Weekly meeting - Miscellaneous updates … · Weekly meeting Miscellaneous updates J er emie Decock...
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Introduction Benchmark Energy conservation Conclusion References Weekly meeting Miscellaneous updates J´ er´ emie Decock CEA Saclay - Irfu/SAp October 5, 2016 Decock CEA Saclay - Irfu/SAp Weekly meeting
Transcript of Weekly meeting - Miscellaneous updates … · Weekly meeting Miscellaneous updates J er emie Decock...
Introduction Benchmark Energy conservation Conclusion References
Weekly meetingMiscellaneous updates
Jeremie Decock
CEA Saclay - Irfu/SAp
October 5, 2016
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Introduction
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Quick overview
Quick overview
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Quick overview
What’s new
I Refactor the library (for future changes)
I Fix the issue about the non null background with WT
I Make additional plots plot: Npe , scores vs Npe
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Reminder of the main issue: background > 0 with Wavelets
Reminder of the main issue: background > 0 withWavelets
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Reminder of the main issue: background > 0 with Wavelets
Look at the scale of the cleaned image...
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Reminder of the main issue: background > 0 with Wavelets
It’s more obvious with ’vmin=0’ in matplotlib...
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Reminder of the main issue: background > 0 with Wavelets
I It explains the shift on all plots for WT
I It should explain why the classification works despite thatmistake (the background is ignored by the classifier)
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Solution
Solution
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Solution
Solution
I Remove the largest plane in the wavelet space
I I took advantage of this to switch from mrtransform tomrfilter (both are part of Cosmostat’s Sparce 2D suite)
I Mrfilter advantages:I has more options to solve the background issue, remove
isolated pixels, handle negative values, ...I has more options for filtering and handle more complex
wavelets reconstructionI should be faster
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Solution
MrFilter
Used options:
I K Suppress the last scale (to have background pixels = 0)
I k Suppress isolated pixels in the support
I C1 Coef Detection Method: K-SigmaNoise Threshold
I s3 K-SigmaNoise Threshold = 3 sigma
I m3 Noise model (m10 works better but is much slower)...
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Benchmark
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Definition
Definition
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Definition
Benchmark function
The error function E is given by:
E(s, s∗) = (E1(s, s∗), E2(s, s∗))T
E1(s, s∗) = mean
(abs
(s∑i si− s∗∑
i s∗i
))E2(s, s∗) =
abs (∑
i si −∑
i s∗i )∑
i s∗i
Where:
I s is the output image (the ”cleaned” image) ∈ IRd
I s∗ is the reference image (the ”clean” image) ∈ IRd
I i is the index of a PMT (i.e. of a pixel) within an image
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Gammas
Gammas
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Gammas
I ASTRI mini-array (calibrated data)
I Telescopes 1 to 33 only (ASTRI)
I Polychromatic event set
I Input files: sapcta:/dsm/manip/cta/DATA/astri mini array/fits/gamma/
I Source file:sapcta:/dsm/manip/cta/DATA/astri mini array/gamma/run1001.simtel.gz
I Num samples: 14899 images
Cleaning algorithms:
I Tailcut: JD’s implementation
I FFT: Numpy implementation
I Wavelets: Cosmostat Sparce2D (mr transform) b-Spline wavelet transform
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Gammas
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npe
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∑is ∗i (γ)
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Gammas
Tailcut (JD) WT (mr_transform) WT (mr_filter)10-4
10-3
10-2
Sco
reE1 (γ)
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Gammas
0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4
Score 1e 3
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Tailcut (JD)
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Gammas
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score
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Gammas
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score
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Tailcut (JD)
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score
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E1 (γ)
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Gammas
Tailcut (JD) WT (mr_transform) WT (mr_filter)10-4
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reE2 (γ)
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Gammas
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Score
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E2 (γ)
WT (mr_filter)
Tailcut (JD)
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Gammas
10-4 10-3 10-2
Score 0
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10-3
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Tailcut (JD) scores correlation
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Gammas
10-4 10-3 10-2
Score 0
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10-2
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101
Sco
re 1
WT (mr_filter) scores correlation
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Gammas
10-3 10-2 10-1 100 101
score
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Tailcut (JD)
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Gammas
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score
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Protons
Protons
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Protons
I ASTRI mini-array (calibrated data)
I Telescopes 1 to 33 only (ASTRI)
I Polychromatic event set
I Input files: sapcta:/dsm/manip/cta/DATA/astri mini array/fits/proton/
I Source file:sapcta:/dsm/manip/cta/DATA/astri mini array/proton/run10001.simtel.gz
I Num samples: 2203 images
Cleaning algorithms:
I Tailcut: JD’s implementation
I FFT: Numpy implementation
I Wavelets: Cosmostat Sparce2D (mr transform) b-Spline wavelet transform
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Protons
100 101 102 103 104 105 106
npe
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Count
14899 images
∑is ∗i (γ)
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Protons
Tailcut (JD) WT (mr_transform) WT (mr_filter)10-4
10-3
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Sco
reE1 (protons)
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Protons
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CountE1 (protons)
WT (mr_filter)
Tailcut (JD)
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Protons
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Protons
10-3
score
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Tailcut (JD)
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E1 (protons)
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Protons
Tailcut (JD) WT (mr_transform) WT (mr_filter)10-5
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reE2 (protons)
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Protons
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CountE2 (protons)
WT (mr_filter)
Tailcut (JD)
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Protons
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score
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Protons
10-4 10-3 10-2 10-1 100 101 102
score
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Tailcut (JD)
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Protons
10-4 10-3 10-2
Score 0
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Tailcut (JD) scores correlation
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Protons
10-4 10-3 10-2
Score 0
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re 1
WT (mr_filter) scores correlation
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Energy conservation
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Definition
Definition
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Definition
Definition
The error on energy is given by:∑i si −
∑i s
∗i∑
i s∗i
Where:
I s is the output image (the ”cleaned” image) ∈ IRd
I s∗ is the reference image (the ”clean” image) ∈ IRd
I i is the index of a PMT (i.e. of a pixel) within an image
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Gamma
Gamma
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Gamma
1 0 1 2 3 4 5 6
Score 1e1
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105
Count
Tailcut (JD)
Tailcut (JD)
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Gamma
1.0 0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0
Score
100
101
102
103
104
Count
WT
WT (mr_filter)
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Gamma
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score
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102
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104
105
npe
Tailcut (JD)
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score 1e1
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WT (mr_filter)
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4006008001000120014001600
0
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4006008001000120014001600
Energy conservation (γ)
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Proton
Proton
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Proton
0.5 0.0 0.5 1.0 1.5 2.0 2.5
Score 1e2
100
101
102
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104
Count
Tailcut (JD)
Tailcut (JD)
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Proton
0.5 0.0 0.5 1.0 1.5 2.0 2.5
Score 1e1
100
101
102
103
104
Count
WT
WT (mr_filter)
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Proton
0 50 100 150 200
score
100
101
102
103
104
105
npe
Tailcut (JD)
0.0 0.5 1.0 1.5 2.0
score 1e2
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104
105
npe
WT (mr_filter)
0
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40
6080100120140160
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6080100120140160
Energy conservation (protons)
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Conclusion
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TODO
TODO
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TODO
TODO
I use the ctapipe implementation for Tailcut (work in progress)
I add metadata into input FITS files (EMC , ...) (work inprogress)
I use these metadata to plot log(EMC ) vs Npe , score vslog(EMC ), ... (almost done)
I plot the Chi2
I tag images where the signal is on the border
I optimize meta parameters (multicriteria optimization) (workin progress)
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References I
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Appendix
Appendix
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Appendix
Examples: E1 with gamma photons
Examples: E1 with gamma photons
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Appendix
Examples: E1 with gamma photons
Bad FFT example
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Appendix
Examples: E1 with gamma photons
Good FFT example
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Appendix
Examples: E1 with gamma photons
Bad Tailcut example
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Appendix
Examples: E1 with gamma photons
Good Tailcut example
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Appendix
Examples: E1 with gamma photons
Bad WT example
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Appendix
Examples: E1 with gamma photons
Good WT example
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Appendix
Examples: E2 with gamma photons
Examples: E2 with gamma photons
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Appendix
Examples: E2 with gamma photons
Bad FFT example
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Appendix
Examples: E2 with gamma photons
Good FFT example
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Appendix
Examples: E2 with gamma photons
Bad Tailcut example
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Decock CEA Saclay - Irfu/SAp
Weekly meeting
Appendix
Examples: E2 with gamma photons
Good Tailcut example
0 5 10 15 20 25 30 350
5
10
15
20
25
30
35
Input image
0 5 10 15 20 25 30 350
5
10
15
20
25
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35
Reference image
0 5 10 15 20 25 30 350
5
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30
35
Cleaned image
0.0
1.5
3.0
4.5
6.0
7.5
9.0
0
1
2
3
4
5
6
7
8
9
0.0
1.5
3.0
4.5
6.0
7.5
9.0
Decock CEA Saclay - Irfu/SAp
Weekly meeting
Appendix
Examples: E2 with gamma photons
Bad WT example
0 5 10 15 20 25 30 350
5
10
15
20
25
30
35
Input image
0 5 10 15 20 25 30 350
5
10
15
20
25
30
35
Reference image
0 5 10 15 20 25 30 350
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20
25
30
35
Cleaned image
0
2
4
6
8
10
12
0.0
1.5
3.0
4.5
6.0
7.5
9.0
10.5
0.00
0.15
0.30
0.45
0.60
0.75
0.90
1.05
Decock CEA Saclay - Irfu/SAp
Weekly meeting
Appendix
Examples: E2 with gamma photons
Good WT example
0 5 10 15 20 25 30 350
5
10
15
20
25
30
35
Input image
0 5 10 15 20 25 30 350
5
10
15
20
25
30
35
Reference image
0 5 10 15 20 25 30 350
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10
15
20
25
30
35
Cleaned image
0
3
6
9
12
15
18
21
24
0.0
2.5
5.0
7.5
10.0
12.5
15.0
17.5
20.0
22.5
25.0
0.0
2.5
5.0
7.5
10.0
12.5
15.0
17.5
20.0
22.5
Decock CEA Saclay - Irfu/SAp
Weekly meeting
Appendix
Examples: E1 with protons
Examples: E1 with protons
Decock CEA Saclay - Irfu/SAp
Weekly meeting
Appendix
Examples: E1 with protons
Bad FFT example
0 5 10 15 20 25 30 350
5
10
15
20
25
30
35
Input image
0 5 10 15 20 25 30 350
5
10
15
20
25
30
35
Reference image
0 5 10 15 20 25 30 350
5
10
15
20
25
30
35
Cleaned image
1.5
0.0
1.5
3.0
4.5
6.0
7.5
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
0
1
2
3
4
5
6
7
8
Decock CEA Saclay - Irfu/SAp
Weekly meeting
Appendix
Examples: E1 with protons
Good FFT example
0 5 10 15 20 25 30 350
5
10
15
20
25
30
35
Input image
0 5 10 15 20 25 30 350
5
10
15
20
25
30
35
Reference image
0 5 10 15 20 25 30 350
5
10
15
20
25
30
35
Cleaned image
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250
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0
400
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2800
3200
0
250
500
750
1000
1250
1500
1750
2000
Decock CEA Saclay - Irfu/SAp
Weekly meeting
Appendix
Examples: E1 with protons
Bad Tailcut example
0 5 10 15 20 25 30 350
5
10
15
20
25
30
35
Input image
0 5 10 15 20 25 30 350
5
10
15
20
25
30
35
Reference image
0 5 10 15 20 25 30 350
5
10
15
20
25
30
35
Cleaned image
1.5
0.0
1.5
3.0
4.5
6.0
7.5
9.0
0.0
0.4
0.8
1.2
1.6
2.0
2.4
2.8
3.2
3.6
4.0
0
1
2
3
4
5
6
7
8
9
Decock CEA Saclay - Irfu/SAp
Weekly meeting
Appendix
Examples: E1 with protons
Good Tailcut example
0 5 10 15 20 25 30 350
5
10
15
20
25
30
35
Input image
0 5 10 15 20 25 30 350
5
10
15
20
25
30
35
Reference image
0 5 10 15 20 25 30 350
5
10
15
20
25
30
35
Cleaned image
0
3
6
9
12
15
18
21
24
0.0
2.5
5.0
7.5
10.0
12.5
15.0
17.5
20.0
22.5
0.0
2.5
5.0
7.5
10.0
12.5
15.0
17.5
20.0
22.5
Decock CEA Saclay - Irfu/SAp
Weekly meeting
Appendix
Examples: E1 with protons
Bad WT example
0 5 10 15 20 25 30 350
5
10
15
20
25
30
35
Input image
0 5 10 15 20 25 30 350
5
10
15
20
25
30
35
Reference image
0 5 10 15 20 25 30 350
5
10
15
20
25
30
35
Cleaned image
0.0
1.5
3.0
4.5
6.0
7.5
9.0
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
0.00
0.15
0.30
0.45
0.60
0.75
0.90
1.05
1.20
Decock CEA Saclay - Irfu/SAp
Weekly meeting
Appendix
Examples: E1 with protons
Good WT example
0 5 10 15 20 25 30 350
5
10
15
20
25
30
35
Input image
0 5 10 15 20 25 30 350
5
10
15
20
25
30
35
Reference image
0 5 10 15 20 25 30 350
5
10
15
20
25
30
35
Cleaned image
0
200
400
600
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0
150
300
450
600
750
900
1050
1200
1350
0
150
300
450
600
750
900
1050
1200
1350
Decock CEA Saclay - Irfu/SAp
Weekly meeting
Appendix
Examples: E2 with protons
Examples: E2 with protons
Decock CEA Saclay - Irfu/SAp
Weekly meeting
Appendix
Examples: E2 with protons
Bad FFT example
0 5 10 15 20 25 30 350
5
10
15
20
25
30
35
Input image
0 5 10 15 20 25 30 350
5
10
15
20
25
30
35
Reference image
0 5 10 15 20 25 30 350
5
10
15
20
25
30
35
Cleaned image
0.0
1.5
3.0
4.5
6.0
7.5
9.0
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
0.0
1.5
3.0
4.5
6.0
7.5
9.0
Decock CEA Saclay - Irfu/SAp
Weekly meeting
Appendix
Examples: E2 with protons
Good FFT example
0 5 10 15 20 25 30 350
5
10
15
20
25
30
35
Input image
0 5 10 15 20 25 30 350
5
10
15
20
25
30
35
Reference image
0 5 10 15 20 25 30 350
5
10
15
20
25
30
35
Cleaned image
0
250
500
750
1000
1250
1500
1750
2000
0
400
800
1200
1600
2000
2400
2800
3200
0
250
500
750
1000
1250
1500
1750
2000
Decock CEA Saclay - Irfu/SAp
Weekly meeting
Appendix
Examples: E2 with protons
Bad Tailcut example
0 5 10 15 20 25 30 350
5
10
15
20
25
30
35
Input image
0 5 10 15 20 25 30 350
5
10
15
20
25
30
35
Reference image
0 5 10 15 20 25 30 350
5
10
15
20
25
30
35
Cleaned image
1.5
0.0
1.5
3.0
4.5
6.0
7.5
9.0
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
0
1
2
3
4
5
6
7
8
9
Decock CEA Saclay - Irfu/SAp
Weekly meeting
Appendix
Examples: E2 with protons
Good Tailcut example
0 5 10 15 20 25 30 350
5
10
15
20
25
30
35
Input image
0 5 10 15 20 25 30 350
5
10
15
20
25
30
35
Reference image
0 5 10 15 20 25 30 350
5
10
15
20
25
30
35
Cleaned image
1.5
0.0
1.5
3.0
4.5
6.0
7.5
9.0
0
1
2
3
4
5
6
7
8
9
10
0.0
1.5
3.0
4.5
6.0
7.5
9.0
Decock CEA Saclay - Irfu/SAp
Weekly meeting
Appendix
Examples: E2 with protons
Bad WT example
0 5 10 15 20 25 30 350
5
10
15
20
25
30
35
Input image
0 5 10 15 20 25 30 350
5
10
15
20
25
30
35
Reference image
0 5 10 15 20 25 30 350
5
10
15
20
25
30
35
Cleaned image
1.5
0.0
1.5
3.0
4.5
6.0
7.5
9.0
10.5
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
Decock CEA Saclay - Irfu/SAp
Weekly meeting
Appendix
Examples: E2 with protons
Good WT example
0 5 10 15 20 25 30 350
5
10
15
20
25
30
35
Input image
0 5 10 15 20 25 30 350
5
10
15
20
25
30
35
Reference image
0 5 10 15 20 25 30 350
5
10
15
20
25
30
35
Cleaned image
1.5
0.0
1.5
3.0
4.5
6.0
7.5
9.0
0.0
0.3
0.6
0.9
1.2
1.5
1.8
2.1
2.4
2.7
3.0
0.00
0.25
0.50
0.75
1.00
1.25
1.50
1.75
2.00
2.25
Decock CEA Saclay - Irfu/SAp
Weekly meeting
