C. Lowder | Searcharxiv

arXiv2026

Solar Wind Turbulence Spectra and Correlation Scale from 4 to 50 $r_\odot$: STEREO and PUNCH Observations

We investigate the radial evolution of solar wind turbulence using remote-sensing brightness observations from the Solar Terrestrial Relations Observatory (STEREO) and the first science-ready images from the Polarimeter to Unify the Corona and Heliosphere (PUNCH) mission. Exploiting the combined wide fields of view of STEREO's COR2 coronagraph (4-9 r_sun) and PUNCH's Wide Field Imager (WFI, 21-50 r_sun), we track the radial evolution of the power spectral density and correlation scale of density fluctuations from 4 to 50 r_sun. Line-of-sight integration systematically steepens the "usual" turbulence scaling laws by a constant offset of about -0.5 across all resolved scales, so that the energy-containing range (nominally k^-1) and the inertial range (nominally k^-5/3) appear as k^-1.5 and k^-2.1, respectively -- consistent with previous synthetic-image simulations. The correlation scale increases with heliocentric distance across the full range of heliodistances probed, in accordance with previous theoretical models and observations at closer distances from the Sun.

astro-ph.SR↗

arXiv2022

Square Root Compression and Noise Effects in Digitally Transformed Images

We report on a particular example of noise and data representation interacting to introduce systematic error. Many instruments collect integer digitized values and appy nonlinear coding, in particular square-root coding, to compress the data for transfer or downlink; this can introduce surprising systematic errors when they are decoded for analysis. Square root coding and subsequent decoding typically introduces a variable, $\pm 1$ count value-dependent systematic bias in the data after reconstitution. This is significant when large numbers of measurements (e.g., image pixels) are averaged together. Using direct modeling of the probabiliity distribution of particular coded values in the presence of instrument noise, one may apply Bayes' Theorem to construct a decoding table that reduces this error source to a very small fraction of a digitizer step; in our example, systematic error from square root coding is reduced by a factor of 20 from 0.23 count RMS to 0.013 count RMS. The method is suitable both for new experiments such as the upcoming PUNCH mission, and also for post facto application to existing data sets -- even if the instrument noise properties are only loosely known. Further, the method does not depend on the specifics of the coding formula, and may be applied to other forms of nonlinear coding or representation of data values.

astro-ph.IM↗