LBTI/NOMIC data analysis

1. LBTI/NOMIC data analysis B. Mennesson, D. Defrère, P. Hinz, B. Hoffmann, O. Absil, B. Danchi, R. Millan-Gabet, and A. Skemer Instrument Status Review Tucson AZ Sep 4 2013

2. Group activities • Detector and background characterization • Noise mitigation strategies • Optimization of chopping/nodding frequency • Definition of data acquisition sequence • Computation of key instrument performance indicators • Adaptation of statistical reduction technique

3. Group activities • Detector and background characterization • Noise mitigation strategies • Optimization of chopping/nodding frequency • Definition of data acquisition sequence • Computation of key instrument performance indicators • Adaptation of statistical reduction technique ✓

4. Group activities • Detector and background characterization • Noise mitigation strategies • Optimization of chopping/nodding frequency • Definition of data acquisition sequence • Computation of key instrument performance indicators • Adaptation of statistical reduction technique ✓ ✓

5. Group activities • Detector and background characterization • Noise mitigation strategies • Optimization of chopping/nodding frequency • Definition of data acquisition sequence • Computation of key instrument performance indicators • Adaptation of statistical reduction technique ✓ ✓ ✓

6. Group activities • Detector and background characterization • Noise mitigation strategies • Optimization of chopping/nodding frequency • Definition of data acquisition sequence • Computation of key instrument performance indicators • Adaptation of statistical reduction technique ✓ ✓ ✓ ✓

7. Group activities • Detector and background characterization • Noise mitigation strategies • Optimization of chopping/nodding frequency • Definition of data acquisition sequence • Computation of key instrument performance indicators • Adaptation of statistical reduction technique ✓ ✓ ✓ ✓ ✓

8. Group activities • Detector and background characterization • Noise mitigation strategies • Optimization of chopping/nodding frequency • Definition of data acquisition sequence • Computation of key instrument performance indicators • Adaptation of statistical reduction technique ✓ ✓ ✓ ✓ ✓ ✗

9. Detector and background • Complex spatiotemporal fluctuations • Flux-dependent detector behavior • Temporal and spatial noise correlation • Must be corrected for accurate null measurements Detector Background

10. Noise mitigation strategies • Investigated various strategies: Concentric Vertical offset Horizontal offset OBVIOUS DRIFT Time series of residual background (DARK frames, June 27th 2013 – 55ms)

11. Detector frame DARKS Noise mitigation strategies Photometric aperture Background regions (optimized for r=0.64l/D) Corrected Raw DIT=21ms

12. Detector frame BACKGROUND Noise mitigation strategies Photometric aperture Background regions (optimized for r=0.64l/D) chopping/nodding Corrected Raw DIT=55ms

13. Noise mitigation strategies • 40-min of sky data nodding every ~1min30 (June 27th 2013) • Offset reduced to ~8 ADU/PSF (+ Gaussian noise) DIT=55ms DIT=55ms WITHOUT NODDING SUBTRACTION WITH NODDING SUBTRACTION

14. Noise mitigation strategies • 40-min of sky data nodding every ~1min30 (June 27th 2013) • Offset reduced to ~8 ADU/PSF (+ Gaussian noise) DIT=55ms DIT=55ms WITHOUT NODDING SUBTRACTION WITH NODDING SUBTRACTION

15. Noise mitigation strategies Vega on June 27th (40 min of integration) DIT=55ms = bias = noise • Measured Vega’s flux ~ 2.2*105ADU/PSF in 55ms (optimum aperture) • Background noise is ~0.2% in 55ms (i.e., 0.07 Jy) • Background bias is ~0.004% (i.e., 0.001 Jy)

16. Background b Leo ~ 10 sec Altair ~ 1 sec Vega ~ 0.6 sec Minimum integration time necessary to achieve 3-zodi sensitivity (assuming 1 zodi = 5.10-5). Comparing shot noise on constant background (ideal non realistic case) with current measured background uncertainty (after spatio/temporal correction of fluctuations)

17. Chopping/nodding frequency • Nodding frequency: • Remove quasi-static offsets between photometric aperture and background regions • Can be slow (a few minutes or more) • Chopping frequency: • Relaxed thanks to simultaneous background subtraction technique • Will be constrained by photometric calibration (more data needed) • Likely to be slow • Still needed in conjunction of nodding for accurate background removal

18. Data acquisition sequence 4 1 2 3 REF R+L L R NOD 0 REF R R+L L INTERFEROMETRIC FRAME - Chop positions: (1,1) - Nod positions: (0,0) PHOTOMETRIC FRAME - Chop positions: (1,2) - Nod positions: (0,0) INTERFEROMETRIC FRAME - Chop positions: (2,2) - Nod positions: (0,0) PHOTOMETRIC FRAME - Chop positions: (2,1) - Nod positions: (0,0) 8 5 6 7 REF R+L L R NOD 1 REF R R+L L INTERFEROMETRIC FRAME - Chop positions: (1,1) - Nod positions: (1,1) PHOTOMETRIC FRAME - Chop positions: (1,2) - Nod positions: (1,1) INTERFEROMETRIC FRAME - Chop positions: (2,2) - Nod positions: (1,1) PHOTOMETRIC FRAME - Chop positions: (2,1) - Nod positions: (1,1)

19. Ongoing and future analysis • Statistical reduction technique. Adaptation from NIR Palomar Fiber • Nuller not straightforward: • 1D to 2D data • Higher background at 10microns • No single-mode fibers used -> higher phase orders than piston • Computation of chopping frequency (photometric calibration) • Determination of OPD reset frequency • - How long does the NIR OPD target remain valid in the MIR ? • - Transverse atm dispersion • - Other chromatic effects?