The paper introduces the phase‑induced amplitude apodisation Zernike wavefront sensor (PIAA‑ZWFS), a modified ZWFS that uses lossless pupil apodisation to concentrate starlight in the focal plane. Using a differentiable modelling framework and Bayesian experimental design, the authors optimise the sensor to minimise the variance of a maximum‑likelihood estimator in the high‑Strehl regime. Simulations show that the PIAA‑ZWFS achieves state‑of‑the‑art performance across various apertures, bandwidths, photon fluxes, and source sizes, closing the gap to the fundamental limit by a factor of 10 (or 2.5) compared to conventional or optimised ZWFS, and outperforming ideal point‑source sensors for stellar diameters larger than 0.8 λ/D without sacrificing dynamic range.
arXiv:2607. 11928v1 Announce Type: new Abstract: Single-shot fringe projection profilometry (FPP) networks that regress depth directly can exploit a shape-prior shortcut, recovering depth from object boundaries rather than from fringe phase.
arXiv:2609.20880v1 Announce Type: cross Abstract: Direct imaging of exoplanets is limited by the extreme contrast between the star and the planets, which is mitigated using a coronagraph. However, op...
arXiv:2606. 10771v1 Announce Type: cross Abstract: Reinforcement learning (RL)-based algorithms have recently emerged as a promising approach for adaptive optics (AO) control.
The study audits the AION-1 foundation model, a 39‑modality transformer trained on over 200 million astronomical objects, and finds that its reliance on a survey detection channel—specifically the segmentation map—introduces a severe systematic bias. By keeping image tokens unchanged and editing only the segmentation map, all model outputs (flux, size, ellipticity, redshift) shift by factors of 110–4400 compared to a placebo, revealing that the model’s predictions are driven more by detection gating than by the actual light distribution. This bias propagates into cosmological analyses, shifting tomographic mean redshifts by a median 0.71 × the LSST DESC requirement and exceeding it in multiple assignments, while removing the detection channel eliminates the effect without measurable cost. whyItMatters":"The bias in the detection channel directly inflates errors in key astronomical measurements, potentially compromising the precision of cosmological studies that rely on accurate redshift estimates."
arXiv:2609.23161v1 Announce Type: new Abstract: A conventional camera uses millions of pixels to measure radiance from all directions within its field of view. We present an omnidirectional irradianc...
LensBridge is a two‑stage framework that extends reusable aberration‑correction models to handle both lens aberrations and veiling glare (VG). Stage I builds a PSF‑aware diffusion foundation using discrete degradation priors from a large Lens Library, enabling aberration correction without explicit PSF input. Stage II adapts this foundation to compound degradation via frequency‑guided techniques—Frequency‑guided Degradation Completion (FDC) synthesizes training pairs and Frequency‑guided Pseudo Decomposition (FPD) conditions separate adaptation branches—allowing joint aberration correction and VG removal with only a few unpaired target observations.
arXiv:2609.35966v1 Announce Type: cross Abstract: Radio-interferometric images are reconstructed from sparsely sampled visibilities, and CLEAN-based imaging can struggle with spatial filtering, compl...
STAR (Structure-aware Test-time Adaptation for diffusion-based light field Reconstruction) addresses the ill‑posed problem of reconstructing a light field from limited, noisy focal stack measurements by adapting a pre‑trained diffusion prior to each test scene. The method freezes the diffusion model and trains three lightweight adapters that jointly capture within‑view spatial details, cross‑view angular dependencies, and disparity across views. Experiments show that STAR surpasses state‑of‑the‑art approaches in both two‑ and three‑focal‑sheet settings while achieving faster inference times than methods that update parameters at test time.
The paper introduces the Measurement-Adaptive Light-Cone Transform (MALCT), a physics-guided framework that maintains the efficient Fourier-domain inversion of traditional light-cone transform methods while adapting to real-world measurement noise and deviations in non-line-of-sight imaging. MALCT integrates an analytical response with a learned response via measurement-conditioned weighting and predicts a frequency-dependent regularization map within a unified variational formulation. An attention-enhanced spatio-temporal feature extraction module captures long-range temporal dependencies and local spatial correlations, enabling robust reconstruction under low-signal conditions and with reduced acquisition time, as demonstrated on synthetic data and real measurements from various NLOS systems.
The paper introduces 3D Point Splatting (3DPS), a differentiable point renderer for millimeter‑wave radar that directly implements the radar equation with an explicit material model and complex‑valued outputs. Unlike previous methods, 3DPS preserves phase information and supports multiple output formats (ADC, CRP, RA) without retraining. On six outdoor ColoRadar scenes, it achieves a mean Pearson correlation of 0.587 on held‑out RA images, outperforming optical‑NVS baselines by 1.7× to 5.2× and trains in roughly three minutes per scene on a single RTX 4090.
EncoTESS is a compact Time Series Foundation Model trained on TESS 2‑minute light curves that encodes stellar variability into a fixed‑size latent space, handling noise, irregular sampling, and data gaps. It improves age estimation for young K and M stars (≤100 Myr) and older M stars (≤1 Gyr) by outperforming traditional rotation period and variability amplitude indicators. The model’s lightweight architecture (~1 % of typical TSFMs) allows deployment on standard laptops and can be extended to other TESS cadences and missions like Kepler and PLATO.
The paper introduces 3D Point Splatting (3DPS), a differentiable point renderer for millimeter-wave radar that directly implements the radar equation with an explicit material model and complex-valued outputs. Unlike prior methods, 3DPS simultaneously provides physical fidelity, complex-valued rendering, and multi-viewpoint tractability, enabling a single optimized scene to generate ADC, complex range profile, and range-azimuth images via FFT pipelines. On six outdoor ColoRadar scenes, 3DPS achieves a mean Pearson correlation of 0.587 on held-out range-azimuth images, outperforming optical-NVS baselines by 1.7x to 5.2x, and trains in about three minutes per scene on an RTX 4090.