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Register a new userNew Astrophysical Opportunities Exploiting Spatio-Temporal Optical Correlations
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The space-time correlations of streams of photons can provide fundamentally new channels of information about the Universe. Todays astronomical observations essentially measure certain amplitude coherence functions produced by a source. The spatial correlations of wave fields has traditionally been exploited in Michelson-style amplitude interferometry. However the technology of the past was largely incapable of fine timing resolution and recording multiple beams. When time and space correlations are combined it is possible to achieve spectacular measurements that are impossible by any other means. Stellar intensity interferometry is ripe for development and is one of the few unexploited mechanisms to obtain potentially revolutionary new information in astronomy. As we discuss below, the modern use of stellar intensity interferometry can yield unprecedented measures of stellar diameters, binary stars, distance measures including Cepheids, rapidly rotating stars, pulsating stars, and short-time scale fluctuations that have never been measured before.
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Dynamic magnetic resonance imaging (MRI) exhibits high correlations in k-space and time. In order to accelerate the dynamic MR imaging and to exploit k-t correlations from highly undersampled data, here we propose a novel deep learning based approach for dynamic MR image reconstruction, termed k-t NEXT (k-t NEtwork with X-f Transform). In particular, inspired by traditional methods such as k-t BLAST and k-t FOCUSS, we propose to reconstruct the true signals from aliased signals in x-f domain to exploit the spatio-temporal redundancies. Building on that, the proposed method then learns to recover the signals by alternating the reconstruction process between the x-f space and image space in an iterative fashion. This enables the network to effectively capture useful information and jointly exploit spatio-temporal correlations from both complementary domains. Experiments conducted on highly undersampled short-axis cardiac cine MRI scans demonstrate that our proposed method outperforms the current state-of-the-art dynamic MR reconstruction approaches both quantitatively and qualitatively.
The promise of any small improvement in the performance of light-harvesting devices, is sufficient to drive enormous experimental efforts. However these efforts are almost exclusively focused on enhancing the power conversion efficiency with specific material properties and harvesting layers thickness, without exploiting the correlations present in sunlight -- in part because such correlations are assumed to have negligible effect. Here we show, by contrast, that these spatio-temporal correlations are sufficiently relevant that the use of specific detector geometries would significantly improve the performance of harvesting devices. The resulting increase in the absorption efficiency, as the primary step of energy conversion, may also act as a potential driving mechanism for artificial photosynthetic systems. Our analysis presents design guidelines for optimal detector geometries with realistic incident intensities based on current technological capabilities
The dynamical response of Coulomb-interacting particles in nano-clusters are analyzed at different temperatures characterizing their solid- and liquid-like behavior. Depending on the trap-symmetry, both the spatial and temporal correlations undergo slow, stretched exponential relaxations at long times, arising from spatially correlated motion in string-like paths. Our results indicate that the distinction between the `solid and `liquid is soft: While particles in a `solid flow producing dynamic heterogeneities, motion in `liquid yields unusually long tail in the distribution of particle-displacements. A phenomenological model captures much of the subtleties of our numerical simulations.
Astrophysical measurements away from the 1 AU orbit of Earth can enable several astrophysical science cases that are challenging or impossible to perform from Earthbound platforms, including: building a detailed understanding of the extragalactic background light throughout the electromagnetic spectrum; measurements of the properties of dust and ice in the inner and outer solar system; determinations of the mass of planets and stellar remnants far from luminous stars using gravitational microlensing; and stable time-domain astronomy. Though potentially transformative for astrophysics, opportunities to fly instrumentation capable of these measurements are rare, and a mission to the distant solar system that includes instrumentation expressly designed to perform astrophysical science, or even one primarily for a different purpose but capable of precise astronomical investigation, has not yet been flown. In this White Paper, we describe the science motivations for this kind of measurement, and advocate for future flight opportunities that permit intersectional collaboration and cooperation to make these science investigations a reality.
We present a spatio-temporal AI framework that concurrently exploits both the spatial and time-variable features of gravitationally lensed supernovae in optical images to ultimately aid in the discovery of such exotic transients in wide-field surveys. Our spatio-temporal engine is designed using recurrent convolutional layers, while drawing from recent advances in variational inference to quantify approximate Bayesian uncertainties via a confidence score. Using simulated Young Supernova Experiment (YSE) images as a showcase, we find that the use of time-series images yields a substantial gain of nearly 20 per cent in classification accuracy over single-epoch observations, with a preliminary application to mock observations from the Legacy Survey of Space and Time (LSST) yielding around 99 per cent accuracy. Our innovative deep learning machinery adds an extra dimension in the search for gravitationally lensed supernovae from current and future astrophysical transient surveys.
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