Statistical analysis of RF conditioning and breakdowns Jorge GINER NAVARRO CLIC Workshop /01/2015 J. Giner Navarro - CLIC WS20151

Overview Introduction Conditioning data from test stands Magnitudes to describe conditioning status Comparison of different structure conditionings Conclusions 26/01/2015 J. Giner Navarro - CLIC WS2015 2

Introduction Performance of CLIC accelerating structures has been tested in klystron-powered test stands at KEK, SLAC and CERN. New prototypes of accelerating structures need to be conditioned to achieve the CLIC main requirements of gradient 100 MV/m at a pulse length of around 200 ns and a low breakdown rate (BDR) of bpp/m. RF conditioning process needs to be understood in order to minimize time and costs. 26/01/2015 J. Giner Navarro - CLIC WS2015 3

Xbox-1: TD26CC#1 conditioning history Automatic operation by a conditioning algorithm [see J.Tagg presentation] 26/01/2015 J. Giner Navarro - CLIC WS BDs B. Woolley - CLIC WS2014

Rescaled gradient 26/01/2015 J. Giner Navarro - CLIC WS BDR= 7e-5 bpp2e-5 bpp2e-6 bpp CONDITIONING BDR measure TD26CC#1 raw data

Describing conditioning status 26/01/2015 J. Giner Navarro - CLIC WS These magnitudes gives us the conditioning status of the structure according to our requirements.

NEXTEF (KEK): TD24R05#4 test history Conditioning and fixed-gradient tests carried out in NEXTEF test stand for the TD24R05 structure provides a source of comparison with our data. Data courtesy of T. Higo. 26/01/2015 J. Giner Navarro - CLIC WS Normalized gradient here TD24R05_#4

Comparison of conditioning evolution 26/01/2015 J. Giner Navarro - CLIC WS Scaled gradient vs cumulative number of PULSES Scaled gradient vs cumulative number of BREAKDOWNS Conditioning to high-gradient is given by the pulses not the breakdowns! #Pulses #BDs

Further studies According to these results, pulsing at constant gradient would slowly decrease the breakdown rate in the structure, which means that the surface is well influenced by the RF high-powered pulses. Study of long term trends, whether there is an asymptotic BDR or not, and the time needed to complete the conditioning is hard to determine. High-repetition rate systems are more efficient in this study. [see A. Korsback presentation] 26/01/2015 J. Giner Navarro - CLIC WS2015 9

HRR Fixed-Gap system data analysis 26/01/2015 J. Giner Navarro - CLIC WS In the Fixed-gap system, at DC Spark lab (CERN), high electric fields are reproduced between two Cu electrodes, pulsing at a repetition rate up to 1 kHz. Analogous studies to RF accelerating structure tests can be driven in less time. Here we compare its conditioning evolution in terms of surface electric field. Data courtesy of N.Shipman

RF Breakdown statistics 26/01/2015 J. Giner Navarro - CLIC WS e-6 bpp 6.5e-6 bpp 2.0e-6 bpp 2.1e-3 bpp2.3e-3 bpp 3.1e-4 bpp 1.1e-4 bpp Analysis in Breakdown statistics shows different regimes of the BDR. [See Anders Korsback presentation for full analysis in a DC system]

Conclusions Working on data analysis from test stands provides a better understanding about the conditioning process, the goal of which is the feasibility and the proper performance of the accelerating structure in the linear collider. Scaling laws are used to compare different structure conditionings and the same trends are found with the number of pulses, but not with the number of breakdowns. Different models (dislocations, local tips…) are being studied to describe the effect that the wall’s surface resists more power with increasing pulses. High repetition rate systems would provide valuable information in this study. The Fixed-Gap system in the DC Spark lab is running to acquire new fresh data. Results from this study lead to more strategies to carry out during the conditioning of the RF structure. Optimization in time (and cost) will be essential when producing new structures. Thank you for your attention! 26/01/2015 J. Giner Navarro - CLIC WS Acknowledgement to A. Degiovanni and W. Wuensch for their contribution in this work!

EXTRA SLIDES 26/01/2015 J. Giner Navarro - CLIC WS

Pulse and BD dependence 26/01/2015 J. Giner Navarro - CLIC WS A. Degiovanni

Normalized BDR in LOG-LOG scale 15

Normalized BDR in LOG-LOG scale – Linear fit A’ = / A’ = /

Pivot model BDR = 1 (limit for operation by definition) E max (assumed limit for gradient) The exponent X increases with the number of pulses (n) log(BDR)=X(n)*log(E0) E meas = α E max A.D

Pivot model fit results (TD26R05CC) 27/05/ ns100ns150ns200ns250ns X 0 =8.0 X=14.7X=18.6X=20.7X=24.0X=26.6