UMI: GPU-Accelerated Asymmetric Robust Estimator for Photometric Detrending in Exoplanet Transit Searches
Abstract
We present UMI (Unified Median Iterative), a novel robust location estimator for detrending photometric time series in exoplanet transit surveys. UMI modifies the standard Tukey bisquare M-estimator with two innovations: (1) an asymmetric weight function that penalizes downward deviations (transit dips) more aggressively than upward ones, exploiting the physical constraint that transits are always below the stellar continuum, and (2) an upper-RMS scale estimator computed from above-median residuals only, ensuring that transit dips never contaminate the noise estimate. Implemented as a fused HIP/CUDA GPU kernel, UMI achieves 69x faster detrending (3.4 ms vs 234 ms per star) and 37x faster full pipeline throughput compared to the wotan biweight implementation. Injection-recovery tests across TESS, Kepler, and K2 show that UMI's advantage is concentrated at planet-scale transit depths above the photometric noise floor: at 0.1% transit depth, UMI reduces median depth recovery error from 20.5% to 15.8% on TESS (23% improvement) and from 14.6% to 4.2% on Kepler (71% improvement). At shallower depths approaching the noise floor, all sliding-window methods converge to comparable performance. Validated across 802 confirmed exoplanets from TESS and Kepler, UMI occupies a previously unfilled region of the speed-accuracy tradeoff for transit detrending. The tool is publicly available as pip install torchflat.
Cite
@article{arxiv.2604.06602,
title = {UMI: GPU-Accelerated Asymmetric Robust Estimator for Photometric Detrending in Exoplanet Transit Searches},
author = {Omar Khan},
journal= {arXiv preprint arXiv:2604.06602},
year = {2026}
}
Comments
10 pages, 7 figures, 4 tables. v2: expanded validation including held-out train/test split, multi-mission consistency across TESS/Kepler/K2, depth-regime characterization (5-1000 ppm), explicit limitations section, and recommended parameter table. Code available at https://github.com/omarkhan2217/TorchFlat