arXiv subjects

Sungwook E. Hong

Publications and source records attributed to Sungwook E. Hong.

At least 37 records · Page 2Linked to original sources

arXiv2023

The Horizon Run 5 Cosmological Hydrodynamic Simulation: Probing Galaxy Formation from Kilo- to Giga-parsec Scales

Horizon Run 5 (HR5) is a cosmological hydrodynamical simulation which captures the properties of the Universe on a Gpc scale while achieving a resolution of 1kpc. Inside the simulation box we zoom-in on a high-resolution cuboid region with a volume of $1049\times119\times127\,{\rm cMpc}^3$.The sub-grid physics chosen to model galaxy formation includes radiative heating/cooling, UV background, star formation, supernova feedback, chemical evolution tracking the enrichment of oxygen and iron, the growth of supermassive black holes and feedback from active galactic nuclei (AGN) in the form of a dual jet-heating mode. For this simulation we implemented a hybrid MPI-OMP version of RAMSES, specifically targeted for modern many-core many thread parallel architectures. In addition to the traditional simulation snapshots, light-cone data was generated on the fly. For the post-processing, we extended the Friends-of-Friend (FoF) algorithm and developed a new galaxy finder PGalF to analyse the outputs of HR5. The simulation successfully reproduces observations, such as the cosmic star formation history and connectivity of galaxy distribution, We identify cosmological structures at a wide range of scales, from filaments with a length of several cMpc, to voids with a radius of ~100 cMpc. The simulation also indicates that hydrodynamical effects on small scales impact galaxy clustering up to very large scales near and beyond the baryonic acoustic oscillation (BAO) scale. Hence, caution should be taken when using that scale as a cosmic standard ruler: one needs to carefully understand the corresponding biases. The simulation is expected to be an invaluable asset for the interpretation of upcoming deep surveys of the Universe.

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arXiv2023

Cosmological Parameter Constraints from the SDSS Density and Momentum Power Spectra

We extract the galaxy density and momentum power spectra from a subset of early-type galaxies in the SDSS DR7 main galaxy catalog. Using galaxy distance information inferred from the improved fundamental plane described in \citet{Yoon_2020}, we reconstruct the peculiar velocities of the galaxies and generate number density and density-weighted velocity fields, from which we extract the galaxy density and momentum power spectra. We compare the measured values to the theoretical expectation of the same statistics, assuming an input $Λ$CDM model and using a third-order perturbative expansion. After validating our analysis pipeline with a series of mock data sets, we apply our methodology to the SDSS data and arrive at constraints $fσ_{8} = 0.471_{-0.080}^{+0.077}$ and $b_{1}σ_{8} = 0.920_{-0.070}^{+0.070}$ at a mean redshift $\bar{z} = 0.04$. Our result is consistent with the Planck cosmological best fit parameters for the $Λ$CDM model. The momentum power spectrum is found to be strongly contaminated by small scale velocity dispersion, which suppresses power by $\sim {\cal O}(30\%)$ on intermediate scales $k \sim 0.05 \, h \, {\rm Mpc}^{-1}$.

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arXiv2023

Reconstructing the cosmological density and velocity fields from redshifted galaxy distributions using V-net

The distribution of matter that is measured through galaxy redshift and peculiar velocity surveys can be harnessed to learn about the physics of dark matter, dark energy, and the nature of gravity. To improve our understanding of the matter of the Universe, we can reconstruct the full density and velocity fields from the galaxies that act as tracer particles. In this paper, we use the simulated halos as proxies for the galaxies. We use a convolutional neural network, a V-net, trained on numerical simulations of structure formation to reconstruct the density and velocity fields. We find that, with detailed tuning of the loss function, the V-net could produce better fits to the density field in the high-density and low-density regions, and improved predictions for the probability distribution of the amplitudes of the velocities. However, the weights will reduce the precision of the estimated $β$ parameter. We also find that the redshift-space distortions of the halo catalogue do not significantly contaminate the reconstructed real-space density and velocity field. We estimate the velocity field $β$ parameter by comparing the peculiar velocities of halo catalogues to the reconstructed velocity fields, and find the estimated $β$ values agree with the fiducial value at the 68\% confidence level.

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arXiv2023

Cluster-counterpart Voids: Void Identification from Galaxy Density Field

We identify cosmic voids from galaxy density fields under the theory of void-cluster correspondence. We extend the previous novel void-identification method developed for the matter density field to the galaxy density field for practical applications. From cosmological N-body simulations, we construct galaxy number- and mass-weighted density fields to identify cosmic voids that are counterparts of galaxy clusters of specific mass. The parameters for the cluster-counterpart void identification such as Gaussian smoothing scale, density threshold, and core volume fraction are found for galaxy density fields. We achieve about $60$--$67\%$ of completeness and reliability for identifying the voids of corresponding cluster mass above $3\times10^{14}h^{-1}M_{\odot}$ from a galaxy sample with the mean number density, $\bar{n}=4.4\times10^{-3} (h^{-1}{\rm Mpc})^{-3}$. When the mean density is increased to $\bar{n}=10^{-2} (h^{-1}{\rm Mpc})^{-3}$, the detection rate is enhanced by $\sim2$--$7\%$ depending on the `mass scale' of voids. We find that the detectability is insensitive to the density weighting scheme applied to generate the density field. Our result demonstrates that we can apply this method to the galaxy redshift survey data to identify cosmic voids corresponding statistically to the galaxy clusters in a given mass range.

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arXiv2023

Tomographic Alcock-Paczynski Test with Redshift-Space Correlation Function: Evidence for the Dark Energy Equation of State Parameter w>-1

The apparent shape of galaxy clustering depends on the adopted cosmology used to convert observed redshift to comoving distance, the $r(z)$ relation, as it changes the line elements along and across the line of sight differently. The Alcock-Paczyński (AP) test exploits this property to constrain the expansion history of the universe. We present an extensive review of past studies on the AP test. We adopt an extended AP test method introduced by Park et al. (2019), which uses the full shape of redshift-space two-point correlation function (CF) as the standard shape, and apply it to the SDSS DR7, BOSS, and eBOSS LRG samples covering the redshift range up to $z=0.8$.We calibrate the test against the nonlinear cosmology-dependent systematic evolution of the CF shape using the Multiverse simulations. We focus on examining whether or not the flat $Λ$CDM `concordance' model is consistent with observation. We constrain the flat $w$CDM model to have $w=-0.892_{-0.050}^{+0.045}$ and $Ω_m=0.282_{-0.023}^{+0.024}$ from our AP test alone, which is significantly tighter than the constraints from the BAO or SNe I$a$ methods by a factor of 3 - 6. When the AP test result is combined with the recent BAO and SNe I$a$ results, we obtain $w=-0.903_{-0.023}^{+0.023}$ and $Ω_m=0.285_{-0.009}^{+0.014}$. This puts a strong tension with the flat $Λ$CDM model with $w=-1$ at $4.2σ$ level. Consistency with $w=-1$ is obtained only when the Planck CMB observation is combined. It remains to see if this tension between observations of galaxy distribution at low redshifts and CMB anisotropy at the decoupling epoch becomes greater in the future studies and leads us to a new paradigm of cosmology.

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arXiv2022

Panel Discussion: Practical Problem Solving for Machine Learning

Machine Learning is a powerful tool for astrophysicists, which has already had significant uptake in the community. But there remain some barriers to entry, relating to proper understanding, the difficulty of interpretability, and the lack of cohesive training. In this discussion session we addressed some of these questions, and suggest how the field may move forward.

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arXiv2022

Machine-guided Exploration and Calibration of Astrophysical Simulations

We apply a novel method with machine learning to calibrate sub-grid models within numerical simulation codes to achieve convergence with observations and between different codes. It utilizes active learning and neural density estimators. The hyper parameters of the machine are calibrated with a well-defined projectile motion problem. Then, using a set of 22 cosmological zoom simulations, we tune the parameters of a popular star formation and feedback model within Enzo to match simulations. The parameters that are adjusted include the star formation efficiency, coupling of thermal energy from stellar feedback, and volume into which the energy is deposited. This number translates to a factor of more than three improvements over manual calibration. Despite using fewer simulations, we obtain a better agreement to the observed baryon makeup of a Milky-Way (MW) sized halo. Switching to a different strategy, we improve the consistency of the recommended parameters from the machine. Given the success of the calibration, we then apply the technique to reconcile metal transport between grid-based and particle-based simulation codes using an isolated galaxy. It is an improvement over manual exploration while hinting at a less known relation between the diffusion coefficient and the metal mass in the halo region. The exploration and calibration of the parameters of the sub-grid models with a machine learning approach is concluded to be versatile and directly applicable to different problems.

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arXiv2021

Weak-lensing Mass Reconstruction of Galaxy Clusters with Convolutional Neural Network

We introduce a novel method for reconstructing the projected matter distributions of galaxy clusters with weak-lensing (WL) data based on convolutional neural network (CNN). Training datasets are generated with ray-tracing through cosmological simulations. We control the noise level of the galaxy shear catalog such that it mimics the typical properties of the existing ground-based WL observations of galaxy clusters. We find that the mass reconstruction by our multi-layered CNN with the architecture of alternating convolution and trans-convolution filters significantly outperforms the traditional reconstruction methods. The CNN method provides better pixel-to-pixel correlations with the truth, restores more accurate positions of the mass peaks, and more efficiently suppresses artifacts near the field edges. In addition, the CNN mass reconstruction lifts the mass-sheet degeneracy when applied to our projected cluster mass estimation from sufficiently large fields. This implies that this CNN algorithm can be used to measure cluster masses in a model-independent way for future wide-field WL surveys.

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arXiv2021

Minkowski Functionals of SDSS-III BOSS : Hints of Possible Anisotropy in the Density Field?

We present measurements of the Minkowski functionals extracted from the SDSS-III BOSS catalogs. After defining the Minkowski functionals, we describe how an unbiased reconstruction of these statistics can be obtained from a field with masked regions and survey boundaries, validating our methodology with Gaussian random fields and mock galaxy snapshot data. From the BOSS galaxy data we generate a set of four density fields in three dimensions corresponding to the northern and southern skies of LOWZ and CMASS catalogs, smoothing over large scales such that the field is perturbatively non-Gaussian. We extract the Minkowski functionals from each data set separately, and measure their shapes and amplitudes by fitting a Hermite polynomial expansion. For the shape parameter of the Minkowski functional curves $a_0$, that is related to the bispectrum of the field, we find that the LOWZ-South data presents a systematically lower value of $a_0 = -0.080 \pm 0.040$ than its northern sky counterpart $a_0 = 0.032 \pm 0.024$. Although the significance of this discrepancy is low, it potentially indicates some systematics in the data or that the matter density field exhibits anisotropy at low redshift. By assuming a standard isotropic flat $Λ$CDM cosmology, the amplitudes of Minkowski functionals from the combination of northern and southern sky data give the constraints $Ω_{\rm c} h^2 n_{\rm s} = 0.110 \pm 0.006$ and $0.111 \pm 0.008$ for CMASS and LOWZ, respectively, which is in agreement with the Planck $Λ$CDM best-fit $Ω_{\rm c}h^{2} n_{\rm s} = 0.116 \pm 0.001$.

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arXiv2021

Panspermia in a Milky Way-like Galaxy

We study the process of panspermia in Milky Way-like galaxies by modeling the probability of successful travel of organic compounds between stars harboring potentially habitable planets. To this end, we apply the modified habitability recipe of Gobat & Hong (2016) to a model galaxy from the MUGS suite of zoom-in cosmological simulations. We find that, unlike habitability, which only occupies narrow dynamic range over the entire galaxy, the panspermia probability can vary be orders of magnitude between the inner ($R, b = 1-4 {\rm kpc}$) and outer disk. However, only a small fraction of star particles have very large values of panspermia probability and, consequently, the fraction of star particles where the panspermia process is more effective than prebiotic evolution is much lower than from naïve expectations based on the ratio between panspermia probability and natural habitability.

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arXiv2021

Revealing the Local Cosmic Web from Galaxies by Deep Learning

The 80% of the matter in the Universe is in the form of dark matter that comprises the skeleton of the large-scale structure called the Cosmic Web. As the Cosmic Web dictates the motion of all matter in galaxies and inter-galactic media through gravity, knowing the distribution of dark matter is essential for studying the large-scale structure. However, the Cosmic Web's detailed structure is unknown because it is dominated by dark matter and warm-hot inter-galactic media, both of which are hard to trace. Here we show that we can reconstruct the Cosmic Web from the galaxy distribution using the convolutional-neural-network-based deep-learning algorithm. We find the mapping between the position and velocity of galaxies and the Cosmic Web using the results of the state-of-the-art cosmological galaxy simulations, Illustris-TNG. We confirm the mapping by applying it to the EAGLE simulation. Finally, using the local galaxy sample from Cosmicflows-3, we find the dark-matter map in the local Universe. We anticipate that the local dark-matter map will illuminate the studies of the nature of dark matter and the formation and evolution of the Local Group. High-resolution simulations and precise distance measurements to local galaxies will improve the accuracy of the dark-matter map.

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arXiv2021

Cosmological Parameter Estimation from the Two-Dimensional Genus Topology -- Measuring the Expansion History using the Genus Amplitude as a Standard Ruler

We measure the genus of the galaxy distribution in two-dimensional slices of the SDSS-III BOSS catalog to constrain the cosmological parameters governing the expansion history of the Universe. The BOSS catalogs are divided into twelve concentric shells over the redshift range $0.25 < z < 0.6$ and we repeatedly measure the genus from the two-dimensional galaxy density fields, each time varying the cosmological parameters used to infer the distance-redshift relation to the shells. We also indirectly reconstruct the two-dimensional genus amplitude using the three-dimensional genus measured from SDSS Main Galaxy Sample with galaxies at low redshift $z < 0.12$. We combine the low- and high-redshift measurements, finding the cosmological model which minimizes the redshift evolution of the genus amplitude, using the fact that this quantity should be conserved. Being a distance measure, the test is sensitive to the matter density parameter ($Ω_{\rm m}$) and equation of state of dark energy ($w_{\rm de}$). We find a constraint of $w_{\rm de} = -1.05^{+0.13}_{-0.12}$, $Ω_{\rm m} = 0.303 \pm 0.036$ after combining the high- and low-redshift measurements and combining with Planck CMB data. Higher redshift data and combining data sets at low redshift will allow for stronger constraints.

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arXiv2020

Deep-Learning Study of the 21cm Differential Brightness Temperature During the Epoch of Reionization

We propose a deep learning analyzing technique with convolutional neural network (CNN) to predict the evolutionary track of the Epoch of Reionization (EoR) from the 21-cm differential brightness temperature tomography images. We use 21cmFAST, a fast semi-numerical cosmological 21-cm signal simulator, to produce mock 21-cm maps between $z=6 \sim 13$. We then apply two observational effects into those 21-cm maps, such as instrumental noise and limit of (spatial and depth) resolution somewhat suitable for realistic choices of the Square Kilometre Array (SKA). We design our deep learning model with CNN to predict the sliced-averaged neutral hydrogen fraction from the given 21-cm map. The estimated neutral fraction from our CNN model has a great agreement with its true value even after coarsely smoothing with broad beamsize and frequency bandwidth, and also heavily covered by noise with narrow. Our results have shown that deep learning analyzing method has a large potential to efficiently reconstruct the EoR history from the 21-cm tomography surveys in future.

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arXiv2020

SCUBA-2 Ultra Deep Imaging EAO Survey (STUDIES) IV: Spatial clustering and halo masses of 450-$μ$m-selected sub-millimeter galaxies

We analyze an extremely deep 450-$μ$m image ($1σ=0.56$\,mJy\,beam$^{-1}$) of a $\simeq 300$\,arcmin$^{2}$ area in the CANDELS/COSMOS field as part of the SCUBA-2 Ultra Deep Imaging EAO Survey (STUDIES). We select a robust (signal-to-noise ratio $\geqslant 4$) and flux-limited ($\geqslant 4$\,mJy) sample of 164 sub-millimeter galaxies (SMGs) at 450-$μ$m that have $K$-band counterparts in the COSMOS2015 catalog identified from radio or mid-infrared imaging. Utilizing this SMG sample and the 4705 $K$-band-selected non-SMGs that reside within the noise level $\leqslant 1$\,mJy\,beam$^{-1}$ region of the 450-$μ$m image as a training set, we develop a machine-learning classifier using $K$-band magnitude and color-color pairs based on the thirteen-band photometry available in this field. We apply the trained machine-learning classifier to the wider COSMOS field (1.6\,deg$^{2}$) using the same COSMOS2015 catalog and identify a sample of 6182 450-$μ$m SMG candidates with similar colors. The number density, radio and/or mid-infrared detection rates, redshift and stellar mass distributions, and the stacked 450-$μ$m fluxes of these SMG candidates, from the S2COSMOS observations of the wide field, agree with the measurements made in the much smaller CANDELS field, supporting the effectiveness of the classifier. Using this 450-$μ$m SMG candidate sample, we measure the two-point autocorrelation functions from $z=3$ down to $z=0.5$. We find that the 450-$μ$m SMG candidates reside in halos with masses of $\simeq (2.0\pm0.5) \times10^{13}\,h^{-1}\,\rm M_{\odot}$ across this redshift range. We do not find evidence of downsizing that has been suggested by other recent observational studies.

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arXiv2020

Cosmological Information from the Small-scale Redshift Space Distortions

The redshift-space distortion (RSD) in the observed distribution of galaxies is known as a powerful probe of cosmology. Observations of large-scale RSD have given tight constraints on the linear growth rate of the large-scale structures in the universe. On the other hand, the small-scale RSD, caused by galaxy random motions inside clusters, has not been much used in cosmology, but also has cosmological information because universes with different cosmological parameters have different halo mass functions and virialized velocities. We focus on the projected correlation function $w(r_p)$ and the multipole moments $ξ_l$ on small scales ($1.4$ to $30\ h^{-1}\rm{Mpc}$). Using simulated galaxy samples generated from a physically motivated most bound particle (MBP)-galaxy correspondence scheme in the Multiverse Simulation, we examine the dependence of the small-scale RSD on the cosmological matter density parameter $Ω_m$, the satellite velocity bias with respect to MBPs, $b_v^s$, and the merger-time-scale parameter $α$. We find that $α=1.5$ gives an excellent fit to the $w(r_p)$ and $ξ_l$ measured from the SDSS-KIAS value added galaxy catalog. We also define the ``strength'' of Fingers-of-God as the ratio of the parallel and perpendicular size of the contour in the two-point correlation function set by a specific threshold value and show that the strength parameter helps constraining $(Ω_m, b_v^s, α)$ by breaking the degeneracy among them. The resulting parameter values from all measurements are $(Ω_m,b_v^s)=(0.272\pm0.013,0.982\pm0.040)$, indicating a slight reduction of satellite galaxy velocity relative to the MBP. However, considering that the average MBP speed inside haloes is $0.94$ times the dark matter velocity dispersion, the main drivers behind the galaxy velocity bias are gravitational interactions, rather than baryonic effects.

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arXiv2020

Cosmological Parameter Estimation from the Two-Dimensional Genus Topology -- Measuring the Shape of the Matter Power Spectrum

We present measurements of the two-dimensional genus of the SDSS-III BOSS catalogs to constrain cosmological parameters governing the shape of the matter power spectrum. The BOSS data are divided into twelve concentric shells over the redshift range $0.2 < z < 0.6$, and we extract the genus from the projected two-dimensional galaxy density fields. We compare the genus amplitudes to their Gaussian expectation values, exploiting the fact that this quantity is relatively insensitive to non-linear gravitational collapse. The genus amplitude provides a measure of the shape of the linear matter power spectrum, and is principally sensitive to $Ω_{\rm c}h^{2}$ and scalar spectral index $n_{\rm s}$. A strong negative degeneracy between $Ω_{\rm c}h^{2}$ and $n_{\rm s}$ is observed, as both can increase small scale power by shifting the peak and tilting the power spectrum respectively. We place a constraint on the particular combination $n_{\rm s}^{3/2} Ω_{\rm c}h^{2}$ -- we find $n_{\rm s}^{3/2} Ω_{\rm c}h^{2} = 0.1121 \pm 0.0043$ after combining the LOWZ and CMASS data sets, assuming a flat $Λ$CDM cosmology. This result is practically insensitive to reasonable variations of the power spectrum amplitude and linear galaxy bias. Our results are consistent with the Planck best fit $n_{\rm s}^{3/2}Ω_{\rm c}h^{2} = 0.1139 \pm 0.0009$.

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arXiv2019

Alcock-Paczynski Test with the Evolution of Redshift-Space Galaxy Clustering Anisotropy

We develop an improved Alcock-Paczynski (AP) test method that uses the redshift-space two-point correlation function (2pCF) of galaxies. Cosmological constraints can be obtained by examining the redshift dependence of the normalized 2pCF, which should not change apart from the expected small non-linear evolution. An incorrect choice of cosmology used to convert redshift to comoving distance will manifest itself as redshift-dependent 2pCF. Our method decomposes the redshift difference of the two-dimensional correlation function into the Legendre polynomials whose amplitudes are modeled by radial fitting functions. Our likelihood analysis with this 2-D fitting scheme tightens the constraints on $Ω_m$ and ${w}$ by $\sim 40\%$ compared to the method of Li et al. (2016, 2017, 2018) that uses one dimensional angular dependence only. We also find that the correction for the non-linear evolution in the 2pCF has a non-negligible cosmology dependence, which has been neglected in previous similar studies by Li et al.. With an accurate accounting for the non-linear systematics and use of full two-dimensional shape information of the 2pCF down to scales as small as $5~h^{-1}{\rm Mpc}$ it is expected that the AP test with redshift-space galaxy clustering anisotropy can be a powerful method to constrain the expansion history of the universe.

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arXiv2019

Constraining Cosmology with Big Data Statistics of Cosmological Graphs

By utilizing large-scale graph analytic tools implemented in the modern Big Data platform, Apache Spark, we investigate the topological structure of gravitational clustering in five different universes produced by cosmological $N$-body simulations with varying parameters: (1) a WMAP 5-year compatible $Λ$CDM cosmology, (2) two different dark energy equation of state variants, and (3) two different cosmic matter density variants. For the Big Data calculations, we use a custom build of stand-alone Spark/Hadoop cluster at Korea Institute for Advanced Study (KIAS) and Dataproc Compute Engine in Google Cloud Platform (GCP) with the sample size ranging from 7 millions to 200 millions. We find that among the many possible graph-topological measures, three simple ones: (1) the average of number of neighbors (the so-called average vertex degree) $α$, (2) closed-to-connected triple fraction (the so-called transitivity) $τ_Δ$, and (3) the cumulative number density $n_{s\ge5}$ of subcomponents with connected component size $s \ge 5$, can effectively discriminate among the five model universes. Since these graph-topological measures are in direct relation with the usual $n$-points correlation functions of the cosmic density field, graph-topological statistics powered by Big Data computational infrastructure opens a new, intuitive, and computationally efficient window into the dark Universe.

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