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Traditional weak-lensing mass reconstruction techniques suffer from various artifacts, including noise amplification and the mass-sheet degeneracy. In Hong et al. (2021), we demonstrated that many of these pitfalls of traditional mass reconstruction can be mitigated using a deep learning approach based on a convolutional neural network (CNN). In this paper, we present our improvements and report on the detailed performance of our CNN algorithm applied to next-generation wide-field observations. Assuming the field of view ($3\deg.5 \times 3\deg.5$) and depth (27 mag at $5\sigma$) of the Vera C. Rubin Observatory, we generated training datasets of mock shear catalogs with a source density of 33 arcmin$^{-2}$ from cosmological simulation ray-tracing data. We find that the current CNN method provides high-fidelity reconstructions consistent with the true convergence field, restoring both small and large-scale structures. In addition, the cluster detection utilizing our CNN reconstruction achieves $\sim75$% completeness down to $\sim 10^{14}M_{\odot}$. We anticipate that this CNN-based mass reconstruction will be a powerful tool in the Rubin era, enabling fast and robust wide-field mass reconstructions on a routine basis., Comment: 11 pages, 8 figures, submitted to ApJ

Metallicity offers a unique window into the baryonic history of the cosmos, being instrumental in probing evolutionary processes in galaxies between different cosmic environments. We aim to quantify the contribution of these environments to the scatter in the mass-metallicity relation (MZR) of galaxies. By analysing the galaxy distribution within the cosmic skeleton of the Horizon Run 5 cosmological hydrodynamical simulation at redshift $z = 0.625$, computed using a careful calibration of the T-ReX filament finder, we identify galaxies within three main environments: nodes, filaments and voids. We also classify galaxies based on the dynamical state of the clusters and the length of the filaments in which they reside. We find that the cosmic environment significantly contributes to the scatter in the MZR; in particular, both the gas metallicity and its average relative standard deviation increase when considering denser large-scale environments. The difference in the average metallicity between galaxies within relaxed and unrelaxed clusters is $\approx 0.1 \text{ dex}$, with both populations displaying positive residuals, $\delta Z_{g}$, from the averaged MZR. Moreover, the difference in metallicity between node and void galaxies accounts for $\approx 0.14 \, \text{dex}$ in the scatter of the MZR at stellar mass $M_{\star} \approx 10^{9.35}\,\text{M}_{\odot}$. Finally, both the average [O/Fe] in the gas and the galaxy gas fraction decrease when moving to higher large-scale densities in the simulation, suggesting that the cores of cosmic environments host, on average, older and more massive galaxies, whose enrichment is affected by a larger number of Type Ia Supernova events., Comment: 17 pages, 17 figures

We introduce a novel halo/galaxy matching technique between two cosmological simulations with different resolutions, which utilizes the positions and masses of halos along their subhalo merger tree. With this tool, we conduct a study of resolution biases through the {\it galaxy-by-galaxy} inspection of a pair of simulations that have the same simulation configuration but different mass resolutions, utilizing a suite of {\sc IllustrisTNG} simulations to assess the impact on galaxy properties. We find that, with the subgrid physics model calibrated for TNG100-1, subhalos in TNG100-1 (high resolution) have $\lesssim0.5$ dex higher stellar masses than their counterparts in the TNG100-2 (low-resolution). It is also discovered that the subhalos with $M_{\mathrm{gas}}\sim10^{8.5}\,{\rm M}_\odot$ in TNG100-1 have $\sim0.5$ dex higher gas mass than those in TNG100-2. The mass profiles of the subhalos reveal that the dark matter masses of subhalos in TNG100-2 converge well with those from TNG100-1, except within 4 kpc of the resolution limit. The differences in stellar mass and hot gas mass are most pronounced in the central region. We exploit machine learning to build a correction mapping for the physical quantities of subhalos from low- to high-resolution simulations (TNG300-1 and TNG100-1), which enables us to find an efficient way to compile a high-resolution galaxy catalog even from a low-resolution simulation. Our tools can easily be applied to other large cosmological simulations, testing and mitigating the resolution biases of their numerical codes and subgrid physics models., Comment: 24 pages, 14 figures; Accepted for publication in to ApJ

When two galaxies merge, they often produce a supermassive black hole binary (SMBHB) at their center. Numerical simulations with stars and cold dark matter show that SMBHBs typically stall out at a distance of a few parsecs apart and take billions of years to coalesce. This is known as the final parsec problem. We suggest that ultralight dark matter (ULDM) halos around SMBHBs can generate dark matter waves due to dynamical friction. These waves can effectively carry away orbital energy from the black holes, rapidly driving them together. To test this hypothesis, we performed numerical simulations of black hole binaries inside ULDM halos. Due to gravitational cooling and quasi-normal modes, the loss-cone problem can be avoided. The decay time scale gives lower bounds on masses of the ULDM particles and SMBHBs comparable to observational data. Our results imply that ULDM waves can lead to the rapid orbital decay of black hole binaries., Comment: Accepted for publication in Physics Letters B

The two-point statistics of the cosmic velocity field, measured from galaxy peculiar velocity (PV) surveys, can be used as a dynamical probe to constrain the growth rate of large-scale structures in the universe. Most works use the statistics on scales down to a few tens of Megaparsecs, while using a theoretical template based on the linear theory. In addition, while the cosmic velocity is volume-weighted, the observable line-of-sight velocity two-point correlation is density-weighted, as sampled by galaxies, and therefore the density-velocity correlation term also contributes, which has often been neglected. These effects are fourth order in powers of the linear density fluctuation $\delta_{\rm L}^4$, compared to $\delta_{\rm L}^2$ of the linear velocity correlation function, and have the opposite sign. We present these terms up to $\delta_{\rm L}^4$ in real space based on the standard perturbation theory, and investigate the effect of non-linearity and the density-velocity contribution on the inferred growth rate $f\sigma_8$, using $N$-body simulations. We find that for a next-generation PV survey of volume $\sim {\cal O}(500 \, h^{-1} \, {\rm Mpc})^3$, these effects amount to a shift of $f\sigma_8$ by $\sim 10$ per cent and is comparable to the forecasted statistical error when the minimum scale used for parameter estimation is $r_{\rm min} = 20 \, h^{-1} \, {\rm Mpc}$., Comment: 17 pages, 6 figures. Minor corrections to non-linear calculation in section 2.2, conclusions unchanged. Accepted for publication in MNRAS

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., Comment: 10 pages, 5 figures, 1 table. Accepted for publication in ApJ

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 $\Lambda$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 $f\sigma_{8} = 0.471_{-0.080}^{+0.077}$ and $b_{1}\sigma_{8} = 0.920_{-0.070}^{+0.070}$ at a mean redshift $\bar{z} = 0.04$. Our result is consistent with the Planck cosmological best fit parameters for the $\Lambda$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 $k \sim 0.05 \, h \, {\rm Mpc}^{-1}$., Comment: 15 figures, 4 tables, accepted for ApJ

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-Paczy\'nski (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 $\Lambda$CDM `concordance' model is consistent with observation. We constrain the flat $w$CDM model to have $w=-0.892_{-0.050}^{+0.045}$ and $\Omega_m=0.282_{-0.023}^{+0.024}$ 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 $\Omega_m=0.285_{-0.009}^{+0.014}$. This puts a strong tension with the flat $\Lambda$CDM model with $w=-1$ at $4.2\sigma$ 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., Comment: 21 pages, 11 figures, accepted by ApJ

We present a novel approach for estimating cosmological parameters, $\Omega_m$, $\sigma_8$, $w_0$, and one derived parameter, $S_8$, from 3D lightcone data of dark matter halos in redshift space covering a sky area of $40^\circ \times 40^\circ$ and redshift range of $0.3 < z < 0.8$, binned to $64^3$ voxels. Using two deep learning algorithms, Convolutional Neural Network (CNN) and Vision Transformer (ViT), we compare their performance with the standard two-point correlation (2pcf) function. Our results indicate that CNN yields the best performance, while ViT also demonstrates significant potential in predicting cosmological parameters. By combining the outcomes of Vision Transformer, Convolution Neural Network, and 2pcf, we achieved a substantial reduction in error compared to the 2pcf alone. To better understand the inner workings of the machine learning algorithms, we employed the Grad-CAM method to investigate the sources of essential information in activation maps of the CNN and ViT. Our findings suggest that the algorithms focus on different parts of the density field and redshift depending on which parameter they are predicting. This proof-of-concept work paves the way for incorporating deep learning methods to estimate cosmological parameters from large-scale structures, potentially leading to tighter constraints and improved understanding of the Universe., Comment: 23 pages, 10 figures

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 $\beta$ 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 $\beta$ parameter by comparing the peculiar velocities of halo catalogues to the reconstructed velocity fields, and find the estimated $\beta$ values agree with the fiducial value at the 68\% confidence level., Comment: 25 pages, 18 figures, 2 tables. Published JCAP

We introduce a new clustering algorithm, MulGuisin (MGS), that can identify distinct galaxy over-densities using topological information from the galaxy distribution. This algorithm was first introduced in an LHC experiment as a Jet Finder software, which looks for particles that clump together in close proximity. The algorithm preferentially considers particles with high energies and merges them only when they are closer than a certain distance to create a jet. MGS shares some similarities with the minimum spanning tree (MST) since it provides both clustering and network-based topology information. Also, similar to the density-based spatial clustering of applications with noise (DBSCAN), MGS uses the ranking or the local density of each particle to construct clustering. In this paper, we compare the performances of clustering algorithms using controlled data and some realistic simulation data as well as the SDSS observation data, and we demonstrate that our new algorithm finds networks most efficiently and defines galaxy networks in a way that most closely resembles human vision., Comment: 26 pages, 13 figures

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., Comment: 6 pages. Prepared for the proceedings of the International Astronomical Union Symposium 368 "Machine Learning in Astronomy: Possibilities and Pitfalls"

We consider a simple system consisting of matter, radiation and vacuum components to model the impact of thermal inflation on the evolution of primordial perturbations. The vacuum energy magnifies the primordial modes entering the horizon before its domination, making them potentially observable, and the resulting transfer function reflects the phase changes and energy contents. To determine the transfer function, we follow the curvature perturbation from well outside the horizon during radiation domination to well outside the horizon during vacuum domination and evaluate it on a constant radiation density hypersurface, as is appropriate for the case of thermal inflation. The shape of the transfer function is determined by the ratio of vacuum energy to radiation at matter-radiation equality, which we denote by $\upsilon$, and has two characteristic scales, $k_{\rm a}$ and $k_{\rm b}$, corresponding to the horizon sizes at matter radiation equality and the beginning of the inflation, respectively. If $\upsilon \ll 1$, the universe experiences radiation, matter and vacuum domination eras and the transfer function is flat for $k \ll k_{\rm b}$, oscillates with amplitude $1/5$ for $ k_{\rm b} \ll k \ll k_{\rm a}$ and oscillates with amplitude $1$ for $k \gg k_{\rm a}$. For $\upsilon \gg 1$, the matter domination era disappears, and the transfer function reduces to being flat for $k \ll k_{\rm b}$ and oscillating with amplitude $1$ for $k \gg k_{\rm b}$., Comment: 22 pages, 6 figures, accepted to the Class. Quant. Grav

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.

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 $\Lambda$CDM cosmology, the amplitudes of Minkowski functionals from the combination of northern and southern sky data give the constraints $\Omega_{\rm c} h^2 n_{\rm s} = 0.110 \pm 0.006$ and $0.111 \pm 0.008$ for CMASS and LOWZ, respectively, which is in agreement with the Planck $\Lambda$CDM best-fit $\Omega_{\rm c}h^{2} n_{\rm s} = 0.116 \pm 0.001$.

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 na\"ive expectations based on the ratio between panspermia probability and natural habitability., Comment: 22 pages, 18 figures, ApJ accepted

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., Comment: 18 pages, 13 figures, ApJ accepted

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 ($\Omega_{\rm m}$) and equation of state of dark energy ($w_{\rm de}$). We find a constraint of $w_{\rm de} = -1.05^{+0.13}_{-0.12}$, $\Omega_{\rm m} = 0.303 \pm 0.036$ 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., Comment: Accepted for publication in the Astrophysical Journal. 19 pages, 13 figures

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., Comment: 18 pages, 9 figures, ApJ accepted, added more data analyses

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., Comment: 25 pages, 8 figures, added references

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., Comment: The dimension of the zoom-in region has been corrected

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 $\xi_l$ 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 $\Omega_m$, the satellite velocity bias with respect to MBPs, $b_v^s$, and the merger-time-scale parameter $\alpha$. We find that $\alpha=1.5$ gives an excellent fit to the $w(r_p)$ and $\xi_l$ 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 $(\Omega_m, b_v^s, \alpha)$ by breaking the degeneracy among them. The resulting parameter values from all measurements are $(\Omega_m,b_v^s)=(0.272\pm0.013,0.982\pm0.040)$, 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., Comment: 18 pages, 12 figures, accepted for publication in ApJ

We analyze an extremely deep 450-$\mu$m image ($1\sigma=0.56$\,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-$\mu$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-$\mu$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-$\mu$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-$\mu$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-$\mu$m SMG candidate sample, we measure the two-point autocorrelation functions from $z=3$ down to $z=0.5$. We find that the 450-$\mu$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., Comment: ApJ accepted

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 $\Omega_{\rm c}h^{2}$ and scalar spectral index $n_{\rm s}$. A strong negative degeneracy between $\Omega_{\rm c}h^{2}$ 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 $n_{\rm s}^{3/2} \Omega_{\rm c}h^{2}$ -- we find $n_{\rm s}^{3/2} \Omega_{\rm c}h^{2} = 0.1121 \pm 0.0043$ after combining the LOWZ and CMASS data sets, assuming a flat $\Lambda$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 $n_{\rm s}^{3/2}\Omega_{\rm c}h^{2} = 0.1139 \pm 0.0009$., Comment: ApJ submitted, comments welcome

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 $\Omega_m$ 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., Comment: 11 pages, 8 figures, accepted for publication

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 $\Lambda$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) $\alpha$, (2) closed-to-connected triple fraction (the so-called transitivity) $\tau_\Delta$, 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., Comment: 16 pages, 11 figures, submitted to MNRAS

Devasthal Optical Telescope Integral Field Spectrograph (DOTIFS) is a new multi-Integral Field Unit (IFU) instrument, planned to be mounted on the 3.6m Devasthal optical telescope in Nainital, India. It has eight identical, fiber-fed spectrographs to disperse light coming from 16 IFUs. The spectrographs produce 2,304 spectra over a 370-740nm wavelength range simultaneously with a spectral resolution of R=1200-2400. It is composed of all-refractive, all spherical optics designed to achieve on average 26.0% throughput from the telescope to the CCD with the help of high transmission spectrograph optics, volume phase holographic grating, and graded coated e2v 2K by 4K CCD. We present the optical and opto-mechanical design of the spectrograph as well as current development status. Optics and optomechanical components for the spectrographs are being fabricated., Comment: 14 pages, 10 figures, Conference poster at SPIE Astronomical Telescopes + Instrumentation, 2018, Austin, Texas, United States

IUCAA Digital Sampling Array Controller (IDSAC) is a flexible and generic yet powerful CCD controller which can handle a wide range of scientific detectors. Based on an easily scalable modular backplane architecture consisting of Single Board Controllers (SBC), IDSAC can control large detector arrays and mosaics. Each of the SBCs offers the full functionality required to control a CCD independently. The SBCs can be cold swapped without the need to reconfigure them. Each SBC can handle data from up to four video channels with or without dummy outputs at speeds up to 500 kilo Pixels Per Second (kPPS) Per Channel with a resolution of 16 bits. Communication with Linux based host computer is through a USB3.0 interface, with the option of using copper or optical fibers. A Field Programmable Gate Array (FPGA) is used as the master controller in each SBC which allows great flexibility in optimizing performance by adjusting gain, timing signals, bias levels, etc. using user-editable configuration files without altering the circuit topology. Elimination of thermal kTC noise is achieved via Digital Correlated Double Sampling (DCDS). We present the results of noise performance characterization of IDSAC through simulation, theoretical modeling, and actual measurements. The contribution of different types of noise sources is modeled using a tool to predict noise of a generic DCDS signal chain analytically. The analytical model predicts the net input referenced noise of the signal chain to be 5 electrons for 200k pixels per second per channel readout rate with 3 samples per pixel. Using a cryogenic test set up in the lab, the noise is measured to be 5.4 e (24.3 \muV), for the same readout configuration., Comment: 21 Pages, 13 Figures, 6 Tables

A new field re-configuration technique, Multiple Rooks of Chess (MRC), for multiple deployable Integral Field Spectrographs has been developed. The method involves mechanical geometry as well as an optimized deployment algorithm. The geometry is found to be simple for mechanical implementation. The algorithm initially assigns the IFUs to the target objects and then devises the movement sequence based on the current and the desired IFU positions. The reconfiguration time using the suitable actuators which runs at 20 cm/s is found to be a maximum of 25 seconds for the circular DOTIFS focal plane (180 mm diameter). The Geometry Algorithm Combination (GAC) has been tested on several million mock target configurations with object-to-IFU ({\tau} ) ratio varying from 0.25 to 16. The MRC method is found to-be efficient in target acquisition in terms of field revisit and deployment time without any collision or entanglement of the fiber bundles. The efficiency of the technique does not get affected by the increase in number density of target objects. The technique is compared with other available methods based on sky coverage, flexibility and overhead time. The proposed geometry and algorithm combination is found to have an advantage in all of the aspects., Comment: 18 Pages, 13 Figures, 1 Table

We perform an anisotropic clustering analysis of 1,133,326 galaxies from the Sloan Digital Sky Survey (SDSS-III) Baryon Oscillation Spectroscopic Survey (BOSS) Data Release (DR) 12 covering the redshift range $0.15

We study the topology of the matter density field in two dimensional slices, and consider how we can use the amplitude $A$ of the genus for cosmological parameter estimation. Using the latest Horizon Run 4 simulation data, we calculate the genus of the smoothed density field constructed from lightcone mock galaxy catalogs. Information can be extracted from the amplitude of the genus by considering both its redshift evolution and magnitude. The constancy of the genus amplitude with redshift can be used as a standard population, from which we derive constraints on the equation of state of dark energy $w_{\rm de}$ - by measuring $A$ at $z \sim 0.1$ and $z \sim 1$, we can place an order $\Delta w_{\rm de} \sim {\cal O}(15\%)$ constraint on $w_{\rm de}$. By comparing $A$ to its Gaussian expectation value we can potentially derive an additional stringent constraint on the matter density $\Delta \Omega_{\rm mat} \sim 0.01$. We discuss the primary sources of contamination associated with the two measurements - redshift space distortion and shot noise. With accurate knowledge of galaxy bias, we can successfully remove the effect of redshift space distortion, and the combined effect of shot noise and non-linear gravitational evolution is suppressed by smoothing over suitably large scales $R_{\rm G} \ge 15 {\rm Mpc}/h$. Without knowledge of the bias, we discuss how joint measurements of the two and three dimensional genus can be used to constrain the growth factor $\beta = f/b$. The method can be applied optimally to redshift slices of a galaxy distribution generated using the drop-off technique., Comment: Published 01/2018. 15 pages, 10 figures

We apply the Minkowski Tensor statistics to two dimensional slices of the three dimensional density field. The Minkowski Tensors are a set of functions that are sensitive to directionally dependent signals in the data, and furthermore can be used to quantify the mean shape of density peaks. We begin by introducing our algorithm for constructing bounding perimeters around subsets of a two dimensional field, and reviewing the definition of Minkowski Tensors. Focusing on the translational invariant statistic $W^{1,1}_{2}$ - a $2 \times 2$ matrix - we calculate its eigenvalues for both the entire excursion set ($\Lambda_{1},\Lambda_{2}$) and for individual connected regions and holes within the set ($\lambda_{1},\lambda_{2}$). The ratio of eigenvalues $\Lambda_{2}/\Lambda_{1}$ informs us of the presence of global anisotropies in the data, and $\langle \lambda_{2}/\lambda_{1} \rangle$ is a measure of the mean shape of peaks and troughs in the density field. We study these quantities for a Gaussian field, then consider how they are modified by the effect of gravitational collapse using the latest Horizon Run 4 cosmological simulation. We find $\Lambda_{1,2}$ are essentially independent of gravitational collapse, as the process maintains statistical isotropy. However, the mean shape of peaks is modified significantly - overdensities become relatively more circular compared to underdensities of the same area. When applying the statistic to a redshift space distorted density field, we find a significant signal in the eigenvalues $\Lambda_{1,2}$, suggesting that they can be used to probe the large-scale velocity field., Comment: 17 pages, accepted for publication in APJ

We develop a methodology to use the redshift dependence of the galaxy 2-point correlation function (2pCF) across the line-of-sight, $\xi(r_{\bot})$, as a probe of cosmological parameters. The positions of galaxies in comoving Cartesian space varies under different cosmological parameter choices, inducing a {\it redshift-dependent scaling} in the galaxy distribution. This geometrical distortion can be observed as a redshift-dependent rescaling in the measured $\xi(r_{\bot})$. We test this methodology using a sample of 1.75 billion mock galaxies at redshifts 0, 0.5, 1, 1.5, 2, drawn from the Horizon Run 4 N-body simulation. The shape of $\xi(r_{\bot})$ can exhibit a significant redshift evolution when the galaxy sample is analyzed under a cosmology differing from the true, simulated one. Other contributions, including the gravitational growth of structure, galaxy bias, and the redshift space distortions, do not produce large redshift evolution in the shape. We show that one can make use of this geometrical distortion to constrain the values of cosmological parameters governing the expansion history of the universe. This method could be applicable to future large scale structure surveys, especially photometric surveys such as DES, LSST, to derive tight cosmological constraints. This work is a continuation of our previous works as a strategy to constrain cosmological parameters using redshift-invariant physical quantities., Comment: 12 pages, 6 figures, Accepted for publication in ApJ

We study the impact of thermal inflation on the formation of cosmological structures and present astrophysical observables which can be used to constrain and possibly probe the thermal inflation scenario. These are dark matter halo abundance at high redshifts, satellite galaxy abundance in the Milky Way, and fluctuation in the 21-cm radiation background before the epoch of reionization. The thermal inflation scenario leaves a characteristic signature on the matter power spectrum by boosting the amplitude at a specific wavenumber determined by the number of e-foldings during thermal inflation ($N_{\rm bc}$), and strongly suppressing the amplitude for modes at smaller scales. For a reasonable range of parameter space, one of the consequences is the suppression of minihalo formation at high redshifts and that of satellite galaxies in the Milky Way. While this effect is substantial, it is degenerate with other cosmological or astrophysical effects. The power spectrum of the 21-cm background probes this impact more directly, and its observation may be the best way to constrain the thermal inflation scenario due to the characteristic signature in the power spectrum. The Square Kilometre Array (SKA) in phase 1 (SKA1) has sensitivity large enough to achieve this goal for models with $N_{\rm bc}\gtrsim 26$ if a 10000-hr observation is performed. The final phase SKA, with anticipated sensitivity about an order of magnitude higher, seems more promising and will cover a wider parameter space., Comment: 28 pages, 8 figures

Starting from a very accurate model for density-in-cells statistics of dark matter based on large deviation theory, a bias model for the tracer density in spheres is formulated. It adopts a mean bias relation based on a quadratic bias model to relate the log-densities of dark matter to those of mass-weighted dark haloes in real and redshift space. The validity of the parametrised bias model is established using a parametrisation-independent extraction of the bias function. This average bias model is then combined with the dark matter PDF, neglecting any scatter around it: it nevertheless yields an excellent model for densities-in-cells statistics of mass tracers that is parametrised in terms of the underlying dark matter variance and three bias parameters. The procedure is validated on measurements of both the one and two point statistics of subhalo densities in the state-of-the-art Horizon Run 4 simulation showing excellent agreement for measured dark matter variance and bias parameters. Finally, it is demonstrated that this formalism allows for a joint estimation of the nonlinear dark matter variance and the bias parameters using solely the statistics of subhaloes. Having verified that galaxy counts in hydrodynamical simulations sampled on a scale of 10 Mpc/h closely resemble those of subhaloes, this work provides important steps towards making theoretical predictions for density-in-cells statistics applicable to upcoming galaxy surveys like Euclid or WFIRST., Comment: 14 pages, 11 figures

Thermal inflation is a second epoch of exponential expansion at typical energy scales $V^{1/4} \sim 10^{6 \sim 8} \mathrm{GeV}$. If the usual primordial inflation is followed by thermal inflation, the primordial power spectrum is only modestly redshifted on large scales, but strongly suppressed on scales smaller than the horizon size at the beginning of thermal inflation, $k > k_{\rm b} = a_{\rm b} H_{\rm b}$. We calculate the spectral distortion of the cosmic microwave background generated by the dissipation of acoustic waves in this context. For $k_{\rm b} \ll 10^3 \mathrm{Mpc}^{-1}$, thermal inflation results in a large suppression of the $\mu$-distortion amplitude, predicting that it falls well below the standard value of $\mu \simeq 2\times 10^{-8}$. Thus, future spectral distortion experiments, similar to PIXIE, can place new limits on the thermal inflation scenario, constraining $k_{\rm b} \gtrsim 10^3 \mathrm{Mpc}^{-1}$ if $\mu \simeq 2\times 10^{-8}$ were found., Comment: 18 pages, 7 figures

We study the two-dimensional topology of the galactic distribution when projected onto two-dimensional spherical shells. Using the latest Horizon Run 4 simulation data, we construct the genus of the two-dimensional field and consider how this statistic is affected by late-time nonlinear effects -- principally gravitational collapse and redshift space distortion (RSD). We also consider systematic and numerical artifacts such as shot noise, galaxy bias, and finite pixel effects. We model the systematics using a Hermite polynomial expansion and perform a comprehensive analysis of known effects on the two-dimensional genus, with a view toward using the statistic for cosmological parameter estimation. We find that the finite pixel effect is dominated by an amplitude drop and can be made less than $1\%$ by adopting pixels smaller than $1/3$ of the angular smoothing length. Nonlinear gravitational evolution introduces time-dependent coefficients of the zeroth, first, and second Hermite polynomials, but the genus amplitude changes by less than $1\%$ between $z=1$ and $z=0$ for smoothing scales $R_{\rm G} > 9 {\rm Mpc/h}$. Non-zero terms are measured up to third order in the Hermite polynomial expansion when studying RSD. Differences in shapes of the genus curves in real and redshift space are small when we adopt thick redshift shells, but the amplitude change remains a significant $\sim {\cal O}(10\%)$ effect. The combined effects of galaxy biasing and shot noise produce systematic effects up to the second Hermite polynomial. It is shown that, when sampling, the use of galaxy mass cuts significantly reduces the effect of shot noise relative to random sampling., Comment: Accepted for publication in ApJ

We apply the methodology developed in \cite{Li2014,Li2015} to BOSS DR12 galaxies and derive cosmological constraints from the redshift dependence of the Alcock-Paczynski (AP) effect. The apparent anisotropy in the distribution of observed galaxies arise from two main sources, the redshift-space distortion (RSD) effect due to the galaxy peculiar velocities, and the geometric distortion when incorrect cosmological models are assumed for transforming redshift to comoving distance, known as the AP effect. Anisotropies produced by the RSD effect are, although large, maintaining a nearly uniform magnitude over a large range of redshift, while the degree of anisotropies from the AP effect varies with redshift by much larger magnitude. We split the DR12 galaxies into six redshift bins, measure the 2-point correlation function in each bin, and assess the redshift evolution of anisotropies. We obtain constraints of $\Omega_m=0.290 \pm 0.053,\ \ w = -1.07 \pm 0.15$, which are comparable with the current constraints from other cosmological probes such as type Ia supernovae, cosmic microwave background, and baryon acoustic oscillation (BAO). Combining these cosmological probes with our method yield tight constraints of $ \Omega_m = 0.301 \pm 0.006,\ w=-1.054 \pm 0.025$. Our method is complementary to the other large scale structure probes like BAO and topology. We expect this technique will play an important role in deriving cosmological constraints from large scale structure surveys., Comment: 21 pages, 14 figures. Accepted for publication in ApJ

The Australian Astronomical Observatory's TAIPAN instrument deploys 150 Starbug robots to position optical fibres to accuracies of 0.3 arcsec, on a 32 cm glass field plate on the focal plane of the 1.2 m UK-Schmidt telescope. This paper describes the software system developed to control and monitor the Starbugs, with particular emphasis on the automated path-finding algorithms, and the metrology software which keeps track of the position and motion of individual Starbugs as they independently move in a crowded field. The software employs a tiered approach to find a collision-free path for every Starbug, from its current position to its target location. This consists of three path-finding stages of increasing complexity and computational cost. For each Starbug a path is attempted using a simple method. If unsuccessful, subsequently more complex (and expensive) methods are tried until a valid path is found or the target is flagged as unreachable., Comment: 10 pages, to be published in Proc. SPIE 9913, Software and Cyberinfrastructure for Astronomy IV; 2016

We develop a galaxy assignment scheme that populates dark matter halos with galaxies by tracing the most bound member particles (MBPs) of simulated halos. Several merger-timescale models based on analytic calculations and numerical simulations are adopted as the survival time of mock satellite galaxies. We build mock galaxy samples from halo merger data of the Horizon Run 4 $N$-body simulation from $z = 12-0$. We compare group properties and two-point correlation functions (2pCFs) of mock galaxies with those of volume-limited SDSS galaxies, with $r$-band absolute magnitudes of $\mathcal{M}_r - 5 \log h < -21$ and $-20$ at $z=0$. It is found that the MBP-galaxy correspondence scheme reproduces the observed population of SDSS galaxies in massive galaxy groups ($M > 10^{14} h^{-1} M_{\odot}$) and the small-scale 2pCF ($r_{\rm p} < 10 h^{-1} {\rm Mpc}$) quite well for the majority of the merger timescale models adopted. The new scheme outperforms the previous subhalo-galaxy correspondence scheme by more than $2\sigma$., Comment: 23 pages, 8 figures

The Horizon Run 4 is a cosmological $N$-body simulation designed for the study of coupled evolution between galaxies and large-scale structures of the Universe, and for the test of galaxy formation models. Using $6300^3$ gravitating particles in a cubic box of $L_{\rm box} = 3150 ~h^{-1}{\rm Mpc}$, we build a dense forest of halo merger trees to trace the halo merger history with a halo mass resolution scale down to $M_s = 2.7 \times 10^{11} h^{-1}{\rm M_\odot}$. We build a set of particle and halo data, which can serve as testbeds for comparison of cosmological models and gravitational theories with observations. We find that the FoF halo mass function shows a substantial deviation from the universal form with tangible redshift evolution of amplitude and shape. At higher redshifts, the amplitude of the mass function is lower, and the functional form is shifted toward larger values of $\ln (1/\sigma)$. We also find that the baryonic acoustic oscillation feature in the two-point correlation function of mock galaxies becomes broader with a peak position moving to smaller scales and the peak amplitude decreasing for increasing directional cosine $\mu$ compared to the linear predictions. From the halo merger trees built from halo data at 75 redshifts, we measure the half-mass epoch of halos and find that less massive halos tend to reach half of their current mass at higher redshifts. Simulation outputs including snapshot data, past lightcone space data, and halo merger data are available at http://sdss.kias.re.kr/astro/Horizon-Run4/., Comment: 17 pages, 17 Figures, Accepted for Publication in JKAS. Data available at http://sdss.kias.re.kr/astro/Horizon-Run4/

Radio relics detected in the outskirts of galaxy clusters are thought to trace radio-emitting relativistic electrons accelerated at cosmological shocks. In this study, using the cosmological hydrodynamic simulation data for the large-scale structure formation and adopting a diffusive shock acceleration (DSA) model for the production of cosmic-ray (CR) electrons, we construct mock radio and X-ray maps of simulated galaxy clusters that are projected in the sky plane. Various properties of shocks and radio relics, including the shock Mach number, radio spectral index and luminosity are extracted from the synthetic maps and compared with observations. A substantial fraction of radio and X-ray shocks identified in these maps involve multiple shock surfaces along line of sights (LoSs), and the morphology of shock distributions in the maps depends on the projection direction. Among multiple shocks in a given LoS, radio observations tend to pick up stronger shocks with flatter radio spectra, while X-ray observations preferentially select weaker shocks with larger kinetic energy flux. As a result, the shock Mach numbers and locations derived from radio and X-ray observations could differ from each other in some cases. We also find that the distributions of the spectral index and radio power of the synthetic radio relics are somewhat inconsistent with those of observed real relics; a bit more radio relics have been observed closer to the cluster core and with steeper spectral indices. We suggest the inconsistency could be explained, if very weak shocks with $M_s \la 2$ accelerate CR electrons more efficiently, compared to the DSA model adopted here., Comment: accepted for publication in ApJ

In cosmological scenarios with thermal inflation, extra eras of moduli matter domination, thermal inflation and flaton matter domination exist between primordial inflation and the radiation domination of Big Bang nucleosynthesis. During these eras, cosmological perturbations on small scales can enter and re-exit the horizon, modifying the power spectrum on those scales. The largest modified scale, $k_\mathrm{b}$, touches the horizon size when the expansion changes from deflation to inflation at the transition from moduli domination to thermal inflation. We analytically calculate the evolution of perturbations from moduli domination through thermal inflation and evaluate the curvature perturbation on the constant radiation density hypersurface at the end of thermal inflation to determine the late time curvature perturbation. Our resulting transfer function suppresses the power spectrum by a factor $\sim 50$ at $k \gg k_\mathrm{b}$, with $k_\mathrm{b}$ corresponding to anywhere from megaparsec to subparsec scales depending on the parameters of thermal inflation. Thus, thermal inflation might be constrained or detected by small scale observations such as CMB distortions or 21cm hydrogen line observations.

The TAIPAN instrument, currently being developed for the Australian Astronomical Observatory's UK Schmidt telescope at Siding Spring Observatory, makes use of the AAO's Starbug technology to deploy 150 science fibres to target positions on the optical plane. This paper describes the software system for controlling and deploying the fibre-bearing Starbug robots. The TAIPAN software is responsible for allocating each Starbug to its next target position based on its current position and the distribution of targets, finding a collision-free path for each Starbug, and then simultaneously controlling the Starbug hardware in a closed loop, with a metrology camera used to determine the position of each Starbug in the field during reconfiguration. The software is written in C++ and Java and employs a DRAMA middleware layer (Farrell et al. 1995)., Comment: 4 pages, to be published in ASP Conf. Ser., Taylor, A. R. and Stil, J. M., Eds.; Proceedings of ADASS XXIV

Starbugs are miniaturised robotic devices that position optical fibres over a telescope's focal plane in parallel operation for high multiplex spectroscopic surveys. The key advantage of the Starbug positioning system is its potential to configure fields of hundreds of targets in a few minutes, consistent with typical detector readout times. Starbugs have been selected as the positioning technology for the TAIPAN (Transforming Astronomical Imaging surveys through Polychromatic Analysis of Nebulae) instrument, a prototype for MANIFEST (Many Instrument Fiber System) on the GMT (Giant Magellan Telescope). TAIPAN consists of a 150-fibre Starbug positioner accessing the 6 degree field-of-view of the AAO's UK Schmidt Telescope at Siding Spring Observatory. For TAIPAN, it is important to optimise the target allocation and routing algorithms to provide the fastest configurations times. We present details of the algorithms and results of the simulated performance., Comment: 21 pages, 16 figures, to be published in the Proc. SPIE 9152 (2014)

The outskirts of galaxy clusters are continuously disturbed by mergers and gas infall along filaments, which in turn induce turbulent flow motions and shock waves. We examine the properties of shocks that form within $r_{200}$ in sample galaxy clusters from structure formation simulations. While most of these shocks are weak and inefficient accelerators of cosmic rays (CRs), there are a number of strong, energetic shocks which can produce large amounts of CR protons via diffusive shock acceleration. We show that the energetic shocks reside mostly in the outskirts and a substantial fraction of them are induced by infall of the warm-hot intergalactic medium from filaments. As a result, the radial profile of the CR pressure in the intracluster medium is expected to be broad, dropping off more slowly than that of the gas pressure, and might be even temporarily inverted, peaking in the outskirts. The volume-integrated momentum spectrum of CR protons inside $r_{200}$ has the power-law slope of $4.25 - 4.5$, indicating that the average Mach number of the shocks of main CR production is in the range of $\left< M_s \right>_{\rm CR} \approx 3 - 4$. We suggest that some radio relics with relatively flat radio spectrum could be explained by primary electrons accelerated by energetic infall shocks with $M_s >~ 3$ induced in the cluster outskirts., Comment: To appear in ApJ. Pdf with full resolution figures can be downloaded from http://canopus.cnu.ac.kr/ryu/hrkc.pdf