The relation between gas density and velocity power spectra in galaxy clusters: high-resolution hydrodynamic simulations and the role of conduction

Exploring the ICM power spectrum can help us to probe the physics of galaxy clusters. Using high-resolution 3D plasma simulations, we study the statistics of the velocity field and its relation with the thermodynamic perturbations. The normalization of the ICM spectrum (density, entropy, or pressure) is linearly tied to the level of large-scale motions, which excite both gravity and sound waves due to stratification. For low 3D Mach number M~0.25, gravity waves mainly drive entropy perturbations, traced by preferentially tangential turbulence. For M>0.5, sound waves start to significantly contribute, passing the leading role to compressive pressure fluctuations, associated with isotropic (or slightly radial) turbulence. Density and temperature fluctuations are then characterized by the dominant process: isobaric (low M), adiabatic (high M), or isothermal (strong conduction). Most clusters reside in the intermediate regime, showing a mixture of gravity and sound waves, hence drifting towards isotropic velocities. Remarkably, regardless of the regime, the variance of density perturbations is comparable to the 1D Mach number. This linear relation allows to easily convert between gas motions and ICM perturbations, which can be exploited by Chandra, XMM data and by the forthcoming Astro-H. At intermediate and small scales (10-100 kpc), the turbulent velocities develop a Kolmogorov cascade. The thermodynamic perturbations act as effective tracers of the velocity field, broadly consistent with the Kolmogorov-Obukhov-Corrsin advection theory. Thermal conduction acts to damp the gas fluctuations, washing out the filamentary structures and steepening the spectrum, while leaving unaltered the velocity cascade. The ratio of the velocity and density spectrum thus inverts the downtrend shown by the non-diffusive models, allowing to probe the presence of significant conductivity in the ICM.Comment: Accepted by A&A; 15 pages, 10 figures; added insights and references - thank you for the positive feedbac

Quantifying properties of ICM inhomogeneities

We present a new method to identify and characterize the structure of the intracluster medium (ICM) in simulated galaxy clusters. The method uses the median of gas properties, such as density and pressure, which we show to be very robust to the presence of gas inhomogeneities. In particular, we show that the radial profiles of median gas properties are smooth and do not exhibit fluctuations at locations of massive clumps in contrast to mean and mode properties. It is shown that distribution of gas properties in a given radial shell can be well described by a log-normal PDF and a tail. The former corresponds to a nearly hydrostatic bulk component, accounting for ~99% of the volume, while the tail corresponds to high density inhomogeneities. We show that this results in a simple and robust separation of the diffuse and clumpy components of the ICM. The FWHM of the density distribution grows with radius and varies from ~0.15 dex in cluster centre to ~0.5 dex at 2r_500 in relaxed clusters. The small scatter in the width between relaxed clusters suggests that the degree of inhomogeneity is a robust characteristic of the ICM. It broadly agrees with the amplitude of density perturbations in the Coma cluster. We discuss the origin of ICM density variations in spherical shells and show that less than 20% of the width can be attributed to the triaxiality of the cluster gravitational potential. As a link to X-ray observations of real clusters we evaluated the ICM clumping factor with and without high density inhomogeneities. We argue that these two cases represent upper and lower limits on the departure of the observed X-ray emissivity from the median value. We find that the typical value of the clumping factor in the bulk component of relaxed clusters varies from ~1.1-1.2 at r_500 up to ~1.3-1.4 at r_200, in broad agreement with recent observations.Comment: 16 pages, 12 figure, accepted to MNRA

The Radial Distribution of Galaxies in LCDM clusters

We study the radial distribution of subhalos and galaxies using high-resolution cosmological simulations of galaxy clusters formed in the concordance LCDM cosmology. In agreement with previous studies, we find that the radial distribution of subhalos is significantly less concentrated than that of the dark matter, when subhalos are selected using their present-day gravitationally bound mass. We show that the difference in the radial distribution is not a numerical artifact and is due to tidal stripping. The subhalos in the cluster core lose more than 70% of their initial mass since accretion, while the average tidal mass loss for halos near the virial radius is ~30%. This introduces a radial bias in the spatial distribution of subhalos when they are selected using their tidally truncated mass. We demonstrate that the radial bias disappears almost entirely if subhalos are selected using their mass or circular velocity at the accretion epoch. The comparisons of the results of dissipationless simulations to the observed distribution of galaxies in clusters are therefore sensitive to the selection criteria used to select subhalo samples. Using the simulations that include cooling and starformation, we show that the radial distribution of subhalos is in reasonable agreement with the observed radial distribution of galaxies in clusters for 0.1<R/R200<2.0, if subhalos are selected using the stellar mass of galaxies. The radial bias is minimized in this case because the stars are located in the centers of dark matter subhalos and are tightly bound. The stellar mass of an object is therefore approximately conserved as the dark matter is stripped from the outer regions. Nevertheless, the concentration of the radial distribution of galaxies is systematically lower than that of the dark matter.Comment: submitted to ApJ, 12 pages, 12 figure

CMB Lensing Power Spectrum Biases from Galaxies and Clusters using High-angular Resolution Temperature Maps

The lensing power spectrum from cosmic microwave background (CMB) temperature maps will be measured with unprecedented precision with upcoming experiments, including upgrades to ACT and SPT. Achieving significant improvements in cosmological parameter constraints, such as percent level errors on sigma_8 and an uncertainty on the total neutrino mass of approximately 50 meV, requires percent level measurements of the CMB lensing power. This necessitates tight control of systematic biases. We study several types of biases to the temperature-based lensing reconstruction signal from foreground sources such as radio and infrared galaxies and the thermal Sunyaev-Zel'dovich effect from galaxy clusters. These foregrounds bias the CMB lensing signal due to their non-Gaussian nature. Using simulations as well as some analytical models we find that these sources can substantially impact the measured signal if left untreated. However, these biases can be brought to the percent level if one masks galaxies with fluxes at 150 GHz above 1 mJy and galaxy clusters with masses above M_vir = 10^14 M_sun. To achieve such percent level bias, we find that only modes up to a maximum multipole of l_max ~ 2500 should be included in the lensing reconstruction. We also discuss ways to minimize additional bias induced by such aggressive foreground masking by, for example, exploring a two-step masking and in-painting algorithm.Comment: 14 pages, 14 figures, to be submitted to Ap

[Commentary] What does left-right autonomic asymmetry signify?

The situation-dependent lateralization of sympathetic electrodermal arousal during real-life stress (Picard, Fedor, & Ayzenberg, 2016) may challenge a unitary notion of arousal, and call into question the practice of unilateral electrodermal recording, but there are broader implications. Here we consider a potential relationship between stress-induced lateralized shifts in electrodermal activity, and a theory concerning lateralized emotion-induced cardiac arrhythmia

A Deep Learning Approach to Galaxy Cluster X-ray Masses

We present a machine-learning approach for estimating galaxy cluster masses from Chandra mock images. We utilize a Convolutional Neural Network (CNN), a deep machine learning tool commonly used in image recognition tasks. The CNN is trained and tested on our sample of 7,896 Chandra X-ray mock observations, which are based on 329 massive clusters from the IllustrisTNG simulation. Our CNN learns from a low resolution spatial distribution of photon counts and does not use spectral information. Despite our simplifying assumption to neglect spectral information, the resulting mass values estimated by the CNN exhibit small bias in comparison to the true masses of the simulated clusters (-0.02 dex) and reproduce the cluster masses with low intrinsic scatter, 8% in our best fold and 12% averaging over all. In contrast, a more standard core-excised luminosity method achieves 15-18% scatter. We interpret the results with an approach inspired by Google DeepDream and find that the CNN ignores the central regions of clusters, which are known to have high scatter with mass.Comment: 10 pages, 6 figures, accepted for publication in The Astrophysical Journa