All Outputs (12)

Spey: Smooth inference for reinterpretation studies (2024)
Journal Article
Araz, J. Y. (2024). Spey: Smooth inference for reinterpretation studies. SciPost Physics, 16(1), Article 032. https://doi.org/10.21468/scipostphys.16.1.032

Statistical models serve as the cornerstone for hypothesis testing in empirical studies. This paper introduces a new cross-platform Python-based package designed to utilize different likelihood prescriptions via a flexible plug-in system. This framew... Read More about Spey: Smooth inference for reinterpretation studies.

Quantum-probabilistic Hamiltonian learning for generative modeling and anomaly detection (2023)
Journal Article
Araz, J. Y., & Spannowsky, M. (2023). Quantum-probabilistic Hamiltonian learning for generative modeling and anomaly detection. Physical Review A, 108(6), Article 062422. https://doi.org/10.1103/physreva.108.062422

The Hamiltonian of an isolated quantum-mechanical system determines its dynamics and physical behavior. This study investigates the possibility of learning and utilizing a system's Hamiltonian and its variational thermal state estimation for data ana... Read More about Quantum-probabilistic Hamiltonian learning for generative modeling and anomaly detection.

Searches for new physics with boosted top quarks in the MadAnalysis 5 and Rivet frameworks (2023)
Journal Article
Araz, J. Y., Buckley, A., & Fuks, B. (2023). Searches for new physics with boosted top quarks in the MadAnalysis 5 and Rivet frameworks. The European Physical Journal C, 83(7), Article 664. https://doi.org/10.1140/epjc/s10052-023-11779-2

High-momentum top quarks are a natural physical system in collider experiments for testing models of new physics, and jet substructure methods are key both to exploiting their largest decay mode and to assuaging resolution difficulties as the boosted... Read More about Searches for new physics with boosted top quarks in the MadAnalysis 5 and Rivet frameworks.

Strength in numbers: Optimal and scalable combination of LHC new-physics searches (2023)
Journal Article
Araz, J. Y., Buckley, A., Fuks, B., Reyes-González, H., Waltenberger, W., Williamson, S. L., & Yellen, J. (2023). Strength in numbers: Optimal and scalable combination of LHC new-physics searches. SciPost Physics, 14(4), Article 077. https://doi.org/10.21468/scipostphys.14.4.077

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 c... Read More about Strength in numbers: Optimal and scalable combination of LHC new-physics searches.

Toward a quantum simulation of nonlinear sigma models with a topological term (2023)
Journal Article
Araz, J. Y., Schenk, S., & Spannowsky, M. (2023). Toward a quantum simulation of nonlinear sigma models with a topological term. Physical Review A, 107(3), https://doi.org/10.1103/physreva.107.032619

We determine the mass gap of a two-dimensional O(3) nonlinear sigma model augmented with a topological θ-term using tensor network and digital quantum algorithms. As proof of principle, we consider the example θ=π and study its critical behavior on a... Read More about Toward a quantum simulation of nonlinear sigma models with a topological term.

Signal region combination with full and simplified likelihoods in MadAnalysis 5 (2023)
Journal Article
Alguero, G., Araz, J., Fuks, B., & Kraml, S. (2023). Signal region combination with full and simplified likelihoods in MadAnalysis 5. SciPost Physics, 14(1), Article 009. https://doi.org/10.21468/scipostphys.14.1.009

The statistical combination of disjoint signal regions in reinterpretation studies uses more of the data of an analysis and gives more robust results than the single signal region approach. We present the implementation and usage of signal region com... Read More about Signal region combination with full and simplified likelihoods in MadAnalysis 5.

Recasting LHC searches for long-lived particles with MadAnalysis 5 (2022)
Journal Article
Araz, J. Y., Fuks, B., Goodsell, M. D., & Utsch, M. (2022). Recasting LHC searches for long-lived particles with MadAnalysis 5. The European Physical Journal C, 82(7), Article 597 (2022). https://doi.org/10.1140/epjc/s10052-022-10511-w

We present an extension of the simplified fast detector simulator of MADANALYSIS 5 – the SFS framework – with methods making it suitable for the treatment of long-lived particles of any kind. This allows users to make use of intuitive PYTHON commands... Read More about Recasting LHC searches for long-lived particles with MadAnalysis 5.

Cross-fertilising extra gauge boson searches at the LHC (2021)
Journal Article
Araz, J. Y., Frank, M., Fuks, B., Moretti, S., & Özdal, Ö. (2021). Cross-fertilising extra gauge boson searches at the LHC. Journal of High Energy Physics, 2021(11), Article 14 (2021). https://doi.org/10.1007/jhep11%282021%29014

For the purpose of cross-fertilising currently separate experimental approaches, we connect results of LHC analyses attempting to access the properties of additional W′ and Z′ bosons from Drell-Yan processes. Under theoretical assumptions linking the... Read More about Cross-fertilising extra gauge boson searches at the LHC.

Quantum-inspired event reconstruction with Tensor Networks: Matrix Product States (2021)
Journal Article
Araz, J. Y., & Spannowsky, M. (2021). Quantum-inspired event reconstruction with Tensor Networks: Matrix Product States. Journal of High Energy Physics, 2021(8), https://doi.org/10.1007/jhep08%282021%29112

Tensor Networks are non-trivial representations of high-dimensional tensors, originally designed to describe quantum many-body systems. We show that Tensor Networks are ideal vehicles to connect quantum mechanical concepts to machine learning techniq... Read More about Quantum-inspired event reconstruction with Tensor Networks: Matrix Product States.

Combine and conquer: event reconstruction with Bayesian Ensemble Neural Networks (2021)
Journal Article
Araz, J. Y., & Spannowsky, M. (2021). Combine and conquer: event reconstruction with Bayesian Ensemble Neural Networks. Journal of High Energy Physics, 2021(4), Article 296. https://doi.org/10.1007/jhep04%282021%29296

Ensemble learning is a technique where multiple component learners are combined through a protocol. We propose an Ensemble Neural Network (ENN) that uses the combined latent-feature space of multiple neural network classifiers to improve the represen... Read More about Combine and conquer: event reconstruction with Bayesian Ensemble Neural Networks.

Precision SMEFT bounds from the VBF Higgs at high transverse momentum (2021)
Journal Article
Araz, J. Y., Banerjee, S., Gupta, R. S., & Spannowsky, M. (2021). Precision SMEFT bounds from the VBF Higgs at high transverse momentum. Journal of High Energy Physics, 2021(4), Article 125. https://doi.org/10.1007/jhep04%282021%29125

We study the production of Higgs bosons at high transverse momenta via vector-boson fusion (VBF) in the Standard Model Effective Field Theory (SMEFT). We find that contributions from four independent operator combinations dominate in this limit. Thes... Read More about Precision SMEFT bounds from the VBF Higgs at high transverse momentum.

Proceedings of the second MadAnalysis 5 workshop on LHC recasting in Korea (2021)
Journal Article
Araz, J. Y., Conte, E., Ducrocq, R., Flacke, T., Fuks, B., Jeon, S. H., Kim, T., Ko, P., Lee, S. J., Ruiz, R., & Sengupta, D. (2021). Proceedings of the second MadAnalysis 5 workshop on LHC recasting in Korea. Modern Physics Letters A, 36(01), Article 2102001. https://doi.org/10.1142/s0217732321020016

We document the activities performed during the second MadAnalysis 5 workshop on LHC recasting, that was organised in KIAS (Seoul, Korea) on February 12-20, 2020. We detail the implementation of 12 new ATLAS and CMS searches in the MadAnalysis 5 Publ... Read More about Proceedings of the second MadAnalysis 5 workshop on LHC recasting in Korea.