Lens magnification by CL0024+1654 in the U and R band

[ABRIDGED] We estimate the total mass distribution of the galaxy cluster CL0024+1654 from the measured source depletion due to lens magnification in the R band. Within a radius of 0.54Mpc/h, a total projected mass of (8.1+/-3.2)*10^14 M_sol/h (EdS) is measured, which corresponds to a mass- to-light ratio of M/L(B)=470+/-180. We compute the luminosity function of CL0024+1654 in order to estimate contamination of the background source counts from cluster galaxies. Three different magnification-based reconstruction methods are employed using both local and non-local techniques. We have modified the standard single power-law slope number count theory to incorporate a break and applied this to our observations. Fitting analytical magnification profiles of different cluster models to the observed number counts, we find that the cluster is best described either by a NFW model with scale radius r_s=334+/-191 kpc/h and normalisation kappa_s=0.23+/-0.08 or a power-law profile with slope xi=0.61+/-0.11, central surface mass density kappa_0=1.52+/-0.20 and assuming a core radius of r_core=35 kpc/h. The NFW model predicts that the cumulative projected mass contained within a radius R scales as M(<R)=2.9*10^14*(R/1')^[1.3-0.5lg (R/1')] M_sol/h. Finally, we have exploited the fact that flux magnification effectively enables us to probe deeper than the physical limiting magnitude of our observations in searching for a change of slope in the U band number counts. We rule out both a total flattening of the counts with a break up to U_AB<=26.6 and a change of slope, reported by some studies, from dlog N/dm=0.4->0.15 up to U_AB<=26.4 with 95% confidence.Comment: 19 pages, 12 figures, submitted to A&A. New version includes more robust U band break analysis and contamination estimates, plus new plot

Deformation at Krafla and Bjarnarflag geothermal areas, Northern Volcanic Zone of Iceland, 1993-2015

The Krafla volcanic system has geothermal areas within the Krafla caldera and at Bjarnarflag in the Krafla fissure swarm, 9-km south of the Krafla caldera. Arrays of boreholes extract geothermal fluids for power plants in both areas. We collected and analyzed InSAR, GPS, and leveling data spanning 1993–2015 in order to investigate crustal deformation in these areas. The volcanic zone hosting the geothermal areas is also subject to large scale regional deformation processes, including plate spreading and deflation of the Krafla volcanic system. These deformation processes have to be taken into account in order to isolate the geothermal deformation signal. Plate spreading produces the largest horizontal displacements, but the regional deformation pattern also suggests readjustment of the Krafla system at depth after the 1975–1984 Krafla rifting episode. Observed deformation can be fit by an inflation source at about 20 km depth north of Krafla and a deflation source at similar depth directly below the Krafla caldera. Deflation signal along the fissure swarm can be reproduced by a 1-km wide sill at 4 km depth closing by 2–4 cm per year. These sources are considered to approximate the combined effects of vertical deformation associated with plate spreading and post-rifting response. Local deformation at the geothermal areas is well resolved in addition to these signals. InSAR shows that deformation at Bjarnarflag is elongated along the direction of the Krafla fissure swarm (∼4 km by ∼2 km) while it is circular at Krafla (∼5 km diameter). Rates of deflation at Krafla and Bjarnarflag geothermal areas have been relatively steady. Average volume decrease of about 6.6 ×10⁵ m³/yr for Krafla and 3.9 ×10⁵ m³/yr for Bjanarflag are found at sources located at ∼1.5 km depth, when interpreted by a spherical point source of pressure. This volume change represents about 8 ×10 −3 m³/ton of the mass of geothermal fluid extracted per year, indicating important renewal of the geothermal reservoir by water flow

Post-eruptive volcano inflation following major magma drainage: Interplay between models of viscoelastic response influence and models of magma inflow at Bárðarbunga caldera, Iceland, 2015-2018

&amp;lt;p&amp;gt;Unrest at B&amp;amp;#225;r&amp;amp;#240;arbunga after a caldera collapse in 2014-2015 includes elevated seismicity beginning about six months after the eruption ended, including nine Mw&amp;gt;4.5 earthquakes. The earthquakes occurred mostly on the northern and southern parts of a caldera ring fault. Global Navigation Satellite System (GNSS, in particular, Global Positioning System; GPS) and Interferometric Synthetic Aperture Radar (InSAR) geodesy are applied to evaluate the spatial and temporal pattern of ground deformation around B&amp;amp;#225;r&amp;amp;#240;arbunga caldera outside the icecap, in 2015-2018, when deformation rates were relatively steady. The aim is to study the role of viscoelastic relaxation following major magma drainage versus renewed magma inflow as an explanation for the ongoing unrest.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The largest horizontal velocity is measured at GPS station KISA (3 km from caldera rim), 141 mm/yr in direction N47&amp;lt;sup&amp;gt;o&amp;lt;/sup&amp;gt;E relative to the Eurasian plate in 2015-2018. GPS and InSAR observations show that the velocities decay rapidly outward from the caldera. We correct our observations for Glacial Isostatic Adjustment and plate spreading to extract the deformation related to volcanic activity. After this correction, some GPS sites show subsidence.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We use a reference Earth model to initially evaluate the contribution of viscoelastic processes to the observed deformation field. We model the deformation within a half-space composed of a 7-km thick elastic layer on top of a viscoelastic layer with a viscosity of 5 x 10&amp;lt;sup&amp;gt;18&amp;lt;/sup&amp;gt; Pa s, considering two co-eruptive contributors to the viscoelastic relaxation: &amp;amp;#8220;non-piston&amp;amp;#8221; magma withdrawal at 10 km depth (modelled as pressure drop in a spherical source) and caldera collapse (modelled as surface unloading). The other model we test is the magma inflow in an elastic half-space. Both the viscoelastic relaxation and magma inflow create horizontal outward movements around the caldera, and uplift at the surface projection of the source center in 2015-2018. Viscoelastic response due to magma withdrawal results in subsidence in the area outside the icecap. Magma inflow creates rapid surface velocity decay as observed.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We explore further two parameters in the viscoelastic reference model: the viscosity and the &amp;quot;non-piston&amp;quot; magma withdrawal volume. Our comparison between the corrected InSAR velocities and viscoelastic models suggests a viscosity of 2.6&amp;amp;#215;10&amp;lt;sup&amp;gt;18&amp;lt;/sup&amp;gt; Pa s and 0.36 km&amp;lt;sup&amp;gt;3&amp;lt;/sup&amp;gt; of &amp;amp;#8220;non-piston&amp;amp;#8221; magma withdrawal volume, given by the optimal reduced Chi-squared statistic. When the deformation is explained using only magma inflow into a single spherical source (and no viscoelastic response), the optimal model suggests an inflow rate at 1&amp;amp;#215;10&amp;lt;sup&amp;gt;7&amp;lt;/sup&amp;gt; m&amp;lt;sup&amp;gt;3&amp;lt;/sup&amp;gt;/yr at 700 m depth. A magma inflow model with more model parameters is also a possible explanation, including sill inflation at 10 km together with slip on caldera ring faults. Our reference Earth model and the two end-member models suggest that there is a trade-off between the viscoelastic relaxation and the magma inflow, since they produce similar deformation signals outside the icecap. However, to reproduce details of the observed deformation, both processes are required. A viscoelastic-only model cannot fully explain the fast velocity decay away from the caldera, whereas a magma inflow-only model cannot explain the subsidence observed at several locations.&amp;lt;/p&amp;gt; </jats:p

Unexpected large eruptions from buoyant magma bodies within viscoelastic crust

Large volume effusive eruptions with relatively minor observed precursory signals are at odds with widely used models to interpret volcano deformation. Here we propose a new modelling framework that resolves this discrepancy by accounting for magma buoyancy, viscoelastic crustal properties, and sustained magma channels. At low magma accumulation rates, the stability of deep magma bodies is governed by the magma-host rock density contrast and the magma body thickness. During eruptions, inelastic processes including magma mush erosion and thermal effects, can form a sustained channel that supports magma flow, driven by the pressure difference between the magma body and surface vents. At failure onset, it may be difficult to forecast the final eruption volume; pressure in a magma body may drop well below the lithostatic load, create under-pressure and initiate a caldera collapse, despite only modest precursors

Gradual caldera collapse at Bárdarbunga volcano, Iceland, regulated by lateral magma outflow

Large volcanic eruptions on Earth commonly occur with a collapse of the roof of a crustal magma reservoir, forming a caldera. Only a few such collapses occur per century, and the lack of detailed observations has obscured insight into the mechanical interplay between collapse and eruption.We usemultiparameter geophysical and geochemical data to show that the 110-squarekilometer and 65-meter-deep collapse of Bárdarbunga caldera in 2014-2015 was initiated through withdrawal of magma, and lateral migration through a 48-kilometers-long dike, from a 12-kilometers deep reservoir. Interaction between the pressure exerted by the subsiding reservoir roof and the physical properties of the subsurface flow path explain the gradual, nearexponential decline of both collapse rate and the intensity of the 180-day-long eruption

Time Estimation Predicts Mathematical Intelligence

Background: Performing mental subtractions affects time (duration) estimates, and making time estimates disrupts mental subtractions. This interaction has been attributed to the concurrent involvement of time estimation and arithmetic with general intelligence and working memory. Given the extant evidence of a relationship between time and number, here we test the stronger hypothesis that time estimation correlates specifically with mathematical intelligence, and not with general intelligence or working-memory capacity. Methodology/Principal Findings: Participants performed a (prospective) time estimation experiment, completed several subtests of the WAIS intelligence test, and self-rated their mathematical skill. For five different durations, we found that time estimation correlated with both arithmetic ability and self-rated mathematical skill. Controlling for non-mathematical intelligence (including working memory capacity) did not change the results. Conversely, correlations between time estimation and non-mathematical intelligence either were nonsignificant, or disappeared after controlling for mathematical intelligence. Conclusions/Significance: We conclude that time estimation specifically predicts mathematical intelligence. On the basis of the relevant literature, we furthermore conclude that the relationship between time estimation and mathematical intelligence is likely due to a common reliance on spatial ability

Reduction of Pavlovian bias in schizophrenia: Enhanced effects in clozapine-administered patients

The negative symptoms of schizophrenia (SZ) are associated with a pattern of reinforcement learning (RL) deficits likely related to degraded representations of reward values. However, the RL tasks used to date have required active responses to both reward and punishing stimuli. Pavlovian biases have been shown to affect performance on these tasks through invigoration of action to reward and inhibition of action to punishment, and may be partially responsible for the effects found in patients. Forty-five patients with schizophrenia and 30 demographically-matched controls completed a four-stimulus reinforcement learning task that crossed action ("Go" or "NoGo") and the valence of the optimal outcome (reward or punishment-avoidance), such that all combinations of action and outcome valence were tested. Behaviour was modelled using a six-parameter RL model and EEG was simultaneously recorded. Patients demonstrated a reduction in Pavlovian performance bias that was evident in a reduced Go bias across the full group. In a subset of patients administered clozapine, the reduction in Pavlovian bias was enhanced. The reduction in Pavlovian bias in SZ patients was accompanied by feedback processing differences at the time of the P3a component. The reduced Pavlovian bias in patients is suggested to be due to reduced fidelity in the communication between striatal regions and frontal cortex. It may also partially account for previous findings of poorer "Go-learning" in schizophrenia where "Go" responses or Pavlovian consistent responses are required for optimal performance. An attenuated P3a component dynamic in patients is consistent with a view that deficits in operant learning are due to impairments in adaptively using feedback to update representations of stimulus value

magma mixing history and dynamics of an eruption trigger

The most violent and catastrophic volcanic eruptions on Earth have been triggered by the refilling of a felsic volcanic magma chamber by a hotter more mafic magma. Examples include Vesuvius 79 AD, Krakatau 1883, Pinatubo 1991, and Eyjafjallajokull 2010. Since the first hypothesis, plenty of evidence of magma mixing processes, in all tectonic environments, has accumulated in the literature allowing this natural process to be defined as fundamental petrological processes playing a role in triggering volcanic eruptions, and in the generation of the compositional variability of igneous rocks. Combined with petrographic, mineral chemistry and geochemical investigations, isotopic analyses on volcanic rocks have revealed compositional variations at different length scales pointing to a complex interplay of fractional crystallization, mixing/mingling and crustal contamination during the evolution of several magmatic feeding systems. But to fully understand the dynamics of mixing and mingling processes, that are impossible to observe directly, at a realistically large scale, it is necessary to resort to numerical simulations of the complex interaction dynamics between chemically different magmas

Nurses' perceptions of aids and obstacles to the provision of optimal end of life care in ICU

Contains fulltext : 172380.pdf (publisher's version ) (Open Access