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.
By Adam Haroon, Cody Fleming, Beiwen Li
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...
By Manuela Casta\~neda-Medina (LIRA), Yann Gutierrez (LIRA), Johan Mazoyer (LIRA, CNRS), Baptiste Abeloos, Laurent Mugnier, Olivier Herscovici-Schiller
arXiv:2606. 10771v1 Announce Type: cross Abstract: Reinforcement learning (RL)-based algorithms have recently emerged as a promising approach for adaptive optics (AO) control.
By Jalo Nousiainen, Vincent Chambouleyron, Benoit Neichel, Sylvain Cetre, Jean-Francois Sauvage, Angelie Alagao, Markus Kasper, Jonathan Dray, Romain Fetick, Byron Engler
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."
By Ihor Kendiukhov
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...
By Jeremy Klotz, Shree K. Nayar
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.
By Xiaolong Qian, Zhonghua Yi, Qi Jiang, Kailun Yang, Shuhang Xie, Shaohua Gao, Kaiwei Wang