Flavoured Leptogenesis

Sacha Davidson

Home/Archive/arXiv

arXiv · 0705.1590

Flavoured Leptogenesis

Sacha Davidson

View original source ↗Read source document ↗Export metadata ↓

Abstract

Thermal leptogenesis, in the seesaw model, is a popular mechanism for generating the Baryon Asymmetry of the Universe. It was noticed recently, that including lepton flavour can modify significantly the results. These proceedings aim to discuss why and when flavour matters, in the thermal leptogenesis scenario for hierarchical right-handed neutrinos. No Boltzmann Equations are introduced.

Explore related subjects

hep-ph

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Sacha Davidson. 2007-05-18. Flavoured Leptogenesis. https://arxiv.org/abs/0705.1590

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

PINNs for Solving Two-Flavor Neutrino Oscillations in Vacuum and Matter Environments for Atmospheric and Reactor Neutrinos: A detailed analysis

Neutrino oscillations provide crucial insights into fundamental particle physics, with two-flavor approximations offering a robust framework for analyzing reactor and atmospheric phenomena. Currently, standard numerical integrators represent the gold standard for solving the associated evolution equations, achieving machine precision at negligible computational cost. However, Physics-Informed Neural Networks (PINNs) have recently emerged as a powerful surrogate model for handling high-dimensional systems, complex geometries, and inverse parameter estimation. In this paper, we establish a foundational proof-of-concept by applying PINNs to solve the coupled ordinary differential equations governing neutrino evolution in vacuum and constant-density matter profiles. We also discuss the theoretical framework encompassing vacuum mixing and the matter enhanced neutrino oscillations and rigorously benchmark the PINNs against standard NuFast and RK4 (Runge-Kutta 4) solutions. By carefully optimizing our network architecture, we demonstrate that PINNs can achieve high-precision agreement with experimental standards for reactor (low-energy) and atmospheric (high-energy) neutrinos, which we use as our case study. This paper evaluates the robustness of the PINNs approach through a comprehensive error analysis, establishing a baseline validation for its application to more challenging problems in neutrino physics, such as variable density profiles.

hep-ph↗

arXiv2026

From Gradient Postulates to Gauge Structure: Chirality, Conserved Charges, and Constraints on Internal Symmetry

We propose a conceptual framework in which, in the non-relativistic static limit, conservative forces arise as gradients of guidance potentials, and the charges that generate these potentials are conserved and form a Lie algebra. We examine what constraints these postulates, together with the empirical facts of parity violation and non-Abelian weak isospin, impose on the internal symmetry group of a gauge theory. We argue that the non-Abelian character of the gauge sector follows from the conserved-charge algebra together with the observed non-Abelian weak isospin algebra. The requirement that the internal group accommodate chiral fermions constrains the admissible representations. Classical stability, charge quantization, and anomaly cancellation provide three physical consistency conditions that strongly constrain the internal group to be compact. With a minimality criterion, the Standard Model group emerges as the minimal candidate among a small set of compact anomaly-free groups with chiral content. We explicitly identify the minimality criterion as a selective assumption rather than a derived result. We do not claim a unique derivation of the Standard Model group. We also show that the scalar guidance potential reproduces Newtonian gravity and Coulomb's law in the non-relativistic limit, and that relativistic consistency requires its upgrade to a metric tensor and gauge vector fields. The postulates substantially constrain the space of viable gauge theories and provide a conceptual basis for treating the gauge group as a constrained rather than arbitrary input.

hep-ph↗

arXiv2026

Learning Astrophysical Uncertainties in Dark Matter Direct Detection with Simulation-Based Inference

We present a proof-of-concept study of neural ratio estimation (NRE) - a simulation-based inference algorithm - applied to WIMP direct detection using the XENONnT experiment as an example. A binary classifier is trained on simulated parameter-observation pairs to approximate the likelihood ratio for the WIMP mass $m_χ$ and spin-independent proton coupling $c_p$, using realistic scintillation and ionization signals produced by the fuse detector simulation. We demonstrate that astrophysical uncertainties from halo model variations are marginalized over implicitly and at no additional inference cost by including multiple halo models in the training data. Using extreme $\pm5σ$ Standard Halo Model variations, we show that training on a combined dataset recovers well-calibrated posteriors even under severe misspecification, while more realistic halo models produce nearly indistinguishable results. The signal-plus-background classifier yields an exclusion limit that is both qualitatively and (after accounting for differences between frequentist and Bayesian approaches) quantitatively consistent with the XENONnT expected sensitivity. These results establish NRE as a scalable complement to the profile likelihood ratio approach, with particular advantages as experiments probe deeper into the neutrino fog.

hep-ph↗