DfA Garching NIR wavefront sensing 1 Low-Noise IR Wavefront Sensing with a Teledyne HxRG David Hale Gustavo Rahmer & Roger Smith Caltech 1

Why a Natural Guide Star for Laser AO? Wavefront tilt is not seen by a laser guide star, since the laser light retraces its outward path, so… The TMT/IRIS OIWFS will use three natural guide stars to measure tilt, rotation and scale changes. The brightest guide star will pass through a 2×2 Shack- Hartmann sensor to measure focus and astigmatism. –Each of three probes can be reconfigured on the fly to be either TT only, or TTFA DfA Garching NIR wavefront sensing 22

Why the NIR ? Goal: increase fraction of sky over which adequate AO performance is achieved. Need to guide on AO corrected image to close the tilt loop. –Need NIR to get good Strehl and thus adequate tilt sensitivity. The most common stars are brightest in the NIR. Diffraction core of 30m telescope is so small that background per pixel is negligible in J+H band –Although Strehl is better for K band, sky is much brighter and diffraction core is larger DfA Garching NIR wavefront sensing 3

Some Detector Options Considered Intevac: electron bombarded CCD with InGaAs photocathode. –Dark current way too high and uncontrolled –>100 Hz frame rates not available (until CCD upgraded to CMOS imager) due to ROI overheads –Current format not ideal (1024×256) HgCdTe APD arrays: –Attractive promises, but not ready enough yet HxRG –Well understood –Large format –High QE. –Noise on recent devices good enough after multiple sampling. DfA Garching NIR wavefront sensing 4

Format (not to scale) Current baseline is ~ 1K×1K operable region within H2RG rather than H1RG, since Teledyne advises this will be no more expensive and that the H1RG may be discontinued. –Size of capture region is set by seeing and probe positioning accuracy Spot will be small when high order correction is turned on, moving on scale of seeing profile until low order loop closed. Final guide window size may be as large as 14×14 pixels to handle impulse perturbations. DfA Garching NIR wavefront sensing 5 5 H2RG 2048 Capture region ~ 1k × 1k 4×4 guide window 2 mas/pix 2 arcsec 14×14 recapture window

Zoom to Capture Start with seeing limited image (>1/4 detector area) –Big, fuzzy, low contrast. –Move to center by adjusting probe position or telescope pointing. Turn on high order correction. –Tiny spot scribbles lines over the seeing profile. Not much change seen in long exposure. Window down on seeing limited image. –Faster frame rate makes wiggly line shorter. Close loop at low gain to drive centroid towards center of window. –Steadily reduce window size to increase frame rate and loop gain. –Servo keeps spot within shrinking window. Zoom in to 4×4 window –avoid bad or noisy pixels DfA Garching NIR wavefront sensing 6

Why Such a Big Detector? (… just to replace a quad cell !) Big telescope aperture makes tiny diffraction core: FWHM = /D = arcsec at 1.2µm Quad cell requires ~0.004 arcsec/pixel Prefer arcsec/pixel so that positioning on pixel boundary not required to maximize centroiding sensitivity. 2 arcsec field of view needed to capture seeing profile. We could go larger to aid acquisition. Thus need >1K 2 Freebie: science image can be acquired around guide star, since H2RG allows nested windows and independent reset. DfA Garching NIR wavefront sensing 7

Maximizing Frame Rate The spot is compact throughout the zoom, since laser guide star sensor is blind to tip tilt, but it is smeared by image motion. –Our problem is to locate it (short frame time) and –Measure tilt accurately (low noise) to feed back to servo. Flux per pixel depends on image motion rather than exposure time, so maximize frame rate. –Pixel time has been minimized. –For window >64 pixels: 32 ch readout, skipping unwanted lines. –For window <64 pixels: single channel, window mode. By 64×64, frame rate = 50Hz … tilt error already << window size. –Readout time = (5.16*N *N ) µs = 21.8ms For fainter guide stars final rate ~100Hz. For frames <44×44, can use multiple sampling to reduce the read noise below the ~11 e - for CDS. For brighter guide stars final rate ~800 Hz, with less noise averaging. DfA Garching NIR wavefront sensing 8

Noise & Frame Rate During Zoom DfA Garching NIR wavefront sensing 9 Start zoom Big frames, CDS noise Faint stars Bright stars 4x4 1024x1024 NIR wavefront sensing Begin multiple sampling here

Noise & Frame Rate During Zoom DfA Garching NIR wavefront sensing 10 Final noise is a weak function of frame rate

Readout Mode Fowler sampling usually incurs a loss of duty cycle. To avoid this, reset only occasionally, synthesizing fowler sampling by coadding and differencing non-destructive reads in what might be called “differential multi-accumulate”. Final samples for one exposure serve as the initial samples for the next, so duty cycle is 100%. All the photons are used! For fluxes where read noise matters resets are only needed every 100 to 1000 frames. …. interpolate over gaps. DfA Garching NIR wavefront sensing 11

Correlated Double Sampling Exposure delay = p dummy reads for constant self heating Subtract first frame from last frame Equivalent to Fowler sampling with m = 1 DfA Garching NIR wavefront sensing 12 Ignore p scans e = 1 = number of exposures to do …. not shown here r = number of reset scans between exposures m = 1 = number of scans to coadd then store. p = 10 = number of dummy scans between coadded groups k = 2 = number of store cycles per exposure At least one reset between frames Reset while idling Initial scan Final scan Exposure time Frame time Let’s review common readout timing options….

Fowler “m” Exposure delay is in units of full scan ties but need not be multiple of m. Subtract mean of first group from mean of last group. DfA Garching NIR wavefront sensing 13 Coadd m Ignore p scansCoadd m e = 1 = number of exposures to do …. not shown here r = number of reset scans between exposures m = 3 = number of scans to coadd then store. p = 6 = number of dummy scans between coadded groups k = 2 = number of store cycles per exposure Exposure time Frame time Duty cycle < 1

Sample up the ramp. Store every scan (no real time coadd) Use post facto least squares fit to measure slope with best S/N; Effective exposure duty cycle due to weighting of shot noise by least squares ~ 90%; reduce this to include effect of the reset overhead. Equivalent MultiAccumulate with m=1. DfA Garching NIR wavefront sensing 14 e = 1 = number of exposures to do …. not shown here r = number of reset scans between exposures m = 1 = number of scans to coadd then store. p = 0 = number of dummy scans between coadded groups k = 12 = number of stores per exposure

Multi-Accumulate (JWST terminology) Coadd in real time, store every m scans, total exposure duration is multiple of m scan times. Least squares fit of stored (coadded) scans is used to estimate noise. Advantage of coadd over single samples with gaps is lower noise and better cosmic ray detection ( which appears as jump in ramp). One or more reset scans between exposures. DfA Garching NIR wavefront sensing 15 Coadd m Reset r scans e = number of exposures to do …. not shown here r = 2 = number of reset scans between exposures m = 3 = number of scans to coadd then store. p = 0 = number of dummy scans between coadded groups k = 4 = number of stores per exposure

Proposed mode for OIWFS (used in the noise tests presented here) Differential Multi-Accumulate Using previous frame as baseline for next frame (without reset) makes duty cycle ~100%, except for a gap when reset occurs. Gaps at time of reset can be reduced in duration by using single scan or significantly fewer scans to establish first baseline instead of averaging m scans. This will produce a noisier result instead of a missing result. Which is better? The reset scan and initial read scan can be combined so the reset time is hidden. DfA Garching NIR wavefront sensing 16 Coadd m Reset Coadd m Difference = frame 1 Difference = frame 2 Difference = frame 3 Difference = frame 4 Occasional gap ! Exposure time

Nested Windows TMT operates at such a fine plate scale that there is concern over loss of lock of the tip tilt servo due to imperfections in the M3 bearing. Nested windows: multiple sample small guide window, then CDS read surrounding window during the exposure delay for the small window. Thus if the spot jumps out of the guide window we can get a snapshot just half a frame time later… DfA Garching NIR wavefront sensing 17

Nested Windows Read a 4x4 window multiple times and coadd to beat down noise. Read surrounding, larger window once, then revert to small window. Calculate differences of (coadds of) small windows and differences of large windows separately. Exposure times for each window size are same (though offset by half an exposure time). Noise is lower for the central 4x4 window since it is sampled more often. Size of the large window depends on frame rate and fraction of time allocated to big window as opposed to beating down the noise in the 2x2 window. If 50% of time is allocated to the larger window at 100 Hz, it can be 31 pixels across with 11 e- read noise, or a 14 pixel window can be read five times reducing read noise to ~5e-. DfA Garching NIR wavefront sensing 18 coadd subtract

Noise vs. Frame Rate measured for various frame sizes Desired 100Hz operating point gives <3e - read noise for 4×4 window. DfA Garching NIR wavefront sensing 19 Why this bump? Mean noise for all pixels in window. Negligible dark current at 80K. Latest low noise 2.5µm recipe Higher CDS noise Why these turn-ups? NIR wavefront sensing

“Dark Signal” (…mentioned in Roger’s talk this morning) Slope depends on number of reads per pixel, not time DfA Garching NIR wavefront sensing e - / read for 6µs pixel

“Dark Signal” Effects 8×8 dark image after 160,000 frames (75.5s), showing central peak, suggesting self heating profile… 10e-/sec dark current at peak is >1000 times normal DfA Garching NIR wavefront sensing 21

“Dark Signal” Effects 32×32 dark image taken immediately following 8×8 shows remnant hot spot at location of 8×8… DfA Garching NIR wavefront sensing 22

Noise vs. Frame Rate measured for various frame sizes DfA Garching NIR wavefront sensing 23 Latest low noise 2.5µm recipe NIR wavefront sensing

“Dark Signal” Effect on Measured Noise DfA Garching NIR wavefront sensing 24 Latest low noise 2.5µm recipe NIR wavefront sensing Just 1/f noise Still have these… The small turn-ups are caused by the “dark signal” Subtracting mean effect leaves us with 1/f noise But… still have the “bump” and the unexplained large turn-up at very low frequencies

2×2 Window DfA Garching NIR wavefront sensing 25 Latest low noise 2.5µm recipe Individual pixels in the 2×2 window … mean was contaminated by noisy pixels NIR wavefront sensing CDS noise is poor predictor of final noise floor !

64×64 Window DfA Garching NIR wavefront sensing 26 Latest low noise 2.5µm recipe NIR wavefront sensing Hot pixels

Tip-Tilt Wavefront Error Median WFE 26.3 nm DfA Garching NIR wavefront sensing 27 Demonstrated: 2.8e 80Hz, 4×4 window >80% QE Read noise contours Zero read noise device needs QE ≥ 65% to be as good as H2RG Neither QE nor noise improvements offer significant WFE reduction in this regime

Sky Coverage Analysis DfA Garching NIR wavefront sensing 28 Requirements: 2mas 50% Exceeded, ~90% sky coverage 2mas

Let’s party ! The End DfA Garching NIR wavefront sensing