arXiv subjects

Simeon Bird

Publications and source records attributed to Simeon Bird.

At least 55 records · Page 3Linked to original sources

arXiv2024

Investigating the mixing between two black hole populations in LIGO-Virgo-KAGRA GWTC-3

We introduce a population model to analyze the mixing between hypothesised power-law and $\sim 35 M_\odot$ Gaussian bump black hole populations in the latest gravitational wave catalog, GWTC-3, estimating their co-location and separation. We find a relatively low level of mixing, $3.1^{+5.0}_{-3.1}\%$, between the power-law and Gaussian populations, compared to the percentage of mergers containing two Gaussian bump black holes, $5.0^{+3.2}_{-1.7}\%$. Our analysis indicates that black holes within the Gaussian bump are generally separate from the power-law population, with only a minor fraction engaging in mixing and contributing to the $\mathcal{M} \sim 14 M_\odot$ peak in the chirp mass. This leads us to identify a distinct population of Binary Gaussian Black Holes (BGBHs) that arise from mergers within the Gaussian bump. We suggest that current theories for the formation of the massive $35 M_\odot$ Gaussian bump population may need to reevaluate the underlying mechanisms that drive the preference for BGBHs.

astro-ph.HE↗

arXiv2024

Cosmological Constraints from the eBOSS Lyman-$α$ Forest using the PRIYA Simulations

We present new cosmological parameter constraints from the eBOSS Lyman-$α$ forest survey. We use a new theoretical model and likelihood based on the PRIYA simulation suite. PRIYA is the first suite to resolve the Lyman-$α$ forest in a ($120$~Mpc/h~)$^3$ volume, using a multi-fidelity emulation technique. We use PRIYA to predict Lyman-$α$ forest observables with $\lesssim 1\%$ interpolation error over an $11$ dimensional ($9$ simulated, $2$ in post-processing) parameter space. We identify an internal tension within the flux power spectrum data. Once the discrepant data is removed, we find the primeval scalar spectral index measured at a pivot scale of $k_0 = 0.78$ Mpc$^{-1}$ to be $n_P = 1.009^{+0.027}_{-0.018}$ at 68\% confidence. This measurement from the Lyman-$α$ forest flux power spectrum alone is in reasonable agreement with Planck, and in tension with earlier eBOSS analyses. The amplitude of matter fluctuations is $σ_8 = 0.733^{+0.026}_{-0.029}$ at 68\% confidence, in agreement with Dark Energy Survey weak lensing measurements and other small-scale structure probes and in tension with CMB measurements from Planck and ACT. The effective optical depth to Lyman-$α$ photons from our pipeline is in good agreement with earlier high resolution measurements. We find a linear power at $z=3$ and $k = 0.009$ s/km of $Δ_L^2 = 0.302^{+0.024}_{-0.027}$ with a slope $n_\mathrm{eff} = -2.264^{+0.026}_{-0.018}$. Our flux power spectrum only chains prefer a low level of heating during helium reionization. When we add IGM temperature data we find $n_P = 0.983\pm 0.020$ and $σ_8 = 0.703^{+0.023}_{-0.027}$. Our chains prefer an early and long helium reionization event, as suggested by measurements from the helium Lyman-$α$ forest. In the near future we will use our pipeline to infer cosmological parameters from the DESI Lyman-$α$ data.

astro-ph.CO↗

arXiv2024

Forecast Cosmological Constraints with the 1D Wavelet Scattering Transform and the Lyman-$α$ forest

We make forecasts for the constraining power of the 1D Wavelet Scattering Transform (WST) when used with a Lyman-$α$ forest cosmology survey. Using mock simulations and a Fisher matrix, we show that there is considerable cosmological information in the scattering transform coefficients not captured by the flux power spectrum. We estimate mock covariance matrices assuming uncorrelated Gaussian pixel noise for each quasar, at a level drawn from a simple lognormal model. The extra information comes from a smaller estimated covariance in the first-order wavelet power, and from second-order wavelet coefficients which probe non-Gaussian information in the forest. Forecast constraints on cosmological parameters from the WST are more than an order of magnitude tighter than for the power spectrum, shrinking a $4D$ parameter space by a factor of $10^6$. Should these improvements be realised with DESI, inflationary running would be constrained to test common inflationary models predicting $α_s = - 6\times 10^{-4}$ and neutrino mass constraints would be improved enough for a $5-σ$ detection of the minimal neutrino mass.

astro-ph.CO↗

arXiv2024

MAGICS I. The First Few Orbits Encode the Fate of Seed Massive Black Hole Pairs

The elusive massive black hole (MBH) seeds stand to be revealed by the Laser Space Antenna Interferometer through mergers. As an aftermath of galaxy mergers, MBH coalescence is a vastly multi-scale process connected to galaxy formation. We introduce the "Massive black hole Assembly in Galaxies Informed by Cosmological Simulations" (MAGICS) suite, with galaxy/MBH properties and orbits recovered from large-volume cosmological simulation ASTRID. The simulations include subgrid star formation, supernovae feedback, and MBH accretion/feedback. In this first suite, we extract fifteen representative galaxy mergers with seed MBHs to examine their dynamics at an improved mass and spatial resolution (by $\sim2000$ and $\sim20$) and follow MBH orbits down to $\sim10\,\text{pc}$. We find that the seed MBH energy loss and orbital decay are largely governed by global torques induced by the galaxy merger process on scales resolvable by cosmological simulations. Specifically, pairs sink quickly if their orbits shrink rapidly below $1\,\text{kpc}$ during the first $\sim200\,\text{Myr}$ of pairing due to effective energy loss in major galaxy mergers, whereas MBHs gaining energy in minor galaxy mergers with head-on collisions are likely to stall. High initial eccentricities ($e_\text{init}>0.5$) and high stellar densities at kpc scales ($ρ_\text{star}>0.05\,M_\odot/\text{pc}^3$) also lead to most efficient decays. $\sim50\%$ high-redshift seed MBH pairs experience consecutive galaxy mergers and are more likely to stall at $\sim1\,\text{kpc}$. For a subset of systems, we carry out N-Body re-simulations until binary formation and find that some stalled systems merge at high-z when embedded in sufficient nuclear star clusters.

astro-ph.GA↗

arXiv2024

Astrometric Microlensing by Primordial Black Holes with The Roman Space Telescope

Primordial Black Holes (PBHs) could explain some fraction of dark matter and shed light on many areas of early-universe physics. Despite over half a century of research interest, a PBH population has so far eluded detection. The most competitive constraints on the fraction of dark matter comprised of PBHs ($f_{\rm DM}$) in the $(10^{-9}-10)M_{\odot}$ mass-ranges come from photometric microlensing and bound $f_{\rm DM}\lesssim10^{-2}-10^{-1}$. With the advent of the Roman Space Telescope with its sub-milliarcsecond (mas) astrometric capabilities and its planned Galactic Bulge Time Domain Survey (GBTDS), detecting astrometric microlensing signatures will become routine. Compared with photometric microlensing, astrometric microlensing signals are sensitive to different lens masses-distance configurations and contains different information, making it a complimentary lensing probe. At sub-mas astrometric precision, astrometric microlensing signals are typically detectable at larger lens-source separations than photometric signals, suggesting a microlensing detection channel of pure astrometric events. We use a Galactic simulation to predict the number of detectable microlensing events during the GBTDS via this pure astrometric microlensing channel. Assuming an absolute astrometric precision floor for bright stars of 0.1 mas for the GBTDS, we find that the number of detectable events peaks at $\approx 10^{3} f_{\rm DM}$ for a population of $ 1 M_{\odot}$ PBHs and tapers to $\approx 10f_{\rm DM}$ and $\approx 100f_{\rm DM}$ at $10^{-4}M_{\odot}$ and $10^{3}M_{\odot}$, respectively. Accounting for the distinguishability of PBHs from Stellar lenses, we conclude the GBTDS will be sensitive to a PBH population at $f_{\rm DM}$ down to $\approx10^{-1}-10^{-3}$ for $(10^{-1}-10^{2})M_{\odot}$ likely yielding novel PBH constraints.

astro-ph.GA↗

arXiv2024

PRIYA: A New Suite of Lyman-alpha Forest Simulations for Cosmology

We present the PRIYA suite of cosmological simulations, based on the code and hydrodynamic model of the ASTRID simulation, and designed for cosmological analyses of the Lyman-$α$ forest. Our simulation suite spans a $9$-dimensional parameter space, including $4$ cosmological parameters and $5$ astrophysical/thermal parameters. We have run $48$ low fidelity simulations with $1536^3$ particles in a $120$ Mpc/h box and $3$ high fidelity simulations with $3072^3$ particles in a $120$ Mpc/h box. All our simulations include a full physics model for galaxy formation, including supernova and AGN feedback, and thus also contain a realistic population of DLAs. We advance on earlier simulations suites by larger particle loads, by incorporating new physical models for patchy hydrogen and helium reionization, and by self-consistently incorporating a model for AGN feedback. We show that patchy helium reionization imprints an excess in the 1D flux power spectrum on large scales, which may allow future measurements of helium reionization bubble sizes. Simulation parameters are chosen based on a Latin hypercube design and a Gaussian process is used to interpolate to arbitrary parameter combinations. We build a multi-fidelity emulator for the 1D flux power spectrum and the mean IGM temperature. We show that our final interpolation error is $< 1\%$ and that our simulations produce a flux power spectrum converged at the percent level for $z=5.4$ - $2.2$. Our simulation suite will be used to interpret Lyman-$α$ forest 1D flux power spectra from SDSS and future DESI data releases.

astro-ph.CO↗

arXiv2023

An Exploration of AGN and Stellar Feedback Effects in the Intergalactic Medium via the Low Redshift Lyman-$α$ Forest

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

astro-ph.GA↗

arXiv2023

Boosting Line Intensity Map Signal-to-Noise with the Ly-$α$ Forest Cross-Correlation

We forecast the prospects for cross-correlating future line intensity mapping (LIM) surveys with the current and future Ly-$α$ forest data. We use large cosmological hydrodynamic simulations to model the expected emission signal for the CO rotational transition in the COMAP LIM experiment at the 5-year benchmark and the Ly-$α$ forest absorption signal for various surveys, including eBOSS, DESI, and PFS. We show that CO$\times$Ly-$α$ forest can significantly enhance the detection signal-to-noise ratio of CO, with a $200$ to $300 \%$ improvement when cross-correlated with the forest observed in the Prime Focus Spectrograph (PFS) survey and a $50$ to $75\%$ enhancement for the currently available eBOSS or the upcoming DESI observations. We compare to the signal-to-noise improvements expected for a galaxy survey and show that CO$\times$Ly-$α$ is competitive with even a spectroscopic galaxy survey in raw signal-to-noise. Furthermore, our study suggests that the clustering of CO emission is tightly constrained by CO$\times$Ly-$α$ forest, due to the increased signal-to-noise ratio and the simplicity of Ly-$α$ absorption power spectrum modeling. Any foreground contamination or systematics are expected not to be shared between LIM surveys and Ly-$α$ forest observations; this provides an unbiased inference. Our findings highlight the potential benefits of utilizing the Ly-$α$ forest to aid in the initial detection of signals in line intensity experiments. For example, we also estimate that [CII]$\times$Ly-$α$ forest measurements from EXCLAIM and DESI/eBOSS, respectively, should have a larger signal-to-noise ratio than planned [CII]$\times$quasar observations by about an order of magnitude. Our results can be readily applied to actual data thanks to the observed quasar spectra in eBOSS Stripe 82, which overlaps with several LIM surveys.

astro-ph.CO↗

arXiv2023

LATIS: Constraints on the Galaxy-halo Connection at $z \sim 2.5$ from Galaxy-galaxy and Galaxy-Ly$α$ Clustering

The connection between galaxies and dark matter halos is often quantified using the stellar mass-halo mass (SMHM) relation. Optical and near-infrared imaging surveys have led to a broadly consistent picture of the evolving SMHM relation based on measurements of galaxy abundances and angular correlation functions. Spectroscopic surveys at $z \gtrsim 2$ can also constrain the SMHM relation via the galaxy autocorrelation function and through the cross-correlation between galaxies and Ly$α$ absorption measured in transverse sightlines; however, such studies are very few and have produced some unexpected or inconclusive results. We use $\sim$3000 spectra of $z\sim2.5$ galaxies from the Lyman-alpha Tomography IMACS Survey (LATIS) to measure the galaxy-galaxy and galaxy-Ly$α$ correlation functions in four bins of stellar mass spanning $10^{9.2} \lesssim M_* / M_{\odot} \lesssim 10^{10.5}$. Parallel analyses of the MultiDark N-body and ASTRID hydrodynamic cosmological simulations allow us to model the correlation functions, estimate covariance matrices, and infer halo masses. We find that results of the two methods are mutually consistent and are broadly in accord with standard SMHM relations. This consistency demonstrates that we are able to accurately measure and model Ly$α$ transmission fluctuations $δ_F$ in LATIS. We also show that the galaxy-Ly$α$ cross-correlation, a free byproduct of optical spectroscopic galaxy surveys at these redshifts, can constrain halo masses with similar precision to galaxy-galaxy clustering.

astro-ph.CO↗

arXiv2023

Disentangling the Black Hole Mass Spectrum with Photometric Microlensing Surveys

From the formation mechanisms of stars and compact objects to nuclear physics, modern astronomy frequently leverages surveys to understand populations of objects to answer fundamental questions. The population of dark and isolated compact objects in the Galaxy contains critical information related to many of these topics, but is only practically accessible via gravitational microlensing. However, photometric microlensing observables are degenerate for different types of lenses, and one can seldom classify an event as involving either a compact object or stellar lens on its own. To address this difficulty, we apply a Bayesian framework that treats lens type probabilistically and jointly with a lens population model. This method allows lens population characteristics to be inferred despite intrinsic uncertainty in the lens-class of any single event. We investigate this method's effectiveness on a simulated ground-based photometric survey in the context of characterizing a hypothetical population of primordial black holes (PBHs) with an average mass of $30 M_{\odot}$. On simulated data, our method outperforms current black hole (BH) lens identification pipelines and characterizes different subpopulations of lenses while jointly constraining the PBH contribution to dark matter to ${\approx}25$\%. Key to robust inference, our method can marginalize over population model uncertainty. We find the lower mass cutoff for stellar origin BHs, a key observable in understanding the BH mass gap, particularly difficult to infer in our simulations. This work lays the foundation for cutting-edge PBH abundance constraints to be extracted from current photometric microlensing surveys.

astro-ph.IM↗

arXiv2023

MF-Box: Multi-fidelity and multi-scale emulation for the matter power spectrum

We introduce MF-Box, an extended version of MFEmulator, designed as a fast surrogate for power spectra, trained using N-body simulation suites from various box sizes and particle loads. To demonstrate MF-Box's effectiveness, we design simulation suites that include low-fidelity suites (L1 and L2) at $256 \,\mathrm{Mpc}/h$ and $100 \,\mathrm{Mpc}/h$, each with $128^3$ particles, and a high-fidelity suite (HF) with $512^3$ particles at $256 \,\mathrm{Mpc}/h$, representing a higher particle load compared to the low-fidelity suites. MF-Box acts as a probabilistic resolution correction function, learning most of the cosmological dependencies from L1 and L2 simulations and rectifying resolution differences with just 3 HF simulations using a Gaussian process. MF-Box successfully emulates power spectra from our HF testing set with a relative error of $< 3\%$ up to $k \simeq 7 \,h/\mathrm{Mpc}$ at $z \in [0, 3]$, while maintaining a cost similar to our previous multi-fidelity approach, which was accurate only up to $z = 1$. The addition of an extra low-fidelity node in a smaller box significantly improves emulation accuracy for MF-Box at $k > 2 \,h/\mathrm{Mpc}$, increasing it by a factor of $10$. We conduct an error analysis of MF-Box based on computational budget, providing guidance for optimizing budget allocation per fidelity node. Our proposed MF-Box enables future surveys to efficiently combine simulation suites of varying quality, effectively expanding the range of emulation capabilities while ensuring cost efficiency.

astro-ph.CO↗

arXiv2023

Machine Learning Uncovers the Universe's Hidden Gems: A Comprehensive Catalogue of CIV Absorption Lines in SDSS DR12

We assemble the largest CIV absorption line catalogue to date, leveraging machine learning, specifically Gaussian processes, to remove the need for visual inspection for detecting CIV absorbers. The catalogue contains probabilities classifying the reliability of the absorption system within a quasar spectrum. Our training set was a sub-sample of DR7 spectra that had no detectable CIV absorption in a large visually inspected catalogue. We used Bayesian model selection to decide between our continuum model and our absorption-line models. Using a random hold-out sample of 1301 spectra from all of the 26,030 investigated spectra in DR7 CIV catalogue, we validated our pipeline and obtained an 87% classification performance score. We found good purity and completeness values, both ~80%, when a probability of ~95% is used as the threshold. Our pipeline obtained similar CIV redshifts and rest equivalent widths to our training set. Applying our algorithm to 185,425 selected quasar spectra from SDSS DR12, we produce a catalogue of 113,775 CIV doublets with at least 95% confidence. Our catalogue provides maximum a posteriori values and credible intervals for CIV redshift, column density, and Doppler velocity dispersion. We detect CIV absorption systems with a redshift range of 1.37 $\!-\!$ 5.1, including 33 systems with a redshift larger than 5 and 549 absorbers systems with a rest equivalent width greater than 2 A at more than 95% confidence. Our catalogue can be used to investigate the physical properties of the circumgalactic and intergalactic media.

astro-ph.GA↗

arXiv2023

Modeling Quasar Proximity Zones in a Realistic Cosmological Environment with a Self-consistent Light Curve

We study quasar proximity zones in a simulation that includes a self-consistent quasar formation model and realistic IGM environments. The quasar host halo is $10^{13}\ M_{\mathrm{\odot}}$ at $z=6$, more massive than typical halos studied in previous work. Between $6<z<7.5$, the quasar luminosity varies rapidly, with a mean magnitude of $M_{UV,mean}=-24.8$ and the fluctuation reaching up to two orders of magnitude. Using this light curve to post-process the dense environment around the quasar, we find that the proximity zone size ($R_{p}$) ranges between $0.5-5$ pMpc. We show that the light curve variability causes a similar degree of scatter in $R_{p}$ as does the density fluctuation, both of which result in a standard deviation of $\sim 0.3$ pMpc). The $R_{p}$ traces the light curve fluctuations closely but with a time delay of $\sim 10^4\ \mathrm{yr}$, breaking the correspondence between the $R_{p}$ and the contemporaneous $M_{UV}$. This also indicates that we can only infer quasar activity within the past $\sim 10^4$ years instead of the integrated lifetime from $R_{p}$ in the later part of cosmic reionization. Compared with the variable light curve, a constant light curve underestimates the $R_{p}$ by 13% at the dim end ($M_{UV}\sim -23.5$), and overestimates the $R_{p}$ by 30% at the bright end ($M_{UV}\sim -26$). By calculating the $R_{p}$ generated by a number of quasars, we show that variable light curves predict a wider $R_{p}$ distribution than lightbulb models, and readily explain the extremely small $R_{p}$ values that have been observed.

astro-ph.CO↗

arXiv2023

Efficient Reionization in a Large Hydrodynamic Galaxy Formation Simulation

Accuracy in the topology and statistics of a simulated Epoch of Reionization (EoR) are vital to draw connections between observations and physical processes. While full radiative transfer models produce the most accurate reionization models, they are highly computationally expensive, and are infeasible for the largest cosmological simulations. Instead, large simulations often include EoR models that are pre-computed via the initial density field, or post-processed where feedback effects are ignored. We introduce Astrid-ES, a resimulation of the Astrid epoch of reionisation $20 > z > 5.5$ which includes an on-the-fly excursion-set reionization algorithm. Astrid-ES produces more accurate reionization histories without significantly impacting the computational time. This model directly utilises the star particles produced in the simulation to calculate the EoR history and includes a UV background which heats the gas particles after their reionization. We contrast the reionization topology and statistics in Astrid-ES with the previously employed parametric reionisation model, finding that in Astrid-ES, ionised regions are more correlated with galaxies, and the 21cm power-spectrum shows an increase in large scale power. We calculate the relation between the size of HII regions and the UV luminosity of the brightest galaxy within them. Prior to the overlap phase, we find a power-law fit of $\mathrm{log} (R) = -0.314 M_\mathrm{UV} - 2.550 \mathrm{log}(1+z) + 7.408$ with a standard deviation $σ_R < 0.15 \mathrm{dex}$ across all mass bins. We also examine the properties of halos throughout reionization, finding that while the properties of halos in the simulation are correlated with the redshift of reionisation, they are not greatly affected by reionisation itself.

astro-ph.CO↗

arXiv2023

Fly-by galaxy encounters with multiple black holes produce star-forming linear wakes

We look for simulated star-forming linear wakes such as the one recently discovered by van Dokkum et al. (2023) in the cosmological hydrodynamical simulation ASTRID. Amongst the runaway black holes in ASTRID, none are able to produce clear star-forming wakes. Meanwhile, fly-by encounters, typically involving a compact galaxy (with a central black hole) and a star-forming galaxy (with a duo of black holes) reproduce remarkably well many of the key properties (its length and linearity; recent star formation, etc.) of the observed star-forming linear feature. We predict the feature to persist for approximately 100 Myr in such a system and hence constitute a rare event. The feature contains a partly stripped galaxy (with $M_{\rm gal}=10^9 \sim 10^{10}M_\odot$) and a dual BH system ($M_{\rm BH}=10^5 \sim 10^7\,M_\odot$) in its brightest knot. X-ray emission from AGN in the knot should be detectable in such systems. After $100\sim 200\,{\rm Myrs}$ from the first fly-by, the galaxies merge leaving behind a triple black hole system in a (still) actively star-forming early-type remnant of mass $\sim 5\times 10^{10}\,M_\odot$. Follow-up JWST observations may be key for revealing the nature of these linear features by potentially detecting the older stellar populations constituting the bright knot. Confirmation of such detections may therefore help discriminate a fly-by encounter from a massive BH wake to reveal the origin of such features.

astro-ph.GA↗

arXiv2023

AI-assisted super-resolution cosmological simulations III: Time evolution

In this work, we extend our recently developed super-resolution (SR) model for cosmological simulations to produce fully time consistent evolving representations of the particle phase-space distribution. We employ a style-based constrained generative adversarial network (Style-GAN) where the changing cosmic time is an input style parameter to the network. The matter power spectrum and halo mass function agree well with results from high-resolution N-body simulations over the full trained redshift range ($10 \le z \le 0$). Furthermore, we assess the temporal consistency of our SR model by constructing halo merger trees. We examine progenitors, descendants and mass growth along the tree branches. All statistical indicators demonstrate the ability of our SR model to generate satisfactory high-resolution simulations based on low-resolution inputs.

astro-ph.CO↗

arXiv2023

Snowmass2021 Cosmic Frontier White Paper: Dark Matter Physics from Halo Measurements

The non-linear process of cosmic structure formation produces gravitationally bound overdensities of dark matter known as halos. The abundances, density profiles, ellipticities, and spins of these halos can be tied to the underlying fundamental particle physics that governs dark matter at microscopic scales. Thus, macroscopic measurements of dark matter halos offer a unique opportunity to determine the underlying properties of dark matter across the vast landscape of dark matter theories. This white paper summarizes the ongoing rapid development of theoretical and experimental methods, as well as new opportunities, to use dark matter halo measurements as a pillar of dark matter physics.

hep-ph↗

arXiv2023

The CAMELS project: Expanding the galaxy formation model space with new ASTRID and 28-parameter TNG and SIMBA suites

We present CAMELS-ASTRID, the third suite of hydrodynamical simulations in the Cosmology and Astrophysics with MachinE Learning (CAMELS) project, along with new simulation sets that extend the model parameter space based on the previous frameworks of CAMELS-TNG and CAMELS-SIMBA, to provide broader training sets and testing grounds for machine-learning algorithms designed for cosmological studies. CAMELS-ASTRID employs the galaxy formation model following the ASTRID simulation and contains 2,124 hydrodynamic simulation runs that vary 3 cosmological parameters ($Ω_m$, $σ_8$, $Ω_b$) and 4 parameters controlling stellar and AGN feedback. Compared to the existing TNG and SIMBA simulation suites in CAMELS, the fiducial model of ASTRID features the mildest AGN feedback and predicts the least baryonic effect on the matter power spectrum. The training set of ASTRID covers a broader variation in the galaxy populations and the baryonic impact on the matter power spectrum compared to its TNG and SIMBA counterparts, which can make machine-learning models trained on the ASTRID suite exhibit better extrapolation performance when tested on other hydrodynamic simulation sets. We also introduce extension simulation sets in CAMELS that widely explore 28 parameters in the TNG and SIMBA models, demonstrating the enormity of the overall galaxy formation model parameter space and the complex non-linear interplay between cosmology and astrophysical processes. With the new simulation suites, we show that building robust machine-learning models favors training and testing on the largest possible diversity of galaxy formation models. We also demonstrate that it is possible to train accurate neural networks to infer cosmological parameters using the high-dimensional TNG-SB28 simulation set.

astro-ph.CO↗