Exploiting PhoSim for Data Management Algorithm Development::Simulated Effects (3)

1. Exploiting PhoSim for Data Management Algorithm Development::Simulated Effects (3) A.Rasmussen (SLAC)

2. Are our current models good enough? What about other models of physical sensor effects outside of Phosim?  Should we incorporate them or use them differently?-or-How can sensor characterization data be used to apply a realistic instrument signature onto simulated data..?(to help close loop between simulations, DM, find and quantify systematics in data) A.Rasmussen LSST2014 PhoSim for DM AlgorDev

3. Good enough? • It appears that LSST sensors will inject astrometric and shape transfer errors into the data if it is assumed that the sky is recorded onto a continuous, regular grid of pixels with square boundaries. • By the same mechanisms (and assumptions), flat field response will contain anomalies/distortions. • Physical effects are broadly divided into fixed pattern and dynamic effects. • Short answer to 1st question: we don’t know yet, it depends on how data will be used, but quantitative comparisons are still being made between sensor characterization data and modeled response: • Heuristic (deduce distortions from raw/stacked flat field response) • Ad-hoc (insert pixel boundary, etc., errs to impart FF & sky distortions) • Physical (grounding to provide dependence on physical conditions, “”) A.Rasmussen LSST2014 PhoSim for DM AlgorDev

4. Good enough? (2) • Heuristic response has gained traction following confirmation of pixel size variation as origin specific spot projector tests that confirmed pixel size variation as origin of anomalies in flat field response (edge & midline) and DECam instrumental magnitude error correlation with flat field response (tree rings) • Ad-hoc approach may qualitatively reproduce characterization (flat field) data while injecting self-consistent distortions into PhoSim images. • Physical (electrostatic) treatment appears to provide quantitative agreement to flat field response. Focused targets, flat field response and autocorrelation matrices prepared and analyzed within a simulation framework. (calculation result files, not drift calculation code, may provide efficient interface) A.Rasmussen LSST2014 PhoSim for DM AlgorDev

5. Some leading terms • Excluding: • PRNU driving fixed pattern pixel boundary noise σy • PRNU driving fixed pattern channel stop noise σξ • Exotic flat field response problems like “Bamboo”: Backside bias voltage dependence of bimodal fixed pattern flat field distortion Flat field distortion (rel.) Astrometric shift (pixels) A.Rasmussen LSST2014 PhoSim for DM AlgorDev

6. Results & comparisons to data: midline midline feature modeled as a isolated channel stop implant extending across sensor’s width cf. a (black:data; red:drift calculation) Flat field distortion (rel.) Astrometric shift (pixels)

7. Quantitative agreements between drift model & flat field distortions: “tearing” onset features Black: data; Red: calculation “dark border” (occurs between adjacent amplifier segments) ξ=+5ξ0 ξ=+1ξ0 ξ=-5ξ0 ξ=-1ξ0 “bright finger” (occurs in column pairs straddling isolated, hole-saturated channel stops)

8. Results & comparisons to data: edge rolloff cf. b (black:data; red:drift calculation) Flat field distortion (rel.) nb: no guard drain bias included in calc. Edge rolloff modeled as a truncation in the channel stop array of implants Astrometric shift (pixels)

9. Example for edge roll-off:Predicted depth- or wavelength-dependence Surface conversions (100um from channel) Midway conversions (50um from channel) Deep conversions (20um from channel) A.Rasmussen LSST2014 PhoSim for DM AlgorDev

10. Depth dependence of pixel boundaries near sensor edge Wavelength dependence of edge response (p.doherty) - Depth dependence of column boundaries, first 16 pixels (drift calc): Note differences in curve shapes! A.Rasmussen LSST2014 PhoSim for DM AlgorDev

11. Additional dependencies to the roll-off measured in lab (p.doherty) Difference by manufacturer/design Guard ring drain bias voltage dependence A.Rasmussen LSST2014 PhoSim for DM AlgorDev

12. Summarize or continue? • Modeling & data comparison: Tree rings • Modeling & data comparison: channel content (B/F) • Detailed pixel distortion data appears to be available in flat field response measurements • Keep abreast of distortion flux dependence (FF) • Coordinate with DM to understand what sort of ancillary pixel data* should be prepared, that can realistically be extracted from data • Efficiently generate flat field, focused spot and generalized mean-variance (autocorrelations) A.Rasmussen LSST2014 PhoSim for DM AlgorDev

13. Tree rings A.Rasmussen LSST2014 PhoSim for DM AlgorDev

14. Backdrop field validation (4) Tree ring distortion feature amplitude depends on backside bias: Tree ring feature extraction 1% 1% 1%

15. Backdrop field validation (5) Functional derivative Impurity gradient Drift coefficient function is drawn from the drift calculation

16. Example of a drift coefficient calculation – for tree ring flat field-, astrometric- and pixel shape distortions Drift coefficient curves specific to backside bias setting Scaling parameter determination by fitting observable quantities Pixel-level and PSF-level distortions arising from a periodic function in underlying “hidden variable” Predicted wavelength dependence of PSF-level distortions (excl. pixel-level)

17. Channel content Greens function A.Rasmussen LSST2014 PhoSim for DM AlgorDev

18. Cross-cuts in the periodic electrostatic barrier (perturbative) field Depth (distance from gates) Depth (distance from gates) Serial address (x) Parallel address (y)

19. Launching position: (x,y,z)=(9.31,8.71,100)um BSS=-48,-58,-68,-78V Drift calculation examples Depth (distance from gates) Serial address (x) Parallel address (y) 2um 8um [01] [11] y y 2um 2um x x [00] [10] 8um 8um @ (-5,-5)um @ (-5,-5)um

20. Pixel distortion Greens function(induced by collected charge dipole moment) 1p0 8p0 2p0 16p0 (4p0) 4p0 Detailed mean-variance curves, autocorrelation matrices and point-source distortion may be computed (also for adjacent BSS, barrier clock and wavelength/SED)

21. Relative barrier strengths (channel stops vs. barrier clocks) Antilogus et al. 2014: Pixel correlations vary By factor of 3 (A01 vs. A10) Channel stop barrier strength is tuned to reproduce the factor of 3 between pixel area distortion response to collected charge in (0,0) (A01vs A10) Parameter estimation may be further constrained with inclusion of additional autocorrelation matrix element ratios

22. Autocorrelation maps from simulated flat fields(two drift coefficients, all distance effects applied < 0.5 pixels) Mean-variance term Shifted terms (i != 0 && j !=0 )

23. Ellipticity kernel S2E1 Brighter/Fatter Systematic effects to focused images consistent with autocorrelation features Source input parameters: aspect ratio = 1.05:1.0 FWHM = 3.0 pix centroid = (0.25,0.25) Orientation = 30° parallel φ E1 component E2 component serial Black: no covariance Red: covariance model parameterized by the drift coefficients: Orientation (delivered) Ellipticity(delivered)

24. Ancillary pixel data corresponding to channel content Greens function ( ) Pixel astrometric errors Pixel 2nd moments (S2, E1, E2) A.Rasmussen LSST2014 PhoSim for DM AlgorDev