Obtainment of Prototypical images and Detector performance simulations. Guillaume Potdevin for the XFEL-HPAD-Consortium

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1 Obtainment of Prototypical images and Detector performance simulations

2 Overview of the analysis Outlook Prototypical images: Single object imaging & XPCS short presentation (reminder ) Presentation of the data Dynamic range analysis Detector Simulation Software Presentation Waiting for your inputs

3 Overview of the Analysis Obtainment of Prototypical input images Simulation of the detector s response Analysis of the data, discussion with users, dev team Simulation of Data Simulation of Noise Experimental Data Feedback, update images & det. characteristics

4 Overview of the analysis Outlook Prototypical images: Single object imaging & XPCS short presentation (reminder ) Presentation of the data Dynamic range analysis Detector Simulation Software Presentation Waiting for your inputs

5 Single object Imaging Coherent Light Source Imaging in the far field region Fourrier Transform

Single object Imaging, the reconstruction Algorithm starts with an image (random) Apply projections Iteratively modify image until converge hybrid

6 Single object Imaging, the reconstruction Algorithm starts with an image (random) Apply projections Iteratively modify image until converge hybrid input-output (Fienup, Appl. Opt. 21, 2759 (1982)) Random start difference map: Elser, J. Opt. Soc. Am. A 4, 118 (2002) Slide robbed to J. Kirz, ALS Berkeley

7 Single object Sources Slide robbed to Henry Chapman, CFel Hamburg

8 XPCS X-ray Photon Correlation Spectroscopy Coherent radiation Random scatterer θ Random diffraction (speckle) pattern e.g. laser light e.g. particle suspension Screen As scattering medium evolves, speckle pattern fluctuates I(q,t) Intensity at small detector T C ~ 10-6 to 10 4 s <I(q,t) I(q,t+τ)> Intensity autocorrelation function <I 2 > <I > 2 ~ T C I [ f ( q )] 2 2,τ Delay time τ ~ T C Time t Structure of intermediate scattering function, f(q,τ), gives information on scatterer dynamics Slide Robbed to P. Pusey, University of Edinburgh

XPCS @ XFel Sources Sequential Mode XPCS (Sequential): 0.1 s τ 200ns τ 600 µs Desire 5 MHz frame-rate.

9 XFel Sources Sequential Mode XPCS (Sequential): 0.1 s τ 200ns τ 600 µs Desire 5 MHz frame-rate. Eventually logarithmic time bins. (record less than 3000 frames) Timing limited. Slide Robbed to G. Gruebel, DESY

XPCS @ XFel Sources Delay line Mode Delay Line Mode: 1ps < Δt < 10ns (1

10 XFel Sources Delay line Mode Delay Line Mode: 1ps < Δt < 10ns (1 ps 0.3 mm; 1ns 300 mm) luminosity limited. Slide Robbed to G. Gruebel, DESY

11 XFel Sources Pump Probe Mode Slide Robbed to G. Gruebel, DESY

12 Overview of the analysis Outlook Prototypical images: Single object imaging & XPCS short presentation (reminder ) Presentation of the data Dynamic range analysis Which sort of data The data Detector Simulation Software Presentation Waiting for your inputs

13 Obtainment of prototypical images : Use of relevant experimental data Use relevant data: Single object scattering XPCS patterns Example: single nanocrystal study (courtesy, C.Mocuta ESRF) Example: Surface Colloid (courtesy, C.Gutt DESY) # hv [001] [-110] [110] Sample Substrate

intensities (complex) Map of Most Likely intensities for each pixels

14 Obtainment of prototypical images : Simulation of Small Objects Scattering Compute the contribution of each atom for each pixel Sum the intensities (complex) Map of Most Likely intensities for each pixels (real numbers) Poisson statistics analysis Gives Intensity (integers) Then Add noise

15 Data: Case of very small single object Simulation of a dwarf Virus single virus (simulation by F.Pfeiffer, SLS) Log 10 ( hv ) 10 9 hv 1 hv 100 hv Mostly a central peak, plus a few scattered photons.

16 Data: Case of small clusters Ex. Simulation Ferritin in 5*5*5 crystal unit crystal (large) 250 molecules of 20kDa each. (simulation by F.Pfeiffer, SLS) Log 10 ( hv ) 10 9 hv 1 hv 30px 6000μm 10 4 hv

17 Data: The background problem Lithographed samples with a CCD (courtesy Schropp, esrf id01) Log 10 ( hv ) Intensity ~ 1/q 3 + features.

18 Data: An example of nano object Ex. Exp data from single object on Si Substrate object : ~800nm size (courtesy of data: C.Mocutta, ESRF) Linear # hv 1200 hv 50 hv 220px 2*10 2 hv

19 Data: Case of the XPCS Ex. XPCS data, colloids on a surface (courtesy of data esrf id10) Linear scale, (# hv) 6 hv Non Bkg substracted Speckles are localized (i.e. density of pixel matters, not only surface)

20 Summary of available images Sample Remark Contributor Simulation of Ferritin Single Mol, 1, 3, 5, crystal units Pfeiffer, et al. Simulation of Dwarf Virus Standalone Pfeiffer, et al. Exp. data of lithographed sample on SiN Exp. data of nanostructures Contribution from SiN membrane to signal Missing information for ADU Photons conversion Vartaniants, Schrupp, et al. C.Mocuta, et al. Simulation of Pd (irrelevant for now). No scaling in photons Vartaniants, Blumes, et al. XPCS exp. data 8 kev, Relevant regarding intensities. For noise? C.Gutt, et al.

21 Dynamic Range analysis Lots of photons at the center (not only direct beam) Central hole to let high intensities go: Hole ~ 2mm >> Central Beam Another adapted detector further (low q info)? Randomly scattered photons smaller Intensities Per region shiftable dynamic range? Then Add: Background should reinforce this tendency Bragg Peaks (nano-crystals) can be a problem (increased dynamics at the edge) Case of experiments like liquid scattering (donuts shape)

22 Detector Geometry Problem of the detector geometry: central Hole? For XPCS experiments, it could be interesting to split the detector in 4 (or more)

inelastic & elastic scattering, fluorescence Cannot distinguish from Signal Shape of the

23 Background simulation signal Background Sources of photons: Signal = Sample Coherent scattered photons Background = Sample Fluorescence + Inelastic scattering Chamber Impurities inelastic & elastic scattering, fluorescence Cannot distinguish from Signal Shape of the background: Probably an exp or 1/q n decay Simulations are on the way Use of Penelope Monte Carlo code

24 Overview of the analysis Outlook Prototypical images: Single object imaging & XPCS short presentation (reminder ) Presentation of the data Dynamic range analysis Detector Simulation Software Presentation Sensor Asic noise Waiting for your inputs

25 Simulation of the detector Performances The code is built on a modular structure Internal Fluorescence Bump bonds Sensor Detector Geometry Photon Absorption Electron creation Electron Drift Electron collection Amplification Electron storage Readout Module Tiling Thickness Material Fano Factor Charge spreading Charge sharing Amplificator noise Leakage Special pixels at asics border Parallax Charge Explosion Cf. Weierstrass Dark current ADC

26 Sensor Absorption simulation hv Absorption ~Monte Carlo Conversion to electron (Fano) Pixel σ = f Electron Spreading Gaussian f ( t, T, E) ( E profile,charge explosion) Charge Sharing

27 ASIC noise simulation Basic Charge amplifier coupled to CR-RC pulse shaper

ASIC noise simulation: Model 1 Q 2 n = exp 8 Normalization of noise to signal gain ( 2) 4 kt 2 + + + ( 2 2eI i τ 4 ktr + e ) d Shot Noise in Sensor R p Shunt resistances noise na Amplifier noise

28 ASIC noise simulation: Model 1 Q 2 n = exp 8 Normalization of noise to signal gain ( 2) 4 kt ( 2 2eI i τ 4 ktr + e ) d Shot Noise in Sensor R p Shunt resistances noise na Amplifier noise current Serie resistances noise s na Amplifier voltage noise 2 C τ + 4 A f 1/f noise C 2 C = C d + C parasitic i na 2 = 2 e I gate leakage 1 from H.Spieler, Semiconductor detector system, oxford science publication

29 ASIC noise simulation: Values Q 2 n = exp 8 I d = 1nA R p = 100MΩ T = 345 K i 2 na = 2eIgate = 2e 134pA leakage τ = 50ns R 50 s = Ω 2 2 4kt e na 3 gm gm= 300μA / V ( 2 ) 4 kt ei i τ + ( 4 ktr + e ) Normalization of noise to signal gain d Shot Noise in Sensor R p Shunt resistances noise na Amplifier noise current s Series resistances noise = 12 2 C = 150 ff A f = 10 V na Amplifier voltage noise C τ A f C 1/f noise 2 46 e - 46 e- 23 e- 9 e- 2 e- 4 e- ( Basic parametters) = e Q n 70

30 Code Test Intensity pixels pixels

31 Code Test 2 hv 40 hv e - = 40 * e - Difference hv e -

32 Future Finish the whole simulation code Develop Background simulation tool Get more images Loop back, ex. Try reconstruction algorithms with various parameters (hole size, Sensor Thickness )

Speckle Spectroscopy - X-ray Photon Correlation Spectroscopy

Speckle Spectroscopy - X-ray Photon Correlation Spectroscopy Olaf Leupold Deutsches Elektronen Synchrotron DESY / Hamburg Grenoble, September 2, 2005 Layout Introduction to coherent scattering Dynamics