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Matthieu Schaller

Publications and source records attributed to Matthieu Schaller.

At least 127 records · Page 7Linked to original sources

Revisiting the Extreme Clustering of $z \approx 4$ Quasars with Large Volume Cosmological Simulations

Observations from wide-field quasar surveys indicate that the quasar auto-correlation length increases dramatically from $z\approx2.5$ to $z\approx4$. This large clustering amplitude at $z\approx4$ has proven hard to interpret theoretically, as it implies that quasars are hosted by the most massive dark matter halos residing in the most extreme environments at that redshift. In this work, we present a model that simultaneously reproduces both the observed quasar auto-correlation and quasar luminosity functions. The spatial distribution of halos and their relative abundance are obtained via a novel method that computes the halo mass and halo cross-correlation functions by combining multiple large-volume dark-matter-only cosmological simulations with different box sizes and resolutions. Armed with these halo properties, our model exploits the conditional luminosity function framework to describe the stochastic relationship between quasar luminosity, $L$, and halo mass, $M$. Assuming a simple power-law relation $L\propto M^γ$ with log-normal scatter, $σ$, we are able to reproduce observations at $z\sim 4$ and find that: (a) the quasar luminosity-halo mass relation is highly non-linear ($γ\gtrsim2$), with very little scatter ($σ\lesssim 0.3$ dex); (b) luminous quasars ($\log_{10} L/{\rm erg s}^{-1} \gtrsim 46.5-47$) are hosted by halos with mass $\log_{10} M/{\rm M}_\odot\gtrsim 13-13.5$; and (c) the implied duty cycle for quasar activity approaches unity ($\varepsilon_{\rm DC}\approx10-60\%$). We also consider observations at $z\approx2.5$ and find that the quasar luminosity-halo mass relation evolves significantly with cosmic time, implying a rapid change in quasar host halo masses and duty cycles, which in turn suggests concurrent evolution in black hole scaling relations and/or accretion efficiency.

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On the anisotropic distribution of clusters in the local Universe

In his 2021 lecture to the Canadian Association of Physicists Congress, P.J.E. Peebles pointed out that the brightest extra-galactic radio sources tend to be aligned with the plane of the de Vaucouleur Local Supercluster up to redshifts of $z=0.02$ ($d_{\rm MW}\approx 85~\rm{Mpc}$). He then asked whether such an alignment of clusters is anomalous in the standard $Λ$CDM framework. In this letter, we employ an alternative, absolute orientation agnostic, measure of the anisotropy based on the inertia tensor axis ratio of these brightest sources and use a large cosmological simulation from the FLAMINGO suite to measure how common such an alignment of structures is. We find that only 3.5% of randomly selected regions display an anisotropy of their clusters more extreme than the one found in the local Universe's radio data. This sets the region around the Milky Way as a $1.85σ$ outlier. Varying the selection parameters of the objects in the catalogue, we find that the clusters in the local Universe are never more than $2σ$ away from the simulations' prediction for the same selection. We thus conclude that the reported anisotropy, whilst note-worthy, is not in tension with the $Λ$CDM paradigm.

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Winds versus jets: a comparison between black hole feedback modes in simulations of idealized galaxy groups and clusters

Using the SWIFT simulation code we study different forms of active galactic nuclei (AGN) feedback in idealized galaxy groups and clusters. We first present a physically motivated model of black hole (BH) spin evolution and a numerical implementation of thermal isotropic feedback (representing the effects of energy-driven winds) and collimated kinetic jets that they launch at different accretion rates. We find that kinetic jet feedback is more efficient at quenching star formation in the brightest cluster galaxies (BCGs) than thermal isotropic feedback, while simultaneously yielding cooler cores in the intracluster medium (ICM). A hybrid model with both types of AGN feedback yields moderate star formation rates, while having the coolest cores. We then consider a simplified implementation of AGN feedback by fixing the feedback efficiencies and the jet direction, finding that the same general conclusions hold. We vary the feedback energetics (the kick velocity and the heating temperature), the fixed efficiencies and the type of energy (kinetic versus thermal) in both the isotropic and the jet case. The isotropic case is largely insensitive to these variations. In particular, we highlight that kinetic isotropic feedback (used e.g. in IllustrisTNG) is similar in its effects to its thermal counterpart (used e.g. in EAGLE). On the other hand, jet feedback must be kinetic in order to be efficient at quenching. We also find that it is much more sensitive to the choice of energy per feedback event (the jet velocity), as well as the efficiency. The former indicates that jet velocities need to be carefully chosen in cosmological simulations, while the latter motivates the use of BH spin evolution models.

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Realistic simulated galaxies form [$α$/Fe]-[Fe/H] knees due to a sustained decline in their star formation rates

We examine the stellar [$α$/Fe]-[Fe/H] distribution of $\simeq1000$ present-day galaxies in a high-resolution EAGLE simulation. A slight majority of galaxies exhibit the canonical distribution, characterised by a sequence of low-metallicity stars with high [$α$/Fe] that transitions at a "knee" to a sequence of declining [$α$/Fe] with increasing metallicity. This population yields a knee metallicity - galaxy-mass relation similar to that observed in Local Group galaxies, both in slope and scatter. However, many simulated galaxies lack a knee or exhibit more complicated distributions. Knees are found only in galaxies with star formation histories (SFHs) featuring a sustained decline from an early peak ($t\simeq7~{\rm Gyr}$), which enables enrichment by Type Ia supernovae to dominate that due to Type II supernovae (SN II), reducing [$α$/Fe] in the interstellar gas. The simulation thus indicates that, contrary to the common interpretation implied by analytic galactic chemical evolution (GCE) models, knee formation is not a consequence of the onset of enrichment by SN Ia. We use the SFH of a simulated galaxy exhibiting a knee as input to the VICE GCE model, finding it yields an $α$-rich plateau enriched only by SN II, but the plateau comprises little stellar mass and the galaxy forms few metal-poor ([Fe/H]$\lesssim - 1$) stars. This follows from the short, constant gas consumption timescale typically assumed by GCEs, which implies the presence of a readily-enriched, low-mass gas reservoir. When an initially longer, evolving consumption timescale is adopted, VICE reproduces the simulated galaxy's track through the [$α$/Fe]-[Fe/H] plane and its metallicity distribution function.

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FLAMINGO: Calibrating large cosmological hydrodynamical simulations with machine learning

To fully take advantage of the data provided by large-scale structure surveys, we need to quantify the potential impact of baryonic effects, such as feedback from active galactic nuclei (AGN) and star formation, on cosmological observables. In simulations, feedback processes originate on scales that remain unresolved. Therefore, they need to be sourced via subgrid models that contain free parameters. We use machine learning to calibrate the AGN and stellar feedback models for the FLAMINGO cosmological hydrodynamical simulations. Using Gaussian process emulators trained on Latin hypercubes of 32 smaller-volume simulations, we model how the galaxy stellar mass function and cluster gas fractions change as a function of the subgrid parameters. The emulators are then fit to observational data, allowing for the inclusion of potential observational biases. We apply our method to the three different FLAMINGO resolutions, spanning a factor of 64 in particle mass, recovering the observed relations within the respective resolved mass ranges. We also use the emulators, which link changes in subgrid parameters to changes in observables, to find models that skirt or exceed the observationally allowed range for cluster gas fractions and the stellar mass function. Our method enables us to define model variations in terms of the data that they are calibrated to rather than the values of specific subgrid parameters. This approach is useful, because subgrid parameters are typically not directly linked to particular observables, and predictions for a specific observable are influenced by multiple subgrid parameters.

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The FLAMINGO project: cosmological hydrodynamical simulations for large-scale structure and galaxy cluster surveys

We introduce the Virgo Consortium's FLAMINGO suite of hydrodynamical simulations for cosmology and galaxy cluster physics. To ensure the simulations are sufficiently realistic for studies of large-scale structure, the subgrid prescriptions for stellar and AGN feedback are calibrated to the observed low-redshift galaxy stellar mass function and cluster gas fractions. The calibration is performed using machine learning, separately for three resolutions. This approach enables specification of the model by the observables to which they are calibrated. The calibration accounts for a number of potential observational biases and for random errors in the observed stellar masses. The two most demanding simulations have box sizes of 1.0 and 2.8 Gpc and baryonic particle masses of $1\times10^8$ and $1\times10^9 \text{M}_\odot$, respectively. For the latter resolution the suite includes 12 model variations in a 1 Gpc box. There are 8 variations at fixed cosmology, including shifts in the stellar mass function and/or the cluster gas fractions to which we calibrate, and two alternative implementations of AGN feedback (thermal or jets). The remaining 4 variations use the unmodified calibration data but different cosmologies, including different neutrino masses. The 2.8 Gpc simulation follows $3\times10^{11}$ particles, making it the largest ever hydrodynamical simulation run to $z=0$. Lightcone output is produced on-the-fly for up to 8 different observers. We investigate numerical convergence, show that the simulations reproduce the calibration data, and compare with a number of galaxy, cluster, and large-scale structure observations, finding very good agreement with the data for converged predictions. Finally, by comparing hydrodynamical and `dark-matter-only' simulations, we confirm that baryonic effects can suppress the halo mass function and the matter power spectrum by up to $\approx20$ per cent.

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The FLAMINGO project: revisiting the $S_8$ tension and the role of baryonic physics

A number of recent studies have found evidence for a tension between observations of large-scale structure (LSS) and the predictions of the standard model of cosmology with the cosmological parameters fit to the cosmic microwave background (CMB). The origin of this '$S_8$ tension' remains unclear, but possibilities include new physics beyond the standard model, unaccounted for systematic errors in the observational measurements and/or uncertainties in the role that baryons play. Here we carefully examine the latter possibility using the new FLAMINGO suite of large-volume cosmological hydrodynamical simulations. We project the simulations onto observable harmonic space and compare with observational measurements of the power and cross-power spectra of cosmic shear, CMB lensing, and the thermal Sunyaev-Zel'dovich (tSZ) effect. We explore the dependence of the predictions on box size and resolution, cosmological parameters including the neutrino mass, and the efficiency and nature of baryonic 'feedback'. Despite the wide range of astrophysical behaviours simulated, we find that baryonic effects are not sufficiently large to remove the $S_8$ tension. Consistent with recent studies, we find the CMB lensing power spectrum is in excellent agreement with the standard model, whilst the cosmic shear power spectrum, tSZ effect power spectrum, and the cross-spectra between shear, CMB lensing, and the tSZ effect are all in varying degrees of tension with the CMB-specified standard model. These results suggest that some mechanism is required to slow the growth of fluctuations at late times and/or on non-linear scales, but that it is unlikely that baryon physics is driving this modification.

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Tests of subgrid models for star formation using simulations of isolated disk galaxies

We use smoothed-particle hydrodynamics simulations of isolated Milky Way-mass disk galaxies that include cold, interstellar gas to test subgrid prescriptions for star formation (SF). Our fiducial model combines a Schmidt law with a gravitational instability criterion, but we also test density thresholds and temperature ceilings. While SF histories are insensitive to the prescription for SF, the Kennicutt-Schmidt (KS) relations between SF rate and gas surface density can discriminate between models. We show that our fiducial model, with an SF efficiency per free-fall time of 1 per cent, agrees with spatially-resolved and azimuthally-averaged observed KS relations for neutral, atomic and molecular gas. Density thresholds do not perform as well. While temperature ceilings selecting cold, molecular gas can match the data for galaxies with solar metallicity, they are unsuitable for very low-metallicity gas and hence for cosmological simulations. We argue that SF criteria should be applied at the resolution limit rather than at a fixed physical scale, which means that we should aim for numerical convergence of observables rather than of the properties of gas labelled as star-forming. Our fiducial model yields good convergence when the mass resolution is varied by nearly 4 orders of magnitude, with the exception of the spatially-resolved molecular KS relation at low surface densities. For the gravitational instability criterion, we quantify the impact on the KS relations of gravitational softening, the SF efficiency, and the strength of supernova feedback, as well as of observable parameters such as the inclusion of ionized gas, the averaging scale, and the metallicity.

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The impact of stochastic modeling on the predictive power of galaxy formation simulations

All modern galaxy formation models employ stochastic elements in their sub-grid prescriptions to discretise continuous equations across the time domain. In this paper, we investigate how the stochastic nature of these models, notably star formation, black hole accretion, and their associated feedback, that act on small ($<$ kpc) scales, can back-react on macroscopic galaxy properties (e.g. stellar mass and size) across long ($>$ Gyr) timescales. We find that the scatter in scaling relations predicted by the EAGLE model implemented in the SWIFT code can be significantly impacted by random variability between re-simulations of the same object, even when galaxies are resolved by tens of thousands of particles. We then illustrate how re-simulations of the same object can be used to better understand the underlying model, by showing how correlations between galaxy stellar mass and black hole mass disappear at the highest black hole masses ($M_{\rm BH} > 10^8$ M$_\odot$), indicating that the feedback cycle may be interrupted by external processes. We find that although properties that are collected cumulatively over many objects are relatively robust against random variability (e.g. the median of a scaling relation), the properties of individual galaxies (such as galaxy stellar mass) can vary by up to 25\%, even far into the well-resolved regime, driven by bursty physics (black hole feedback) and mergers between galaxies. We suggest that studies of individual objects within cosmological simulations be treated with caution, and that any studies aiming to closely investigate such objects must account for random variability within their results.

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Non-linear CMB lensing with neutrinos and baryons: FLAMINGO simulations vs. fast approximations

Weak lensing of the cosmic microwave background is rapidly emerging as a powerful probe of neutrinos, dark energy, and new physics. We present a fast computation of the non-linear CMB lensing power spectrum which combines non-linear perturbation theory at early times with power spectrum emulation using cosmological simulations at late times. Comparing our calculation with lightcones from the FLAMINGO 5.6 Gpc cube dark-matter-only simulation, we confirm its accuracy to 1% (2%) up to multipoles L = 3000 (L = 5000) for a nuLambdaCDM cosmology consistent with current data. Clustering suppression due to small-scale baryonic phenomena such as feedback from active galactic nuclei can reduce the lensing power by of order 10%. To our perturbation theory and emulator-based calculation we add SP(k), a new fitting function for this suppression, and confirm its accuracy compared to the FLAMINGO hydrodynamic simulations to 4% at L = 5000, with similar accuracy for massive neutrino models. We further demonstrate that scale-dependent suppression due to neutrinos and baryons approximately factorize, implying that a careful treatment of baryonic feedback can limit biasing neutrino mass constraints.

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Hydrodynamic simulations of the Disk of Gas Around Supermassive black holes (HDGAS) -I; Molecular Gas Dynamics

We present hydrodynamic simulations of the interstellar medium (ISM) within the circumnuclear disk (CND) of a typical AGN-dominated galaxy influenced by mechanical feedback from an active galactic nucleus(AGN). The simulations are coupled with the CHIMES non-equilibrium chemistry network to treat the radiative-cooling and AGN-heating. A focus is placed on the central 100 pc scale where AGN outflows are coupled to the ISM and constrained by observational Seyfert-2 galaxies. AGN-feedback models are implemented with different wind-velocity and mass-loading factors. We post-process the simulation snapshots with a radiative-transfer code to obtain the molecular emission lines. We find that the inclusion of an AGN promotes the formation of CO in clumpy and dense regions surrounding supermassive-blackholes (SMBH). The CO(1-0) intensity maps ($<$6 Myr) in the CND seem to match well with observations of NGC 1068 with a best match for a model with 5000 $\rm km/s$ wind-velocity and a high mass-loading factor. We attempt to discern between competing explanations for the apparent counter-rotating gas disk in the NGC 1068 through an analysis of kinematic maps of the CO line emission. We suggest that mechanical AGN-feedback could explain the alignment-stability of position-angle across the different CND radii around the SMBH through momentum and energy loading of the wind. It is the wind-velocity that drives the disk out of alignment on a 100 pc scale for a long period of time. The position-velocity diagrams are in broad agreement with the predicted Keplerian rotation-curve in the model without-AGN, but the AGN models exhibit a larger degree of scatter, in better agreement with NGC 1068 observations.

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A thermal-kinetic subgrid model for supernova feedback in simulations of galaxy formation

We present a subgrid model for supernova feedback designed for cosmological simulations of galaxy formation that may include a cold interstellar medium (ISM). The model uses thermal and kinetic channels of energy injection, which are built upon the stochastic kinetic and thermal models for stellar feedback used in the OWLS and EAGLE simulations, respectively. In the thermal channel, the energy is distributed statistically isotropically and injected stochastically in large amounts per event, which minimizes spurious radiative energy losses. In the kinetic channel, we inject the energy in small portions by kicking gas particles in pairs in opposite directions. The implementation of kinetic feedback is designed to conserve energy, linear and angular momentum, and is statistically isotropic. To test the model, we run simulations of isolated Milky Way-mass and dwarf galaxies, in which the gas is allowed to cool down to 10 K. Using the thermal and kinetic channels together, we obtain smooth star formation histories and powerful galactic winds with realistic mass loading factors. Furthermore, the model produces spatially resolved star formation rates (SFRs) and velocity dispersions that are in agreement with observations. We vary the numerical resolution by several orders of magnitude and find excellent convergence of the global SFRs and wind mass loading. We show that large thermal-energy injections generate a hot phase of the ISM and modulate the star formation by ejecting gas from the disc, while the low-energy kicks increase the turbulent velocity dispersion in the neutral ISM, which in turn helps suppress star formation.

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KURVS: The outer rotation curve shapes and dark matter fractions of $z \sim 1.5 $ star-forming galaxies

We present first results from the KMOS Ultra-deep Rotation Velocity Survey (KURVS), aimed at studying the outer rotation curves shape and dark matter content of 22 star-forming galaxies at $z\sim1.5$. These galaxies represent `typical' star-forming discs at $z \sim 1.5$, being located within the star-forming main sequence and stellar mass-size relation with stellar masses $9.5\leqslant$log$(M_{\star}/\mathrm{M_{\odot}})\leqslant11.5$. We extract individual rotation curves out to 4 times the effective radius, on average, or $\sim 10-15$ kpc. Most rotation curves are flat or rising between three- and six-disc scale radii. Only three objects with dispersion-dominated dynamics ($v_{\rm rot}/σ_0\sim0.2$) have declining outer rotation curves at more than 5$σ$ significance. After accounting for seeing and pressure support, the nine rotation-dominated discs with $v_{\rm rot}/σ_0\geqslant1.5$ have average dark matter fractions of $50 \pm 20\%$ at the effective radius, similar to local discs. Together with previous observations of star-forming galaxies at cosmic noon, our measurements suggest a trend of declining dark matter fraction with increasing stellar mass and stellar mass surface density at the effective radius. Simulated EAGLE galaxies are in quantitative agreement with observations up to log$(M_{\star}R_{\rm eff}^{-2}/\mathrm{M_{\odot}kpc^{-2}}) \sim 9.2$, and over-predict the dark matter fraction of galaxies with higher mass surface densities by a factor of $\sim 3$. We conclude that the dynamics of typical rotationally-supported discs at $z \sim 1.5$ is dominated by dark matter from effective radius scales, in broad agreement with cosmological models. The tension with observations at high stellar mass surface density suggests that the prescriptions for baryonic processes occurring in the most massive galaxies (such as bulge growth and quenching) need to be reassessed.

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A machine learning approach to mapping baryons onto dark matter haloes using the EAGLE and C-EAGLE simulations

High-resolution cosmological hydrodynamic simulations are currently limited to relatively small volumes due to their computational expense. However, much larger volumes are required to probe rare, overdense environments, and measure clustering statistics of the large scale structure. Typically, zoom simulations of individual regions are used to study rare environments, and semi-analytic models and halo occupation models applied to dark matter only (DMO) simulations are used to study the Universe in the large-volume regime. We propose a new approach, using a machine learning framework to explore the halo-galaxy relationship in the periodic EAGLE simulations, and zoom C-EAGLE simulations of galaxy clusters. We train a tree based machine learning method to predict the baryonic properties of galaxies based on their host dark matter halo properties. The trained model successfully reproduces a number of key distribution functions for an infinitesimal fraction of the computational cost of a full hydrodynamic simulation. By training on both periodic simulations as well as zooms of overdense environments, we learn the bias of galaxy evolution in differing environments. This allows us to apply the trained model to a larger DMO volume than would be possible if we only trained on a periodic simulation. We demonstrate this application using the $(800 \; \mathrm{Mpc})^3$ P-Millennium simulation, and present predictions for key baryonic distribution functions and clustering statistics from the EAGLE model in this large volume.

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Sensitivity of non-radiative cloud-wind interactions to the hydrodynamics solver

Cloud-wind interactions are common in the interstellar and circumgalactic media. Many studies have used simulations of such interactions to investigate the effect of particular physical processes, but the impact of the choice of hydrodynamics solver has largely been overlooked. Here we study the cloud-wind interaction, also known as the "blob test", using seven different hydrodynamics solvers: Three flavours of SPH, a moving mesh, adaptive mesh refinement and two meshless schemes. The evolution of masses in dense gas and intermediate-temperature gas, as well as the covering fraction of intermediate-temperature gas, are systematically compared for initial density contrasts of 10 and 100, and five numerical resolutions. To isolate the differences due to the hydrodynamics solvers, we use idealised non-radiative simulations without physical conduction. We find large differences between these methods. SPH methods show slower dispersal of the cloud, particularly for the higher density contrast, but faster convergence, especially for the lower density contrast. Predictions for the intermediate-temperature gas differ particularly strongly, also between non-SPH codes, and converge most slowly. We conclude that the hydrodynamical interaction between a dense cloud and a supersonic wind remains an unsolved problem. Studies aiming to understand the physics or observational signatures of cloud-wind interactions should test the robustness of their results by comparing different hydrodynamics solvers.

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EAGLE-like simulation models do not solve the entropy core problem in groups and clusters of galaxies

Recent high-resolution cosmological hydrodynamic simulations run with a variety of codes systematically predict large amounts of entropy in the intra-cluster medium at low redshift, leading to flat entropy profiles and a suppressed cool-core population. This prediction is at odds with X-ray observations of groups and clusters. We use a new implementation of the EAGLE galaxy formation model to investigate the sensitivity of the central entropy and the shape of the profiles to changes in the sub-grid model applied to a suite of zoom-in cosmological simulations of a group of mass $M_{500} = 8.8 \times 10^{12}~{\rm M}_\odot$ and a cluster of mass $2.9 \times 10^{14}~{\rm M}_\odot$. Using our reference model, calibrated to match the stellar mass function of field galaxies, we confirm that our simulated groups and clusters contain hot gas with too high entropy in their cores. Additional simulations run without artificial conduction, metal cooling or AGN feedback produce lower entropy levels but still fail to reproduce observed profiles. Conversely, the two objects run without supernova feedback show a significant entropy increase which can be attributed to excessive cooling and star formation. Varying the AGN heating temperature does not greatly affect the profile shape, but only the overall normalisation. Finally, we compared runs with four AGN heating schemes and obtained similar profiles, with the exception of bipolar AGN heating, which produces a higher and more uniform entropy distribution. Our study leaves open the question of whether the entropy core problem in simulations, and particularly the lack of power-law cool-core profiles, arise from incorrect physical assumptions, missing physical processes, or insufficient numerical resolution.

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A sparse regression approach for populating dark matter halos and subhalos with galaxies

We use sparse regression methods (SRM) to build accurate and explainable models that predict the stellar mass of central and satellite galaxies as a function of properties of their host dark matter halos. SRM are machine learning algorithms that provide a framework for modelling the governing equations of a system from data. In contrast with other machine learning algorithms, the solutions of SRM methods are simple and depend on a relatively small set of adjustable parameters. We collect data from 35,459 galaxies from the EAGLE simulation using 19 redshift slices between $z=0$ and $z=4$ to parameterize the mass evolution of the host halos. Using an appropriate formulation of input parameters, our methodology can model satellite and central halos using a single predictive model that achieves the same accuracy as when predicted separately. This allows us to remove the somewhat arbitrary distinction between those two galaxy types and model them based only on their halo growth history. Our models can accurately reproduce the total galaxy stellar mass function and the stellar mass-dependent galaxy correlation functions ($ξ(r)$) of EAGLE. We show that our SRM model predictions of $ξ(r)$ is competitive with those from sub-halo abundance matching and might be comparable to results from extremely randomized trees. We suggest SRM as an encouraging approach for populating the halos of dark matter only simulations with galaxies and for generating mock catalogues that can be used to explore galaxy evolution or analyse forthcoming large-scale structure surveys.

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A common origin for the Fundamental Plane of quiescent and star-forming galaxies in the EAGLE simulations

We use the EAGLE cosmological simulations to perform a comprehensive and systematic analysis of the $z=0.1$ Fundamental Plane (FP), the tight relation between galaxy size, mass and velocity dispersion. We first measure the total mass and velocity dispersion (including both random and rotational motions) within the effective radius to show that simulated galaxies obey a total mass FP that is very close to the virial relation ($<10\%$ deviation), indicating that the effects of non-homology are weak. When we instead use the stellar mass, we find a strong deviation from the virial plane, which is driven by variations in the dark matter content. The dark matter fraction is a smooth function of the size and stellar mass, and thereby sets the coefficients of the stellar mass FP without substantially increasing the scatter. Hence, both star-forming and quiescent galaxies obey the same FP, with equally low scatter (0.02 dex). We employ simulations with a variable stellar initial mass function (IMF) to show that IMF variations have a modest additional effect on this FP. Moreover, when we use luminosity-weighted mock observations of the size and spatially-integrated velocity dispersion, the inferred FP changes only slightly. However, the scatter increases significantly, due to the luminosity-weighting and line-of-sight projection of the velocity dispersions, and measurement uncertainties on the half-light radii. Importantly, we find significant differences between the simulated FP and observations, which likely reflects a systematic difference in the stellar mass distributions. Therefore, we suggest the stellar mass FP offers a simple test for cosmological simulations, requiring minimal post-processing of simulation data.

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