Effects of beam-beam interactions on luminosity decay rates at the LHC in 2012 Anton Esmail-Yakas, 2015 For completion of the 3 rd year of the Physics.

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Presentation transcript:

Effects of beam-beam interactions on luminosity decay rates at the LHC in 2012 Anton Esmail-Yakas, 2015 For completion of the 3 rd year of the Physics with a Year in Europe Msci at Imperial College London in relation with École Polytechnique Fédérale de Lausanne as part of the Swiss European Mobility Program.

Special thanks to Xavier Buffat for help with the analysis and Danilo Banfi for help with the initial Python codes used Main supervisor: Tatiana Pieloni Supervisor from Imperial College London: Gavin Davies Supervisor from EPFL: Leonid Rivkin

Aims Analyse different available models for luminosity decay Analyse the dependency of decay rates on BBIs Analyse the time dependency of decay rates

Different fitting methods Simple exponential model C = 0, variable C Tevatron model (“Fitting the luminosity decay in the Tevatron, E. McCrory et al., 2005) Brightness model (Xavier Buffat)

Simple exponential model Constant decay rate

Tevatron model Theory attempts to account for IBS, beam burn-up due to luminosity, and beam-beam interactions. Time varying decay rate

Brightness model Two decay rates – C varying in initial hours C = 0 in later hours

C = 0 fit

C varying fit

Brightness fit

Tevatron fit

Residual analysis – Whole Fill C = 0 fit qualitatively worse. 3 other fits produce similar results.

Residual analysis – 1 st Hour Whole fill does not model 1 st hour accurately – need to fit 1 st hour separately Little difference between Variable C fit and other models when fitting first hour. other models when fitting first hour.

Filling scheme First half 2012

Decay rates pop out for bunches missing IP2s and IP8s Luminosity and Specific luminosity decay rates follow the same pattern

Strong and varying correlations with the LRs

Bunches seem to also follow HOs – only noticeable when they are missing

Very different pattern over whole fill compared to 1 st hour compared to 1 st hour Some shifts at bunches with different HOs/LRs Related to drop in HOs?

Negative correlation with LRs? Or a different effect? IBS?

Specific luminosity decays show some relation to HOs Whole fill decay shows stronger relation to HOs rather than LRs? Likely due to stronger effect of HOs persisting throughout fill

Filling scheme Second half 2012

Decay rates pop out for bunches missing IP8s Luminosity and Specific luminosity decay rates follow the similar pattern Very different pattern compared to fill 2710

Strong and varying correlations with the LRs Also for spec. lumi.

Very different pattern over whole fill compared to 1 st hour compared to 1 st hour Drops in decay rates for bunches with fewer total LRs

But no bunch by bunch correlation with LRs

Large change in pattern after 1 st hour Expanding time window

Similar change in pattern

Equivalent analysis using 1hr sliding window – results are very different and not consistent enough to produce results for each hour enough to produce results for each hour Modelling sections by single hours is not effective outside of the first hour is not effective outside of the first hour

However, the expanding window is not necessarily better, as it only shows a different pattern for the first hour, as it only shows a different pattern for the first hour, and misses it in the following hours – e.g. hours 1-2, and misses it in the following hours – e.g. hours 1-2, as seen below… as seen below…

Decay rate vs time analysis Decay rate is calculated by an expanding window as for the bunch-bunch analysis, though expanding in 5 minute steps. bunch-bunch analysis, though expanding in 5 minute steps. Sharp drop after first hours Though somewhat unreliable due to expanding window

More examples…

Importance of model used to fit luminosity – simple exponential (C=0) is not suitable. Models based around varying decay regimes are much better Importance of the time period that is looked at – currently no model can fit to the whole fill and still accurately model behaviour contained within the first hour. Need to find a better model for consistent time analysis Effects in the first hours are VERY different to effects over the whole fill – Strong BBI dependencies – not yet obvious the nuances within this Studies on a larger number of fills and relating to injection intensities of fills would be useful Closing ideas…

Thank you! Questions?