MERIT analysis - Beam spot size Goran Skoro More details: UKNF Meeting, Oxford, 16 September 2008

MERcury Intense Target Purpose: To provide a proof-of- principle for the NF/MC 4- MW target concept. To study the effects of high-magnetic fields on the beam/jet interaction Syringe Pump Secondary Containment Jet Chamber Proton Beam Solenoid “Each beam pulse is a separate experiment” Estimate of energy density is based on the beam optics calculations Beam size has to be measured for each pulse The MERIT Experiment

Beam monitoring MERITCamera 484Camera 454Camera 414

Beam monitoring (example) CERN software for online (and offline) beam monitoring X, Y projections At the beginning (May, 2008), the goals of this analysis were: To make a better fitting algorithm To find x,y positions of the beam To find ratios of the x,y sigmas Problem: saturation For most shots the light intensity is saturated Find a shadow Fit a tail 2 nd approach: Devils Tower Wyoming Distribution looks like this To extract ASCII from SDDS

How to extract a beam size? z(x,y) distribution is in a saturation here 1 st approach: To fit projections* Mean = 0.31(7) mmSigma = 6.42(12) mm XYXY 2 nd approach: To fit shadows** Mean = -4.64(3) mmMean = 0.16(4) mmMean = -4.71(3) mmSigma = 2.21(3) mmSigma = 2.22(4) mmSigma = 4.82(5) mm We have 3 beam ‘cameras’ -> 3 images for each beam pulse Shot from Camera 484 * Projection for X is similarly for Y.,** Shadow for X is similarly for Y., Fitting: Procedure

Simple fitting function: Gaussian + ‘background’ Fitting algorithm (how to avoid gaps; how to choose initial value of the ‘background’ term, etc…) was based on the analysis of the randomly selected images (after this, completely ‘blind’ analysis -> no parameters tuning) In total: 520 beam pulses* x 3 cameras x 2 projections = 3120 distributions have been fitted Result: Table – ntuple (part of it shown below) Fitting: Procedure Camera 414 Camera 454 Camera 484 TARGET BEAM Camera 414 Camera 454 Camera 484 Date (ddmmyyyy) Time (hhmmss) X mean (mm) X mean (mm) Y mean (mm) Y mean (mm) Sigma x (mm) Sigma x (mm) Sigma y (mm) ………… This will be used to reconstruct the Run number and to attach this table to the ‘global’ table with experimental results. This will be used to recognize a shot with the ‘suspicious’ fitting result and to fit it ‘manually’. * Period: 23 Oct 2007 – 11 Nov 2007

X mean (mm) Y mean (mm) Relative intensity Camera 414 Camera 414 Camera 484 Camera 454 Camera 454 Camera TARGET BEAM 414 Results: ShadowsDistributions of the Gaussian means

Distributions of the Gaussian sigmas  x (mm) Relative intensity Camera 414 Camera 414 Camera 484 Camera 454 Camera 454 Camera 484  x (mm)  y (mm) (empty shots, beam on the edge of the ‘visible field’, etc…) -Suspicious results Find the corresponding event in the table (Slide 6) and fit it manually (if possible) TARGET BEAM 414 Results: Projections

X mean (shadow)/X mean (projection) Relative intensity Camera 414 Camera 414 Camera 484 Camera 454 Camera 454 Camera TARGET BEAM 414 Cross-checking: Projections vs Shadows Distributions of the ratios (shadow/projection) of the Gaussian means X mean (shadow)/X mean (projection) Y mean (shadow)/Y mean (projection) Everything is (more or less) symmetrical around 1. As expected, both approaches return similar values of x/y means.

Relative intensity Camera 414 Camera 414 Camera 484 Camera 454 Camera 454 Camera 484  x (shadow)/  x (projection) TARGET BEAM 414  y (shadow)/  y (projection) Distributions of the ratios (shadow/projection) of the Gaussian sigmas Distributions are not symmetrical around 1 (shifted towards left). It means that sigmas for projections are, in general, bigger than sigmas for shadows. Cross-checking: Projections vs Shadows

Distributions of the ratios of the Gaussian sigmas Relative intensity TARGET BEAM 414 Nice agreement with ‘Beam Optics’ values Results: Shadows BO ~ 1 BO ~ 1.33

Relative intensity TARGET BEAM 414 Distributions of the ratios of the Gaussian sigmasResults: Shadows ‘Beam Optics’ value = 1.3‘Beam Optics’ value = 1.7

Beam position on target * From ‘Beam Spot Information’ talk, I. Efthymiopoulos, VRVS Meeting, November 30, TARGET m-4.17 m TARGET BEAM 414 EXP Taken online (estimated by the eye from the screen data) FIT Calculated by using: 1) the fitted beam positions for Camera454 and Camera484 (see Slide 4, for example); 2) the Camera454, Camera484 and target positions* EXP FIT EXP FIT

TARGET BEAM 414 Relative intensity Distributions of the ratios of the Gaussian sigmas Mean = 1.07 Mean = 1.41 Mean = 1.80 ‘Beam optics’ ~ 1 ‘Beam optics’ ~ 1.33 ‘Beam optics’ = 1.7 Beam position on target So far, everything (sigmas ratios, beam positions) looks nice… … except the absolute values of the beam widths!!! Beam optics calculations: beam sigmas are much smaller

Projections If we assume that the ‘light intensity’ (from the screens) is proportional to beam intensity (before we reach a ‘saturation intensity’) we can, at least, estimate the correction factor when fitting the projections. Similar results have been obtained by fitting of shadows. Objections: 1) Saturation is a problem (‘we could have many sigmas hidden here’) 2) Shadows approach looks problematic for the highest beam intensities (only a few points left to fit tails) This is not a problem (intensity is below the saturation level) and a projection approach will give us correct value of beam width(s) This is a problem (intensity is 10x higher than the saturation level) Results: Beam size vs beam intensity

Simulation: Saturation effects (1) (2) (3) Intensity = 10x ‘saturation intensity’ Intensity = 100x ‘saturation intensity’ Intensity <= ‘saturation intensity’  x =  y = 2 mm It is obvious that an extraction of projections from (2) and (3) will not give us gaussians Projections X, Y (mm) (1)  x,y = 2.00 (2) (2)  x,y = 2.97 (3) (3)  x,y = 3.78 (4) Intensity But, what will happen if we try to fit corresponding projections by using gaussian(s)?

Simulation: Saturation effects Intensity = 10x ‘saturation intensity’ Intensity = 100x ‘saturation intensity’ Intensity <= ‘saturation intensity’  x = 3 mm  y = 1.5 mm  x = 3 mm  y = 1.5 mm  x = 3 mm  y = 1.5 mm - In our case, expected value of sigma_x/sigma_y ~ 2 ‘Symmetry’ between x and y is broken Sigma_fit/sigma_input Intensity / ’Saturation intensity’ - Previous slide is for sigma_x = sigma_y - By plotting sigma_output/sigma_input as a function of intensity we can estimate a correction function Next step: To find a value of ‘saturation intensity’ in our case x y

Results: Beam intensity below 0.2 Tp TARGET BEAM 414 There are shots (23 Oct 2007) where beam intensity is below 0.2 Tp. The distributions (few examples are shown below) look like perfect double-Gaussians for all shots. Camera 484 No saturation here BUT sigmas are ~2x bigger than expected from BO calculations!!!

Results of fitting of the shadows X (mm) Y (mm) (1)  x = 2.93 (2) (2)  x = 3.26 (2) (3)  x = 3.22 (3) (1)  y = 1.90 (1) (2)  y = 2.05 (2) (3)  y = 1.66 (2) TARGET BEAM 414 For low beam intensity shots, in around 50% of the cases the situation is similar to (1) and (2). Even when we have a beam shot similar to case (3) the x/y widths ratio is close to 2. The plot above shows the results of the fitting of these 3 distributions. (1) (2) (3) Saturation Saturation (but not so clear) ‘Proper’ Gaussian Camera 484 It looks that saturation starts somewhere here Relative intensity Results: Beam intensity around 0.3 Tp

Results: ‘Correction’ function Sigma_fit/sigma_input Intensity / ’Saturation intensity’ ~ 0.3 Tp ’Saturation intensity’ ~ 0.3 Tp 0.3 Tp30 Tp We can use these ‘functions’ to correct the data xy ~ 30 Tp

TARGET BEAM 414 Beam size vs beam intensity (after correction) Dots: from beam monitors data Lines: from beam optics calculations Summary Analysis of the MERIT beam monitors data has been completed Two approaches: cross-checked Measurements vs. calculations: controversy remains More details: