arXiv subjects

Giulio Dujany

Publications and source records attributed to Giulio Dujany.

7 recordsLinked to original sources

arXiv2026

The $B^+ \to K^+ \nu \bar \nu$ decay as a QCD axion search: comparing reinterpretation approaches

Two recent independent analyses of Belle II $B^+ \! \to \! K^+\nu\bar\nu$ data yield limits on ${\mathcal B}(B^+ \! \to \! K^+ a)$ -- the two-body mode to a light invisible particle such as the QCD axion -- differing by a factor of roughly four; we trace this to the choice of kinematic variable space. The central figure of merit is the resolution in the reconstructed di-neutrino invariant mass $q^2_{\rm rec}$: fine-grained binning resolves the narrow axion signal, while coarse binning dilutes it into a background-dominated range. A BDT axis trained on $B^+ \! \to \! K^+\nu\bar\nu$ adds little discriminating power for $B^+ \! \to \! K^+ a$, as this axis is largely uncorrelated with $q^2$. These expectations are confirmed by a set of numerical tests. The subleading shape systematics omitted from our $q^2_{\rm rec}$-based approach {\em lower}, not raise, the $B^+ \! \to \! K^+ a$ limit: by better accommodating the $B^+ \! \to \! K^+\nu\bar\nu$ shape, they leave less room for the axion signal, making our $q^2_{\rm rec}$-based bound conservative, if anything. A dedicated reanalysis confirms that the kinematic-axes choice alone accounts for the factor-of-four sensitivity difference, and that the $B^+ \! \to \! K^+ a$ bound varies sizeably within the $q^2_{\rm rec}\times\eta({\rm BDT}_2)$ space, depending on the SM-likeness of $B^+ \! \to \! K^+\nu\bar\nu$, thus losing the dual-probe feature of our $q^2_{\rm rec}$-based approach. These results point to a broader consideration: likelihoods dominated by BDT variables are of limited use for reinterpretations when the signal shape differs appreciably from the BDT's training signal. We therefore advocate that experimental collaborations publish likelihood projections in physical variable spaces alongside BDT-based likelihoods, to maximise the reinterpretability of their measurements.

hep-ph↗

arXiv2025

The $B^+ \to K^+ \nu \bar \nu$ decay as a search for the QCD axion

We introduce a model-independent framework to reinterpret Belle II results using only public data, analytically reconstructing the mapping between true and reconstructed kinematic variables within the statistically dominant Inclusive Tagging Analysis. This enables rare-decay measurements to probe light invisible particles -- such as the QCD axion or axion-like particles, collectively denoted $a$ -- without relying on internal simulations. Applying the method to $B^+ \! \to \! K^+ \nu \bar\nu$ yields the strongest bound on the branching fraction for $B^+ \! \to \! K^+ a$, improving existing limits by about a factor of nine and constraining the axion's fundamental flavour-changing coupling to $b$ and $s$ quarks. The approach establishes $B^+ \! \to \! K^+ \nu \bar\nu$ as a dual probe -- simultaneously testing short-distance new physics and light invisible states, the two probes working independently to an excellent approximation -- and provides a general strategy for model-independent reinterpretation of collider data.

hep-ph↗

arXiv2025

French HEP community input to the European Strategy for Particle Physics

In view of the European Strategy for Particle Physics process, the French HEP community has organized a national process of collecting written contributions and has pursued a series of workshops culminating with a national symposium held in Paris on January 20-21, 2025 that involved over 280 scientists https://indico.in2p3.fr/event/34662/. The present document summarises the main conclusions of this bottom-up approach centred on the physics and technology motivations.

hep-ex↗

arXiv2025

Graph-based Full Event Interpretation: a graph neural network for event reconstruction in Belle II

In this work we present the Graph-based Full Event Interpretation (GraFEI), a machine learning model based on graph neural networks to inclusively reconstruct events in the Belle~II experiment. Belle~II is well suited to perform measurements of $B$ meson decays involving invisible particles (e.g. neutrinos) in the final state. The kinematical properties of such particles can be deduced from the energy-momentum imbalance obtained after reconstructing the companion $B$ meson produced in the event. This task is performed by reconstructing it either from all the particles in an event but the signal tracks, or using the Full Event Interpretation, an algorithm based on Boosted Decision Trees and limited to specific, hard-coded decay processes. A recent example involving the use of the aforementioned techniques is the search for the $B^+ \to K^+ \nu \bar \nu$ decay, that provided an evidence for this process at about 3 standard deviations. The GraFEI model is trained to predict the structure of the decay chain by exploiting the information from the detected final state particles only, without making use of any prior assumptions about the underlying event. By retaining only signal-like decay topologies, the model considerably reduces the amount of background while keeping a relatively high signal efficiency. The performances of the model when applied to the search for $B^+ \to K^+ \nu \bar \nu$ are presented.

hep-ex↗

arXiv2022

Learning Tree Structures from Leaves For Particle Decay Reconstruction

In this work, we present a neural approach to reconstructing rooted tree graphs describing hierarchical interactions, using a novel representation we term the Lowest Common Ancestor Generations (LCAG) matrix. This compact formulation is equivalent to the adjacency matrix, but enables learning a tree's structure from its leaves alone without the prior assumptions required if using the adjacency matrix directly. Employing the LCAG therefore enables the first end-to-end trainable solution which learns the hierarchical structure of varying tree sizes directly, using only the terminal tree leaves to do so. In the case of high-energy particle physics, a particle decay forms a hierarchical tree structure of which only the final products can be observed experimentally, and the large combinatorial space of possible trees makes an analytic solution intractable. We demonstrate the use of the LCAG as a target in the task of predicting simulated particle physics decay structures using both a Transformer encoder and a Neural Relational Inference encoder Graph Neural Network. With this approach, we are able to correctly predict the LCAG purely from leaf features for a maximum tree-depth of $8$ in $92.5\%$ of cases for trees up to $6$ leaves (including) and $59.7\%$ for trees up to $10$ in our simulated dataset.

physics.comp-ph↗

arXiv2021

Track Finding at Belle II

This paper describes the track-finding algorithm that is used for event reconstruction in the Belle II experiment operating at the SuperKEKB B-factory in Tsukuba, Japan. The algorithm is designed to balance the requirements of a high efficiency to find charged particles with a good track parameter resolution, a low rate of spurious tracks, and a reasonable demand on CPU resources. The software is implemented in a flexible, modular manner and employs a diverse selection of global and local track-finding algorithms to achieve an optimal performance.

physics.ins-det↗

arXiv2017

On model-independent searches for direct CP violation in multi-body decays

Techniques for performing model-independent searches for direct CP violation in three and four-body decays are discussed. Comments on the performance and the optimisation of a binned chisquare approach and an unbinned approach, known as the energy test, are made. The use of the energy test in the presence of background is also studied. The selection and treatment of the coordinates used to describe the phase-space of the decay are discussed. The conventional model-independent techniques, which test for P-even CP violation, are modified to create a new approach for testing for P-odd CP violation. An implementation of the energy test using GPUs is described.

hep-ex↗