1,505 research outputs found
Image patch analysis of sunspots and active regions. I. Intrinsic dimension and correlation analysis
The flare-productivity of an active region is observed to be related to its
spatial complexity. Mount Wilson or McIntosh sunspot classifications measure
such complexity but in a categorical way, and may therefore not use all the
information present in the observations. Moreover, such categorical schemes
hinder a systematic study of an active region's evolution for example. We
propose fine-scale quantitative descriptors for an active region's complexity
and relate them to the Mount Wilson classification. We analyze the local
correlation structure within continuum and magnetogram data, as well as the
cross-correlation between continuum and magnetogram data. We compute the
intrinsic dimension, partial correlation, and canonical correlation analysis
(CCA) of image patches of continuum and magnetogram active region images taken
from the SOHO-MDI instrument. We use masks of sunspots derived from continuum
as well as larger masks of magnetic active regions derived from the magnetogram
to analyze separately the core part of an active region from its surrounding
part. We find the relationship between complexity of an active region as
measured by Mount Wilson and the intrinsic dimension of its image patches.
Partial correlation patterns exhibit approximately a third-order Markov
structure. CCA reveals different patterns of correlation between continuum and
magnetogram within the sunspots and in the region surrounding the sunspots.
These results also pave the way for patch-based dictionary learning with a view
towards automatic clustering of active regions.Comment: Accepted for publication in the Journal of Space Weather and Space
Climate (SWSC). 23 pages, 11 figure
Monte Carlo Techniques for Addressing Large Errors and Missing Data in Simulation-based Inference
Upcoming astronomical surveys will observe billions of galaxies across cosmic
time, providing a unique opportunity to map the many pathways of galaxy
assembly to an incredibly high resolution. However, the huge amount of data
also poses an immediate computational challenge: current tools for inferring
parameters from the light of galaxies take hours per fit. This is
prohibitively expensive. Simulation-based Inference (SBI) is a promising
solution. However, it requires simulated data with identical characteristics to
the observed data, whereas real astronomical surveys are often highly
heterogeneous, with missing observations and variable uncertainties determined
by sky and telescope conditions. Here we present a Monte Carlo technique for
treating out-of-distribution measurement errors and missing data using standard
SBI tools. We show that out-of-distribution measurement errors can be
approximated by using standard SBI evaluations, and that missing data can be
marginalized over using SBI evaluations over nearby data realizations in the
training set. While these techniques slow the inference process from
sec to min per object, this is still significantly faster than
standard approaches while also dramatically expanding the applicability of SBI.
This expanded regime has broad implications for future applications to
astronomical surveys.Comment: 8 pages, 2 figures, accepted to the Machine Learning and the Physical
Sciences workshop at NeurIPS 202
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