Regression with noisy input features shrinks predictions toward the mean by a factor 1 divided by (1 plus the squared ratio of noise to signal spread), and this bias persists regardless of training sample size, label accuracy, or sample distribution.
The Data Reduction Pipeline for the Apache Point Observatory Galactic Evolution Experiment
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abstract
The Apache Point Observatory Galactic Evolution Experiment (APOGEE), part of the Sloan Digital Sky Survey III, explores the stellar populations of the Milky Way using the Sloan 2.5-m telescope linked to a high resolution (R~22,500), near-infrared (1.51-1.70 microns) spectrograph with 300 optical fibers. For over 150,000 predominantly red giant branch stars that APOGEE targeted across the Galactic bulge, disks and halo, the collected high S/N (>100 per half-resolution element) spectra provide accurate (~0.1 km/s) radial velocities, stellar atmospheric parameters, and precise (~0.1 dex) chemical abundances for about 15 chemical species. Here we describe the basic APOGEE data reduction software that reduces multiple 3D raw data cubes into calibrated, well-sampled, combined 1D spectra, as implemented for the SDSS-III/APOGEE data releases (DR10, DR11 and DR12). The processing of the near-IR spectral data of APOGEE presents some challenges for reduction, including automated sky subtraction and telluric correction over a 3 degree diameter field and the combination of spectrally dithered spectra. We also discuss areas for future improvement.
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Why Machine Learning Models Systematically Underestimate Extreme Values
Regression with noisy input features shrinks predictions toward the mean by a factor 1 divided by (1 plus the squared ratio of noise to signal spread), and this bias persists regardless of training sample size, label accuracy, or sample distribution.