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EOS -- A Software for Flavor Physics Phenomenology

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arxiv 2111.15428 v1 pith:WLNTZPS3 submitted 2021-11-30 hep-ph hep-ex

classification hep-phhep-ex
keywords physicssoftwareexamplesflavorprocessestheoryvarietyachieve
verification ladder T0 review T1 audit T2 compute T3 formal
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EOS is an open-source software for a variety of computational tasks in flavor physics. Its use cases include theory predictions within and beyond the Standard Model of particle physics, Bayesian inference of theory parameters from experimental and theoretical likelihoods, and simulation of pseudo events for a number of signal processes. EOS ensures high-performance computations through a C++ back-end and ease of usability through a Python front-end. To achieve this flexibility, EOS enables the user to select from a variety of implementations of the relevant decay processes and hadronic matrix elements at run time. In this article, we describe the general structure of the software framework and provide basic examples. Further details and in-depth interactive examples are provided as part of the EOS online documentation.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Dispersive Look at Rare $B$-meson Semileptonic Decays

    hep-ph 2026-07 conditional novelty 6.0 of 10

    Lattice-only Dispersive Matrix form factors enlarge large-recoil uncertainties and, with new angular data, favour long-distance hadronic effects over a short-distance C9 New Physics shift.

  2. Time-Dependent Precision Measurement of $B_s^0\rightarrow \phi \mu^+\mu^-$ Decay at FCC-$ee$

    hep-ph 2025-06 conditional novelty 6.0 of 10

    FCC-ee could measure B_s -> phi mu+ mu- branching ratio to 0.5% and time-dependent CP observables D_f, C_f, S_f to 0.1, 0.02, and 0.02, giving order-of-magnitude better Wilson coefficient constraints than pre-FCC projections.

  3. Joint LHCb--Belle II Prospects to Constrain New Physics in $B\to D^{(*)}\tau\nu$

    hep-ex 2026-04 conditional novelty 5.0 of 10

    A likelihood-level combined fit of LHCb- and Belle II-like B→D(*)τν data, with shared form-factor nuisance parameters, is more sensitive and less biased than post-fit combinations of independent fits.

  4. Communicating Likelihoods with Normalising Flows

    hep-ph 2025-02 conditional novelty 4.0 of 10

    A normalizing-flow workflow compresses sample-based likelihoods into small files, validated with a radial Kolmogorov-Smirnov test on three high-energy physics examples.

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