arXiv subjects

Shy Genel

Publications and source records attributed to Shy Genel.

At least 73 records · Page 4Linked to original sources

arXiv2022

Breaking baryon-cosmology degeneracy with the electron density power spectrum

Uncertain feedback processes in galaxies affect the distribution of matter, currently limiting the power of weak lensing surveys. If we can identify cosmological statistics that are robust against these uncertainties, or constrain these effects by other means, then we can enhance the power of current and upcoming observations from weak lensing surveys such as DES, Euclid, the Rubin Observatory, and the Roman Space Telescope. In this work, we investigate the potential of the electron density auto-power spectrum as a robust probe of cosmology and baryonic feedback. We use a suite of (magneto-)hydrodynamic simulations from the CAMELS project and perform an idealized analysis to forecast statistical uncertainties on a limited set of cosmological and physically-motivated astrophysical parameters. We find that the electron number density auto-correlation, measurable through either kinematic Sunyaev-Zel'dovich observations or through Fast Radio Burst dispersion measures, provides tight constraints on $Ω_{m}$ and the mean baryon fraction in intermediate-mass halos, $\bar{f}_{\mathrm{bar}}$. By obtaining an empirical measure for the associated systematic uncertainties, we find these constraints to be largely robust to differences in baryonic feedback models implemented in hydrodynamic simulations. We further discuss the main caveats associated with our analysis, and point out possible directions for future work.

astro-ph.CO↗

arXiv2022

Cosmology with one galaxy?

Galaxies can be characterized by many internal properties such as stellar mass, gas metallicity, and star-formation rate. We quantify the amount of cosmological and astrophysical information that the internal properties of individual galaxies and their host dark matter halos contain. We train neural networks using hundreds of thousands of galaxies from 2,000 state-of-the-art hydrodynamic simulations with different cosmologies and astrophysical models of the CAMELS project to perform likelihood-free inference on the value of the cosmological and astrophysical parameters. We find that knowing the internal properties of a single galaxy allow our models to infer the value of $Ω_{\rm m}$, at fixed $Ω_{\rm b}$, with a $\sim10\%$ precision, while no constraint can be placed on $σ_8$. Our results hold for any type of galaxy, central or satellite, massive or dwarf, at all considered redshifts, $z\leq3$, and they incorporate uncertainties in astrophysics as modeled in CAMELS. However, our models are not robust to changes in subgrid physics due to the large intrinsic differences the two considered models imprint on galaxy properties. We find that the stellar mass, stellar metallicity, and maximum circular velocity are among the most important galaxy properties to determine the value of $Ω_{\rm m}$. We believe that our results can be explained taking into account that changes in the value of $Ω_{\rm m}$, or potentially $Ω_{\rm b}/Ω_{\rm m}$, affect the dark matter content of galaxies. That effect leaves a distinct signature in galaxy properties to the one induced by galactic processes. Our results suggest that the low-dimensional manifold hosting galaxy properties provides a tight direct link between cosmology and astrophysics.

astro-ph.CO↗

arXiv2022

The MUSE Hubble Ultra Deep Field Survey XVI. The angular momentum of low-mass star-forming galaxies. A cautionary tale and insights from TNG50

We investigate the specific angular momentum (sAM) $ j(<r)$ profiles of intermediate redshift ($0.4<z<1.4$) star-forming galaxies (SFGs) in the relatively unexplored regime of low masses (down to $M_\star\sim 10^8$M$_{\odot}$), and small sizes (down to $R_{\rm e}\sim 1.5$ kpc) and characterize the sAM scaling relation and its redshift evolution. We have developed a 3D methodology to constrain sAM profiles of the star-forming gas using a forward modeling approach with \galpak{} that incorporates the effects of beam smearing, yielding the intrinsic morpho-kinematic properties even with limited spatial resolution data. Using mock observations from the TNG50 simulation, we find that our 3D methodology robustly recovers the star formation rate (SFR)-weighted $j(<r)$ profiles down to low effective signal-to-noise ratio (SNR) of $\gtrapprox3$. We applied our methodology blindly to a sample of 494 \OII{}-selected SFGs in the MUSE Ultra Deep Field (UDF) 9~arcmin$^2$ mosaic data, covering the unexplored $8<\log M_*/$M$_{\odot}<9$ mass range. We find that the (SFR-weighted) sAM relation follows $j\propto M_\star^α$ with an index $α$ varying from $α=0.3$ to $α=0.5$, from $\log M_\star/$M$_{\odot}=8$ to $\log M_*/$M$_{\odot}=10.5$. The UDF sample supports a redshift evolution consistent with the $(1+z)^{-0.5}$ expectation from a Universe in expansion. The scatter of the sAM sequence is a strong function of the dynamical state with $\log j|_{M_*}\propto 0.65 \times \log(V_{\rm max}/σ)$ where $σ$ is the velocity dispersion at $2 R_{\rm e}$. In TNG50, SFGs also form a $j-M_{\star}-(V/σ)$ plane but it correlates more with galaxy size than with morphological parameters. Our results suggest that SFGs might experience a dynamical transformation before their morphological transformation to becoming passive via either merging or secular evolution.

astro-ph.GA↗

arXiv2022

The CAMELS project: public data release

The Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project was developed to combine cosmology with astrophysics through thousands of cosmological hydrodynamic simulations and machine learning. CAMELS contains 4,233 cosmological simulations, 2,049 N-body and 2,184 state-of-the-art hydrodynamic simulations that sample a vast volume in parameter space. In this paper we present the CAMELS public data release, describing the characteristics of the CAMELS simulations and a variety of data products generated from them, including halo, subhalo, galaxy, and void catalogues, power spectra, bispectra, Lyman-$α$ spectra, probability distribution functions, halo radial profiles, and X-rays photon lists. We also release over one thousand catalogues that contain billions of galaxies from CAMELS-SAM: a large collection of N-body simulations that have been combined with the Santa Cruz Semi-Analytic Model. We release all the data, comprising more than 350 terabytes and containing 143,922 snapshots, millions of halos, galaxies and summary statistics. We provide further technical details on how to access, download, read, and process the data at \url{https://camels.readthedocs.io}.

astro-ph.CO↗

arXiv2021

A Comparison of Circumgalactic MgII Absorption between the TNG50 Simulation and the MEGAFLOW Survey

The circumgalactic medium (CGM) contains information on gas flows around galaxies, such as accretion and supernova-driven winds, which are difficult to constrain from observations alone. Here, we use the high-resolution TNG50 cosmological magnetohydrodynamical simulation to study the properties and kinematics of the CGM around star-forming galaxies in $10^{11.5}-10^{12}\;M_{\odot}$ halos at $z\simeq$ 1 using mock MgII absorption lines, which we generate by postprocessing halos to account for photoionization in the presence of a UV background. We find that the MgII gas is a very good tracer of the cold CGM, which is accreting inward at inflow velocities of up to 50 km s$^{-1}$. For sight lines aligned with the galaxy's major axis, we find that MgII absorption lines are kinematically shifted due to the cold CGM's significant corotation at speeds up to 50% of the virial velocity for impact parameters up to 60 kpc. We compare mock MgII spectra to observations from the MusE GAs FLow and Wind (MEGAFLOW) survey of strong MgII absorbers ($\rm{EW}^{2796Å}_{0}>0.5 \; Å$). After matching the equivalent-width (EW) selection, we find that the mock MgII spectra reflect the diversity of observed kinematics and EWs from MEGAFLOW, even though the sight lines probe a very small fraction of the CGM. MgII absorption in higher-mass halos is stronger and broader than in lower-mass halos but has qualitatively similar kinematics. The median-specific angular momentum of the MgII CGM gas in TNG50 is very similar to that of the entire CGM and only differs from non-CGM components of the halo by normalization factors of $\lesssim$ 1 dex.

astro-ph.GA↗

arXiv2021

Weighing the Milky Way and Andromeda with Artificial Intelligence

We present new constraints on the masses of the halos hosting the Milky Way and Andromeda galaxies derived using graph neural networks. Our models, trained on thousands of state-of-the-art hydrodynamic simulations of the CAMELS project, only make use of the positions, velocities and stellar masses of the galaxies belonging to the halos, and are able to perform likelihood-free inference on halo masses while accounting for both cosmological and astrophysical uncertainties. Our constraints are in agreement with estimates from other traditional methods.

astro-ph.GA↗

arXiv2021

Supermassive black holes in cosmological simulations II: the AGN population and predictions for upcoming X-ray missions

In large-scale hydrodynamical cosmological simulations, the fate of massive galaxies is mainly dictated by the modeling of feedback from active galactic nuclei (AGN). The amount of energy released by AGN feedback is proportional to the mass that has been accreted onto the BHs, but the exact sub-grid modeling of AGN feedback differs in all simulations. Whilst modern simulations reliably produce populations of quiescent massive galaxies at z<2, it is also crucial to assess the similarities and differences of the responsible AGN populations. Here, we compare the AGN population of the Illustris, TNG100, TNG300, Horizon-AGN, EAGLE, and SIMBA simulations. The AGN luminosity function (LF) varies significantly between simulations. Although in agreement with current observational constraints at z=0, at higher redshift the agreement of the LFs deteriorates with most simulations producing too many AGN of L_{x, 2-10 keV}~10^43-10^44 erg/s. AGN feedback in some simulations prevents the existence of any bright AGN with L_{x, 2-10 keV}>=10^45 erg/s (although this is sensitive to AGN variability), and leads to smaller fractions of AGN in massive galaxies than in the observations at z<=2. We find that all the simulations fail at producing a number density of AGN in good agreement with observational constraints for both luminous (L_{x, 2-10 keV}~10^43-10^45 erg/s) and fainter (L_{x, 2-10 keV}~10^42-10^43 erg/s) AGN, and at both low and high redshift. These differences can aid us in improving future BH and galaxy subgrid modeling in simulations. Upcoming X-ray missions (e.g., Athena, AXIS, and LynX) will bring faint AGN to light and new powerful constraints. After accounting for AGN obscuration, we find that the predicted number density of detectable AGN in future surveys spans at least one order of magnitude across the simulations, at any redshift.

astro-ph.GA↗

arXiv2021

Supermassive black holes in cosmological simulations I: M_BH-M_star relation and black hole mass function

The past decade has seen significant progress in understanding galaxy formation and evolution using large-scale cosmological simulations. While these simulations produce galaxies in overall good agreement with observations, they employ different sub-grid models for galaxies and supermassive black holes (BHs). We investigate the impact of the sub-grid models on the BH mass properties of the Illustris, TNG100, TNG300, Horizon-AGN, EAGLE, and SIMBA simulations, focusing on the M_BH-M_star relation and the BH mass function. All simulations predict tight M_BH-M_star relations, and struggle to produce the lowest (M_BH< 10^7.5 Msun) in galaxies of M_star~10^10.5-10^11.5 Msun. While the time evolution of the mean M_BH-M_star relation is mild (<1 dex in BH mass for 0 10^9 Msun in most of the simulations. The BH mass function is dominated by efficiently accreting BHs (log10 f_Edd >-2$) at high redshifts, and transitions progressively from the high-mass to the low-mass end to be governed by inactive BHs. The transition time and the contribution of active BHs are different among the simulations, and can be used to evaluate models against observations.

astro-ph.GA↗

arXiv2021

The CAMELS Multifield Dataset: Learning the Universe's Fundamental Parameters with Artificial Intelligence

We present the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) Multifield Dataset, CMD, a collection of hundreds of thousands of 2D maps and 3D grids containing many different properties of cosmic gas, dark matter, and stars from 2,000 distinct simulated universes at several cosmic times. The 2D maps and 3D grids represent cosmic regions that span $\sim$100 million light years and have been generated from thousands of state-of-the-art hydrodynamic and gravity-only N-body simulations from the CAMELS project. Designed to train machine learning models, CMD is the largest dataset of its kind containing more than 70 Terabytes of data. In this paper we describe CMD in detail and outline a few of its applications. We focus our attention on one such task, parameter inference, formulating the problems we face as a challenge to the community. We release all data and provide further technical details at https://camels-multifield-dataset.readthedocs.io.

cs.LG↗

arXiv2021

Robust marginalization of baryonic effects for cosmological inference at the field level

We train neural networks to perform likelihood-free inference from $(25\,h^{-1}{\rm Mpc})^2$ 2D maps containing the total mass surface density from thousands of hydrodynamic simulations of the CAMELS project. We show that the networks can extract information beyond one-point functions and power spectra from all resolved scales ($\gtrsim 100\,h^{-1}{\rm kpc}$) while performing a robust marginalization over baryonic physics at the field level: the model can infer the value of $Ω_{\rm m} (\pm 4\%)$ and $σ_8 (\pm 2.5\%)$ from simulations completely different to the ones used to train it.

astro-ph.CO↗

arXiv2021

Multifield Cosmology with Artificial Intelligence

Astrophysical processes such as feedback from supernovae and active galactic nuclei modify the properties and spatial distribution of dark matter, gas, and galaxies in a poorly understood way. This uncertainty is one of the main theoretical obstacles to extract information from cosmological surveys. We use 2,000 state-of-the-art hydrodynamic simulations from the CAMELS project spanning a wide variety of cosmological and astrophysical models and generate hundreds of thousands of 2-dimensional maps for 13 different fields: from dark matter to gas and stellar properties. We use these maps to train convolutional neural networks to extract the maximum amount of cosmological information while marginalizing over astrophysical effects at the field level. Although our maps only cover a small area of $(25~h^{-1}{\rm Mpc})^2$, and the different fields are contaminated by astrophysical effects in very different ways, our networks can infer the values of $Ω_{\rm m}$ and $σ_8$ with a few percent level precision for most of the fields. We find that the marginalization performed by the network retains a wealth of cosmological information compared to a model trained on maps from gravity-only N-body simulations that are not contaminated by astrophysical effects. Finally, we train our networks on multifields -- 2D maps that contain several fields as different colors or channels -- and find that not only they can infer the value of all parameters with higher accuracy than networks trained on individual fields, but they can constrain the value of $Ω_{\rm m}$ with higher accuracy than the maps from the N-body simulations.

astro-ph.CO↗

arXiv2021

Inpainting hydrodynamical maps with deep learning

From 1,000 hydrodynamic simulations of the CAMELS project, each with a different value of the cosmological and astrophysical parameters, we generate 15,000 gas temperature maps. We use a state-of-the-art deep convolutional neural network to recover missing data from those maps. We mimic the missing data by applying regular and irregular binary masks that cover either $15\%$ or $30\%$ of the area of each map. We quantify the reliability of our results using two summary statistics: 1) the distance between the probability density functions (pdf), estimated using the Kolmogorov-Smirnov (KS) test, and 2) the 2D power spectrum. We find an excellent agreement between the model prediction and the unmasked maps when using the power spectrum: better than $1\%$ for $k<20 h/$Mpc for any irregular mask. For regular masks, we observe a systematic offset of $\sim5\%$ when covering $15\%$ of the maps while the results become unreliable when $30\%$ of the data is missing. The observed KS-test p-values favor the null hypothesis that the reconstructed and the ground-truth maps are drawn from the same underlying distribution when irregular masks are used. For regular-shaped masks on the other hand, we find a strong evidence that the two distributions do not match each other. Finally, we use the model, trained on gas temperature maps, to perform inpainting on maps from completely different fields such as gas mass, gas pressure, and electron density and also for gas temperature maps from simulations run with other codes. We find that visually, our model is able to reconstruct the missing pixels from the maps of those fields with great accuracy, although its performance using summary statistics depends strongly on the considered field.

astro-ph.CO↗

arXiv2021

Finding universal relations in subhalo properties with artificial intelligence

We use a generic formalism designed to search for relations in high-dimensional spaces to determine if the total mass of a subhalo can be predicted from other internal properties such as velocity dispersion, radius, or star-formation rate. We train neural networks using data from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project and show that the model can predict the total mass of a subhalo with high accuracy: more than 99% of the subhalos have a predicted mass within 0.2 dex of their true value. The networks exhibit surprising extrapolation properties, being able to accurately predict the total mass of any type of subhalo containing any kind of galaxy at any redshift from simulations with different cosmologies, astrophysics models, subgrid physics, volumes, and resolutions, indicating that the network may have found a universal relation. We then use different methods to find equations that approximate the relation found by the networks and derive new analytic expressions that predict the total mass of a subhalo from its radius, velocity dispersion, and maximum circular velocity. We show that in some regimes, the analytic expressions are more accurate than the neural networks. We interpret the relation found by the neural network and approximated by the analytic equation as being connected to the virial theorem.

astro-ph.CO↗

arXiv2021

The CAMELS project: Cosmology and Astrophysics with MachinE Learning Simulations

We present the Cosmology and Astrophysics with MachinE Learning Simulations --CAMELS-- project. CAMELS is a suite of 4,233 cosmological simulations of $(25~h^{-1}{\rm Mpc})^3$ volume each: 2,184 state-of-the-art (magneto-)hydrodynamic simulations run with the AREPO and GIZMO codes, employing the same baryonic subgrid physics as the IllustrisTNG and SIMBA simulations, and 2,049 N-body simulations. The goal of the CAMELS project is to provide theory predictions for different observables as a function of cosmology and astrophysics, and it is the largest suite of cosmological (magneto-)hydrodynamic simulations designed to train machine learning algorithms. CAMELS contains thousands of different cosmological and astrophysical models by way of varying $Ω_m$, $σ_8$, and four parameters controlling stellar and AGN feedback, following the evolution of more than 100 billion particles and fluid elements over a combined volume of $(400~h^{-1}{\rm Mpc})^3$. We describe the simulations in detail and characterize the large range of conditions represented in terms of the matter power spectrum, cosmic star formation rate density, galaxy stellar mass function, halo baryon fractions, and several galaxy scaling relations. We show that the IllustrisTNG and SIMBA suites produce roughly similar distributions of galaxy properties over the full parameter space but significantly different halo baryon fractions and baryonic effects on the matter power spectrum. This emphasizes the need for marginalizing over baryonic effects to extract the maximum amount of information from cosmological surveys. We illustrate the unique potential of CAMELS using several machine learning applications, including non-linear interpolation, parameter estimation, symbolic regression, data generation with Generative Adversarial Networks (GANs), dimensionality reduction, and anomaly detection.

astro-ph.CO↗

arXiv2021

Inferring the Morphology of Stellar Distribution in TNG50: Twisted and Twisted-Stretched shapes

We investigate the morphology of the stellar distribution in a sample of Milky Way (MW) like galaxies in the TNG50 simulation. Using a local in shell iterative method (LSIM) as the main approach, we explicitly show evidence of twisting (in about 52% of halos) and stretching (in 48% of them) in the real space. This is matched with the re-orientation observed in the eigenvectors of the inertia tensor and gives us a clear picture of having a re-oriented stellar distribution. We make a comparison between the shape profile of dark matter (DM) halo and stellar distribution and quite remarkably see that their radial profiles are fairly close, especially at small galactocentric radii where the stellar disk is located. This implies that the DM halo is somewhat aligned with stars in response to the baryonic potential. The level of alignment mostly decreases away from the center. We study the impact of substructures in the orbital circularity parameter. It is demonstrated that in some cases, far away substructures are counter-rotating compared with the central stars and may flip the sign of total angular momentum and thus the orbital circularity parameter. Truncating them above 150 kpc, however, retains the disky structure of the galaxy as per initial selection. Including the impact of substructures in the shape of stars, we explicitly show that their contribution is subdominant. Overlaying our theoretical results to the observational constraints from previous literature, we establish fair agreement.

astro-ph.GA↗

arXiv2021

DM halo morphological types of MW-like galaxies in the TNG50 simulation: Simple, Twisted, or Stretched

We present a comprehensive analysis of the shape of dark matter (DM) halos in a sample of 25 Milky Way-like galaxies in TNG50 simulation. Using an Enclosed Volume Iterative Method (EVIM), we infer an oblate-to-triaxial shape for the DM halo with the median $T \simeq 0.24 $. We group DM halos in 3 different categories. Simple halos (32% of population) establish principal axes whose ordering in magnitude does not change with radius and whose orientations are almost fixed throughout the halo. Twisted halos (32% of population), experience levels of gradual rotations throughout their radial profiles. Finally, stretched halos (36% of population) demonstrate a stretching in their principal axes lengths where the ordering of different eigenvalues change with radius. Subsequently, the halo experiences a "rotation" of $\sim$90 deg where the stretching occurs. Visualizing the 3D ellipsoid of each halo, for the first time, we report signs of re-orienting ellipsoid in twisted and stretched halos. We examine the impact of baryonic physics on DM halo shape through a comparison to dark matter only (DMO) simulations. This suggests a triaxial (prolate) halo. We analyze the impact of substructure on DM halo shape in both hydro and DMO simulations and confirm that their impacts are subdominant. We study the distribution of satellites in our sample. In simple and twisted halos, the angle of satellites' angular momentum with galaxy's angular momentum grows with radius. However, stretched halos show a flat distribution of angles. Overlaying our theoretical outcome on the observational results presented in the literature establishes a fair agreement.

astro-ph.GA↗

arXiv2021

The IllustrisTNG Simulations: Public Data Release

We present the full public release of all data from the TNG50, TNG100 and TNG300 simulations of the IllustrisTNG project. IllustrisTNG is a suite of large volume, cosmological, gravo-magnetohydrodynamical simulations run with the moving-mesh code Arepo. TNG includes a comprehensive model for galaxy formation physics, and each TNG simulation self-consistently solves for the coupled evolution of dark matter, cosmic gas, luminous stars, and supermassive blackholes from early time to the present day, z=0. Each of the flagship runs -- TNG50, TNG100, and TNG300 -- are accompanied by lower-resolution and dark-matter only counterparts, and we discuss scientific and numerical cautions and caveats relevant when using TNG. Full volume snapshots are available at 100 redshifts; halo and subhalo catalogs at each snapshot and merger trees are also released. The data volume now directly accessible online is ~1.1 PB, including 2,000 full volume snapshots and ~110,000 high time-resolution subbox snapshots. Data access and analysis examples are available in IDL, Python, and Matlab. We describe improvements and new functionality in the web-based API, including on-demand visualization and analysis of galaxies and halos, exploratory plotting of scaling relations and other relationships between galactic and halo properties, and a new JupyterLab interface. This provides an online, browser-based, near-native data analysis platform which supports user computation with fully local access to TNG data, alleviating the need to download large simulated datasets.

astro-ph.GA↗

arXiv2021

The Role of Machine Learning in the Next Decade of Cosmology

In recent years, machine learning (ML) methods have remarkably improved how cosmologists can interpret data. The next decade will bring new opportunities for data-driven cosmological discovery, but will also present new challenges for adopting ML methodologies and understanding the results. ML could transform our field, but this transformation will require the astronomy community to both foster and promote interdisciplinary research endeavors.

astro-ph.IM↗