arXiv subjects

Björn Sarrazin

Publications and source records attributed to Björn Sarrazin.

5 recordsLinked to original sources

arXiv2018

Constraining new physics with collider measurements of Standard Model signatures

A new method providing general consistency constraints for Beyond-the-Standard-Model (BSM) theories, using measurements at particle colliders, is presented. The method, `Constraints On New Theories Using Rivet', Contur, exploits the fact that particle-level differential measurements made in fiducial regions of phase-space have a high degree of model-independence. These measurements can therefore be compared to BSM physics implemented in Monte Carlo generators in a very generic way, allowing a wider array of final states to be considered than is typically the case. The Contur approach should be seen as complementary to the discovery potential of direct searches, being designed to eliminate inconsistent BSM proposals in a context where many (but perhaps not all) measurements are consistent with the Standard Model. We demonstrate, using a competitive simplified dark matter model, the power of this approach. The Contur method is highly scaleable to other models and future measurements.

hep-ph↗

arXiv2017

SCYNet: Testing supersymmetric models at the LHC with neural networks

SCYNet (SUSY Calculating Yield Net) is a tool for testing supersymmetric models against LHC data. It uses neural network regression for a fast evaluation of the profile likelihood ratio. Two neural network approaches have been developed: one network has been trained using the parameters of the 11-dimensional phenomenological Minimal Supersymmetric Standard Model (pMSSM-11) as an input and evaluates the corresponding profile likelihood ratio within milliseconds. It can thus be used in global pMSSM-11 fits without time penalty. In the second approach, the neural network has been trained using model-independent signature-related objects, such as energies and particle multiplicities, which were estimated from the parameters of a given new physics model. While the calculation of the energies and particle multiplicities takes up computation time, the corresponding neural network is more general and can be used to predict the LHC profile likelihood ratio for a wider class of new physics models.

hep-ph↗

arXiv2014

How alive is constrained SUSY really?

Constrained supersymmetric models like the CMSSM might look less attractive nowadays because of fine tuning arguments. They also might look less probable in terms of Bayesian statistics. The question how well the model under study describes the data, however, is answered by frequentist p-values. Thus, for the first time, we calculate a p-value for a supersymmetric model by performing dedicated global toy fits. We combine constraints from low-energy and astrophysical observables, Higgs boson mass and rate measurements as well as the non-observation of new physics in searches for supersymmetry at the LHC. Using the framework Fittino, we perform global fits of the CMSSM to the toy data and find that this model is excluded at more than 95% confidence level.

hep-ph↗

arXiv2013

Constrained Supersymmetry after the Higgs Boson Discovery: A global analysis with Fittino

We present preliminary results from the latest global fit analysis of the constrained minimal supersymmetric standard model (CMSSM) performed within the Fittino framework. The fit includes low-energy and astrophysical observables as well as collider constraints from the non-observation of new physics in supersymmetric searches at the LHC. Furthermore, the Higgs boson mass and signal rate measurements from both the LHC and Tevatron experiments are included via the program HiggsSignals. Although the LHC exclusion limits and the Higgs mass measurements put tight constraints on the viable parameter space, we find an acceptable fit quality once the Higgs signal rates are included.

hep-ph↗

arXiv2011

What if the LHC does not find supersymmetry in the sqrt(s)=7 TeV run?

We investigate the implications for supersymmetry from an assumed absence of any signal in the first period of LHC data taking at 7 TeV center-of-mass energy and with 1 to 7 fb^(-1) of integrated luminosity. We consider the zero-lepton plus four jets and missing transverse energy signature, and perform a combined fit of low-energy measurements, the dark matter relic density constraint and potential LHC exclusions within a minimal supergravity model. A non-observation of supersymmetry in the first period of LHC data taking would still allow for an acceptable description of low-energy data and the dark matter relic density in terms of minimal supergravity models, but would exclude squarks and gluinos with masses below 1 TeV.

hep-ph↗