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\u27s 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

Learning tree structures from leaves for particle decay reconstruction

International audienceIn 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 of cases for trees up to 6 leaves (including) and for trees up to 10 in our simulated dataset

Learning tree structures from leaves for particle decay reconstruction

International audienceIn 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 of cases for trees up to 6 leaves (including) and for trees up to 10 in our simulated dataset

Measurement of the Ωc0\Omega_c^0 lifetime at Belle II

We report on a measurement of the $\Omega_c^0$ lifetime using $\Omega_c^0 \to \Omega^-\pi^+$ decays reconstructed in $e^+e^-\to c\bar{c}$ data collected by the Belle II experiment and corresponding to $207~{\rm fb^{-1}}$ of integrated luminosity. The result, $\rm\tau(\Omega_c^0)=243\pm48( stat)\pm11(syst)~fs$, agrees with recent measurements indicating that the $\Omega_c^0$ is not the shortest-lived weakly decaying charmed baryon

Measurement of the Ωc0\Omega_c^0 lifetime at Belle II

We report on a measurement of the $\Omega_c^0$ lifetime using $\Omega_c^0 \to \Omega^-\pi^+$ decays reconstructed in $e^+e^-\to c\bar{c}$ data collected by the Belle II experiment and corresponding to $207~{\rm fb^{-1}}$ of integrated luminosity. The result, $\rm\tau(\Omega_c^0)=243\pm48( stat)\pm11(syst)~fs$, agrees with recent measurements indicating that the $\Omega_c^0$ is not the shortest-lived weakly decaying charmed baryon