喷气推进实验室

A polarization-aware direction-of-arrival (DoA) detection scheme is conceived that leverages the intrinsic vector sensitivity of a single Rydberg atomic vapor cell to achieve quantum-enhanced angle resolution. Our core idea lies in the fact that the vector nature of an electromagnetic wave is uniquely determined by its orthogonal electric and magnetic field components, both of which can be retrieved by a single Rydberg atomic receiver via electromagnetically induced transparency (EIT)-based spectroscopy. To be specific, in the presence of a static magnetic bias field that defines a stable quantization axis, a pair of sequential EIT measurements is carried out in the same vapor cell. Firstly, the electric-field polarization angle is extracted from the Zeeman-resolved EIT spectrum associated with an electric-dipole transition driven by the radio frequency (RF) field. Within the same experimental cycle, the RF field is then retuned to a magnetic-dipole resonance, producing Zeeman-resolved EIT peaks for decoding the RF magnetic-field orientation. This scheme exhibits a dual yet independent sensitivity on both angles, allowing for precise DoA reconstruction without the need for spatial diversity or phase referencing. Building on this foundation, we derive the quantum Fisher-information matrix (QFIM) and obtain a closed-form quantum Cramer-Rao bound (QCRB) for the joint estimation of polarization and orientation angles. Finally, simulation results spanning various quantum parameters validate the proposed approach and identify optimal operating regimes. With appropriately chosen polarization and magnetic-field geometries, a single vapor cell is expected to achieve sub-0.1 degrees angle resolution at moderate RF-field driving strengths.

We have surveyed all conventional methods proposed or conceivable for obtaining resolved images of an Earth-like exoplanet. Generating a 10 x 10 pixel map of a 1 R circle plus world at 10 pc demands approximate to 0.85 pas angular resolution and photon collection sufficient for SNR >= 5 per "micropixel." We derived diffraction-limit and photon-budget requirements for (i) large single-aperture space telescopes with internal coronagraphs, extremely large telescopes with extreme adaptive optics, (5) pupil-densified "hypertelescopes," (vi) indirect reconstructions (rotational light-curve inversion, eclipse mapping, intensity interferometry), and (vii) diffraction-occultation by Solar System bodies. Even though these approaches serve their primary goals- exoplanet discovery and initial coarse characterization-each remains orders of magnitude away from delivering a spatially resolved image. In every case, technology readiness falls short, and fundamental barriers leave them 2-5 orders of magnitude below the angular resolution and photon-budget thresholds needed to map an Earth analog even on decadal timescales. Ultimately, an in situ platform delivered to less than or similar to 0.1 AU of the target could, in principle, overcome both diffraction and photon-starvation limits-but such a mission far exceeds current propulsion, autonomy, and communications capabilities. By contrast, the solar gravitational lens-providing on-axis gain of similar to 1010 and inherent pas-scale focusing once an imaging spacecraft reaches heliocentric distances beyond >= 550 AU-appears uniquely capable of simultaneously meeting both the resolution and photon-budget requirements. If the several missionspecific risks (coronal calibration, focal-plane scanning, deconvolution) can be retired, the solar gravitational lens could enable true, resolved surface images and spatially resolved spectroscopy of Earth-like exoplanets in our stellar neighborhood.

This paper introduces a high resolution, machine learning-ready heliophysics dataset derived from NASA’s Solar Dynamics Observatory (SDO), specifically designed to advance machine learning (ML) applications in solar physics and space weather forecasting. The dataset includes processed imagery from the Atmospheric Imaging Assembly (AIA) and Helioseismic and Magnetic Imager (HMI), spanning a solar cycle from May 2010 to December 2024. To ensure suitability for ML tasks, the data has been preprocessed, including correction of spacecraft roll angles, orbital adjustments, exposure normalization, and degradation compensation. We also provide auxiliary application benchmark datasets complementing the core SDO dataset. These provide benchmark applications for central heliophysics and space weather tasks such as active region segmentation, active region emergence forecasting, coronal field extrapolation, solar flare prediction, solar Extreme Ultraviolet (EUV) spectra prediction, and solar wind speed estimation. By establishing a unified, standardized data collection, this dataset aims to facilitate benchmarking, enhance reproducibility, and accelerate the development of AI-driven models for critical space weather prediction tasks, bridging gaps between solar physics, machine learning, and operational forecasting.

The laser interferometer lunar antenna (LILA), a concept for measuring sub-Hz gravitational waves on the Moon, would use laser strainmeters to obtain extremely sensitive strain measurements from 1 mHz to 1 Hz. With proposed strain sensitivities, LILA would also be able to measure the normal modes of the Moon from 1-10 mHz at high signal-to-noise ratio. Such measurements would enable significant advances in our understanding of both the spherically symmetric and even 3D deep internal structure of the Moon. Strainmeter measurements may even be able to detect the translational mode of the solid inner core of the Moon at frequencies below 0.1 mHz. Inertial seismometers, on the other hand, are unlikely to reach the performance of similar to 10(-16) m s(-2)/ Hz required to reliably detect normal modes below 5-10 mHz, even with optimistic assumptions on future projected performance, although they would offer excellent ability to perform body wave measurements which give a complementary view of the lunar interior.

We present a unified post-Newtonian framework for relativistic timing and coordinate transformations across the Barycentric, Geocentric, and Lunicentric Celestial Reference Systems (LCRS) and six timescales: Barycentric Coordinate Time (TCB), Geocentric Coordinate Time (TCG), Terrestrial Time (TT), Barycentric Dynamical Time (TDB), Lunicentric Coordinate Time (TCL), and Lunar Time (TL). Following IAU Resolution II (2024), we implement an LCRS metric with time coordinate TCL, linked to TCB via the IAU B1.5 prescription with lunar quantities. We truncate all series to retain contributions above a fractional threshold of 5 x 10-18 and timing terms exceeding 0.1 ps, expanding the lunar gravity to degree & ell; = 9 with Love number variations and including external tidal and inertial multipoles through the octupole. We derive closed-form mappings among TCB, TCG, TT, TDB, TCL, and TL, giving proper-to-coordinate time conversions and one-/two-way time-transfer corrections at subpicosecond accuracy. Secular rates and periodic perturbations from kinematic dilation, lunar monopole and multipoles, Earth tides, and gravitomagnetism are evaluated for clocks on the lunar surface in Very Low and Low Lunar Orbits, in elliptical lunar frozen orbits, at the Earth-Moon L1 point, and in Near-rectilinear Halo Orbits (NRHOs). Harmonics through & ell; = 9 and tides through & ell; = 8 suffice to meet 5 x 10-18 stability in deep cislunar regimes (e.g., NRHO, L1), supporting subpicosend synchronization and centimeter-level navigation, whereas near-surface and Very Low Lunar Orbits require much higher degree (typically & ell;max greater than or similar to 300 ). The framework implements the IAU LCRS/TCL prescription and supplies accuracy budgets for high-precision time/frequency transfer, relativistic geodesy, quantum links, and fundamental tests beyond low Earth orbit.