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A bstract We interpret within the phenomenological MSSM (pMSSM) the results of SUSY searches published by the CMS collaboration based on the first ~1 fb −1 of data taken during the 2011 LHC run at 7 TeV. The pMSSM is a 19-dimensional parametrization of the MSSM that captures most of its phenomenological features. It encompasses, and goes beyond, a broad range of more constrained SUSY models. Performing a global Bayesian analysis, we obtain posterior probability densities of parameters, masses and derived observables. In contrast to constraints derived for particular SUSY breaking schemes, such as the CMSSM, our results provide more generic conclusions on how the current data constrain the MSSM.

We present a set of recommendations for the presentation of LHC results on searches for new physics, which are aimed at providing a more efficient flow of scientific information between the experimental collaborations and the rest of the high energy physics community, and at facilitating the interpretation of the results in a wide class of models. Implementing these recommendations would aid the full exploitation of the physics potential of the LHC.

Analysis description languages are declarative interfaces for HEP data analysis that allow users to avoid writing event loops, simplify code, and enable performance improvements to be decoupled from analysis development. One example is FuncADL, inspired by functional programming and developed using Python as a host language. FuncADL borrows concepts from database query languages to isolate the interface from the underlying physical and logical schemas. The same query can be used to select data from different sources and formats and with different execution mechanisms. FuncADL is one of the tools being developed by IRIS-HEP for highly scalable physics analysis for the LHC and HL-LHC. FuncADL is demonstrated by implementing example analysis tasks designed by HSF and IRIS-HEP. Another language example is ADL, which expresses the physics content of an analysis in a standard and unambiguous way, independent of computing frameworks. In ADL, analyses are described in human-readable text files composed of blocks with a keyword-expression structure. Two infrastructures are available to render ADL executable: CutLang, a runtime interpreter written in C++; and adl2tnm, a transpiler converting ADL into C++ or Python code. ADL/CutLang are already used in several physics studies and educational projects, and are adapted for use with LHC Open Data.

The ability to model and sample from conditional densities is important in many physics applications. Implicit quantile networks (IQN) have been successfully applied to this task in domains outside physics. In this work, we illustrate the potential of IQNs as components of emulators using the simulation of jets as an example. Specifically, we use an IQN to map jets described by their 4-momenta at the generation level to jets at the event reconstruction level. The conditional densities emulated by our model closely match those generated by \\(\\texttt{Delphes}\\), while also enabling faster jet simulation.

Besides their intrinsic nuclear-structure value, nuclear mass models are essential for astrophysical applications, such as r-process nucleosynthesis and neutron-star structure. To overcome the intrinsic limitations of existing \"state-of-the-art\" mass models, we propose a refinement based on a Bayesian Neural Network (BNN) formalism. A novel BNN approach is implemented with the goal of optimizing mass residuals between theory and experiment. A significant improvement (of about 40%) in the mass predictions of existing models is obtained after BNN refinement. Moreover, these improved results are now accompanied by proper statistical errors. Finally, by constructing a \"world average\" of these predictions, a mass model is obtained that is used to predict the composition of the outer crust of a neutron star. The power of the Bayesian neural network method has been successfully demonstrated by a systematic improvement in the accuracy of the predictions of nuclear masses. Extension to other nuclear observables is a natural next step that is currently under investigation.

We interpret within the phenomenological MSSM (pMSSM) the results of SUSY searches published by the CMS collaboration based on the first ~1 fb^-1 of data taken during the 2011 LHC run at 7 TeV. The pMSSM is a 19-dimensional parametrization of the MSSM that captures most of its phenomenological features. It encompasses, and goes beyond, a broad range of more constrained SUSY models. Performing a global Bayesian analysis, we obtain posterior probability densities of parameters, masses and derived observables. In contrast to constraints derived for particular SUSY breaking schemes, such as the CMSSM, our results provide more generic conclusions on how the current data constrain the MSSM.

We present a set of recommendations for the presentation of LHC results on searches for new physics, which are aimed at providing a more efficient flow of scientific information between the experimental collaborations and the rest of the high energy physics community, and at facilitating the interpretation of the results in a wide class of models. Implementing these recommendations would aid the full exploitation of the physics potential of the LHC.

This report presents the activities of the `New Physics' working group for the `Physics at TeV Colliders' workshop (Les Houches, France, 10--28 June, 2019). These activities include studies of direct searches for new physics, approaches to exploit published data to constrain new physics, as well as the development of tools to further facilitate these investigations. Benefits of machine learning for both the search for new physics and the interpretation of these searches are also presented.

We present the activities of the `New Physics' working group for the `Physics at TeV Colliders' workshop (Les Houches, France, 5--23 June, 2017). Our report includes new physics studies connected with the Higgs boson and its properties, direct search strategies, reinterpretation of the LHC results in the building of viable models and new computational tool developments.

We put forth conclusions and suggestions regarding the presentation of the LHC Higgs results that may help to maximize their impact and their utility to the whole High Energy Physics community.