DELOS is a deep‑learning framework that uses contrastive scoring to perform blind searches for shallow transits in Kepler photometry. It combines GPU‑accelerated phase folding, optimized phase binning, and a custom one‑dimensional convolutional encoder to produce a transit‑likeness score periodogram without relying on pre‑detected events. In tests on synthetic data and controlled injection‑recovery experiments, DELOS outperforms traditional methods (BLS and TLS) in precision‑recall and speed, and successfully recovers all known shallow intermediate‑to‑long‑period transit signals in a selected Kepler sample.
By Qingtian Liu, Jian Ge, XingChen Yan, Kevin Willis, Xinyu Yao, QuanQuan Hu, Jiapeng Zhu
arXiv:2606. 02778v1 Announce Type: cross Abstract: I present EXOVEIL, a transit detection system that learns what a star's brightness should look like and flags when reality disagrees.
By Pratik Priyanshu
arXiv:2606. 23766v1 Announce Type: cross Abstract: The detection and atmospheric characterization of exoplanets have entered a new data-intensive era driven by the James Webb Space Telescope and the upcoming Ariel mission.
By Muallim Yakubu, Vwavware Oruaode Jude
arXiv:2602. 10330v3 Announce Type: replace-cross Abstract: Context: The characterization of exoplanetary atmospheres has been transformed by the James Webb Space Telescope (JWST), whose infrared sensitivity enables transmission spectroscopy at unprecedented precision.
By David S. Duque-Casta\~no, Lauren Flor-Torres, Jorge I. Zuluaga
arXiv:2609.14735v1 Announce Type: cross
Abstract: Deep Learning (DL)-based channel estimation has shown high accuracy and low latency in terrestrial 5G NR, but Low Earth Orbit (LEO) Non-Terrestrial N...
By Miguel Camelo Botero, Nina Slamnik-Krije\v{s}torac, Johann Marquez-Barja
arXiv:2606. 18464v1 Announce Type: cross Abstract: Detecting the tiny Doppler shifts induced by Earth-mass planets in stellar radial-velocity measurements remains extremely challenging due to stellar activity.
By Isidro G\'omez-Vargas, Xavier Dumusque, Yinan Zhao, Khaled Al Moulla, Michael Cretignier
arXiv:2606. 18338v1 Announce Type: new Abstract: The search for life beyond Earth will depend on detecting faint signatures in the atmospheres of potentially habitable exoplanets.
By Edward T. Stevenson, Mei Ting Mak, Eric Wolf, Denis E. Sergeev, Tobi Hammond, N. J. Mayne, Miles Cranmer
arXiv:2608.24516v1 Announce Type: cross
Abstract: Satellite-based distributed learning promises to train machine-learning models directly in orbit using massive, globally dispersed sensor data, there...
By Hao Wu, Kin Whye Chew, Yizhan Han, Han Li, Jingxian Wang
arXiv:2608.29609v1 Announce Type: new
Abstract: Semantic segmentation is a crucial task for understanding Mars, the most Earth-like planet in our solar system. However, it is challenging because the...
By Ming-Han Lee, Chi-Yeh Chen
The paper presents a new monocular spacecraft pose estimation model that achieves the lowest reported mean rotation errors on the SPEED+ lightbox and sunlamp test sets. By replacing smaller encoders with a large self‑supervised ViT foundation model (DINOv3) and scaling up to 840 M parameters, the authors improve accuracy from 300 M to 840 M parameters without saturation. The 840 M model also runs on a Jetson Orin NX 16 GB with 133.8 ms per crop and 32.0 W power draw, demonstrating embedded inference feasibility while training solely on synthetic data.
By John Church, Vazghen Nikolian
Satellite-based distributed learning promises to train machine-learning models directly in orbit using massive, globally dispersed sensor data, thereby avoiding large-scale data downloads to ground se...
arXiv:2609.26631v1 Announce Type: new
Abstract: Accurate ground-based cloud classification is important for atmospheric monitoring, solar-energy forecasting, aviation weather assessment, and climate...
By Esther Bou Dagher, Viktoriya Bu-Dager, Boguslaw Zegarlinski