1806.05676
The Clusters Hiding in Plain Sight (CHiPS) survey: a first discovery of a massive nearby cluster around PKS1353-341
Somboonpanyakul, et al
Introduce the first result of the CHiPS survey, which aims to discover new, nearby, and massive galaxy clusters that were incorrectly identified as isolated point sources in the ROSAT All-Sky Survey. Present a Chandra X-ray observation of the first newly discovered low-redshift (z=0.223) galaxy cluster with a central X-ray bright point source, PKS1353-341. After removing the point source contribution to the cluster core, determine various properties of the cluster. The presence of a relaxed X-ray morphology, a central temperature drop, and a central cooling time around 400 Myr point to it being a strong cool-core cluster. The central galaxy appears to be forming stars at the rate of 6.2±3.6 Msun/yr, corresponding to ~1% of the classical cooling prediction. The SMBH in the central galaxy appear to be accreting at ~0.1% of the Eddington rate with the total power output of ~5e45 ergs/s, split nearly equally between radiative and mechanical power. Comparing the cluster's bulk properties with those of other known clusters (e.g., M500=6.9(+4.3)(-2.6)e14 Msun, and L_X=7e44 erg/s), show that this cluster is sufficiently luminous that it would have been identified as a cluster in the ROSAT All Sky-Survey data, if it did not have such a bright central point source. This discovery demonstrate the potential of the CHiPS survey to find massive nearby clusters with extreme central properties that may have been missed or misidentified by previous surveys.
1806.05685
Compact groups analysis using weak gravitational lensing II: CFHT Stripe 82 data
Chalela, et al
In this work, present a lensing study of CGs using data obtained from the high quality CFHT Stripe 82 Survey. Using stacking techniques, obtain the average density contrast profile. Analyise the lensing signal dependence on the groups surface brightness and morphological content, for CGs in the z range z=0.2-0.4. Obtain a larger lensing signal for CGs with higher surface brightness, probably due to their lower contamination by interlopers. Also, find a strong dependence of the lensing signal on the group concentration parameter, with the most concentrated quintile showing a significant lensing signal, consistent with an isothermal sphere with sigma_V=336 ± 28 km/s and a NFW profile with R_200=0.60±0.05 Mpc/h_70. Also compare lensing results with dynamical estimates finding a good agreement with lensing determinations for CGs with higher surface brightness and higher concentration indexes. On the other hand, CGs that are more contaminated by interlopers show larger dynamical dispersions, since interlopers bias dynamical estimate to larger values, although the lensing signal is weakened.
1806.05697
Electromagnetic emission from supermassive binary black holes approaching merger
d'Ascoli, et al
Present a fully relativistic prediction fo the EM emission from the surrounding gas for a SMBH system approaching merger. Using a rays-racing code to post-process data from a general relativistic 3d MHD sim, generate images and spectra, and analyze the viewing angle dependence of the light emitted. When the accretion rate is relatively high, the circumbinary disk accretion stream, and mini-disks combine to emit light in the UV/EUV bands. Posit a thermal Compton hard X-ray spectrum for coronal emission; at high accretion rates, it is almost entirely produced in the mini-disks, but at lower accretion rates it is the primary radiation mechanism in the mini-disks and accretion streams as well. Due to relativistic beaming and gravitational lensing, the angular distribution of the power radiated is strongly anisotropic, especially near the equatorial plane.
1806.05870
BAM: Bias assignment method to generate mock catalogs
Balaguera-Antolínez, Kitaura, et al
Present BAM: a novel Bias Assignment Method envisaged to generate mock catalogs by linking the continuous cosmic dark matter field to a discrete population of tracers, such as DM haloes or galaxies. Using a reference high resolution cosmo N-body sim to extract a bias scheme, can generate halo catalogues starting from a much coarser density fields calculated from downsampled initial conditions using efficient structure formation solvers. Characterize the halo-bias relation as a function of a number of properties (e.g. local density, cosmic web type) to the DM density field defined on a mesh of a 3 Mpc/h cell side resolution, derived from the fast structure formation solvers. In this way, the bias description automatically includes stochastic, deterministic, local and non-local component directly extracted from full N-body sims. Sample the halo density field according to the observed halo bias, such that the 2pt statistics of the mock halo catalog follows the same statistics as the reference. By construction, the approach reaches percentage accuracy, 1%, in the majority of the k-range up to the Nyquist frequency without systematic deviations for the power spectra (about k~1 h/Mpc) using either particle mesh or Lagrangian perturbation theory biased solvers. When using phase-space mapping to compensate the low resolution of the approximate gravity solvers, the method is able to reproduce the bispectra of the reference within 10% precision studying configurations tracing the quasi-nonlinear regime. Therefore BAM promises to become a standard technique to produce mock halo and galaxy catalogs for future galaxy surveys and cosmological studies being highly accurate, efficient and parameter free.
1806.05995
Learning from deep learning: better cosmological parameter inference from weak lensing maps
Ribli, et al
WL: Due to NL on small scales, the traditional analysis with 2pt statistics does not fully capture all the underlying information. Multiple inference method were prosed to extract more details based on higher order statistics, peak statistics, Minkowski functions and recently convolutional neural networks (CNN). Present an improved CNN that gives significantly better estimates of Omega_m and sigma8 cosmological parameters from simulated convergence maps than the state of the art method and also is free of systematic bias. Going beyond "black box" style predictions, the investigation of the features learned by a high performing CNN revealed interesting insights. Without direct human assistance, only from the training data, the CNN discovered 2 familiar convolutional operators: the discrete Laplace operator and a Roberts cross kernel, which both characterize the steepness of the peaks. Using this insight, constructed a new, easy-to-understand, and robust peak counting algorithm which uses these operators, instead of the heights of the peaks. The new scheme significantly reduced prediction errors, and turned out to be even more accurate than the neural network.
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