The future of FLUOR
nic scott
NASA ARC
FLUOR to JouFLU
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Science Case: Exozodis
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Exozodiacal Disks
- Not to be confused with debris disks
- Require interferometry to detect
- High levels (100-1000 zodi) even in >100 Myr systems
- Confound the detection of exoEarths
- Probe the structure of inner system
1 AU terrestrial planet with gap
0.5 – 1.5 AU warm dust disk 500K
0.1 – 0.5 AU hot dust disk > 1000K
Center A0 star @ 10 pcc
< 100K
100 AU
100-1400K
< 10 AU
HZ
![](https://s3.amazonaws.com/media-p.slid.es/uploads/709806/images/3786880/ligthhouse.jpg)
Resonant structures could indicate planets indirectly [7]
Zodi levels of dust affect Earth detection
Our disk is the most luminous object in SS after the Sun.
Earth would be a clump in the zodi at visible and IR [1]
10-20 zodi would compromise exoEarth detection [2,3] Interferometric, astrometric, direct, photometric, ...
exoEarth detection is divided by factor of 2 for exozodi level increase of 10 [5]
exoEarth detection becomes challenging if exozodi level is ~20 zodis and clumpy [4]
\(\geq\)10% of Gyr old MS stars may have enough exozodi dust to complicate exoEarth imaging [6]
Correlation with spectral type or outer reservoir?
\(\leftarrow\) tentative correlation with stellar rotation supports the magnetic trapping model but not conclusive.
Nuñez et al. 2017
6/33 new circumstellar excesses at \(\geq\)1% level
- 2 of these detections can be attributed to uniform CSE
- 4 are known or suspected binaries.
The difference of between the instrumental noise and the JouFLU significance distribution yields an estimate of 9 undetected excesses.
Nuñez et al. 2017
NIR
VLTI - PIONIER
Ertel et al 2014 merged FLUOR+PIONIER samples (n~125) reaching 0.25% precision
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Rate decreases across spectral type
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Matches cold disk trend. Common origin?
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No correlation b/t hot dust and cold dust.*
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Different origin for hot and cold discs?
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Slight increase in exozodi detection with stellar age
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Stochastic rather than steady-state process [46,33,34,35]?
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No correlation b/t exoplanets and exozodi.
HD 7788 shows variability
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excess disappeared for a year
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Near Infrared Exozodi Variability Study
Dust production mechanism poorly understood
Destruction factors:
- Sublimation
- Radiation Pressure
- Poynting-Robertson (P-R) drag
Models:
- Steady state/continuous replenishment
- Steady state/trapped nano-grains
- LHB & outgassing
- Keplerian time scale ~weeks/months
iot Psc
ups And
kap CrB
gam Ser
HD 98058
- \(\Phi\) Leo spectra shows signs of exocomet infall and evaporation [45]
HD 210418
- A-type with 1.7 ± 0.5% excess from 2013
HD 222368
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F-type with 1.3 ± 0.3% excess from 2013
Tet Boo
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Solar type star with no previously known dust excess
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Significant excess at 10 micron with the LBTI nuller.
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Potentially huge implications on our understanding of exozodi level upper limits, and dust generation mechanisms around such stars.
From LBTI | |||
|
10700 | exoplanet host | |
13 Uma | 78154 | LBTI excess | |
kap01 ceti | 20630 | exoplanet host | |
1 Ori | 30652 | ||
tau Boo | 120136 |
The Problem
Signs of problems
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differential polarization rotation
differential polarization phase delay
Added Lithium Niobate plates to correct polarization, but decreases throughput.
Limiting Kmag ~4.5-5.2 from 2015-2016 is now ~3
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1. Connect Beam 5 fiber to input A and Beam 6 fiber to input B. (default arrangement)
2. Close the beam 6 shutter and measure the four outputs.
3. Open the beam 6 shutter, close the beam 5 shutter, and measure the four outputs.
4. Open both shutters and measure the four outputs.
5. Move the beam 5 fiber to input B, move the beam 6 fiber to input A, and repeat all 3 measurements.
6. Swap beam 5 and beam 6 on the beam sampler and repeat the complete set of 6 measurments.
determined beam ratio and coupling efficiency for each input
I2 interferometric channel does not see anything from beam A. Could be a broken fiber in MONA?
- no sig difference in coupling efficiency of the two stages.
- no sig difference in light in beam 5 and beam 6
- Most significant difference:
- input A 80% to photometric output
- input B 13% to the photometric output
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Each branch of the fiber bundle transmits basically the same (max counts well w/i +/- 8%)
- Bundle still seems fine
- Confirms that the two stages have essentially the same efficiency.
- MONA seems to be the problematic part.
![](https://s3.amazonaws.com/media-p.slid.es/uploads/709806/images/5814413/Fiber-test.png)
The ratio of light reaching the interferometric output from input A and Input B.
![](https://s3.amazonaws.com/media-p.slid.es/uploads/709806/images/5814421/MONA_Normalize_Count.png)
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"The conclusion we seem to converge upon is that the problem is in the MONA box.
Not enough light coming from Input A to the inteferometric channels."
MONA_Normalized_Count
This shows the total amount of light getting through normalize for Kmag, ie Count / 10\(^{(mag/-2.5)}\). Decline in 2016 after we put the polarization corrector plates in.
Percentage of light from input A(top) and B(bottom) reaching it's photometric output and the two inteferometeric outputs. There is a clear change after the unit was sent back to France. It seems much more light is going to the photometric channel and much less to the interferometric outputs.
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Possible Solutions
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JouFLU upgrade paths
JouFLU prior limit
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JouFLU potential
JouFLU present
Getting to 5th mag could more than double the number of targets observable
CHARA AO is now coming online \(\rightarrow\) greatly improved obs efficiency
- ZBLAN IO chip
- losses ~0.4 db/cm
- get H band IO chip as "bonus"
- v-groove and coupling optics (Ozoptics)
- input and output mounts
- option: 4 beam H+K simultaneous
Saphira Selex detector
- will enable drastically better spectrally dispersed results
- +350k
transmission | <0.01 | db/m |
bandpass | 2 - 2.3 | \(\mu\)m |
NA/lambda_c | 0.089 | \(\mu\)m |
20-30% to photom, 70-80% to Interferometric. I1 & I2 balanced |
NA | 0.17 ± 0.01 |
cutoff | < 1.95 \(\mu\)m |
bandpass | 2.0 - 2.4 \(\mu\)m |
Goal is 1% excess detection at 5σ to mK < 5.
![](https://s3.amazonaws.com/media-p.slid.es/uploads/709806/images/5901173/IMG_20181205_145342.jpg)
Takeaway
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Explore the apparent variability of known exozodis
- long-term monitoring
- clues to source and formation of the dust
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Expand strong exozodi sample
- leveraging LBTI and prior surveys
- from ~100 \(\rightarrow\) ~1000 objects
- Use spectral dispersion to resolve the thermal/scattered dilemma
- Risk mitigation for coronagraphy/starshade missions
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Target selection and characterization for mid/large missions (TESS, LUVOIR, HabEx, etc)
- exozodis likely to be dominant noise source
- Precision diameters and fundamental astrophysics
Science gaps on Exoplanet program office list
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Science gap Number 4
- Planetary System Architecture
- Science gap Number 6
- Yield estimation for exoplanet direct imaging missions
- Science gap number 7
- Improve target lists and compilations of stellar parameters for exoplanet missions in operation or under study
- Science gap number 10
- Precursor surveys of direct image targets
- Science gap Number 11
- Understanding the abundance and distribution of exozodiacal dust
NN-Explore/NASA
ExoZodiacal
Monitoring
Observatory
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References
[1] Kelsall et al. 1998
[2] Beichman et al. 2006 ApJ 652
[3] Roberge et al. 2012
[4] Defrère et al. Proc. SPIE 2012
[5] Stark et al. 2014
[6] Kennedy & Wyatt 2013
[7] Wyatt et al. 1999
[8] Fajardo-Acosta et al. 2000
[9] Mannings & Barlow 1998
[10] Laureijs et al. 2002
[11] Lawler et al. 2009
[12] Wyatt et al. 2007 ApJ 658
[13] Defrère et al. 2015
[14] Mennesson et al. 2014
[15] Absil et al. 2013
[16] Ciardi et al. 2001
[17] di Folco et al. 2004
[18] Absil et al. 2006
[19] di Folco et al. 2007
[20] Absil et al. 2008b
[23] Defrère et al. 2011
[24] Mennesson et al. 2011a
[25] Mawet et al. 2011
[26] Lisse et al. 2012
[27] Weinberger et al. 2011
[28] Defrère et al. 2012a
[29] Lisse et al. 2013
[30] Ertel et al. 2014
[31] Marion et al. 2014
[32] Nuñez et al. 2017
[33] Kral et al. 2013
[34] Krivov et al. 2006
[35] Wyatt et al. 2007 ApJ 663
[36] Defrère et al. 2012 A&A 546
[37] Marshall et al. 2016
[38] van Lieshout et al. 2014 A&A 571
[39] Jackson et al. 2012
[40] Rieke et al. 2016
[41] Su et al. 2016
[42] Wyatt et al. 2008
[43] Su et al. 2013
[44] Lebreton et al. 2013
[45] Eiroa et al. 2016, A&A 594, Oct 2016
[46] Faramaz et al. 2016
[47] Ertel et al. 2018
Scott-CHARA_meeting_2019
By Nic Scott
Scott-CHARA_meeting_2019
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