Real-time Finite Fault Rupture Detector (FinDer) for large earthquakes

To provide rapid estimates of fault rupture extent during large earthquakes, we have developed the Finite Fault Rupture Detector algorithm, ‘FinDer’. FinDer uses image recognition techniques to detect automatically surface-projected fault ruptures in real-time (assuming a line source) by estimating their current centroid position, length L, and strike θ. The approach is based on a rapid high-frequency near/far-source classification of ground motion amplitudes in a dense seismic network (station spacing <50 km), and comparison with a set of pre-calculated templates using ‘Matching by Correlation’. To increase computational efficiency, we perform the correlation in the wavenumber domain. FinDer keeps track of the current dimensions of a rupture in progress. Errors in L are typically on the same order as station spacing in the network. The continuously updated estimates of source geometries as provided by FinDer make predicted shaking intensities more accurate and thus more useful for earthquake early warning, ShakeMaps, and related products. The applicability of the algorithm is demonstrated for several recorded and simulated earthquakes with different focal mechanisms, including the 2009 M_w 6.3 L’Aquila (Italy), the 1999 M_w 7.6 ChiChi (Taiwan) and the M_w 7.8 ShakeOut scenario earthquake on the southern San Andreas Fault (California)

Interactions of Satellite Galaxies in Cosmological Dark Matter Halos

We present a statistical analysis of the interactions between satellite galaxies in cosmological dark matter halos taken from fully self-consistent high-resolution simulations of galaxy clusters. We show that the number distribution of satellite encounters has a tail that extends to as many as 3-4 encounters per orbit. On average 30% of the substructure population had at least one encounter (per orbit) with another satellite galaxy. However, this result depends on the age of the dark matter host halo with a clear trend for more interactions in younger systems. We also report a correlation between the number of encounters and the distance of the satellites to the centre of the cluster: satellite galaxies closer to the centre experience more interactions. However, this can be simply explained by the radial distribution of the substructure population and merely reflects the fact that the density of satellites is higher in those regions. In order to find substructure galaxies we applied (and present) a new technique based upon the N-body code MLAPM. This new halo finder MHF (MLAPM's-Halo-Finder) acts with exactly the same accuracy as the N-body code itself and is therefore free of any bias and spurious mismatch between simulation data and halo finding precision related to numerical effects.Comment: 6 pages, 4 figures, accepted by PASA (refereed contribution to the 5th Galactic Chemodynamics workshop, July 2003

The DEEP2 Galaxy Redshift Survey: Clustering of Groups and Group Galaxies at z~1

We study the clustering properties of groups and of galaxies in groups in the DEEP2 Galaxy Redshift Survey dataset at z~1. Four clustering measures are presented: 1) the group correlation function for 460 groups with estimated velocity dispersions of sigma>200 km/s, 2) the galaxy correlation for the full galaxy sample, using a flux-limited sample of 9800 objects between 0.7<z<1.0, 3) the galaxy correlation for galaxies in groups, and 4) the group-galaxy cross-correlation function. Using the observed number density and clustering amplitude of the groups, the estimated minimum group dark matter halo mass is M_min~6 10^12 h^-1 M_Sun for a flat LCDM cosmology. Groups are more clustered than galaxies, with a relative bias of b=1.7 +/-0.04 on scales r_p=0.5-15 Mpc/h. Galaxies in groups are also more clustered than the full galaxy sample, with a scale-dependent relative bias which falls from b~2.5 +/-0.3 at r_p=0.1 Mpc/h to b~1 +/-0.5 at r_p=10 Mpc/h. The correlation functions for all galaxies and galaxies in groups can be fit by a power-law on scales r_p=0.05-20 Mpc/h. We empirically measure the contribution to the projected correlation function for galaxies in groups from a `one-halo' term and a `two-halo' term by counting pairs of galaxies in the same or in different groups. The projected cross-correlation between shows that red galaxies are more centrally concentrated in groups than blue galaxies at z~1. DEEP2 galaxies in groups appear to have a shallower radial distribution than that of mock galaxy catalogs made from N-body simulations, which assume a central galaxy surrounded by satellite galaxies with an NFW profile. We show that the clustering of galaxies in groups can be used to place tighter constraints on the halo model than can be gained from using the usual galaxy correlation function alone.Comment: 22 pages, 12 figures, in emulateapj format, accepted to ApJ, minor changes made to match published versio

Cosmic voids detection without density measurements

Cosmic voids are effective cosmological probes to discriminate among competing world models. Their identification is generally based on density or geometry criteria that, because of their very nature, are prone to shot noise. We propose two void finders that are based on dynamical criterion to select voids in Lagrangian coordinates and minimise the impact of sparse sampling. The first approach exploits the Zel'dovich approximation to trace back in time the orbits of galaxies located in voids and their surroundings, the second uses the observed galaxy-galaxy correlation function to relax the objects' spatial distribution to homogeneity and isotropy. In both cases voids are defined as regions of the negative velocity divergence, that can be regarded as sinks of the back-in-time streamlines of the mass tracers. To assess the performance of our methods we used a dark matter halo mock catalogue CoDECS, and compared the results with those obtained with the ZOBOV void finder. We find that the void divergence profiles are less scattered than the density ones and, therefore, their stacking constitutes a more accurate cosmological probe. The significance of the divergence signal in the central part of voids obtained from both our finders is 60% higher than for overdensity profiles in the ZOBOV case. The ellipticity of the stacked void measured in the divergence field is closer to unity, as expected, than what is found when using halo positions. Therefore our void finders are complementary to the existing methods, that should contribute to improve the accuracy of void-based cosmological tests.Comment: 12 pages, 18 figures, accepted for publication in MNRA

The Rockstar Phase-Space Temporal Halo Finder and the Velocity Offsets of Cluster Cores

We present a new algorithm for identifying dark matter halos, substructure, and tidal features. The approach is based on adaptive hierarchical refinement of friends-of-friends groups in six phase-space dimensions and one time dimension, which allows for robust (grid-independent, shape-independent, and noise-resilient) tracking of substructure; as such, it is named Rockstar (Robust Overdensity Calculation using K-Space Topologically Adaptive Refinement). Our method is massively parallel (up to 10^5 CPUs) and runs on the largest current simulations (>10^10 particles) with high efficiency (10 CPU hours and 60 gigabytes of memory required per billion particles analyzed). A previous paper (Knebe et al 2011) has shown Rockstar to have class-leading recovery of halo properties; we expand on these comparisons with more tests and higher-resolution simulations. We show a significant improvement in substructure recovery as compared to several other halo finders and discuss the theoretical and practical limits of simulations in this regard. Finally, we present results which demonstrate conclusively that dark matter halo cores are not at rest relative to the halo bulk or satellite average velocities and have coherent velocity offsets across a wide range of halo masses and redshifts. For massive clusters, these offsets can be up to 350 km/s at z=0 and even higher at high redshifts. Our implementation is publicly available at http://code.google.com/p/rockstar .Comment: 20 pages, 14 figures. Minor revisions to match accepted versio

The Characterised Noise Hi source finder: Detecting Hi galaxies using a novel implementation of matched filtering

The spectral line datacubes obtained from the Square Kilometre Array (SKA) and its precursors, such as the Australian SKA Pathfinder (ASKAP), will be sufficiently large to necessitate automated detection and parametrisation of sources. Matched filtering is widely acknowledged as the best possible method for the automated detection of sources. This paper presents the Characterised Noise Hi (CNHI) source finder, which employs a novel implementation of matched filtering. This implementation is optimised for the 3-D nature of the planned Wide-field ASKAP Legacy L-band All- sky Blind surveY's (WALLABY) Hi spectral line observations. The CNHI source finder also employs a novel sparse representation of 3-D objects, with a high compression rate, to implement Lutz one-pass algorithm on datacubes that are too large to process in a single pass. WALLABY will use ASKAP's phenomenal 30 square degree field of view to image \sim 70% of the sky. It is expected that WALLABY will find 500 000 Hi galaxies out to z \sim 0.2.Comment: Part of the 2012 PASA Source Finding Special Issue, 10 figure

The Cross-Correlation between Galaxies and Groups: Probing the Galaxy Distribution in and around Dark Matter Haloes

We determine the cross-correlation function between galaxies and galaxy groups, using both the Two-Degree Field Galaxy Redshift Survey (2dFGRS) and the Sloan Digital Sky Survey (SDSS). We study the cross-correlation as a function of group mass, and as a function of the luminosity, stellar mass, colour, spectral type and specific star formation rate of the galaxies. All these cross-correlation functions show a clear transition from the `1-halo' to the `2-halo' regimes on a scale comparable to the virial radius of the groups in consideration. On scales larger than the virial radius, all cross-correlation functions are roughly parallel, consistent with the linear bias model. In particular, the large scale correlation amplitudes are higher for more massive groups, and for brighter and redder galaxies. In the `1-halo' regime, the cross-correlation function depends strongly on the definition of the group center. We consider both a luminosity-weighted center (LWC) and a center defined by the location of the brightest group galaxy (BGC). With the first definition, the bright early-type galaxies in massive groups are found to be more centrally concentrated than the fainter, late-type galaxies. Using the BGC, and excluding the brightest galaxy from the cross correlation analysis, we only find significant segregation in massive groups (M \gta 10^{13}h^{-1}\msun) for galaxies of different spectral types (or colours or specific star formation rates). In haloes with masses \la 10^{13}h^{-1}\msun, there is a significant deficit of bright satellite galaxies. Comparing the results from the 2dFGRS with those obtained from realistic mock samples, we find that the distribution of galaxies in groups is much less concentrated than dark matter haloes predicted by the current $\Lambda$CDM model. (Abridged)Comment: 18 pages, 11 figures. Accepted for publication in MNRAS, 1 table added, fig7 replace

Void Dynamics

Cosmic voids are becoming key players in testing the physics of our Universe. Here we concentrate on the abundances and the dynamics of voids as these are among the best candidates to provide information on cosmological parameters. Cai, Padilla \& Li (2014) use the abundance of voids to tell apart Hu \& Sawicki $f(R)$ models from General Relativity. An interesting result is that even though, as expected, voids in the dark matter field are emptier in $f(R)$ gravity due to the fifth force expelling away from the void centres, this result is reversed when haloes are used to find voids. The abundance of voids in this case becomes even lower in $f(R)$ compared to GR for large voids. Still, the differences are significant and this provides a way to tell apart these models. The velocity field differences between $f(R)$ and GR, on the other hand, are the same for halo voids and for dark matter voids. Paz et al. (2013), concentrate on the velocity profiles around voids. First they show the necessity of four parameters to describe the density profiles around voids given two distinct void populations, voids-in-voids and voids-in-clouds. This profile is used to predict peculiar velocities around voids, and the combination of the latter with void density profiles allows the construction of model void-galaxy cross-correlation functions with redshift space distortions. When these models are tuned to fit the measured correlation functions for voids and galaxies in the Sloan Digital Sky Survey, small voids are found to be of the void-in-cloud type, whereas larger ones are consistent with being void-in-void. This is a novel result that is obtained directly from redshift space data around voids. These profiles can be used to remove systematics on void-galaxy Alcock-Pacinsky tests coming from redshift-space distortions.Comment: 8 pages, 4 figures, to appear in the proceedings of IAU308 Symposium "The Zeldovich Universe

Data compression using correlations and stochastic processes in the ALICE Time Projection chamber

In this paper lossless and a quasi lossless algorithms for the online compression of the data generated by the Time Projection Chamber (TPC) detector of the ALICE experiment at CERN are described. The first algorithm is based on a lossless source code modelling technique, i.e. the original TPC signal information can be reconstructed without errors at the decompression stage. The source model exploits the temporal correlation that is present in the TPC data to reduce the entropy of the source. The second algorithm is based on a lossy source code modelling technique. In order to evaluate the consequences of the error introduced by the lossy compression, the results of the trajectory tracking algorithms that process data offline are analyzed, in particular, with respect to the noise introduced by the compression. The offline analysis has two steps: cluster finder and track finder. The results on how these algorithms are affected by the lossy compression are reported. In both compression technique entropy coding is applied to the set of events defined by the source model to reduce the bit rate to the corresponding source entropy. Using TPC simulated data, the lossless and the lossy compression achieve a data reduction to 49.2% of the original data rate and respectively in the range of 35% down to 30% depending on the desired precision.In this study we have focused on methods which are easy to implement in the frontend TPC electronics.Comment: 8 pages, 3 figures, Talk from the 2003 Computing in High Energy and Nuclear Physics (CHEP03), La Jolla, Ca, USA, March 2003, PSN THLT00

Bootstrapping bilinear models of robotic sensorimotor cascades

We consider the bootstrapping problem, which consists in learning a model of the agent's sensors and actuators starting from zero prior information, and we take the problem of servoing as a cross-modal task to validate the learned models. We study the class of bilinear dynamics sensors, in which the derivative of the observations are a bilinear form of the control commands and the observations themselves. This class of models is simple yet general enough to represent the main phenomena of three representative robotics sensors (field sampler, camera, and range-finder), apparently very different from one another. It also allows a bootstrapping algorithm based on hebbian learning, and that leads to a simple and bioplausible control strategy. The convergence properties of learning and control are demonstrated with extensive simulations and by analytical arguments