Some ML and AI challenges in current and future optical and near infra imaging datasets

Transcription

1 Some ML and AI challenges in current and future optical and near infra imaging datasets Richard McMahon (Institute of Astronomy, University of Cambridge) and Cameron Lemon, Estelle Pons, Fernanda Ostrovski, Matt Auger, Manda Banerji, Vidhi Lalchand 2019 Sep, ATI 1

2 Telescope landscape 2019 Sep, ATI 2

3 Historical Trends for optical astronomy Future dominated by detector size improvements CCDs Total area of 3m+ telescopes in the world in m 2, total number of CCD pixels in Megapixels Sep, ATI Growth over 25 years is a factor of 30 in total telescope glass collecting area, 3000 in pixels. Glass Moore s Law growth in detector array sizes Gigapixel arrays now available: Gaia in Space Dark Energy Camera, PanSTARRS on ground Improvements in computing and storage track growth in data volume Investment in analysis software is critical Reproducible analysis is paramount

4 Gaia satellite; launch Dec year mission 2 primary mirrors viewing two different areas of sky simultaneously coaligned to a common focal plane Primary mirror 1.49 metre 0.54metre (3:1) Power comsumption; 1.9 kw 0.94 Gigapixel camera Operates in a scanning mode; 63 days per full sky scan 1.7 billion unique sources measured multiple times Positions and fluxes Data release 1 (DR1): Sep 2016 Date release 2 (DR2): April 2018 Data release 3 (DR3): Sep, ATI 4

5 2019 Sep, ATI 5

6 0.1 x 0.3 FWHM 2019 Sep, ATI 6

7 2019 Sep, ATI 7

8 Gaia summary Gaia focal plane consists of 106 CCDs with 4500 pixels in the along-scan direction and 1966 pixels in the across-scan direction. Each pixel is rectangular in the ratio 1:3 similar to the PSF major and minor axes with size microns corresponding to x arcseconds on the sky, i.e. 2 pixels Nyquist sampling of the PSF FWHM 2019 Sep, ATI 8

9 GaiaDR1 nearest neighbour analysis 2019 Sep, ATI 9

10 GaiaDR2 nearest neighbour analysis 1 = 1 microradian 2019 Sep, ATI 10

11 measure Dark Matter content of foreground lensing galaxy constrain Dark Energy via time delays measurement of expansion rate at z>1 from 100 day time delays with 1% uncertainty Measure sizes and masses of supermassive black holes (> Solar Masses) 30 x 30 SDSS J Sep, ATI 11

12 Rate of discovery of lensed quasars 2019 Sep, ATI 12

13 Theory Reality Lemon Sep, ATI 13

14 2019 Sep, ATI 14

15 Left: NIRC2 K Adative Optic imaging of PS J The four images ABCD are in a canonical cusp configuration, but note the presence of two galaxies, G1 and G2, as well as the additional point source E. The additional structure around the images A, B and C is due to poor wavefront correction. Middle: the same as the left-hand panel, but with a model for the AO PSF subtracted from the three brightest images, revealing the presence of a faint arc. Right: same as the middle but with enhanced contrast to better show the lensed quasar host galaxy Sep, ATI 15

16 VDES J a z = 2.7 gravitationally lensed quasar discovered using morphology-independent supervised machine learning: GMM J as a g, r, and i DES Y1 colour composite, an i-band image, an i-band image model and the residuals from subtracting the model from the image. All cutouts are 10.0 arcsec in size. North is up and East is left.

17 Input model spectral energy distributions 2019 Sep, ATI 17

18 Colour colour plots showing the location of J (pink circle) in different colour spaces. The pink square shows the location of the north-most blue object. For comparison, the colour locus of quasars (green), point sources (blue) and extended sources (orange) are populated by the objects present in the GMM training set.

19 ML in practice Gauusian mixture models 2 or 3 labelled classes No lensed quasars in the training set 9 photometric first order features [5 shown on right] No morphology (shape) features used 0.1% of sample selected as candidates 2019 Sep, ATI 19

20 Domain knowledge supervised Boosted Decision Tree Data fusion and data cuts and data of Gaia catalogue with other sources 2 x 10 9 to 2 x 10 6 to 1 x 10 5 candidate gravitationally lensed quasars Final stage was eyeball on ground based imaging data for 1 x 10 5 to reduce to 2 x 10 2 best candidates [ML opportunity] 2019 Sep, ATI 20

21 J Single GMM extended quasars in DES Y3 Single source with RADEC offset in Gaia DR1 Triplet in Gaia DR Sep, ATI 21

22 J Pair of GMM extended quasars in DES Y3 Pair in Gaia DR1 Quad in Gaia DR Sep, ATI 22

23 J Single GMM extended quasars in DES Y3 Triple in Gaia DR1 Triple in Gaia DR Sep, ATI 23

24 Example of junk issues with imaging Data XXL-SDSS-DES sample = 1497 sources But 13% of them (197 sources) do not have a DES ipsf magnitude Not due to a non-detection 2019 Sep, ATI 24

25 Rate of discovery of lensed quasars 2019 Sep, ATI 25

26 Future LSST from Factor of 10 more imaging data in first year compared with all existing imaging data Need training sets for ML for 2020 commissioning phase 2019 Sep, ATI 26

27 Panel: ML and AI in Astronomy How important is hardware design in ML? i/o bottle necks Access to hardware for benchmarking of training for a range of ML methods Which types of astronomical problems would benefit from ML techniques? Low order and high order features lists versus images Which types of ML methods are state of the art? ML in the low signal to noise regime; < 5? Are ML results reproducible between ML methods more than human methods? 2019 Sep, ATI 27

28 ESFRI Roadmap Sep, ATI Main Research Infrastructures in Astronomy and Astroparticle Physics 28