Philip Bechtle
In High Energy Physics, as in many other fields of science, the application of machine learning techniques has been crucial in advancing our understanding of fundamental phenomena. Increasingly, deep learning models are applied to analyze…
It has been argued more than 30 years ago that it is not possible to test locality at colliders, due to the inability to directly measure non-commutating observables such as spin components in current collider experiments. Recently, there…
Circular $e^{+}e^{-}$ colliders operating at energies below the di-Higgs production threshold can provide information on the trilinear Higgs self-coupling $\lambda_{hhh}$ via its loop contributions to single Higgs production processes and…
We introduce a method to study quantum entanglement at a future $e^+e^-$ Higgs factory (here the Future Circular Collider colliding $e^+$ and $e^-$ (FCC-ee) operating at $\sqrt{s}=240\,\mathrm{GeV}$) in the $\tau\tau$ final state. This…
The search for dark matter is an exciting topic that is pursued in different communities over a wide range of masses and using a variety of experimental approaches. The result is a strongly correlated matrix of activities across Europe and…
We present a proposal for a future light dark matter search experiment at the Electron Stretcher Accelerator ELSA in Bonn: Lohengrin. It employs the fixed-target missing momentum based technique for searching for dark-sector particles. The…
Data from particle physics experiments are unique and are often the result of a very large investment of resources. Given the potential scientific impact of these data, which goes far beyond the immediate priorities of the experimental…
Some highlights of the physics case for running an $e^+e^-$ collider at 500 GeV and above are discussed with a particular emphasis on the experimental access to the Higgs potential via di-Higgs and (at sufficiently high energy) triple Higgs…
Large-scale events like the UEFA Euro~2020 football (soccer) championship offer a unique opportunity to quantify the impact of gatherings on the spread of COVID-19, as the number and dates of matches played by participating countries…
Physical theories that depend on many parameters or are tested against data from many different experiments pose unique challenges to statistical inference. Many models in particle physics, astrophysics and cosmology fall into one or both…
The statistical models used to derive the results of experimental analyses are of incredible scientific value and are essential information for analysis preservation and reuse. In this paper, we make the scientific case for systematically…
Experiments in particle physics have hitherto failed to produce any significant evidence for the many explicit models of physics beyond the Standard Model (BSM) that had been proposed over the past decades. As a result, physicists have…
The $\mathcal{CP}$ structure of the Higgs boson in its coupling to the particles of the Standard Model is amongst the most important Higgs boson properties which have not yet been constrained with high precision. In this study, all relevant…
The program HiggsSignals confronts the predictions of models with arbitrary Higgs sectors with the available Higgs signal rate and mass measurements, resulting in a likelihood estimate. A new version of the program, HiggsSignals-2, is…
We describe recent developments of the public computer code HiggsBounds. In particular, these include the incorporation of LHC Higgs search results from Run 2 at a center-of-mass energy of 13 TeV, and an updated and extended framework for…
We report on the status of efforts to improve the reinterpretation of searches and measurements at the LHC in terms of models for new physics, in the context of the LHC Reinterpretation Forum. We detail current experimental offerings in…
We compare the available wind resources for conventional wind turbines and for airborne wind energy systems. Accessing higher altitudes and dynamically adjusting the harvesting operation to the wind resource substantially increases the…
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:…
In this paper we present AWEsome (Airborne Wind Energy Standardized Open-source Model Environment), a test platform for airborne wind energy systems that consists of low-cost hardware and is entirely based on open-source software. It can…
We perform a parameter scan of the phenomenological Minimal Supersymmetric Standard Model (pMSSM) with eight parameters taking into account the experimental Higgs boson results from Run I of the LHC and further low-energy observables. We…