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Institute of Cosmos Sciences - University of Barcelona

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Presentation on theme: "Institute of Cosmos Sciences - University of Barcelona"β€” Presentation transcript:

1 Institute of Cosmos Sciences - University of Barcelona
Gaia Photometry Institute for Space Studies of Catalonia and Institute of Cosmos Sciences - University of Barcelona Claus Fabricius On behalf of DPAC CU5

2 Photometric data in Gaia DR1
Phot_g_mean_flux [e– /s] Phot_g_mean_flux_error s.d./ 𝑛 Phot_g_mean_mag [mag] Vega system Phot_variable_flag string mostly N/A Phot_g_n_obs – # CCD transits Flux_error is a coarse uncertainty measure Zero-point (DR1) Vega: AB: Small set of variables

3 Airbus DS

4 Gaia passbands Jordi et al. 2010

5 Gaia focal plane

6 Observation strategy Sky Mapper detection: magnitude
Exposure time can be restricted by gates: 16, 126, 252, 503, 1006, 2013, ms G ≲ 12 mag 4417 ms G ≳ 12 mag Only small windows are read out G < 13 mag G: 13 – 16 mag G: 16 – 21 mag

7 Windows read out around stars
0.7 arcsec G = 14.9 2.1 arcsec Scan direction Windows measure 12 Γ— 12 pixels (typically) Pixels are binned on chip before reading Lower readout noise Less telemetry

8 One sample masked (saturation)
Recent obs. Bright star 2D window 1006 ms exp. One sample masked (saturation) Not DR1 ! G = 10.8

9 Recent obs. Bright detect 2D window 252 ms exp. Binary Poor centroid
~ 0.25 Μ‹ sep. Poor centroid G = 9.4

10 Simulated BP / RP spectra
Jordi et al. 2010

11 BP spectra G = 17.5 600 400

12 RP spectra G = 16.7

13 Windows in case of conflict
0.7 arcsec Fainter detection Truncated window – not used for DR1 2.1 arcsec Brighter detection Complete window – with contamination Window for brighter detection is the winner Window for the fainter detection will lose Truncated windows are not used in Gaia DR1

14 Conflicts between spectra
Brighter detection Complete window 3.5 arcsec 2.1 arcsec 4.1 arcsec Fainter detection Truncated window Window for brighter detection is the winner We need at least one β€œgood” spectrum for a source Fainter source of a pair closer than 2 Μ‹ is lost in DR1

15 A dense field from DR1 Drop at 4 arcsec separation
Very few below 2 arcsec separation

16 A sparse field from DR1 No drop at 4 arcsec separation
Small peak of binaries

17 AF CCD flat field – pre launch
Response non-uniformities wavelength & gate dependent 400 nm 550 nm 900 nm Carrasco et al. 2016

18 Simulated AC de-centring flux loss
Carrasco et al. 2016

19 Calibration model Response variation Large scale Small scale Gate
FoV & CCD level Small scale CCD column level, ~ 500 per CCD Gate 8 levels of exposure time (ranges of pixel rows) Colour 6 bands Time Strong variations: mirror contamination Large scale: calibration valid for 1 day Small scale: single calibration for DR1

20 Calibration β€œunits” Concerns
Scale CCDs Strategy Telescopes N_AC N_time N_CU AF LS 62 10 2 1 420 SS 492 BP/RP 7 6 35 280 20 664 Concerns Enough observations for the shorter gates ? Enough sources observed with different gates ? Enough sources observed with different window classes ?

21 Spectral shape coefficients
π‘ͺ πŸ‘ π‘ͺ 𝟐 π‘ͺ 𝟏 π‘ͺ πŸ’ π‘ͺ πŸ“ π‘ͺ πŸ” Carrasco et al. 2016

22 Flux extraction PSF fitting (G < 13 mag)
Insensitive to AC de-centring LSF fitting (G > 13 mag) Wide windows (G mag) Narrow windows (G mag) For BP & RP Simple diaphragm photometry NB! Not included in DR1

23 Calibration model π‘“π‘œπ‘π‘ =𝑓𝑖𝑛𝑠𝑑 𝐿 𝑆 𝐿 = 1 6 𝐴 π‘š 𝐢 π‘š + 0 2 𝐡 𝑗 πœ‡ 𝑗
π‘“π‘œπ‘π‘ =𝑓𝑖𝑛𝑠𝑑 𝐿 𝑆 𝐿 : large scale (CCD level) 𝑆 : small scale (CCD column level) 𝐿 = 𝐴 π‘š 𝐢 π‘š 𝐡 𝑗 πœ‡ 𝑗 𝐢 π‘š : spectral shape coefficients πœ‡ : CCD column 𝑆 : one coefficient every 4 columns Coefficients 𝐴 π‘š , 𝐡 𝑗 , 𝑆 : valid over a calibration unit Carrasco et al. 2016

24 Response monitoring using Tycho-2
decontaminations M. Hauser 2016

25 Monitoring vs LargeScale calibration
Evans et al. 2016

26 Small Scale calibr., three time ranges
Evans et al. 2016

27 Scatter of individual CCD observations
Gaia science performance Predicted accuracy Evans et al. 2016

28 Magnitude zeropoint Spectro-photometric standard stars, SPSS
Nominal passband Calculate synthetic G magnitudes for SPSS Compare with instrument G magnitudes Details: Carrasco et al. 2016, A&A, in press Gaia DR1 on-line documentation

29 Zeropoint, Vega-system
Why colour dependence ? Passband ? PSF ? Aperture ? Carrasco et al. 2016

30 Comparison to APASS Carrasco et al. 2016

31 G-RP-f(colour) versus G
Arenou et al. 2016

32 Completeness of TGAS Missing bright stars Several stars too faint for
Tycho-2

33 A very faint TGAS stars Tycho epoch Gaia epoch

34 A couple of RR Lyr: LMC / MW
Clementini et al. 2016

35 Some limitations of DR1 
Processing Simple cross match: source duplication Saturated samples not excluded No aperture correction (astrometry not yet known) Dense fields: few faint stars Extreme colours excluded (fuzzy limit) Many poorly scanned areas

36 Coming to you in DR2  Processing Dense fields: more faint stars
Much improved cross match Saturation library enabled Aperture correction using preliminary DR2 astrometry Dense fields: more faint stars Extreme colours included Few poorly scanned areas


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