5,446 research outputs found
The Helioseismic and Magnetic Imager (HMI) Vector Magnetic Field Pipeline: Overview and Performance
The Helioseismic and Magnetic Imager (HMI) began near-continuous full-disk
solar measurements on 1 May 2010 from the Solar Dynamics Observatory (SDO). An
automated processing pipeline keeps pace with observations to produce
observable quantities, including the photospheric vector magnetic field, from
sequences of filtergrams. The primary 720s observables were released in mid
2010, including Stokes polarization parameters measured at six wavelengths as
well as intensity, Doppler velocity, and the line-of-sight magnetic field. More
advanced products, including the full vector magnetic field, are now available.
Automatically identified HMI Active Region Patches (HARPs) track the location
and shape of magnetic regions throughout their lifetime.
The vector field is computed using the Very Fast Inversion of the Stokes
Vector (VFISV) code optimized for the HMI pipeline; the remaining 180 degree
azimuth ambiguity is resolved with the Minimum Energy (ME0) code. The
Milne-Eddington inversion is performed on all full-disk HMI observations. The
disambiguation, until recently run only on HARP regions, is now implemented for
the full disk. Vector and scalar quantities in the patches are used to derive
active region indices potentially useful for forecasting; the data maps and
indices are collected in the SHARP data series, hmi.sharp_720s. Patches are
provided in both CCD and heliographic coordinates.
HMI provides continuous coverage of the vector field, but has modest spatial,
spectral, and temporal resolution. Coupled with limitations of the analysis and
interpretation techniques, effects of the orbital velocity, and instrument
performance, the resulting measurements have a certain dynamic range and
sensitivity and are subject to systematic errors and uncertainties that are
characterized in this report.Comment: 42 pages, 19 figures, accepted to Solar Physic
Information-Theoretic Bounds on the Moments of the Generalization Error of Learning Algorithms
Generalization error bounds are critical to understanding the performance of
machine learning models. In this work, building upon a new bound of the
expected value of an arbitrary function of the population and empirical risk of
a learning algorithm, we offer a more refined analysis of the generalization
behaviour of a machine learning models based on a characterization of (bounds)
to their generalization error moments. We discuss how the proposed bounds --
which also encompass new bounds to the expected generalization error -- relate
to existing bounds in the literature. We also discuss how the proposed
generalization error moment bounds can be used to construct new generalization
error high-probability bounds
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