I. Plan to Meet PLRA Threshold Requirements

Phil Hinz Principal Investigator

Science Validation Plan Compliance

• This presentation discusses the tasks planned to improve the performance of LBTI to that of the PLRA threshold requirements

• Relationship to ORR Success Criteria: This addresses 1c, describing the plans to meet the threshold requirements

• Concerns: – Terms in the null uncertainty error budget are difficult to isolate and

verify. Precise performance prediction and assessing impact of improvements is therefore difficult.

• Liens: None, though we continue to explore new detector options

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1.c Credible plans* documented to meet PLRA threshold performance requirements (6 zodi, 0.3 mJy) by end of science validation phase with risk that is medium or lower Green

Compliance Matrix

PLRA Section

SuccessCriteria

March 2014

Now (ORR)

End of SVP

In-Guide Plan (FY17)

With Lien (FY18)

L0 4.3 A: 10x better 2.8x better 12x better 24x better 24x better 24x better

L0 4.3 B: Inform missions 6 zodi median

1.13 zodi median

0.51 zodi median

0.51 zodi median

0.50 zodi median

L1 4.1.2 C: 6 zodi, 1σ 54 zodi 12 zodi 6 zodi 6 zodi 6 zodi

L1 4.1.4 D: 50 stars in 4 yrs 1 3 18 35 50

L2 4.1.4 E: 0.3 mJy sens. 0.4 mJy 0.4 mJy 0.3 mJy 0.3 mJy 0.3 mJy

L2 4.1.4 F: 1.5x10-4 null stb 1800 ppm 400 ppm 150 ppm 150 ppm 150 ppm

L2 4.1.4 G: 30% efficiency 30% 32% 40% 40% 40%

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Red = not compliantBlue = compliant, assuming in-guide completionOrange = compliant with ORR criteria, but not PLRAGreen = compliant with PLRA

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1. Efficiency Budget

• Already meets requirement; sufficient to carry out HOSTS survey

• Setup efficiencies will improve during SV phase, providing greater margin against the number of stars that can be surveyed

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Automate process

Yellow=areas of concern for SVP

Blue=projectedImprovement for SVP

2. Photometric Error Budget—Current

• Excess background is the dominant noise term• Throughput improvements will help

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Modeling Throughput and Background

• We have used vendor specifications and on- and off-sky testing to measure the system throughput of LBTI– Spreadsheet captures our current estimate– Throughput is 1.5x lower than expected

• The background dominates the noise for LBTI

• SNR calculation uses the photons/frame and the throughput to track our expected performance relative to the requirements

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Improve Photometric Sensitivity

• Reduce equivalent blackbody background emissivity to 16% from 27% (measured)– Replace poorly performing WFS dichroic and better baffling

• Improve throughput to 6.6% from 4.5% (measured)– Remove warm ZnSe uncoated window

(used as safety between NIC and beam-combiner)– Model suggests an additional factor of 1.5 is possible

• Pipeline optimization during SVP– Improve data rejection to optimize null uncertainty jointly

with photometric uncertainty– Implement PSF photometry

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Impact on Photometric Uncertainty

Photometric requirements will be reached via straightforward improvements to the instrument during SVP

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3. Null Uncertainty: Error Budget

• Areas of improvement are PWV turbulence, vibrations, and photometric bias

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Photometric uncertainty is incorporated into null budget

PWV Turbulence

• Low-frequency variations in the null are seen– These variations are

more prominent on high-PWV nights

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Low PWV

High PWV

Approach to Estimating PWV Effect

• NSC fits a mean (μ) and scatter (σ) to the intensity variations to distinguish these effects from the astrophysical null– The mean and scatter can only be measured to a precision

given as

– N is the number of independent measurements, about 60 per OB

– Resulting setpoint uncertainty is 60 nm

• Null uncertainty given by

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This is consistent with phase drift calculated from NSC fits to successive OBs

PWV Mitigation: 1

• Since PWV mitigation is the largest error budget term, several options are being developed to minimize risk:– Option A: Use group delay measurement of the K-band phase sensor.

• Timescale to implement: Summer 2015• Risk: Group delay metric may not encode water vapor dispersion accurately

enough to improve null– Option B: Measure group delay using the 3- to 5-micron light with

LMIRcam• Timescale to implement: Summer 2015• Risk: Speed of LMIRcam may not be sufficient to track dispersion changes

– Option C: Reconfigure K band phase sensor to more accurately measure group delay• Timescale to implement: Fall 2015• Risk: More extensive change to the phasing code required

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PWV Mitigation: 2

• Queue scheduling will be implemented to use driest nights for HOSTS– Most other LBTI programs don’t require dry

conditions– Steward Observatory has agreed to support this– Queue will be internal to LBTI runs with clearly

defined rules– Weather loss and overall allocations will still be

adhered to, but may be spread over more than one semester

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Vibration

• We see two effects:– Vibrations at 10–20 Hz due

to resonant telescope modes• Removed by phase sensor• Tests done to feed-forward

with accelerometers

– Vibrations at 100–300 Hz due to instrument• Appears to be LBTI box-beam

structures• Cryogenic optics mounting

may also contribute

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Telescope Vibrations

Instrument Vibrations

Approach to Estimating Vibration Effect

• Assume that the residual vibration in the system (that is, whatever is not corrected by phase cam) is all high-frequency– Blurs individual frames by

• This is removed via phase cam telemetry, but adopt a systematic estimation error of 2%.

• We currently see 400 nm RMS of high-frequency vibration (f>1/DIT)

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Vibration Mitigation

• Reduce instrument vibration– Damping of identified resonant beams– Stiffening of identified “soft” mounts

• Feed-forward telescope vibration– Refine proven approach to use accelerometers

and filtering for specific frequencies– Add feed-forward for tertiaries (to be

implemented) as well as secondaries (currently implemented)

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Photometric Bias

• Excess low-frequency noise is seen in our data– This is primarily caused by our BIB detector– Thickness of the detection layer causes this

(Stapelbroek et al. 1984)– A new detector could eliminate this effect

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Approach to Estimating Photometric Bias

• We measure a 2-mJy uncertainty per pointing from the NSC

• For this error budget we assume the calibrators and science object are the same

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Photometric Bias Mitigation

• Minimize Excess Low Frequency Noise (ELFN) by revising observing approach– Increase nod frequency (0.01 → 0.02 Hz, 1.5x improvement)– Improve background subtraction by using closest frames

(1.5x improvement)

• Detector upgrade– Discussing/exploring options with ESO and Raytheon

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Null Uncertainty Improvements

• We expect a 2x improvement of null drift once PWV mitigation is implemented

• We expect a 2x improvement of the vibration residuals once the accelerometer feed-forward and instrument damping work is completed

• We expect a 2x improvement in the photometric bias after shortening the nod cycle time

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Impact on Nulling Uncertainty

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Impact on HOSTS Survey

• Most of the SV tasks can be carried out in parallel with executing the survey

• We will require dedicated engineering time at the level of approximately two nights total in FY16 for the following tasks:– Validating PWV tracking approach– Validating automated operation– Exploring increased nodding frequency sequences– Tuning and optimizing the feed-forward inputs

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SVP Mean Zodi Estimate

• Based on following limits:– 1 mJy photometric bias/pointing– Noise floor of 150 ppm (bright star limit)

• The uncertainty for a general HOSTS star is

• Overall sample provides mean zodi sensitivity:– 4.9 for sample of 32 stars– 6.1 for full sample of 50 stars

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Science Validation Summary

• We have defined a list of tasks to execute over the next 9 months– Detailed schedule developed (see supporting document

on Wiki)

• The planned performance improvements are sufficient to meet the threshold requirements– The modeled performance is not precise, and provides

some level of risk to achieving the threshold performance

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