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Daniel Angles-Alcazar

Publications and source records attributed to Daniel Angles-Alcazar.

At least 19 recordsLinked to original sources

Emission line tracers of galactic outflows driven by stellar feedback in simulations of isolated disk galaxies

Hydrodynamic simulations can connect outflow observables to the physical conditions of outflowing gas. Here, we use simulations of isolated disk galaxies ranging from dwarf mass ($M_{200} = 10^{10}\mathrm{M}_{\odot}$) to Milky Way mass ($M_{200} = 10^{12}\mathrm{M}_{\odot}$), based on the FIRE-2 subgrid models to investigate multiphase galactic outflows. We use the CHIMES non-equilibrium chemistry module to create synthetic spectra of common outflow tracers ([CII]$_{158\rm{\mu m}}$, $\mathrm{CO}_{J(1-0)}$, H$\alpha$ and $[\mathrm{OIII}]_{5007\text{A}}$). Using our synthetic spectra we measure the mass outflow rate, kinetic power and momentum flux using observational techniques. In [CII]$_{158\rm{\mu m}}$ we measure outflow rates of $10^{-4}$ to $1$ $\mathrm{M_{\odot}yr^{-1}}$ across an SFR range of $10^{-3}$ to $1$ $\text{M}_{\odot}\text{yr}^{-1}$, which is in reasonable agreement with observations. The significant discrepancy is in $\mathrm{CO}_{J(1-0)}$, with the simulations lying $\approx1$ dex below the observational sample. We test observational assumptions used to derive outflow properties from synthetic spectra. We find the greatest uncertainty lies in measurements of electron density, as estimates using the SII doublet can overestimate the actual electron density by up to 2 dex, which changes mass outflow rates by up to 4 dex. We also find that molecular outflows are especially sensitive to the conversion factor between CO luminosity and H2 mass, with outflow rates changing by up to 4 dex in our least massive galaxy. Comparing the outflow properties derived from the synthetic spectra to those derived directly from the simulation, we find that [CII]$_{158\rm{\mu m}}$ probes outflows at greater distances from the disk, whilst we find that molecular gas does not survive at large distances within outflows within our modestly star-forming disk galaxies simulated in this work.

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Second public data release of the FIRE-2 cosmological zoom-in simulations of galaxy formation

We describe the second data release (DR2) of the FIRE-2 cosmological zoom-in simulations of galaxy formation, from the Feedback In Realistic Environments (FIRE) project, available at http://flathub.flatironinstitute.org/fire. DR2 includes all snapshots for most simulations, starting at z ~ 99, with all snapshot time spacings <~ 25 Myr. The Core suite -- comprising 14 Milky Way-mass galaxies, 5 SMC/LMC-mass galaxies, and 4 lower-mass galaxies -- includes 601 snapshots to z = 0. For the Core suite, we also release resimulations with physics variations: (1) dark-matter-only versions; (2) a modified ultraviolet background with later reionization at z = 7.8; (3) magnetohydrodynamics, anisotropic conduction, and viscosity in gas; and (4) a model for cosmic-ray injection, transport, and feedback (assuming a constant diffusion coefficient). The Massive Halo suite now includes 8 massive galaxies with 278 snapshots to z = 1. The High Redshift suite includes 34 simulations: in addition to the 22 simulations run to z = 5, we now include 12 additional simulations run to z = 7 and z = 9. We also release 4 dark-matter-only cosmological boxes used to generate zoom-in initial conditions for many FIRE simulations. Most simulations include catalogs of (sub)halos and galaxies at all available snapshots, and most Core simulations to z = 0 include full halo merger trees.

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Zooming In On The Multi-Phase Structure of Magnetically-Dominated Quasar Disks: Radiation From Torus to ISCO Across Accretion Rates

Recent radiation-thermochemical-magnetohydrodynamic simulations resolved formation of quasar accretion disks from cosmological scales down to ~300 gravitational radii $R_{g}$, arguing they were 'hyper-magnetized' (plasma $\beta\ll1$ supported by toroidal magnetic fields) and distinct from traditional $\alpha$-disks. We extend these, refining to $\approx 3\,R_{g}$ around a $10^{7}\,{\rm M_{\odot}}$ BH with multi-channel radiation and thermochemistry, and exploring a factor of 1000 range of accretion rates ($\dot{m}\sim0.01-20$). At smaller scales, we see the disks maintain steady accretion, thermalize and self-ionize, and radiation pressure grows in importance, but large deviations from local thermodynamic equilibrium and single-phase equations of state are always present. Trans-Alfvenic and highly-supersonic turbulence persists in all cases, and leads to efficient vertical mixing, so radiation pressure saturates at levels comparable to fluctuating magnetic and turbulent pressures even for $\dot{m}\gg1$. The disks also become radiatively inefficient in the inner regions at high $\dot{m}$. The midplane magnetic field remains primarily toroidal at large radii, but at super-Eddington $\dot{m}$ we see occasional transitions to a poloidal-field dominated state associated with outflows and flares. Large-scale magnetocentrifugal and continuum radiation-pressure-driven outflows are weak at $\dot{m}<1$, but can be strong at $\dot{m}\gtrsim1$. In all cases there is a scattering photosphere above the disk extending to $\gtrsim 1000\,R_{g}$ at large $\dot{m}$, and the disk is thick and flared owing to magnetic support (with $H/R$ nearly independent of $\dot{m}$), so the outer disk is strongly illuminated by the inner disk and most of the inner disk continuum scatters or is reprocessed at larger scales, giving apparent emission region sizes as large as $\gtrsim 10^{16}\,{\rm cm}$.

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Zooming by in the CARPoolGP lane: new CAMELS-TNG simulations of zoomed-in massive halos

Galaxy formation models within cosmological hydrodynamical simulations contain numerous parameters with non-trivial influences over the resulting properties of simulated cosmic structures and galaxy populations. It is computationally challenging to sample these high dimensional parameter spaces with simulations, particularly for halos in the high-mass end of the mass function. In this work, we develop a novel sampling and reduced variance regression method, CARPoolGP, which leverages built-in correlations between samples in different locations of high dimensional parameter spaces to provide an efficient way to explore parameter space and generate low variance emulations of summary statistics. We use this method to extend the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) to include a set of 768 zoom-in simulations of halos in the mass range of $10^{13} - 10^{14.5} M_\odot\,h^{-1}$ that span a 28-dimensional parameter space in the IllustrisTNG model. With these simulations and the CARPoolGP emulation method, we explore parameter trends in the Compton $Y-M$, black hole mass-halo mass, and metallicity-mass relations, as well as thermodynamic profiles and quenched fractions of satellite galaxies. We use these emulations to provide a physical picture of the complex interplay between supernova and active galactic nuclei feedback. We then use emulations of the $Y-M$ relation of massive halos to perform Fisher forecasts on astrophysical parameters for future Sunyaev-Zeldovich observations and find a significant improvement in forecasted constraints. We publicly release both the simulation suite and CARPoolGP software package.

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FORGE'd in FIRE II: The Formation of Magnetically-Dominated Quasar Accretion Disks from Cosmological Initial Conditions

In a companion paper, we reported the self-consistent formation of quasar accretion disks with inflow rates $\sim 10\,{\rm M_{\odot}\,yr^{-1}}$ down to <300 Schwarzschild radii from cosmological radiation-magneto-thermochemical-hydrodynamical galaxy and star formation simulations. We see the formation of a well-defined, steady-state accretion disk which is stable against star formation at sub-pc scales. The disks are optically thick, with radiative cooling balancing accretion, but with properties that are distinct from those assumed in most previous accretion disk models. The pressure is strongly dominated by (primarily toroidal) magnetic fields, with a plasma $\beta \sim 10^{-4}$ even in the disk midplane. They are qualitatively distinct from magnetically elevated or arrested disks. The disks are strongly turbulent, with trans-Alfvenic and highly super-sonic turbulence, and balance this via a cooling time that is short compared to the disk dynamical time, and can sustain highly super-Eddington accretion rates. Their surface and 3D densities at $\sim 10^{3}-10^{5}$ gravitational radii are much lower than in a Shakura-Sunyaev disk, with important implications for their thermo-chemistry and stability. We show how the magnetic field strengths and geometries arise from rapid advection of flux with the inflow from much weaker galaxy-scale fields in these 'flux-frozen' disks, and how this stabilizes the disk and gives rise to efficient torques. Re-simulating without magnetic fields produces catastrophic fragmentation with a vastly smaller, lower-$\dot{M}$ Shakura-Sunyaev-like disk.

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Predicting the impact of feedback on matter clustering with machine learning in CAMELS

Extracting information from the total matter power spectrum with the precision needed for upcoming cosmological surveys requires unraveling the complex effects of galaxy formation processes on the distribution of matter. We investigate the impact of baryonic physics on matter clustering at $z=0$ using a library of power spectra from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project, containing thousands of $(25\,h^{-1}{\rm Mpc})^3$ volume realizations with varying cosmology, initial random field, stellar and AGN feedback strength and sub-grid model implementation methods. We show that baryonic physics affects matter clustering on scales $k \gtrsim 0.4\,h\,\mathrm{Mpc}^{-1}$ and the magnitude of this effect is dependent on the details of the galaxy formation implementation and variations of cosmological and astrophysical parameters. Increasing AGN feedback strength decreases halo baryon fractions and yields stronger suppression of power relative to N-body simulations, while stronger stellar feedback often results in weaker effects by suppressing black hole growth and therefore the impact of AGN feedback. We find a broad correlation between mean baryon fraction of massive halos ($M_{\rm 200c} > 10^{13.5}$\,\Msun) and suppression of matter clustering but with significant scatter compared to previous work owing to wider exploration of feedback parameters and cosmic variance effects. We show that a random forest regressor trained on the baryon content and abundance of halos across the full mass range $10^{10} \leq M_\mathrm{halo}/$\Msun$< 10^{15}$ can predict the effect of galaxy formation on the matter power spectrum on scales $k = 1.0$--20.0\,$h\,\mathrm{Mpc}^{-1}$.

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FORGE'd in FIRE: Resolving the End of Star Formation and Structure of AGN Accretion Disks from Cosmological Initial Conditions

It has recently become possible to zoom-in from cosmological to sub-pc scales in galaxy simulations to follow accretion onto supermassive black holes (SMBHs). However, at some point the approximations used on ISM scales (e.g. optically-thin cooling and stellar-population-integrated star formation [SF] and feedback [FB]) break down. We therefore present the first cosmological radiation-magnetohydrodynamic (RMHD) simulation which self-consistently combines the FIRE physics (relevant on galactic/ISM scales where SF/FB are ensemble-averaged) and STARFORGE physics (relevant on small scales where we track individual (proto)stellar formation and evolution), together with explicit RMHD (including non-ideal MHD and multi-band M1-RHD) which self-consistently treats both optically-thick and thin regimes. This allows us to span scales from ~100 Mpc down to <100 au (~300 Schwarzschild radii) around a SMBH at a time where it accretes as a bright quasar, in a single simulation. We show that accretion rates up to $\sim 10-100\,{\rm M_{\odot}\,yr^{-1}}$ can be sustained into the accretion disk at $\ll 10^{3}\,R_{\rm schw}$, with gravitational torques between stars and gas dominating on sub-kpc scales until star formation is shut down on sub-pc scales by a combination of optical depth to cooling and strong magnetic fields. There is an intermediate-scale, flux-frozen disk which is gravitoturbulent and stabilized by magnetic pressure sustaining strong turbulence and inflow with persistent spiral modes. In this paper we focus on how gas gets into the small-scale disk, and how star formation is efficiently suppressed.

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Cosmology with multiple galaxies

Recent works have discovered a relatively tight correlation between $Ω_{\rm m}$ and properties of individual simulated galaxies. Because of this, it has been shown that constraints on $Ω_{\rm m}$ can be placed using the properties of individual galaxies while accounting for uncertainties on astrophysical processes such as feedback from supernova and active galactic nuclei. In this work, we quantify whether using the properties of multiple galaxies simultaneously can tighten those constraints. For this, we train neural networks to perform likelihood-free inference on the value of two cosmological parameters ($Ω_{\rm m}$ and $σ_8$) and four astrophysical parameters using the properties of several galaxies from thousands of hydrodynamic simulations of the CAMELS project. We find that using properties of more than one galaxy increases the precision of the $Ω_{\rm m}$ inference. Furthermore, using multiple galaxies enables the inference of other parameters that were poorly constrained with one single galaxy. We show that the same subset of galaxy properties are responsible for the constraints on $Ω_{\rm m}$ from one and multiple galaxies. Finally, we quantify the robustness of the model and find that without identifying the model range of validity, the model does not perform well when tested on galaxies from other galaxy formation models.

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A unified model for the co-evolution of galaxies and their circumgalactic medium: the relative roles of turbulence and atomic cooling physics

The circumgalactic medium (CGM) plays a pivotal role in regulating gas flows around galaxies and thus shapes their evolution. However, the details of how galaxies and their CGM co-evolve remain poorly understood. We present a new time-dependent two-zone model that self-consistently tracks not just mass and metal flows between galaxies and their CGM but also the evolution of the global thermal and turbulent kinetic energy of the CGM. Our model accounts for heating and turbulence driven by both supernova winds and cosmic accretion as well as radiative cooling, turbulence dissipation, and halo outflows due to CGM overpressurization. We demonstrate that, depending on parameters, the CGM can undergo a phase transition (``thermalization'') from a cool, turbulence-supported phase to a virial-temperature, thermally-supported phase. This CGM phase transition is largely determined by the ability of radiative cooling to balance heating from supernova winds and turbulence dissipation. We perform an initial calibration of our model to the FIRE-2 cosmological hydrodynamical simulations and show that it can approximately reproduce the baryon cycles of the simulated halos. In particular, we find that, for these parameters, the phase transition occurs at high-redshift in ultrafaint progenitors and at low redshift in classical $M_{\rm vir}\sim10^{11}M_{\odot}$ dwarfs, while Milky Way-mass halos undergo the transition at $z\approx0.5$. We see a similar transition in the simulations though it is more gradual, likely reflecting radial dependence and multi-phase gas not captured by our model. We discuss these and other limitations of the model and possible future extensions.

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Inferring Warm Dark Matter Masses with Deep Learning

We present a new suite of over 1,500 cosmological N-body simulations with varied Warm Dark Matter (WDM) models ranging from 2.5 to 30 keV. We use these simulations to train Convolutional Neural Networks (CNNs) to infer WDM particle masses from images of DM field data. Our fiducial setup can make accurate predictions of the WDM particle mass up to 7.5 keV at a 95% confidence level from small maps that cover an area of (25 h$^{-1}$ Mpc)$^2$. We vary the image resolution, simulation resolution, redshift, and cosmology of our fiducial setup to better understand how our model is making predictions. Using these variations, we find that our models are most dependent on simulation resolution, minimally dependent on image resolution, not systematically dependent on redshift, and robust to varied cosmologies. We also find that an important feature to distinguish between WDM models is present with a linear size between 100 and 200 h$^{-1}$ kpc. We compare our fiducial model to one trained on the power spectrum alone and find that our field-level model can make 2x more precise predictions and can make accurate predictions to 2x as massive WDM particle masses when used on the same data. Overall, we find that the field-level data can be used to accurately differentiate between WDM models and contain more information than is captured by the power spectrum. This technique can be extended to more complex DM models and opens up new opportunities to explore alternative DM models in a cosmological environment.

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Massive black holes in galactic nuclei: Theory and Simulations

Massive black holes are fundamental constituents of our cosmos, from the Big Bang to today. Understanding their formation from cosmic dawn, their growth, and the emergence of the first, rare quasars in the early Universe remains one of our greatest theoretical and observational challenges. Hydrodynamic cosmological simulations self-consistently combine the processes of structure formation at cosmological scales with the physics of smaller, galaxy scales. They capture our most realistic understanding of massive black holes and their connection to galaxy formation and have become the primary avenue for theoretical research in this field. The space-based gravitational wave interferometer, LISA, will open up new investigations into the dynamical processes involving massive black holes. Multi-messenger astrophysics brings new exciting prospects for tracing the origin, growth and merger history of massive black holes across cosmic ages.

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Cosmology with one galaxy? -- The ASTRID model and robustness

Recent work has pointed out the potential existence of a tight relation between the cosmological parameter $Ω_{\rm m}$, at fixed $Ω_{\rm b}$, and the properties of individual galaxies in state-of-the-art cosmological hydrodynamic simulations. In this paper, we investigate whether such a relation also holds for galaxies from simulations run with a different code that made use of a distinct subgrid physics: Astrid. We find that also in this case, neural networks are able to infer the value of $Ω_{\rm m}$ with a $\sim10\%$ precision from the properties of individual galaxies while accounting for astrophysics uncertainties as modeled in CAMELS. This tight relationship is present at all considered redshifts, $z\leq3$, and the stellar mass, the stellar metallicity, and the maximum circular velocity are among the most important galaxy properties behind the relation. In order to use this method with real galaxies, one needs to quantify its robustness: the accuracy of the model when tested on galaxies generated by codes different from the one used for training. We quantify the robustness of the models by testing them on galaxies from four different codes: IllustrisTNG, SIMBA, Astrid, and Magneticum. We show that the models perform well on a large fraction of the galaxies, but fail dramatically on a small fraction of them. Removing these outliers significantly improves the accuracy of the models across simulation codes.

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Public data release of the FIRE-2 cosmological zoom-in simulations of galaxy formation

We describe a public data release of the FIRE-2 cosmological zoom-in simulations of galaxy formation, available at http://flathub.flatironinstitute.org/fire, from the Feedback In Realistic Environments (FIRE) project. FIRE-2 simulations achieve parsec-scale resolution to explicitly model the multi-phase interstellar medium while implementing direct models for stellar evolution and feedback, including stellar winds, core-collapse and Ia supernovae, radiation pressure, photoionization, and photoelectric heating. We release complete snapshots from 3 suites of simulations. The first comprises 20 simulations that zoom in on 14 Milky Way-mass galaxies, 5 SMC/LMC-mass galaxies, and 4 lower-mass galaxies including 1 ultra-faint; we release 39 snapshots across z = 0 - 10. The second comprises 4 massive galaxies, with 19 snapshots across z = 1 - 10. Finally, a high-redshift suite comprises 22 simulations, with 11 snapshots across z = 5 - 10. Each simulation also includes dozens of resolved lower-mass (satellite) galaxies in its zoom-in region. Snapshots include all stored properties for all dark matter, gas, and star particles, including 11 elemental abundances for stars and gas, and formation times (ages) of star particles. We also release accompanying (sub)halo catalogs, which include galaxy properties and member star particles. For the simulations to z = 0, including all Milky Way-mass galaxies, we release the formation coordinates and an "ex-situ" flag for all star particles, pointers to track particles across snapshots, catalogs of stellar streams, and multipole basis expansions for the halo mass distributions. We describe publicly available python packages for reading and analyzing these simulations.

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A universal equation to predict $Ω_{\rm m}$ from halo and galaxy catalogues

We discover analytic equations that can infer the value of $Ω_{\rm m}$ from the positions and velocity moduli of halo and galaxy catalogues. The equations are derived by combining a tailored graph neural network (GNN) architecture with symbolic regression. We first train the GNN on dark matter halos from Gadget N-body simulations to perform field-level likelihood-free inference, and show that our model can infer $Ω_{\rm m}$ with $\sim6\%$ accuracy from halo catalogues of thousands of N-body simulations run with six different codes: Abacus, CUBEP$^3$M, Gadget, Enzo, PKDGrav3, and Ramses. By applying symbolic regression to the different parts comprising the GNN, we derive equations that can predict $Ω_{\rm m}$ from halo catalogues of simulations run with all of the above codes with accuracies similar to those of the GNN. We show that by tuning a single free parameter, our equations can also infer the value of $Ω_{\rm m}$ from galaxy catalogues of thousands of state-of-the-art hydrodynamic simulations of the CAMELS project, each with a different astrophysics model, run with five distinct codes that employ different subgrid physics: IllustrisTNG, SIMBA, Astrid, Magneticum, SWIFT-EAGLE. Furthermore, the equations also perform well when tested on galaxy catalogues from simulations covering a vast region in parameter space that samples variations in 5 cosmological and 23 astrophysical parameters. We speculate that the equations may reflect the existence of a fundamental physics relation between the phase-space distribution of generic tracers and $Ω_{\rm m}$, one that is not affected by galaxy formation physics down to scales as small as $10~h^{-1}{\rm kpc}$.

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Calibrating cosmological simulations with implicit likelihood inference using galaxy growth observables

In a novel approach employing implicit likelihood inference (ILI), also known as likelihood-free inference, we calibrate the parameters of cosmological hydrodynamic simulations against observations, which has previously been unfeasible due to the high computational cost of these simulations. For computational efficiency, we train neural networks as emulators on ~1000 cosmological simulations from the CAMELS project to estimate simulated observables, taking as input the cosmological and astrophysical parameters, and use these emulators as surrogates to the cosmological simulations. Using the cosmic star formation rate density (SFRD) and, separately, stellar mass functions (SMFs) at different redshifts, we perform ILI on selected cosmological and astrophysical parameters (Omega_m, sigma_8, stellar wind feedback, and kinetic black hole feedback) and obtain full 6-dimensional posterior distributions. In the performance test, the ILI from the emulated SFRD (SMFs) can recover the target observables with a relative error of 0.17% (0.4%). We find that degeneracies exist between the parameters inferred from the emulated SFRD, confirmed with new full cosmological simulations. We also find that the SMFs can break the degeneracy in the SFRD, which indicates that the SMFs provide complementary constraints for the parameters. Further, we find that the parameter combination inferred from an observationally-inferred SFRD reproduces the target observed SFRD very well, whereas, in the case of the SMFs, the inferred and observed SMFs show significant discrepancies that indicate potential limitations of the current galaxy formation modeling and calibration framework, and/or systematic differences and inconsistencies between observations of the stellar mass function.

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Robust field-level inference with dark matter halos

We train graph neural networks on halo catalogues from Gadget N-body simulations to perform field-level likelihood-free inference of cosmological parameters. The catalogues contain $\lesssim$5,000 halos with masses $\gtrsim 10^{10}~h^{-1}M_\odot$ in a periodic volume of $(25~h^{-1}{\rm Mpc})^3$; every halo in the catalogue is characterized by several properties such as position, mass, velocity, concentration, and maximum circular velocity. Our models, built to be permutationally, translationally, and rotationally invariant, do not impose a minimum scale on which to extract information and are able to infer the values of $Ω_{\rm m}$ and $σ_8$ with a mean relative error of $\sim6\%$, when using positions plus velocities and positions plus masses, respectively. More importantly, we find that our models are very robust: they can infer the value of $Ω_{\rm m}$ and $σ_8$ when tested using halo catalogues from thousands of N-body simulations run with five different N-body codes: Abacus, CUBEP$^3$M, Enzo, PKDGrav3, and Ramses. Surprisingly, the model trained to infer $Ω_{\rm m}$ also works when tested on thousands of state-of-the-art CAMELS hydrodynamic simulations run with four different codes and subgrid physics implementations. Using halo properties such as concentration and maximum circular velocity allow our models to extract more information, at the expense of breaking the robustness of the models. This may happen because the different N-body codes are not converged on the relevant scales corresponding to these parameters.

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The black hole population in low-mass galaxies in large-scale cosmological simulations

Recent systematic searches for massive black holes (BHs) in local dwarf galaxies led to the discovery of a population of faint Active Galactic Nuclei (AGN). We investigate the agreement of the BH and AGN populations in the Illustris, TNG, Horizon-AGN, EAGLE, and SIMBA simulations with current observational constraints in low-mass galaxies. We find that some of these simulations produce BHs that are too massive, and that the BH occupation fraction at z=0 is not inherited from the simulation seeding modeling. The ability of BHs and their host galaxies to power an AGN depends on BH and galaxy subgrid modeling. The fraction of AGN in low-mass galaxies is not used to calibrate the simulations, and thus can be used to differentiate galaxy formation models. AGN fractions at z=0 span two orders of magnitude at fixed galaxy stellar mass in simulations, similarly to observational constraints, but uncertainties and degeneracies affect both observations and simulations. The agreement is difficult to interpret due to differences in the masses of simulated and observed BHs, BH occupation fraction affected by numerical choices, and an unknown fraction of obscured AGN. Our work advocates for more thorough comparisons with observations to improve the modeling of cosmological simulations, and our understanding of BH and galaxy physics in the low-mass regime. The mass of BHs, their ability to efficiently accrete gas, and the AGN fraction in low-mass galaxies have important implications for the build-up of the entire BH and galaxy populations with time.

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Hot-mode accretion and the physics of thin-disk galaxy formation

We use FIRE simulations to study disk formation in z~0, Milky Way-mass galaxies, and conclude that a key ingredient for the formation of thin stellar disks is the ability for accreting gas to develop an aligned angular momentum distribution via internal cancellation *prior* to joining the galaxy. Among galaxies with a high fraction (>70%) of their young stars in a thin disk (h/R~0.1) we find that: (i) hot, virial-temperature gas dominates the inflowing gas mass on halo scales (>~20 kpc), with radiative losses offset by compression heating; (ii) this hot accretion proceeds until angular momentum support slows inward motion, at which point the gas cools to T~10^4 K or less; (iii) prior to cooling, the accreting gas develops an angular momentum distribution that is aligned with the galaxy disk, and while cooling transitions from a quasi-spherical spatial configuration to a more flattened, disk-like configuration. We show that the existence of this "rotating cooling flow" accretion mode is strongly correlated with the fraction of stars forming in a thin disk among a sample of 17 z~0 galaxies spanning a halo mass range of 10^10.5 solar masses to 10^12 solar masses, or a stellar mass range 10^8 solar masses to 10^11 solar masses. Notably, galaxies with a thick disk or irregular morphology do not undergo significant angular momentum alignment of gas prior to accretion and show no correspondence between halo gas cooling and flattening. Our results suggest that rotating cooling flows (or, more generally, rotating subsonic flows) that become coherent and angular momentum-supported prior to accretion onto the galaxy are likely a necessary condition for the formation of thin, star-forming disk galaxies in a LambdaCDM universe.

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