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A. Sciandra

Publications and source records attributed to A. Sciandra.

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Infinitesimal braidings and pre-Cartier bialgebras

We propose an infinitesimal counterpart to the notion of braided category. The corresponding infinitesimal braidings are natural transformations which are compatible with an underlying braided monoidal structure in the sense that they constitute a first-order deformation of the braiding. This extends previously considered infinitesimal symmetric or Cartier categories, where involutivity of the braiding and an additional commutativity of the infinitesimal braiding with the symmetry are required. The generalized pre-Cartier framework is then elaborated in detail for the categories of (co)quasitriangular bialgebra (co)modules and we characterize the resulting infinitesimal $\mathcal{R}$-matrices (resp. $\mathcal{R}$-forms) on the bialgebra. It is proven that the latter are Hochschild $2$-cocycles and that they satisfy an infinitesimal quantum Yang-Baxter equation, while they are Hochschild $2$-coboundaries under the Cartier (co)triangular assumption in the presence of an antipode. We provide explicit examples of infinitesimal braidings, particularly on quantum $2\times 2$-matrices, $\mathrm{GL}_q(2)$, Sweedler's Hopf algebra and via Drinfel'd twist deformation. As conceptual tools to produce examples of infinitesimal braidings we prove an infinitesimal version of the FRT construction and we provide a Tannaka-Krein reconstruction theorem for pre-Cartier coquasitriangular bialgebras. We comment on the deformation of infinitesimal braidings and construct a quasitriangular structure on formal power series of Sweedler's Hopf algebra.

math.QA

Avoiding biases in binned fits

Binned maximum likelihood fits are an attractive option when analysing large datasets, but require care when computing likelihoods of continuous PDFs in bins. For many years the widely used statistical modelling package RooFit evaluated probabilities at the bin centre, leading to significant biases for strongly curved probability density functions. We demonstrate the biases with real-world examples, and introduce a PDF class to RooFit that removes these biases. The physics and computation performance of this new class are discussed.

physics.data-an