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Timeline analysis and wavelet multiscale analysis of the AKARI All-Sky Survey at 90 μm
Authors:Lingyu Wang  Michael Rowan-Robinson  Issei Yamamura  Hiroshi Shibai  Rich Savage  Seb Oliver  Matthew Thomson  Nurur Rahman  Dave Clements  Elysandra Figueredo  Tomotsugu Goto  Sunao Hasegawa  Woong-Seob Jeong  Shuji Matsuura  Thomas G Müller  Takao Nakagawa  Chris P Pearson  Stephen Serjeant  Mai Shirahata  Glenn J White
Institution:Astrophysics Group, Blackett Laboratory, Imperial College of Science Technology and Medicine, London SW7 2BZ;Institute of Space and Astronautical Science, Japan Aerospace Exploration Agency, Yoshinodai 3-1-1, Sagamihara, Kanagawa 229-8510, Japan;Graduate School of Sciences, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8602, Japan;Astronomy Centre, Department of Physics and Astronomy, University of Sussex, Brighton BN1 9QH;Department of Physics and Astronomy, The Open University, Milton Keynes MK7 6AA;Space Science Division, Korea Astronomy &Space Science Institute (KASI), 61-1 Whaam-dong, Yuseong-gu, Deajeon 305-348, South Korea;Max Planck Institute for Extraterrestrial Physics, Giessenbachstrasse, 85748 Garching, Germany;Space Science &Technology Department, CCLRC Rutherford Appleton Laboratory, Chilton, Didcot, Oxfordshire OX11 0QX;Department of Physics, University of Lethbridge, 4401 University Drive, Lethbridge, Alberta T1J 1B1, Canada
Abstract:We present a careful analysis of the point-source detection limit of the AKARI All-Sky Survey in the WIDE-S 90-μm band near the North Ecliptic Pole (NEP). Timeline analysis is used to detect IRAS ( Infrared Astronomy Satellite ) sources and then a conversion factor is derived to transform the peak timeline signal to the interpolated 90-μm flux of a source. Combined with a robust noise measurement, the point-source flux detection limit at signal-to-noise ratio  (S/N) > 5  for a single detector row is  1.1 ± 0.1 Jy  which corresponds to a point-source detection limit of the survey of ~0.4 Jy.
Wavelet transform offers a multiscale representation of the Time Series Data ( tsd ). We calculate the continuous wavelet transform of the tsd and then search for significant wavelet coefficients considered as potential source detections. To discriminate real sources from spurious or moving objects, only sources with confirmation are selected. In our multiscale analysis, IRAS sources selected above 4σ can be identified as the only real sources at the Point Source Scales. We also investigate the correlation between the non- IRAS sources detected in timeline analysis and cirrus emission using wavelet transform and contour plots of wavelet power spectrum. It is shown that the non- IRAS sources are most likely to be caused by excessive noise over a large range of spatial scales rather than real extended structures such as cirrus clouds.
Keywords:methods: data analysis  surveys  infrared: galaxies  infrared: ISM
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