New aperture photometry of QSO 0957+561; application to time delay and microlensing

We present a re-reduction of archival CCD frames of the doubly imaged quasar 0957+561 using a new photometry code. Aperture photometry with corrections for both cross contamination between the quasar images and galaxy contamination is performed on about 2650 R-band images from a five year period (1992-1997). From the brightness data a time delay of 424.9 +/- 1.2 days is derived using two different statistical techniques. The amount of gravitational microlensing in the quasar light curves is briefly investigated, and we find unambiguous evidence of both long term and short term microlensing. We also note the unusual circumstance regarding time delay estimates for this gravitational lens. Estimates by different observers from different data sets or even with the same data sets give lag estimates differing by typically 8 days, and error bars of only a day or two. This probably indicates several complexities where the result of each estimate depends upon the details of the calculation.Comment: 14 pages, 16 figures (several in color

Discovery and Validation of Kepler-452b: A 1.6-Re Super Earth Exoplanet in the Habitable Zone of a G2 Star

We report on the discovery and validation of Kepler-452b, a transiting planet identified by a search through the 4 years of data collected by NASA's Kepler Mission. This possibly rocky 1.63$^{+0.23}_{-0.20}$ R$_\oplus$ planet orbits its G2 host star every 384.843$^{+0.007}_{0.012}$ days, the longest orbital period for a small (R$_p$ < 2 R$_\oplus$) transiting exoplanet to date. The likelihood that this planet has a rocky composition lies between 49% and 62%. The star has an effective temperature of 5757$\pm$85 K and a log g of 4.32$\pm$0.09. At a mean orbital separation of 1.046$^{+0.019}_{-0.015}$ AU, this small planet is well within the optimistic habitable zone of its star (recent Venus/early Mars), experiencing only 10% more flux than Earth receives from the Sun today, and slightly outside the conservative habitable zone (runaway greenhouse/maximum greenhouse). The star is slightly larger and older than the Sun, with a present radius of 1.11$^{+0.15}_{-0.09}$ R$_\odot$ and an estimated age of 6 Gyr. Thus, Kepler-452b has likely always been in the habitable zone and should remain there for another 3 Gyr.Comment: 19 pages, 16 figure

On the Origin of a Sunquake during the 29 March 2014 X1 Flare

Helioseismic data from the HMI instrument have revealed a sunquake associated with the X1 flare SOL2014-03-29T17:48 in active region NOAA 12017. We try to discover if acoustic-like impulses or actions of the Lorentz force caused the sunquake. We analyze spectro-polarimetric data obtained with the Facility Infrared Spectrometer (FIRS) at the Dunn Solar Telescope (DST). Fortuitously the FIRS slit crossed the flare kernel close to the acoustic source, during the impulsive phase. The infrared FIRS data remain unsaturated throughout the flare. Stokes profiles of lines of Si I 1082.7 nm and He I 1083.0 nm are analyzed. At the flare footpoint, the Si I 1082.7 nm core intensity increases by a factor of several, the IR continuum increases by 4 +/- 1%. Remarkably, the Si I core resembles the classical Ca II K line's self-reversed profile. With nLTE radiative models of H, C, Si and Fe these properties set the penetration depth of flare heating to 100 +/- 100 km, i.e. photospheric layers. Estimates of the non-magnetic energy flux are at least a factor of two less than the sunquake energy flux. Milne-Eddington inversions of the Si I line show that the local magnetic energy changes are also too small to drive the acoustic pulse. Our work raises several questions: Have we "missed" the signature of downward energy propagation? Is it intermittent in time and/or non-local? Does the 1-2 s photospheric radiative damping time discount compressive modes?Comment: in pres

A methodology for assessing the effect of correlations among muscle synergy activations on task-discriminating information

Muscle synergies have been hypothesized to be the building blocks used by the central nervous system to generate movement. According to this hypothesis, the accomplishment of various motor tasks relies on the ability of the motor system to recruit a small set of synergies on a single-trial basis and combine them in a task-dependent manner. It is conceivable that this requires a fine tuning of the trial-to-trial relationships between the synergy activations. Here we develop an analytical methodology to address the nature and functional role of trial-to-trial correlations between synergy activations, which is designed to help to better understand how these correlations may contribute to generating appropriate motor behavior. The algorithm we propose first divides correlations between muscle synergies into types (noise correlations, quantifying the trial-to-trial covariations of synergy activations at fixed task, and signal correlations, quantifying the similarity of task tuning of the trial-averaged activation coefficients of different synergies), and then uses single-trial methods (task-decoding and information theory) to quantify their overall effect on the task-discriminating information carried by muscle synergy activations. We apply the method to both synchronous and time-varying synergies and exemplify it on electromyographic data recorded during performance of reaching movements in different directions. Our method reveals the robust presence of information-enhancing patterns of signal and noise correlations among pairs of synchronous synergies, and shows that they enhance by 9–15% (depending on the set of tasks) the task-discriminating information provided by the synergy decompositions. We suggest that the proposed methodology could be useful for assessing whether single-trial activations of one synergy depend on activations of other synergies and quantifying the effect of such dependences on the task-to-task differences in muscle activation patterns

Quantum circuit fidelity estimation using machine learning

The computational power of real-world quantum computers is limited by errors. When using quantum computers to perform algorithms which cannot be efficiently simulated classically, it is important to quantify the accuracy with which the computation has been performed. In this work we introduce a machine-learning-based technique to estimate the fidelity between the state produced by a noisy quantum circuit and the target state corresponding to ideal noise-free computation. Our machine learning model is trained in a supervised manner, using smaller or simpler circuits for which the fidelity can be estimated using other techniques like direct fidelity estimation and quantum state tomography. We demonstrate that, for simulated random quantum circuits with a realistic noise model, the trained model can predict the fidelities of more complicated circuits for which such methods are infeasible. In particular, we show the trained model may make predictions for circuits with higher degrees of entanglement than were available in the training set, and that the model may make predictions for non-Clifford circuits even when the training set included only Clifford-reducible circuits. This empirical demonstration suggests classical machine learning may be useful for making predictions about beyond-classical quantum circuits for some non-trivial problems.Comment: 27 pages, 6 figure