1. UPDATE: Study of scattering points on LIGO mirrors
Lamar Glover (Cal State LA)
Riccardo DeSalvo (Cal State LA)
Innocenzo Pinto (Unisannio)
DCC Number G
Special Thanks to Travis Sadecki and Rick Savage of LIGO Hanford for providing recent images

2. Brief Recap of Study Subject Matter
LIGO mirrors present unexpectedly large scattering losses.
Previously, measurements were made in situ by Bill Kells and on samples by Josh Smith at Cal State Fullerton and the Syracuse group.
Newer photos were provided by Travis Sadecki and Rick Savage from LIGO Hanford with updated, better optimized equipment in Nov (i.e. images from CCD cameras with higher resolution, made for infrared photography)

3. Brief Recap of Study Subject Matter (Cont’d)
At Cal State LA, we are continuing our analysis of the scattering points detected in the Advanced LIGO mirrors
In astronomy, looking where there appear to be no stars, the beginning of the universe was seen
In LIGO mirrors, the problem of scatterers may be understood looking away from the large scatterers, in the depth of the coating layer stack

4. Brief Recap of Study Subject Matter (Cont’d)
The largest scatterers are likely accidental
Dirt on the mirror
Impurity-induced defects during deposition
A large number of scatterers were observed where none would be expected

5. Method Review
Individual scatterers were identified using astronomical algorithms for stars in galaxies
Extract apparent amplitude and position of each scatterer
Fit the stored beam position and profile to determine the illumination power on each scatterer.
Re-normalize the amplitude of each scatterer (dividing by the illumination power) to extract actual scattering power of each point.

6. Start with a low exposure image Inspect with increasing large exposure
photo @ sec sum
1st Run: 101 scatterers
2nd Run: 17 scatterers
3rd Run: 16 scatterers
photo @ 0.01 sec 
1st Run: 5,680 scatterers
2nd Run: 6,126 scatterers ,806
photo @ sec
1st Run: 6,216 scatterers
photo @ 0.4 sec (beware of saturation)
1st Run: 183,522 scatterers ,522
2nd Run: 180,267 scatterers ,789

7. Initializing the analysis software
Low exposure.
An obvious cluster is visible
It is probably due to accidental sources (dirt?)
Original Photo :

8. Daophot mechanism: Point Spread Templates
“daophot” creates a point spread template from selected sources within the image.
The template is applied to search scatterers in the image
Magnitude data is derived for each identified source.
Point Spread Template for an exposure time of 1.25 x 10-4 s

9. Types of Scatterers “Well-Mannered”: Used in template construction
Few Not “Well-Mannered”: Could it be due to dirt buried into layers?

10. Identifying/subtracting scatterers
Original Photo in “ds9”
256
100
Resulting Photo from first run of “daophot” package
256
Even at this low exposure few scatterers are VERY bright Saturate CCD
After extracting the bright point sources, the pixel scale is expanded
The gray CCD background becomes obvious.
Black dots mark the place where bright scatterers have been excised
“dirt” is ignored by daophot and is less bright

11. Pixel Intensity
Pixel amplitude histogram of original Image
100
200
Residual pixel amplitude histogram after scatterer extraction
In a good image Daophot is very effective in extracting scatterers
Width of residuals (2-3 pixels FWHM) illustrates Daophot’s good resolution
-50 50

12. Pixel Intensity @ Exposure Time = 1.25x10-4 sec
100
250
Pixel amplitude histogram of original Image
Original Image 50
-50
Residual pixel amplitude after “daophot” Process
Image after “daophot” Process

13. Pixel Intensity @ Exposure Time = 0.01 sec
Pixel amplitude histogram of original Image
Original Image
100
250 50
-50
Residual pixel amplitude after “daophot” Process
Width of residuals widened by saturation
Still a few pixels FWHM, + small tails
Image after “daophot” Process

14. Point Spread Templates
Run 1
Run 2
Run 3
T = 1.25 x 10-4 s
Point Spread Template for different runs
T = 0.01 s
Template quality worsens with:
Increased exposure time
High run number
T = s
T = 0.4 s

15. Pixel Intensity @ Exposure Time = 0.0125 sec
Pixel amplitude histogram of original Image
Original Image
100
250
FWHM still good
Tails grow with exposure and saturation 50
-50
Residual pixel amplitude after “daophot” Process
Image after “daophot” Process

16. Efficiency of Daophot’s cycles
200
Large amplitude scatterers picked up first
Weaker pixels plucked from background noise
100
Scatterer light Exposure Time = 0.01 sec
2000
7000

17. Scatterer light intensity @ Exposure Time = 0.01 sec
Adding up cycles
200 80
Run 1
Run 2
1000
10000
20000
2000
Run 1 + 2
10000
20000
100 5 1
Scatterer light Exposure Time = 0.01 sec

18. Scatterer light intensity @ Exposure Time = 0.0125 sec70 40
5000
20000

19. The origin of scatterer apparent sizes
Local illumination level (correctable)
Physical scatterer size inside host layer
Depth of host layer inside dielectric coating stack
(All scatterers are nanometer scale,
diffraction limited)

20. 1: Crystallite Formation in a single evaporated layer
Growth direction
Side View
Top View
Growth Direction
As mirror layers are formed, crystallites appear at different depths.
Those that appear earlier in the depositing process grow more
Create larger scatterers

21. 1: Crystallite Formation in a single evaporated layer
Deposition starts as a glassy layer
As layer grows, crystallites form in the glassy matrix. Slowly grow.
Columnar growth ensues
Dielectric mirror layers ~ 0.1 µm. Well inside glassy matrix region
Where there “appear” to be NO crystallites
Growth Direction
Chapter 4: Thin Film Deposition
Web:

22. Scattered light detected by CCD
2: Depth in stack effect
Two depth attenuation components:
Attenuation of impinging light due to reflections through depth of scattering layer
Further attenuation from reflection of Scattered light
Scattered light detected by CCD
Complex effect to simulate

23. Analysis of spatial distribution
From previous analysis of initial LIGO mirror
(New mirror still to be completed)
Uniform scatterer density
Uniform scattering amplitude across radius

24. Number of Scatterers vs. Exposure Time
photo @ sec
101 scatterers
photo @ 0.01 sec 
5,680 scatterers
photo @ sec
6,216 scatterers
photo @ 0.4 sec
183,522 scatterers (saturation)
Number increases almost linearly with exposure
Exploring smaller scatterers
But also deeper in the dielectric coating layers ! !

25. Discussion of crystal number and distribution intensity
How can so many scatterers exist, and be so uniformly distributed?
Cannot be “dirt” !
Only a thermodynamical source like the nucleation theory can explain the observations
Image: Old Ligo

26. Competing forces in nucleation
Ordered volume inside crystal
Disordered outside
Inside crystallite bonds are formed
Energy gain:

27. Competing forces in nucleation
In disordered surface
Many more bonds are frustrated than in the glassy matrix
=> Large Energy loss

28. Competing forces in nucleation
This costs energy
Here you gain energy
Ordered volume
Disordered surface
Gain (+)
Loss (-)

29. Grain Critical radius As a result of the competition
between area ΔGs and volume ΔGv
ΔG has a maximum Nucleation Energy ΔG*
Crystallites have a critical size

30. Nucleation speed
An activation energy means that the nucleation probability depends on the deposition temperature

31. Effects of nucleation size
critical size
Below r* there is energetic advantage to disappear
Above r* there is energetic advantage to grow
Nanolayering depresses number and growth of crystallites
Nanolayering below r* may even inhibit crystallite generation

32. Is the surface of Crystallites the source of mechanical losses?
Surface is most disordered
More frustrated bonds exist on the surface than inside the glassy matrix ! !
Large probability of bonds flipping between energy states during vibrations
Typical mechanical energy loss mechanism

33. Effects of depressing crystallites
There is circumstantial experimental evidence that the surface of crystals is a site where large energy losses happens.
Observed when over-annealing generates X-ray-detectable crystallites
Reduced number and size of crystallite may reduce thermal noise, as well as light scatter.