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Wolfgang Waltenberger

Publications and source records attributed to Wolfgang Waltenberger.

At least 19 recordsLinked to original sources

On the coverage of electroweak-inos within the pMSSM with SModelS -- a comparison with the ATLAS pMSSM study

The ATLAS collaboration has recently performed a vast scan of the phenomenological Minimal Supersymmetric Standard Model (pMSSM) with a focus on the electroweak-ino sector, and analysed how their Run 2 searches for electroweak production of supersymmetric (SUSY) particles constrain this dataset. All the SLHA files from the scan as well as the constraints from the eight individual searches considered by ATLAS were made publicly available. We use this material to study how well the ATLAS constraints can be reproduced with SModelS v3.0. Moreover, we explore how the picture changes when also including CMS results, and what can be gained by the statistical combination of analyses. Finally, we discuss the part of parameter space with light electroweak-inos that remains valid despite the stringent LHC limits. Our results underscore the need of a broad, multifaceted approach for maximising sensitivity and closing loopholes in the extensive SUSY parameter space.

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SModelS v3: Going Beyond Z2 Topologies

SModelS is a public tool for fast reinterpretation of LHC searches for new physics based on a large database of simplified model results. While previous versions were limited to models with a Z2-type symmetry, such as R-parity conserving supersymmetry, version 3 can now handle arbitrary signal topologies. To this end, the tool was fully restructured and now relies on a graph-based description of simplified model topologies. In this work, we present the main conceptual changes and novel features of SModelS v3, together with the inclusion of new experimental searches for resonant production of spin-1 and spin-0 mediators with decays to quarks or to dark matter. Applying these results to a model containing two mediators, we discuss the interplay of resonance and missing energy searches, and the model's coverage by the currently available simplified model results.

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Global LHC constraints on electroweak-inos with SModelS v2.3

Electroweak-inos, superpartners of the electroweak gauge and Higgs bosons, play a special role in supersymmetric theories. Their intricate mixing into chargino and neutralino mass eigenstates leads to a rich phenomenology, which makes it difficult to derive generic limits from LHC data. In this paper, we present a global analysis of LHC constraints for promptly decaying electroweak-inos in the context of the minimal supersymmetric standard model, exploiting the SModelS software package. Combining up to 16 ATLAS and CMS searches for which electroweak-ino efficiency maps are available in SModelS, we study which combinations maximise the sensitivity in different regions of the parameter space, how fluctuations in the data in individual analyses influence the global likelihood, and what is the resulting exclusion power of the combination compared to the analysis-by-analysis approach.

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SModelS v2.3: enabling global likelihood analyses

We present version 2.3 of SModelS, a public tool for the fast reinterpretation of LHC searches for new physics on the basis of simplified-model results. The main new features are a database update with the latest available experimental results for full Run 2 luminosity, comprising in particular a large variety of electroweak-ino searches, and the ability to combine likelihoods from different analyses. This enables statistically more rigorous constraints and opens the way for global likelihood analyses for LHC searches. The physics impact is demonstrated for the electroweak-ino sector of the minimal supersymmetric standard model.

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Cait: analysis toolkit for cryogenic particle detectors in Python

Cryogenic solid state detectors are widely used in dark matter and neutrino experiments, and require a sensible raw data analysis. For this purpose, we present Cait, an open source Python package with all essential methods for the analysis of detector modules fully integrable with the Python ecosystem for scientific computing and machine learning. It comes with methods for triggering of events from continuously sampled streams, identification of particle recoils and artifacts in a low signal-to-noise ratio environment, the reconstruction of deposited energies, and the simulation of a variety of typical event types. Furthermore, by connecting Cait with existing machine learning frameworks we introduce novel methods, for better automation in data cleaning and background rejection.

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Strength in numbers: optimal and scalable combination of LHC new-physics searches

To gain a comprehensive view of what the LHC tells us about physics beyond the Standard Model (BSM), it is crucial that different BSM-sensitive analyses can be combined. But in general, search analyses are not statistically orthogonal, so performing comprehensive combinations requires knowledge of the extent to which the same events co-populate multiple analyses' signal regions. We present a novel, stochastic method to determine this degree of overlap and a graph algorithm to efficiently find the combination of signal regions with no mutual overlap that optimises expected upper limits on BSM-model cross-sections. The gain in exclusion power relative to single-analysis limits is demonstrated with models with varying degrees of complexity, ranging from simplified models to a 19-dimensional supersymmetric model.

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Constraining new physics with SModelS version 2

We present version 2 of SModelS, a program package for the fast reinterpretation of LHC searches for new physics on the basis of simplified model results. The major novelty of the SModelS v2 series is an extended topology description with a flexible number of particle attributes, such as spin, charge, decay width, etc. This enables, in particular, the treatment of a wide range of signatures with long-lived particles. Moreover, constraints from prompt and long-lived searches can be evaluated simultaneously in the same run. The current database includes results from searches for heavy stable charged particles, disappearing tracks, displaced jets and displaced leptons, in addition to a large number of prompt searches. The capabilities of the program are demonstrated by two physics applications: constraints on long-lived charged scalars in the scotogenic model, and constraints on the electroweak-ino sector in the Minimal Supersymmetric Standard Model.

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Publishing statistical models: Getting the most out of particle physics experiments

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 publishing the full statistical models and discuss the technical developments that make this practical. By means of a variety of physics cases -- including parton distribution functions, Higgs boson measurements, effective field theory interpretations, direct searches for new physics, heavy flavor physics, direct dark matter detection, world averages, and beyond the Standard Model global fits -- we illustrate how detailed information on the statistical modelling can enhance the short- and long-term impact of experimental results.

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Artificial proto-modelling with simplified-model results from the LHC

We present a novel approach to identify potential dispersed signals of new physics in the slew of published LHC results. It employs a random walk algorithm to introduce sets of new particles, dubbed "proto-models", which are tested against simplified-model results from ATLAS and CMS searches for new physics by exploiting the SModelS software framework. A combinatorial algorithm identifies the set of analyses and/or signal regions that maximally violates the Standard Model hypothesis, while remaining compatible with the entirety of LHC constraints in our database. Crucial to the method is the ability to construct a reliable likelihood in proto-model space; we explain the various approximations which are needed depending on the information available from the experiments, and how they impact the whole procedure.

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Artificial Proto-Modelling: Building Precursors of a Next Standard Model from Simplified Model Results

We present a novel algorithm to identify potential dispersed signals of new physics in the slew of published LHC results. It employs a random walk algorithm to introduce sets of new particles, dubbed "proto-models", which are tested against simplified-model results from ATLAS and CMS (exploiting the SModelS software framework). A combinatorial algorithm identifies the set of analyses and/or signal regions that maximally violates the SM hypothesis, while remaining compatible with the entirety of LHC constraints in our database. Demonstrating our method by running over the experimental results in the SModelS database, we find as currently best-performing proto-model a top partner, a light-flavor quark partner, and a lightest neutral new particle with masses of the order of 1.2 TeV, 700 GeV and 160 GeV, respectively. The corresponding global p-value for the SM hypothesis is approximately 0.19; by construction no look-elsewhere effect applies.

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New developments in SModelS

SModelS is an automatized tool enabling the fast interpretation of simplified model results from the LHC within any model of new physics respecting a $\mathbb{Z}_2$ symmetry. In this contribution, we report on two important updates of SModelS during 2020: the extension of the SModelS' database with 13 ATLAS and 10 CMS analyses, including 5 ATLAS and 1 CMS analyses at full Run~2 luminosity, and the ability to use full likelihoods now provided by ATLAS in the form of pyhf JSON files. Moreover, we briefly explain how to use SModelS and give an overview of ongoing developments.

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A SModelS interface for pyhf likelihoods

SModelS is an automatized tool enabling the fast interpretation of simplified model results from the LHC within any model of new physics respecting a $\mathbb{Z}_2$ symmetry. We here present a new version of SModelS that can use the full likelihoods now provided by ATLAS in the form of pyhf JSON files. This much improves the statistical evaluation and therefore also the limit setting on new physics scenarios.

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SModelS database update v1.2.3

We present an update of the SModelS database with simplified model results from 13 ATLAS and 10 CMS searches for supersymmetry at Run 2. This includes 5 ATLAS and 1 CMS analyses for full Run 2 luminosity, i.e. close to 140/fb of data. In total, 76 official upper limit and efficiency map results have been added. Moreover, 21 efficiency map results have been produced by us using MadAnalysis5, to improve the coverage of gluino-squark production. The constraining power of the new database, v1.2.3, is compared to that of the previous release, v1.2.2. SModelS v1.2.3 is publicly available and can readily be employed for physics studies.

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The Simplified Likelihood Framework

We discuss the simplified likelihood framework as a systematic approximation scheme for experimental likelihoods such as those originating from LHC experiments. We develop the simplified likelihood from the Central Limit Theorem keeping the next-to-leading term in the large $N$ expansion to correctly account for asymmetries. Moreover, we present an efficient method to compute the parameters of the simplified likelihood from Monte Carlo simulations. The approach is validated using a realistic LHC-like analysis, and the limits of the approximation are explored. Finally, we discuss how the simplified likelihood data can be conveniently released in the HepData error source format and automatically built from it, making this framework a convenient tool to transmit realistic experimental likelihoods to the community.

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SModelS v1.2: long-lived particles, combination of signal regions, and other novelties

SModelS is an automatised tool enabling the fast interpretation of simplified model results from the LHC within any model of new physics respecting a $\mathbb{Z}_2$ symmetry. With the version 1.2 we announce several new features. First, previous versions were restricted to missing energy signatures and assumed prompt decays within each decay chain. SModelS v1.2 considers the lifetime of each $\mathbb{Z}_2$-odd particle and appropriately takes into account missing energy, heavy stable charge particle and R-hadron signatures. Second, the current version allows for a combination of signal regions in efficiency map results whenever a covariance matrix is available from the experiment. This is an important step towards fully exploiting the constraining power of efficiency map results. Several other improvements increase the user-friendliness, such as the use of wildcards in the selection of experimental results, and a faster database which can be given as a URL. Finally, smodelsTools provides an interactive plots maker to conveniently visualize the results of a model scan.

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SModelS extension with the CMS supersymmetry search results from Run 2

We present the update of the SModelS database with the simplified model results from CMS searches for supersymmetry at Run 2 with 36 fb$^{-1}$ of data. The constraining power of these new results is compared to that of the 8 TeV results within the context of a full model, the pMSSM. The new database, v1.1.2, is publicly available and can readily be employed for physics studies with SModelS.

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On the coverage of the pMSSM by simplified model results

We investigate to which extent the SUSY search results published by ATLAS and CMS in the context of simplified models actually cover the more realistic scenarios of a full model. Concretely, we work within the phenomenological MSSM (pMSSM) with 19 free parameters and compare the constraints obtained from SModelSv1.1.1 with those from the ATLAS pMSSM study in arXiv:1508.06608. We find that about 40-45% of the points excluded by ATLAS escape the currently available simplified model constraints. For these points we identify the most relevant topologies which are not tested by the current simplified model results. In particular, we find that topologies with asymmetric branches, including 3-jet signatures from gluino-squark associated production, could be important for improving the current constraining power of simplified models results. Furthermore, for a better coverage of light stops and sbottoms, constraints for decays via heavier neutralinos and charginos, which subsequently decay visibly to the lightest neutralino are also needed.

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SModelS v1.1 user manual

SModelS is an automatised tool for the interpretation of simplified model results from the LHC. It allows to decompose models of new physics obeying a Z2 symmetry into simplified model components, and to compare these against a large database of experimental results. The first release of SModelS, v1.0, used only cross section upper limit maps provided by the experimental collaborations. In this new release, v1.1, we extend the functionality of SModelS to efficiency maps. This increases the constraining power of the software, as efficiency maps allow to combine contributions to the same signal region from different simplified models. Other new features of version 1.1 include likelihood and chi-square calculations, extended information on the topology coverage, an extended database of experimental results as well as major speed upgrades for both the code and the database. We describe in detail the concepts and procedures used in SModelS, explaining in particular how upper limits and efficiency map results are dealt with in parallel. Detailed instructions for code usage are also provided.

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