データサイエンス勉強会 20190425/meeting on datascience at RIKEN 20190425

データサイエンスの勉強会@理研 2019-04-27 の発表資料．

Transcript

1. データサイエンスの勉強会 ― 資料 東京大学理学系研究科 天文学教育研究センター データサイエンス勉強会 2019.04.25 大澤 亮
2. Outline Brief self Introduction Topics 1. 2. 3. Finding needles in haystacks. Low-rank and sparse matrices decomposition Lightcurve and Gaussian process
3. Tomo-e Gozen (トモエゴゼン) A 105-cm Schmidt Telescope & a wide-field mosaic CMOS camera Monitoring 20 sq-deg sky (up to) at 2 Hz Wide-area survey for transients and moving objects Kojima, et al. 2018. 10709:107091T; Sako, et al. 2018. 10702:107020J 2K×1K CMOS image sensor by Canon moon
4. Tomo-e Gozen (トモエゴゼン) Kojima, et al. 2018. 10709:107091T; Sako, et al. 2018. 10702:107020J
5. Data in optical and infrared astronomy (ground-based) Data are dominated by noise (low signal-to-noise ratio). Noise statistics is well approximated by a normal distribution. Signals are highly localized.
6. Tomo-e Gozen Supernova Survey Tomo-e Gozen is originally developed to detect explosions in the Universe. Take a 6-second video, move the telescope, take a 6-second video, and .... Patrol the entire visible sky at least twice in a night. ⇉ this data are useful for a blind survey of moving objects For the details of NEO detection system, refer to the master thesis by Yuto Kojima Image by Stellarium
7. Tomo-e Gozen Supernova Survey Tomo-e Gozen is originally developed to detect explosions in the Universe. Take a 6-second video, move the telescope, take a 6-second video, and .... Patrol the entire visible sky at least twice in a night. ⇉ this data are useful for a blind survey of moving objects For the details of NEO detection system, refer to the master thesis by Yuto Kojima Image by Stellarium Near-Earth Object
8. Tomo-e Gozen Supernova Survey 1st frame 12th frame Binary Mask Image Moving Object detected Raw Data 0.5sec x 12frames SExtractor 1.5σ Frame Extraction Dark Subtraction Flat Fielding WCS Mapping AND NOT Masked 1st frame kd-tree Masked 12th frame X X SExtractor SExtractor tracklets For the details of NEO detection system, refer to the master thesis by Yuto Kojima Near-Earth Object
9. Tomo-e Gozen Supernova Survey 1st frame 12th frame Binary Mask Image Raw Data 0.5sec x 12frames SExtractor 1.5σ Frame Extraction Dark Subtraction Flat Fielding WCS Mapping AND NOT Masked 1st frame kd-tree Masked 12th frame X X SExtractor SExtractor tracklets NEOs GEO objects LEO objects Garbage Cosmic Rays For the details of NEO detection system, refer to the master thesis by Yuto Kojima Near-Earth Object
10. Tomo-e Gozen Supernova Survey 1st frame 12th frame Binary Mask Image Raw Data 0.5sec x 12frames SExtractor 1.5σ Frame Extraction Dark Subtraction Flat Fielding WCS Mapping AND NOT Masked 1st frame kd-tree Masked 12th frame X X SExtractor SExtractor tracklets NEOs GEO objects LEO objects Garbage Cosmic Rays 1000 : 200000 moving objects bogus detections For the details of NEO detection system, refer to the master thesis by Yuto Kojima Near-Earth Object
11. Tomo-e Gozen Supernova Survey 1st frame 12th frame Binary Mask Image Raw Data 0.5sec x 12frames SExtractor 1.5σ Frame Extraction Dark Subtraction Flat Fielding WCS Mapping AND NOT Masked 1st frame kd-tree Masked 12th frame X X SExtractor SExtractor tracklets NEOs GEO objects LEO objects Garbage Cosmic Rays 1000 : 200000 moving objects bogus detections 1000 : 1000 moving objects bogus detections put labels observed videos detected candidates inspect complie the characteristics into a table Characteristics Table Random Forest Discriminator Training Validate Candidates Bogus detections For the details of NEO detection system, refer to the master thesis by Yuto Kojima Near-Earth Object
12. Tomo-e Gozen Supernova Survey Near-Earth Object
13. Tomo-e Gozen Supernova Survey Near-Earth Object
14. Tomo-e Gozen Supernova Survey Near-Earth Object
15. Low-rank and sparse matrices decomposition Movie decomposition with GoDec (low-rank, sparse, residual and original) Demonstrated in https:/ /kastnerkyle.github.io/posts/robust-matrix-decomposition/
16. Low-rank and sparse matrices decomposition Zhou, Tianyi, and Dacheng Tao. 2011, 33–40. ICML ’11.
17. Application to the Tomo-e Gozen data Extract flash-like transient signals from 2 Hz video data. Morii, Mikio, Shiro Ikeda, Shigeyuki Sako, and Ryou Ohsawa. 2017, ApJ 835 1. flash meteor
18. Ground-based MIR observations An important tool to investigate Orion KL region in MIR planet formation process; evolution and formation of solid state materials; star formation activity heavily obscured. There are couples of technical difficulties. astronomical objects are intrinsically faint; telescope and atmosphare are quite bright in the MIR; atmospheric emission is highly variable.
19. Chopping observation pixel count signal atmosphere telescope bias noise flat : astronomical signal is weak : poor detector flattness Difference A-B Position A Position B
20. Characteristics of signals Much redundancy in non-scientific signals Scientific Not scientific An: rapidly fluctuating / almost constant in space Tn: almost constant in time Bn: almost constant in time
21. Scanning observation (on-the-fly) Much redundancy in non-scientific signals Scientific Not scientific An: rapidly fluctuating / almost constant in space Tn: almost constant in time Bn: almost constant in time Object Field-of-View V elocity V Time t 1 =0 t 2 t 3 t M =T scan observation Sn: compact and moving sources Less redundancy in scientific signals
22. Application to the Subaru/COMICS data Raw data cube obtained with Subaru/COMICS
23. Application to the Subaru/COMICS data Background subtracted using the proposed algorithm
24. Gaussian Process GP = A generator of smooth functions. Observations constrain a set of functions: 持橋大地 & 大羽成征, 機械学習プロフェッショナルシリーズ『ガウス過程と機械学習』 GPy（Pythonのガウス過程用ライブラリ）の使い方 http:/ /statmodeling.hatenablog.com/entry/how-to-use-GPy obtaining a smoothed continous function inter- or extrapolate data with uncertantines Responsitivity is fainite. Time evolution should be continuous/smooth. Kernel optimization ⇨ characteristics of data.
25. Applications in time-domain astronomy 植村誠, 時間変動データに対する確率過程モデルの有用性, 連星系・変光星・低温度星研究会2018
26. GoDec + Gaussian Process Decompose a lightcurve into components of different timescales Optical lightcurve of MAXI J1820+070, Ryou Ohsawa+, in prep. a smooth baseline + stochastic pulses