Home > 科学研究部コロキウム > 2022.8.8-2022.8.14


Aug 10 Wed 15:30~17:00  NAOJ Science Colloquium zoom/ Instrument Development Bldg. 3 (hybrid)



Seminar:NAOJ Science Colloquium
Regularly Scheduled/Sporadic:Every Wednesday
Date and time:2022 August 10, 15:30-17:00
Place:zoom / Instrument Development Bldg. 3 (hybrid)
Speaker:Tsutomu Takeuchi
Affiliation:Nagoya University
Title:High Dimensional Statistical Analysis of ALMA Map of NGC 253
Abstract:In astronomy, if we denote the dimension of data as $d$ and the number
of samples as $n$, we often meet a case with $n \ll d$. Traditionally, such
a situation is regarded as ill-posed, and there was no choice but to throw
away most of the information in data dimension to let $d < n$. The data
with $n \ll d$ is referred to as high-dimensional low sample size (HDLSS).
To deal with HDLSS problems, a method called high-dimensional statistics
has been developed rapidly in the last decade. In this talk, we introduce
the high-dimensional statistical analysis to the astronomical community.
We apply two representative methods in the high-dimensional statistical
analysis methods, the noise-reduction principal component analysis (NRPCA)
and regularized principal component analysis (RPCA), to a spectroscopic
map of a nearby archetype starburst galaxy NGC~253 taken by the
Atacama Large Millimeter/Submillimeter Array (ALMA). The ALMA map is
a typical HDLSS dataset. First we analyzed the original data including the
Doppler shift due to the systemic rotation. The high-dimensional PCA could
describe the spatial structure of the rotation precisely. We then applied to
the Doppler-shift corrected data to analyze more subtle spectral features.
The NRPCA and RPCA could quantify the very complicated characteristics
of the ALMA spectra. Particularly, we could extract the information of the
global outflow from the center of NGC~253. This method can also be
applied not only to spectroscopic survey data, but also any type of data
with small sample size and large dimension.
-Name:Akimasa Kataoka

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