized compact bursts of star formation in galaxies are disproportionately productive sites of LyC leakage., Comment: 21 pages, 10 figures. Submitted to ApJ. Comments welcome!

Molecular clouds (MCs) are the birthplaces of new stars in galaxies. A key component of MCs are photodissociation regions (PDRs), where far-ultraviolet radiation plays a crucial role in determining the gas's physical and chemical state. Traditional PDR models assume chemical steady state (CSS), where the rates of H$_2$ formation and photodissociation are balanced. However, real MCs are dynamic and can be out of CSS. In this study, we demonstrate that combining H$_2$ emission lines observed in the far-ultraviolet or infrared with column density observations can be used to derive the rates of H$_2$ formation and photodissociation. We derive analytical formulae that relate these rates to observable quantities, which we validate using synthetic H$_2$ line emission maps derived from the SILCC-Zoom hydrodynamical simulation. Our method estimates integrated H$_2$ formation and dissociation rates to within 29\% accuracy. Our simulations cover a wide dynamic range in H$_2$ formation and photodissociation rates, showing significant deviations from CSS, with 74\% of the MC's mass deviating from CSS by a factor greater than 2. Our analytical formulae can effectively distinguish between regions in and out of CSS. When applied to actual H$_2$ line observations, our method can assess the chemical state of MCs, providing insights into their evolutionary stages and lifetimes., Comment: Submitted to ApJ. **Comments Welcome!** This paper is the first ("Paper-I") in 2-paper series. Paper II, entitled "The Molecular Cloud Lifecycle II: Formation and Destruction of Molecular Clouds Diagnosed via H 2 Fluorescent Emission Emission" by Burkhart et al has been recently accepted for publication in ApJ, and may be found here: arXiv:2402.01587

We present an investigation of clustered stellar feedback in the form of superbubbles identified within eleven 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 LMC-mass galaxies and six Milky Way-mass progenitors (with a minimum baryonic particle mass of $m_{b.min} = 7100 M_{\odot}$), for which we calculate the local mass and energy loading factors on 750~pc scales from the identified outflows. We also characterize the multiphase morphology and properties of the identified superbubbles, including the `shell' of cool ($T<10^5$ K) gas and break out of energetic hot ($T>10^5$ K) gas when the shell bursts. For all galaxies, the outflow mass, momentum, and energy fluxes appear to reach their peak during the identified superbubbles, and we investigate the effects on the interstellar medium (ISM), circumgalactic medium (CGM), and subsequent star formation rates. 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.

We investigate the contribution of host galaxies to the overall Dispersion Measures (DMs) for Fast Radio Bursts (FRBs) using the Feedback in Realistic Environments (FIRE-2) cosmological zoom-in simulation suite. We calculate DMs from every star particle in the simulated L* galaxies by ray-tracing through their multi-phase interstellar medium (ISM), summing the line-of-sight free thermal electron column for all gas elements within $\pm$20 kpc of the galactic mid-plane. At $z=0$, we find average (median) host-galaxy DMs of 74 (43) and 210 (94) pc cm$^{-3}$ for older ($\gtrsim$10 Myr) and younger ($\lesssim$10 Myr) stellar populations, respectively. Inclination raises the median DM measured for older populations ($\gtrsim$10 Myr) in the simulations by a factor of $\sim$2, but generally does not affect the younger stars deeply embedded in H{\small II} regions except in extreme edge-on cases (inclination $\gtrsim 85^\circ$). In kinematically disturbed snapshots ($z = 1$ in FIRE), the average (median) host-galaxy DMs are higher: 80 (107) and 266 (795) pc cm$^{-3}$ for older ($\gtrsim$10 Myr) and younger ($\lesssim$10 Myr) stellar populations, respectively. FIRE galaxies tend to have higher DM values than cosmological simulations such as IllustrisTNG\rev{, with larger tails in their distributions to high DMs}. As a result, FRB host galaxies may be closer (lower redshift) than previously inferred. Furthermore, constraining host-galaxy DM distributions may help significantly constrain FRB progenitor models., Comment: 20 pages, 13 figures, accepted to ApJ Letters. Updated with revised, accepted version of manuscript

At high redshifts ($z\gtrsim12$), the relative velocity between baryons and dark matter (the so-called streaming velocity) significantly affects star formation in low-mass objects. Streaming substantially reduces the abundance of low-mass gas objects while simultaneously allowing for the formation of supersonically-induced gas objects (SIGOs) and their associated star clusters outside of dark matter halos. Here, we present a study of the population-level effects of streaming on star formation within both halos and SIGOs in a set of simulations with and without streaming. Notably, we find that streaming actually enhances star formation within individual halos of all masses at redshifts between $z=12$ and $z=20$. This is demonstrated both as an increased star formation rate per object as well as an enhancement of the Kennicutt-Schmidt relation for objects with streaming. We find that our simulations are consistent with some observations at high redshift, but on a population level, they continue to under-predict star formation relative to the majority of observations. Notably, our simulations do not include feedback, and so can be taken as an upper limit on the star formation rate, exacerbating these differences. However, simulations of overdense regions (both with and without streaming) agree with observations, suggesting a strategy for extracting information about the overdensity and streaming velocity in a given survey volume in future observations., Comment: 16 pages, 8 figures

The main goal of the CRESST-III experiment is the direct detection of dark matter particles via their scattering off target nuclei in cryogenic detectors. In this work we present the results of a Silicon-On-Sapphire (SOS) detector with a mass of 0.6$\,$g and an energy threshold of (6.7$\, \pm \,$0.2)$\,$eV with a baseline energy resolution of (1.0$\, \pm \,$0.2)$\,$eV. This allowed for a calibration via the detection of single luminescence photons in the eV-range, which could be observed in CRESST for the first time. We present new exclusion limits on the spin-independent and spin-dependent dark matter-nucleon cross section that extend to dark matter particle masses of less than 100$\,$MeV/c$^{2}$., Comment: 10 pages, 8 figures

The observed rest-UV luminosity function at cosmic dawn ($z \sim 8-14$) measured by JWST revealed an excess of UV-luminous galaxies relative to many pre-launch 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 (\(\Psi_{\mathrm{UV}}\)), the resulting enhanced radiative pressure from young stars could decrease the star formation efficiency (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 neighbourhood-like dust abundance decreases with increasingly top-heavy IMF's -- by $\sim 20 \%$ for an increase of factor 4 in $\Psi_{\mathrm{UV}}$, and by $50 \%$ for a factor $ \sim 10$ in $\Psi_{\mathrm{UV}}$. However, we find that an expected decrease in the dust-to-gas ratio ($\sim 0.01 \times \mathrm{Solar}$) at these redshifts can completely compensate for the enhanced light output. This leads to a (cloud-scale; $\sim 10 \, \mathrm{pc}$) SFE that is $\gtrsim 70\%$ even for a factor 10 increase in $\Psi_{\mathrm{UV}}$, 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., Comment: 12 pages, 4 figures. Accepted to the Astrophysical Journal Letters

In recent times, the sensitivity of low-mass direct dark matter searches has been limited by unknown low energy backgrounds close to the energy threshold of the experiments known as the low energy excess (LEE). The CRESST experiment utilises advanced cryogenic detectors constructed with different types of crystals equipped with Transition Edge Sensors (TESs) to measure signals of nuclear recoils induced by the scattering of dark matter particles in the detector. In CRESST, this low energy background manifests itself as a steeply rising population of events below 200 eV. A novel detector design named doubleTES using two identical TESs on the target crystal was studied to investigate the hypothesis that the events are sensor-related. We present the first results from two such modules, demonstrating their ability to differentiate between events originating from the crystal's bulk and those occurring in the sensor or in its close proximity., Comment: 10 pages, 13 figures

Cryogenic scintillating calorimeters are ultrasensitive particle detectors for rare event searches, particularly for the search for dark matter and the measurement of neutrino properties. These detectors are made from scintillating target crystals generating two signals for each particle interaction. The phonon (heat) signal precisely measures the deposited energy independent of the type of interacting particle. The scintillation light signal yields particle discrimination on an event-by-event basis. This paper presents a likelihood framework modeling backgrounds and a potential dark matter signal in the two-dimensional plane spanned by phonon and scintillation light energies. We apply the framework to data from CaWO$_4$-based detectors operated in the CRESST dark matter search. For the first time, a single likelihood framework is used in CRESST to model the data and extract results on dark matter in one step by using a profile likelihood ratio test. Our framework simultaneously fits (neutron) calibration data and physics (background) data and allows combining data from multiple detectors. Although tailored to CaWO$_4$-targets and the CRESST experiment, the framework can easily be expanded to other materials and experiments using scintillating cryogenic calorimeters for dark matter search and neutrino physics., Comment: 18 pages, 11 figures, additional figures and data in ancillary files (on arXiv or at journal reference)

Molecular hydrogen (H$_2$) formation and dissociation are key processes that drive the gas lifecycle in galaxies. Using the SImulating the LifeCycle of Molecular Clouds (SILCC) zoom-in simulation suite, we explore the utility of future observations of H$_2$ dissociation and formation for tracking the lifecycle of molecular clouds. The simulations used in this work include non-equilibrium H$_2$ formation, stellar radiation, sink particles, and turbulence. We find that, at early times in the cloud evolution, H$_2$ formation rapidly outpaces dissociation and molecular clouds build their mass from the atomic reservoir in their environment. Rapid H$_2$ formation is also associated with a higher early star formation rate. For the clouds studied here, H$_2$ is strongly out of chemical equilibrium during the early stages of cloud formation but settles into a bursty chemical steady-state about 2 Myrs after the first stars form. At the latest stage of cloud evolution, dissociation outweighs formation and the clouds enter a dispersal phase. We discuss how theories for the molecular cloud lifecycle and the star formation efficiency may be distinguished with observational measurements of H$_2$ fluorescence with a space-based high-resolution FUV spectrometer, such as the proposed Hyperion and Eos NASA Explorer missions. Such missions would enable measurements of the H$_2$ dissociation and formation rates, which we demonstrate can be connected to different phases in a molecular cloud's star-forming life, including cloud building, rapidly star-forming, H$_2$ chemical equilibrium, and cloud destruction., Comment: Submitted to ApJ, comments welcome

Recently low-mass dark matter direct searches have been hindered by a low energy background, drastically reducing the physics reach of the experiments. In the CRESST-III experiment, this signal is characterised by a significant increase of events below 200 eV. As the origin of this background is still unknown, it became necessary to develop new detector designs to reach a better understanding of the observations. Within the CRESST collaboration, three new different detector layouts have been developed and they are presented in this contribution., Comment: 8 pages, 4 figures

In cosmology, the quest for primordial $B$-modes in cosmic microwave background (CMB) observations has highlighted the critical need for a refined model of the Galactic dust foreground. We investigate diffusion-based modeling of the dust foreground and its interest for component separation. Under the assumption of a Gaussian CMB with known cosmology (or covariance matrix), we show that diffusion models can be trained on examples of dust emission maps such that their sampling process directly coincides with posterior sampling in the context of component separation. We illustrate this on simulated mixtures of dust emission and CMB. We show that common summary statistics (power spectrum, Minkowski functionals) of the components are well recovered by this process. We also introduce a model conditioned by the CMB cosmology that outperforms models trained using a single cosmology on component separation. Such a model will be used in future work for diffusion-based cosmological inference., Comment: 5+6 pages, 2+3 figures, accepted at the NeurIPS 2023 workshop on "Machine Learning and the Physical Sciences" and selected for a spotlight talk

Rare-event search experiments located on-surface, such as short-baseline reactor neutrino experiments, are often limited by muon-induced background events. Highly efficient muon vetos are essential to reduce the detector background and to reach the sensitivity goals. We demonstrate the feasibility of deploying organic plastic scintillators at sub-Kelvin temperatures. For the NUCLEUS experiment, we developed a cryogenic muon veto equipped with wavelength shifting fibers and a silicon photo multiplier operating inside a dilution refrigerator. The achievable compactness of cryostat-internal integration is a key factor in keeping the muon rate to a minimum while maximizing coverage. The thermal and light output properties of a plastic scintillation detector were examined. We report first data on the thermal conductivity and heat capacity of the polystyrene-based scintillator UPS-923A over a wide range of temperatures extending below one Kelvin. The light output was measured down to 0.8K and observed to increase by a factor of 1.61$\pm$0.05 compared to 300K. The development of an organic plastic scintillation muon veto operating in sub-Kelvin temperature environments opens new perspectives for rare-event searches with cryogenic detectors at sites lacking substantial overburden.

Diamond operated as a cryogenic calorimeter is an excellent target for direct detection of low-mass dark matter candidates. Following the realization of the first low-threshold cryogenic detector that uses diamond as absorber for astroparticle physics applications, we now present the resulting exclusion limits on the elastic spin-independent interaction cross-section of dark matter with diamond. We measured two 0.175 g CVD (Chemical Vapor Deposition) diamond samples, each instrumented with a W-TES. Thanks to the energy threshold of just 16.8 eV of one of the two detectors, we set exclusion limits on the elastic spin-independent interaction of dark matter particles with carbon nuclei down to dark matter masses as low as 0.122 GeV/c2. This work shows the scientific potential of cryogenic detectors made from diamond and lays the foundation for the use of this material as target for direct detection dark matter experiments., Comment: 6 pages, 6 figures

The James Webb Space Telescope (JWST) is capable of probing extremely early eras of our Universe when the supersonic relative motions between dark matter and baryonic overdensities modulate structure formation ($z>\sim 10$). We study low-mass galaxy formation including this "stream velocity" using high resolution AREPO hydrodynamics simulations, and present theoretical predictions of the UV luminosity function (UVLF) and galaxy stellar mass function (GSMF) down to extremely faint and low mass galaxies ($M_{UV}>\sim-15$, $10^4M_\odot<=M_*<=10^8 M_\odot)$. We show that, although the stream velocity suppresses early star formation overall, it induces a short period of rapid star formation in some larger dwarfs, leading to an enhancement in the faint-end of the UVLF at $z=12$. We demonstrate that JWST observations are close to this enhanced regime, and propose that the UVLF may constitute an important probe of the stream velocity at high redshift for JWST and future observatories., Comment: 13 pages, 7 figures. Accepted ApJL

In the setting of finite groups, suppose $J$ acts on $N$ via automorphisms so that the induced semidirect product $N\rtimes J$ acts on some non-empty set $\Omega$, with $N$ acting transitively. Glauberman proved that if the orders of $J$ and $N$ are coprime, then $J$ fixes a point in $\Omega$. We consider the non-coprime case and show that if $N$ is abelian and a Sylow $p$-subgroup of $J$ fixes a point in $\Omega$ for each prime $p$, then $J$ fixes a point in $\Omega$. We also show that if $N$ is nilpotent, $N\rtimes J$ is supersoluble, and a Sylow $p$-subgroup of $J$ fixes a point in $\Omega$ for each prime $p$, then $J$ fixes a point in $\Omega$.

Using CaWO$_4$ crystals as cryogenic calorimeters, the CRESST experiment searches for nuclear recoils caused by the scattering of potential Dark Matter particles. A reliable identification of a potential signal crucially depends on an accurate background model. In this work we introduce an improved normalisation method for CRESST's model of the electromagnetic backgrounds. Spectral templates, based on Geant4 simulations, are normalised via a Bayesian likelihood fit to experimental background data. Contrary to our previous work, no assumption of partial secular equilibrium is required, which results in a more robust and versatile applicability. Furthermore, considering the correlation between all background components allows us to explain 82.7% of the experimental background within [1 keV, 40 keV], an improvement of 18.6% compared to our previous method., Comment: 24 pages, 14 figures, submitted to EPJC

In this paper, we investigate the conditions for the HI-to-H$_{2}$ transition in the solar neighborhood by analyzing HI emission and absorption measurements toward 58 Galactic lines of sight (LOSs) along with $^{12}$CO(1$-$0) (CO) and dust data. Based on the accurate column densities of the cold and warm neutral medium (CNM and WNM), we first perform a decomposition of gas into atomic and molecular phases and show that the observed LOSs are mostly HI-dominated. In addition, we find that the CO-dark H$_{2}$, not the optically thick HI, is a major ingredient of the dark gas in the solar neighborhood. To examine the conditions for the formation of CO-bright molecular gas, we analyze the kinematic association between HI and CO and find that the CNM is kinematically more closely associated with CO than the WNM. When CNM components within CO line widths are isolated, we find the following characteristics: spin temperature $<$ 200 K, peak optical depth $>$ 0.1, CNM fraction of $\sim$0.6, and $V$-band dust extinction $>$ 0.5 mag. These results suggest that CO-bright molecular gas preferentially forms in environments with high column densities where the CNM becomes colder and more abundant. Finally, we confront the observed CNM properties with the steady-state H$_{2}$ formation model of Sternberg et al. and infer that the CNM must be clumpy with a small volume filling factor. Another possibility would be that missing processes in the model, such as cosmic-rays and gas dynamics, play an important role in the HI-to-H$_{2}$ transition., Comment: 25 pages, 12 figures, 6 tables, Accepted for publication in ApJ

We explore the role of galactic feedback on the low redshift Lyman-$\alpha$ (Ly$\alpha$)~forest ($z \lesssim 2$) statistics and its potential to alter the thermal state of the intergalactic medium. Using the Cosmology and Astrophysics with Machine Learning Simulations (CAMELS) suite, we explore variations of the AGN and stellar feedback models in the IllustrisTNG and Simba sub-grid models. We find that both AGN and stellar feedback in Simba play a role in setting the Ly$\alpha$ forest column density distribution function (CDD) and the Doppler width ($b$-value) distribution. The Simba AGN jet feedback mode is able to efficiently transport energy out to the diffuse IGM causing changes in the shape and normalization of the CDD and a broadening of the $b$-value distribution. We find that stellar feedback plays a prominent role in regulating supermassive black hole growth and feedback, highlighting the importance of constraining stellar and AGN feedback simultaneously. In IllustrisTNG, the AGN feedback variations explored in CAMELS do not affect the Ly$\alpha$ forest, but varying the stellar feedback model does produce subtle changes. Our results imply that the low-$z$ Ly$\alpha$ forest can be sensitive to changes in the ultraviolet background (UVB), stellar and black hole feedback, and that AGN jet feedback in particular can have a strong effect on the thermal state of the IGM., Comment: 27 pages, 12 figures, 2 tables, accepted to ApJ

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 up-scaling resolution in both image analysis and numerical simulations (i.e., superresolution). Here we employ and further develop a physics-constrained convolutional neural network (CNN) ML model called "MeshFreeFlowNet'' (MFFN) for superresolution studies of turbulent systems. The model is trained both on the simulation images as well as the evaluated PDEs, making it sensitive to the underlying physics of a particular fluid system. We develop a framework for 2D turbulent Rayleigh-B\'enard convection (RBC) generated with the \textsc{Dedalus} code 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 ($Ra$) of $Ra=10^6-10^{10}$. We evaluate the success of the learned simulations by comparing the power spectra of the direct \textsc{Dedalus} simulation 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 all $Ra$ studied 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., Comment: 11 pages, 10 figures