UMI: GPU-Accelerated Asymmetric Robust Estimator for Photometric Detrending in Exoplanet Transit Searches

TL;DR

UMI is a GPU-accelerated robust estimator for photometric detrending in exoplanet transit searches, improving speed and accuracy especially at detectable transit depths.

Contribution

It introduces a novel asymmetric robust estimator, UMI, optimized with GPU acceleration, significantly enhancing transit detection performance over existing methods.

Findings

UMI achieves 69x faster detrending than previous implementations.

At 0.1% transit depth, UMI reduces median recovery error by up to 71%.

Validated on 802 confirmed exoplanets, UMI improves speed-accuracy tradeoff.

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.