arXiv subjects

Matteo Zennaro

Publications and source records attributed to Matteo Zennaro.

At least 19 recordsLinked to original sources

arXiv2026

CHEFT: A Hybrid Effective Field Theory halo model

We present a hybrid halo model, which improves the description of the 2-halo term by incorporating non-linear information from simulations. A linear computation of the halo-halo power spectrum is inaccurate at the transition between the 1-halo and 2-halo regimes, whereas nonlinear approaches such as Hybrid Effective Field Theory (HEFT) are not naturally compatible with the halo model decomposition. We address this limitation by constructing a collapsed HEFT (CHEFT) framework, in which the power-spectrum templates of the HEFT operator expansion are measured from simulations where 1-halo contributions are removed by collapsing particles to their halo centres. The halo-halo power spectrum is then expressed as a sum over bias operators, with mass-dependent bias parameters deduced from simulation using the probabilistic bias approach. This provides a predictive model in which there are no free bias parameters. We validate the model for a range of weighting schemes designed to mimic the halo-mass dependence of astrophysical observables, including the Sunyaev-Zeldovich effect, the Cosmic Infrared Background, and galaxy abundances described via a halo occupation distribution. For the matter field, the model recovers the power spectrum to percent-level accuracy across the transition regime. For weighted tracers, the baseline model achieves accuracies of $\sim 5-10\%$ in power, which improves to the $\sim 3-5\%$ level when including an effective higher-derivative, Laplacian-like contribution in the bias expansion. The CHEFT model thus retains the precision and flexibility of the EFT approach, while allowing the transparent incorporation of astrophysical effects that are directly associated with haloes.

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arXiv2026

Validation of the Hybrid Bias Expansion model for the galaxy bispectrum

The Hybrid Bias Expansion model (also known as Hybrid Effective Field Theory, HEFT) provides a promising way to extend the range of validity of perturbative large-scale structure modelling by replacing perturbative gravitational evolution with the nonlinear displacement field measured from $N$-body simulations. While this approach has already been shown to improve the modelling of the power spectrum, its validity at the bispectrum level has not yet been established. In this work we perform a first systematic real-space validation of the Hybrid bispectrum model using DESI-like LRG and ELG mock catalogues constructed at fixed cosmology on volumes similar to those of DESI's LRG samples. We find that the model remains self-consistent up to $k_{\rm max}^B \simeq 0.25\,h\,{\rm Mpc}^{-1}$, while clear signs of breakdown appear for a similar EFT tree-level bispectrum approach at $k_{\rm max}^B \gtrsim 0.13\,h\,{\rm Mpc}^{-1}$. We also show that adding matter cross-statistics significantly improves the precision of the recovered bias parameters, while a partial third-order extension including only the $\delta^3$ operator does not extend the validity range. Finally, we find a strong hierarchy among the bispectrum basis terms when grouped by total bias-operator order, with the lowest-order sectors dominating the total amplitude, which has important implications in emulation strategies.

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arXiv2026

Cosmological constraints from the small scale clustering of Emission Line Galaxies

Spectroscopic surveys such as the Dark Energy Spectroscopic Instrument (DESI) and Euclid are mapping the spatial distribution of millions of galaxies, with Emission Line Galaxies (ELGs) serving as the dominant tracer in the redshift range $0.8<z<1.6$. Standard approaches for extracting cosmological information from galaxy clustering, however, typically discard highly constraining measurements from the nonlinear regime. We apply SHAMe-SF - a modification of Subhalo Abundance Matching tailored for star-forming galaxy samples - to analyse the three-dimensional clustering of DESI ELGs from the One-Percent data release, extending their cosmological analysis deep into the nonlinear regime. We validate our pipeline using two mock ELG samples drawn from the state-of-the-art cosmological hydrodynamical simulation MillenniumTNG, demonstrating that our model yields unbiased constraints on $\sigma_8$ and $\Omega_{\rm m}h^2$ down to scales of $0.3~h^{-1}$Mpc on both samples. We find that including scales below $0.8~h^{-1}$Mpc is critical for mitigating projection effects and obtaining unbiased constraints on $\sigma_8$. Applied to the DESI One-Percent measurements, our analysis yields $\sim6$% constraints on $\sigma_8 = 0.81^{+0.05}_{-0.06}$ and $\Omega_{\rm m}h^2=0.146^{+0.009}_{-0.009}$. Remarkably, the accuracy of these constraints is similar to that obtained from the combined full-shape analysis of all DESI DR1 tracers, yet using only 1% of the survey volume. A naive extrapolation of our results from the One-Percent to the full survey area suggests that the complete survey could deliver roughly an order-of-magnitude improvement in precision - a prospect that, while subject to significant practical challenges, illustrates the cosmological potential encoded in the nonlinear regime.

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arXiv2025

Cosmological constraints from the angular power spectrum and bispectrum of luminous red galaxies and CMB lensing

We study the projected clustering of photometric luminous red galaxies from the DESI Legacy Survey, combining their angular power spectrum, bispectrum, and cross-correlation with maps of the CMB lensing convergence from the Planck satellite. We employ a perturbative bias expansion in Eulerian space to describe the clustering of galaxies, modelling the power spectrum and bispectrum at one-loop and tree level, respectively. This allows us to use the power spectrum to self-consistently calibrate the perturbative bias parameters. We validate this model against an $N$-body simulation, and show that it can be used up to scales of at least $k_{\rm max}^P\simeq 0.2\,h{\rm Mpc}^{-1}$ and $k_{\rm max}^B\simeq 0.08\,h{\rm Mpc}^{-1}$, saturating the information recovered from the data. We obtain constraints on the amplitude of matter fluctuations $\sigma_8=0.761\pm 0.020$ and the non-relativistic matter fraction $\Omega_m=0.307\pm 0.015$, as well as the combination $S_8\equiv\sigma_8\sqrt{\Omega_m/0.3}=0.769 \pm 0.020$. Including the galaxy bispectrum leads to a $10$-$20\%$ improvement on the cosmological constraints, which are also in good agreement with previous analyses of the same data, and in mild tension with Planck at the $\sim2.5\sigma$ level. This tension is largely present in the standard two-point function dataset, and the addition of the bispectrum increases it slightly, marginally shifting $\sigma_8$ downwards and $\Omega_m$ upwards. Finally, using the bispectrum allows for a substantially more precise measurement of the bias parameters of this sample, which are in reasonable agreement with existing coevolution relations.

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arXiv2025

Low-redshift constraints on structure growth from CMB lensing tomography

We present constraints on the amplitude of matter fluctuations from the clustering of galaxies and their cross-correlation with the gravitational lensing convergence of the cosmic microwave background (CMB), focusing on low redshifts ($z\lesssim0.3$), where potential deviations from a perfect cosmological constant dominating the growth of structure could be more prominent. Specifically, we make use of data from the 2MASS photometric survey (\tmpz) and the \wisc galaxy survey, in combination with CMB lensing data from \planck. Using a hybrid effective field theory (HEFT) approach to model galaxy bias we obtain constraints on the combination $S_8=\sigma_8\sqrt{\Omega_m/0.3}$, where $\sigma_8$ is the amplitude of matter fluctuations, and $\Omega_m$ is the non-relativistic matter fraction. Using a prior on $\Omega_m$ based on the baryon acoustic oscillation measurements of DESI, we find $S_8=0.79\pm0.06$, in reasonable agreement with CMB constraints. We also find that, in the absence of this prior, the data favours a value of $\Omega_m=0.245\pm0.024$, that is 2.8$\sigma$ lower than \planck. This result is driven by the broadband shape of the galaxy auto-correlation, and may be affected by theoretical uncertainties in the HEFT power spectrum templates. We further reconstruct the low-redshift growth history, finding it to be compatible with the \planck predictions, as well as existing constraints from lensing tomography. Finally, we study our constraints on the HEFT bias parameters of the galaxy samples studied, finding them to be in reasonable agreement with coevolution predictions.

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arXiv2025

The impact of galaxy bias on cross-correlation tomography

The cross-correlation of galaxies at different redshifts with other tracers of the large-scale structure can be used to reconstruct the cosmic mean of key physical quantities, and their evolution over billions of years, at high precision. However, a correct interpretation of these measurements must ensure that they are independent of the clustering properties of the galaxy sample used. In this paper we explore different prescriptions to extract tomographic reconstruction measurements and use the FLAMINGO hydrodynamic simulations to show that a robust estimator, independent of the small-scale galaxy bias, can be constructed. We focus on the tomographic reconstruction of the halo bias-weighted electron pressure $\langle bP_e\rangle$ and star-formation density $\langle b\rho_{\rm SFR}\rangle$, which can be reconstructed from tomographic analysis of Sunyaev-Zel'dovich and cosmic infrared background maps, respectively. We show that these quantities can be reconstructed with an accuracy of 1-3\% over a wide range of redshifts, using different galaxy samples. We also show that these measurements can be accurately interpreted using the halo model, assuming a sufficiently reliable model can be constructed for the halo mass function, large-scale halo bias, and for the dependence of the physical quantities being reconstructed on halo mass.

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arXiv2025

Calibrating baryonic effects in cosmic shear with external data in the LSST era

Cosmological constraints derived from weak lensing (WL) surveys are limited by baryonic effects, which suppress the non-linear matter power spectrum on small scales. By combining WL measurements with data from external tracers of the gas around massive structures, it is possible to calibrate baryonic effects and, therefore, obtain more precise cosmological constraints. In this study, we generate mock data for a Stage-IV weak lensing survey such as the Legacy Survey of Space and Time (LSST), X-ray gas fractions, and stacked kinetic Sunyaev-Zel'dovich (kSZ) measurements, to jointly constrain cosmological and astrophysical parameters describing baryonic effects (using the Baryon Correction Model - BCM). First, using WL data alone, we quantify the level to which the BCM parameters will need to be constrained to recover the cosmological constraints obtained under the assumption of perfect knowledge of baryonic feedback. We identify the most relevant baryonic parameters and determine that they must be calibrated to a precision of $\sim 10$-$20\%$ to avoid significant degradation of the fiducial WL constraints. We forecast that long-term X-ray data from $\sim 5000$ clusters should be able to reach this threshold for the parameters that characterise the abundance of hot virialised gas. Constraining the distribution of ejected gas presents a greater challenge, however, but we forecast that long-term kSZ data from a CMB-S4-like experiment should achieve the level of precision required for full self-calibration.

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arXiv2025

Robust cosmic shear with small-scale nulling

Standard cosmological weak lensing analyses using cosmic shear are inevitably sensitive to small-scale, non-linear clustering from low-redshift structures. The need to adequately model the clustering of matter on this non-linear regime, accounting for both gravitational and baryonic effects, adds significant uncertainty to weak lensing studies, particularly in the context of near-future Stage-IV datasets. In this paper, inspired by previous work on so-called ``nulling'' techniques, we present a general method that selects the linear combinations of a given tomographic cosmic shear dataset that are least sensitive to small-scale non-linearities, by essentially suppressing the contribution from low-redshift structures. We apply this method to the latest public cosmic shear data from the Dark Energy Survey, DES-Y3, that corresponds to 3 years of observation, and show: a) that a large fraction of the signal is dominated by the single mode that is most affected by non-linear scales, and b) that removing this mode leads to a $\sim1\sigma$ upwards shift in the preferred value of $S_8\equiv\sigma_8\sqrt{\Omega_M/0.3}$, alleviating the tension with current CMB data. However, the removal of the most contaminated mode also results in a significant increase in the statistical uncertainties. Taking this into account, we find this shift to be compatible with a random fluctuation caused by removing this most-contaminated mode at the $\sim1.4\sigma$ level. We also show that this technique may be used by future Stage-IV surveys to mitigate the sensitivity of the final constraints to baryonic effects, trading precision for robustness.

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arXiv2024

A 1% accurate method to include baryonic effects in galaxy-galaxy lensing models

Galaxy clustering and galaxy-galaxy lensing are two of the main observational probes in Stage-IV large-scale structure surveys. Unfortunately, the complicated relationship between galaxies and matter limits the exploitation of this data. Galaxy bias models -- such as the hybrid Lagrangian bias expansion -- allow describing galaxy clustering down to scales as small as $k = 0.7h$/Mpc. However, the galaxy-matter cross-power spectra are already affected by baryons on these scales, directly impacting the modelling of galaxy-galaxy lensing. We propose to extend models of the galaxy-matter cross-power spectrum $P_{\rm gm}(k)$ (currently only accounting for dark matter) by including a baryonic correction inferred from the matter component ($S_{\rm mm}(k)$), so that $P_{\rm gm, full \, physics} (k) = \sqrt{S_{\rm mm}} P_{\rm gm, gravity \, only}$. We use the FLAMINGO simulations to measure the effect of baryons on the galaxy-matter cross-power spectrum and to assess the performance of our model. We perform a Bayesian analysis of synthetic data, implementing a model based on BACCO's hybrid Lagrangian bias expansion (for the nonlinear galaxy bias) and Baryon Correction Model. Ignoring baryons in the galaxy-matter cross-power spectrum leads to a biased inference of the galaxy bias, while ignoring baryons in both the galaxy-matter and matter-matter power spectra leads to a biased inference of both the galaxy bias and cosmological parameters. In contrast, our method is 1% accurate compared to all physics variations in FLAMINGO and on all scales described by hybrid perturbative models ($k < 0.7h$/Mpc). Moreover, our model leads to inferred bias and cosmological parameters compatible within 1$\sigma$ with their reference values. We anticipate that our method will be a promising candidate for analysing forthcoming Stage-IV survey data.

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arXiv2024

A Parameter-Masked Mock Data Challenge for Beyond-Two-Point Galaxy Clustering Statistics

The last few years have seen the emergence of a wide array of novel techniques for analyzing high-precision data from upcoming galaxy surveys, which aim to extend the statistical analysis of galaxy clustering data beyond the linear regime and the canonical two-point (2pt) statistics. We test and benchmark some of these new techniques in a community data challenge "Beyond-2pt", initiated during the Aspen 2022 Summer Program "Large-Scale Structure Cosmology beyond 2-Point Statistics," whose first round of results we present here. The challenge dataset consists of high-precision mock galaxy catalogs for clustering in real space, redshift space, and on a light cone. Participants in the challenge have developed end-to-end pipelines to analyze mock catalogs and extract unknown ("masked") cosmological parameters of the underlying $\Lambda$CDM models with their methods. The methods represented are density-split clustering, nearest neighbor statistics, BACCO power spectrum emulator, void statistics, LEFTfield field-level inference using effective field theory (EFT), and joint power spectrum and bispectrum analyses using both EFT and simulation-based inference. In this work, we review the results of the challenge, focusing on problems solved, lessons learned, and future research needed to perfect the emerging beyond-2pt approaches. The unbiased parameter recovery demonstrated in this challenge by multiple statistics and the associated modeling and inference frameworks supports the credibility of cosmology constraints from these methods. The challenge data set is publicly available and we welcome future submissions from methods that are not yet represented.

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arXiv2024

Cosmic shear with small scales: DES-Y3, KiDS-1000 and HSC-DR1

We present a cosmological analysis of the combination of the DES-Y3, KiDS-1000 and HSC-DR1 weak lensing samples under a joint harmonic-space pipeline making use of angular scales down to $\ell_{\rm max}=4500$, corresponding to significantly smaller scales ($\delta\theta\sim2.4'$) than those commonly used in cosmological weak lensing studies. We are able to do so by accurately modelling non-linearities and the impact of baryonic effects using Baccoemu. We find $S_8\equiv\sigma_8\sqrt{\Omega_{\rm m}/0.3}=0.795^{+0.015}_{-0.017}$, in relatively good agreement with CMB constraints from Planck (less than $\sim1.8\sigma$ tension), although we obtain a low value of $\Omega_{\rm m}=0.212^{+0.017}_{-0.032}$, in tension with Planck at the $\sim3\sigma$ level. We show that this can be recast as an $H_0$ tension if one parametrises the amplitude of fluctuations and matter abundance in terms of variables without hidden dependence on $H_0$. Furthermore, we find that this tension reduces significantly after including a prior on the distance-redshift relationship from BAO data, without worsening the fit. In terms of baryonic effects, we show that failing to model and marginalise over them on scales $\ell\lesssim2000$ does not significantly affect the posterior constraints for DES-Y3 and KiDS-1000, but has a mild effect on deeper samples, such as HSC-DR1. This is in agreement with our ability to only mildly constrain the parameters of the Baryon Correction Model with these data

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arXiv2024

syren-halofit: A fast, interpretable, high-precision formula for the $\Lambda$CDM nonlinear matter power spectrum

Rapid and accurate evaluation of the nonlinear matter power spectrum, $P(k)$, as a function of cosmological parameters and redshift is of fundamental importance in cosmology. Analytic approximations provide an interpretable solution, yet current approximations are neither fast nor accurate relative to numerical emulators. We use symbolic regression to obtain simple analytic approximations to the nonlinear scale, $k_\sigma$, the effective spectral index, $n_{\rm eff}$, and the curvature, $C$, which are required for the halofit model. We then re-optimise the coefficients of halofit to fit a wide range of cosmologies and redshifts. We explore the space of analytic expressions to fit the residuals between $P(k)$ and the optimised predictions of halofit. Our results are designed to match the predictions of EuclidEmulator2, but are validated against $N$-body simulations. Our symbolic expressions for $k_\sigma$, $n_{\rm eff}$ and $C$ have root mean squared fractional errors of 0.8%, 0.2% and 0.3%, respectively, for redshifts below 3 and a wide range of cosmologies. The re-optimised halofit parameters reduce the root mean squared fractional error (compared to EuclidEmulator2) from 3% to below 2% for wavenumbers $k=9\times10^{-3}-9 \, h{\rm Mpc^{-1}}$. We introduce syren-halofit (symbolic-regression-enhanced halofit), an extension to halofit containing a short symbolic correction which improves this error to 1%. Our method is 2350 and 3170 times faster than current halofit and hmcode implementations, respectively, and 2680 and 64 times faster than EuclidEmulator2 (which requires running class) and the BACCO emulator. We obtain comparable accuracy to EuclidEmulator2 and BACCO when tested on $N$-body simulations. Our work greatly increases the speed and accuracy of symbolic approximations to $P(k)$, making them significantly faster than their numerical counterparts without loss of accuracy.

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arXiv2023

A precise symbolic emulator of the linear matter power spectrum

Computing the matter power spectrum, $P(k)$, as a function of cosmological parameters can be prohibitively slow in cosmological analyses, hence emulating this calculation is desirable. Previous analytic approximations are insufficiently accurate for modern applications, so black-box, uninterpretable emulators are often used. We utilise an efficient genetic programming based symbolic regression framework to explore the space of potential mathematical expressions which can approximate the power spectrum and $\sigma_8$. We learn the ratio between an existing low-accuracy fitting function for $P(k)$ and that obtained by solving the Boltzmann equations and thus still incorporate the physics which motivated this earlier approximation. We obtain an analytic approximation to the linear power spectrum with a root mean squared fractional error of 0.2% between $k = 9\times10^{-3} - 9 \, h{\rm \, Mpc^{-1}}$ and across a wide range of cosmological parameters, and we provide physical interpretations for various terms in the expression. Our analytic approximation is 950 times faster to evaluate than camb and 36 times faster than the neural network based matter power spectrum emulator BACCO. We also provide a simple analytic approximation for $\sigma_8$ with a similar accuracy, with a root mean squared fractional error of just 0.1% when evaluated across the same range of cosmologies. This function is easily invertible to obtain $A_{\rm s}$ as a function of $\sigma_8$ and the other cosmological parameters, if preferred. It is possible to obtain symbolic approximations to a seemingly complex function at a precision required for current and future cosmological analyses without resorting to deep-learning techniques, thus avoiding their black-box nature and large number of parameters. Our emulator will be usable long after the codes on which numerical approximations are built become outdated.

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arXiv2023

Galaxy bias in the era of LSST: perturbative bias expansions

Upcoming imaging surveys will allow for high signal-to-noise measurements of galaxy clustering at small scales. In this work, we present the results of the LSST bias challenge, the goal of which is to compare the performance of different nonlinear galaxy bias models in the context of LSST Y10 data. Specifically, we compare two perturbative approaches, Lagrangian perturbation theory (LPT) and Eulerian PT (EPT) to two variants of Hybrid Effective Field Theory (HEFT), with our fiducial implementation of these models including terms up to second order in the bias expansion as well as nonlocal bias and deviations from Poissonian stochasticity. We consider different simulated galaxy samples and test the performance of the bias models in a tomographic joint analysis of LSST-Y10-like galaxy clustering, galaxy-galaxy-lensing and cosmic shear. We find both HEFT methods as well as LPT and EPT combined with non-perturbative predictions for the matter power spectrum to yield unbiased constraints on cosmological parameters up to at least a maximal scale of $k_{\mathrm{max}}=0.4 \; \mathrm{Mpc}^{-1}$ for all samples considered, even in the presence of assembly bias. While we find that we can reduce the complexity of the bias model for HEFT without compromising fit accuracy, this is not generally the case for the perturbative models. We find significant detections of non-Poissonian stochasticity in all cases considered, and our analysis shows evidence that small-scale galaxy clustering predominantly improves constraints on galaxy bias rather than cosmological parameters. These results therefore suggest that the systematic uncertainties associated with current nonlinear bias models are likely to be subdominant compared to other sources of error for tomographic analyses of upcoming photometric surveys, which bodes well for future galaxy clustering analyses using these high signal-to-noise data. [abridged]

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arXiv2023

DES Y3 cosmic shear down to small scales: constraints on cosmology and baryons

We present the first analysis of cosmic shear measured in DES Y3 that employs the entire range of angular scales in the data. To achieve this, we build upon recent advances in the theoretical modelling of weak lensing provided by a combination of $N$-body simulations, physical models of baryonic processes, and neural networks. Specifically, we use BACCOemu to model the linear and nonlinear matter power spectrum including baryonic physics, allowing us to robustly exploit scales smaller than those used by the DES Collaboration. We show that the additional data produce cosmological parameters that are tighter but consistent with those obtained from larger scales, while also constraining the distribution of baryons. In particular, we measure the mass scale at which haloes have lost half of their gas, $\log\,M_{\rm c}=14.38^{+0.60}_{-0.56}\log(h^{-1}{\rm M_{ \odot}})$, and a parameter that quantifies the weighted amplitudes of the present-day matter inhomogeneities, $S_8=0.799^{+0.023}_{-0.015}$. Our constraint on $S_8$ is statistically compatible with that inferred from the Planck satellite's data at the $0.9\sigma$ level. We find instead a $1.4\sigma$ shift in comparison to that from the official DES Y3 cosmic shear, because of different choices in the modelling of intrinsic alignment, non-linearities, baryons, and lensing shear ratios. We conclude that small scales in cosmic shear data contain valuable astrophysical and cosmological information and thus should be included in standard analyses.

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arXiv2022

The Bacco Simulation Project: Bacco Hybrid Lagrangian Bias Expansion Model in Redshift Space

We present an emulator that accurately predicts the power spectrum of galaxies in redshift space as a function of cosmological parameters. Our emulator is based on a 2nd-order Lagrangian bias expansion that is displaced to Eulerian space using cosmological $N$-body simulations. Redshift space distortions are then imprinted using the non-linear velocity field of simulated particles and haloes. We build the emulator using a forward neural network trained with the simulations of the BACCO project, which covers an 8-dimensional parameter space including massive neutrinos and dynamical dark energy. We show that our emulator provides unbiased cosmological constraints from the monopole, quadrupole, and hexadecapole of a mock galaxy catalogue that mimics the BOSS-CMASS sample down to nonlinear scales ($k\sim0.6$[$h/$Mpc]$^{3}$). This work opens up the possibility of robustly extracting cosmological information from small scales using observations of the large-scale structure of the Universe.

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arXiv2022

Statistics of biased tracers in variance-suppressed simulations

Cosmological simulations play an increasingly important role in analysing the observed large-scale structure of the Universe. Recently, they have been particularly important in building hybrid models that combine a perturbative bias expansion with displacement fields extracted from N-body simulations to describe the clustering of biased tracers. Here, we show that simulations that employ a technique referred to as "Fixing-and-pairing" (F&P) can dramatically improve the statistical precision of such hybrid models. Specifically, by numerical and analytic means, we show that F&P simulations provide unbiased estimates for all statistics employed by hybrid models while reducing, by up to two orders of magnitude, their uncertainty on large scales. This roughly implies that an EUCLID-like survey could be analysed using simulations of 2Gpc a side -- a 20% of the survey volume. Our work establishes the robustness of F&P for current hybrid theoretical models for galaxy clustering, an important step towards achieving an optimal exploitation of large-scale structure measurements.

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arXiv2021

Priors on Lagrangian bias parameters from galaxy formation modelling

We study the relations among the parameters of the hybrid Lagrangian bias expansion model, fitting biased auto and cross power spectra up to $k_{\rm max} = 0.7 \, h \, \mathrm{Mpc}^{-1}$. We consider $\sim 8000$ halo and galaxy samples, with different halo masses, redshifts, galaxy number densities, and varying the parameters of the galaxy formation model. Galaxy samples are obtained through state-of-the-art extended subhalo abundance matching techniques and include both stellar-mass and star-formation-rate selected galaxies. All of these synthetic galaxies samples are publicly available at https://bacco.dipc.org/galpk.html. We find that the hybrid Lagrangian bias model provides accurate fits to all of our halo and galaxy samples. The coevolution relations between galaxy bias parameters, although roughly compatible with those obtained for haloes, show systematic shifts and larger scatter. We explore possible sources of this difference in terms of dependence on halo occupation and assembly bias of each sample. The bias parameter relations displayed in this work can be used as a prior for future Bayesian analyses employing the hybrid Lagrangian bias expansion model.

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