DELOS provides an efficient and sensitive framework for low-SNR transit searches and represents a practical step toward future searches for longer-period terrestrial planets in Kepler, K2, TESS, PLATO, and Earth 2.0 data.
Abstract
We present DEtection in phase-folded Light curves with cOntrastive Scoring (DELOS), a deep-learning framework that uses contrastive scoring to perform blind searches for shallow transits in Kepler photometry. DELOS combines GPU-accelerated phase folding, optimized phase binning, and a custom one-dimensional convolutional encoder to assign a transit-likeness score to each folded light curve, thereby producing a score periodogram over trial periods without relying on pre-detected threshold-crossing events. Focusing on intermediate-to-long-period signals with orbital periods of 100-150 days, DELOS was trained on 20 million synthetic light curves generated with realistic transit models and Kepler-like noise properties, achieving a validation accuracy of 99.3% on the synthetic validation set. In controlled injection-recovery experiments, DELOS improves the combined precision-recall performance by 15.5% relative to Box-fitting Least Squares (BLS) and 11.25% relative to Transit Least Squares (TLS) in the low Signal-to-Noise Ratios (low-SNR) regime. It also accelerates the search by factors of approximately 3-5 and 74-80 compared with BLS and TLS, respectively. Applied to a selected Kepler validation sample, DELOS recovered all known shallow intermediate-to-long-period transit signals in the tested period range. These results demonstrate that DELOS provides an efficient and sensitive framework for low-SNR transit searches and represents a practical step toward future searches for longer-period terrestrial planets in Kepler, K2, TESS, PLATO, and Earth 2.0 data. Accordingly, this work is intended as a methodological development and validation study, with the detailed astrophysical validation of newly identified candidates deferred to future work.
White dwarfs (WDs) with infrared (IR) excesses probe dusty debris disks, low-mass companions, and the late-stage evolution of planetary and binary systems. Conventional searches usually rely on source-by-source spectral energy distribution (SED) fitting and visual inspection, which become time-consuming for the rapidly growing samples produced by large spectroscopic surveys. We develop a supervised multimodal deep learning framework for scalable preselection of IR-excess WD candidates in the Dark Energy Spectroscopic Instrument (DESI) Data Release 1. The model combines Pan-STARRS1 z- and y-band images, unWISE W1- and W2-band images, and atmospheric, astrometric, and photometric tabular features. On the internal validation set, the model achieved an area under the receiver operating characteristic curve of 0.9765, demonstrating effective separation of literature-reported IR-excess candidates from comparison WDs. Applied to the 10,988 objects in DESI-WD-SEARCH-DATA, the model selected 1886 first-stage candidates for subsequent validation. Image-based screening for Wide-field Infrared Survey Explorer (WISE)-scale contamination and composition-dependent SED validation identified 1041 objects satisfying the adopted excess criteria. Of these, 294 had sufficient photometric coverage for further assessment, and catalog-specific photometric-quality screening yielded a final catalog of 221 candidates, including 204 main-sample and 17 warning candidates. We also provide the complete list of 1886 first-stage candidates with flags recording the outcomes of subsequent screening steps. This catalog provides targets for future high-resolution IR imaging, spectroscopic follow-up, and studies of the physical origins of IR excesses around WDs.
Nan-Lu Huang, Y. Mei, Lei Tan et al.· Astrophysical Journal Supple...· 0 citations
An enhancement pipeline that operates entirely within classical signal processing is proposed, providing a transparent alternative to black-box machine learning methods while remaining practical on standard personal computers.
S. Bhattacharya· Asian journal of applied sci...· 0 citations
Traditional weak-gravitational-lensing shear estimators are carefully calibrated but struggle to fully capture realistic galaxy morphologies, point-spread-function (PSF) effects, blending, and noise in deep surveys, while blindly trained machine learning (ML) models can introduce significant calibration biases. Here, we construct a fully D4-equivariant deep neural network for galaxy shape measurement whose architecture enforces symmetry under 90° rotations and mirror transformations, and adopt the Analytical Calibration framework to calibrate the model using its backpropagated gradients. For isolated galaxies in LSST-like single-band simulations, we demonstrate that our approach achieves ∼10% lower shape noise than the traditional moment-based Fourier Power Function Shapelets estimator in the high-noise regime, equivalent to a 23% gain in effective galaxy number density, while simultaneously achieving multiplicative biases consistent with zero across a wide range of noise levels, PSF sizes and ellipticities, and magnitude selection cuts, with all measurements satisfying ∣m∣ < 10−3 (i.e., within the 0.2% LSST requirement) and most at the ∼10−4 level. We demonstrate this framework on isolated single-band galaxy images with Gaussian noise and known PSFs, establishing a rigorous, physics-informed foundation for future extensions of ML-based shear estimation to blended sources and multiband observations in Stage-IV surveys. All codes and data products will be made publicly available upon acceptance.
Shurui 书睿 Lin 林, Xiangchong Li, Ji Li et al.· Astrophysical Journal· 0 citations
