Speaker
Description
Measurements of neutral, oscillating mesons are a gateway to quantum mechanics and give access to the fundamental interactions of elementary particles. For example, precise measurements of $CP$ violation in neutral $B$ mesons can be taken in order to test the Standard Model of particle physics. These measurements require knowledge of the $B$-meson flavour at the time of its production, which cannot be inferred from its observed decay products. Therefore, multiple LHC experiments employ machine learning-based algorithms, so-called flavour taggers, to exploit particles that are produced in the proton-proton interaction and are associated with the signal $B$ meson to predict the initial $B$ flavour. A state-of-the-art approach to flavour tagging is the inclusive evaluation of all reconstructed tracks from the proton-proton interaction using a Deep Set neural network.
Flavour taggers are desired to achieve optimal performance for data recorded from proton-proton interactions while being trained with a labelled data sample, i.e., with Monte Carlo simulations. However, the limited knowledge of QCD processes introduces inherent differences between simulation and recorded data, especially in the quark-fragmentation processes that are relevant for flavour tagging. Existing flavour taggers neither model these differences nor do they model interactions between tracks explicitly, being at danger of overfitting to simulations, of not providing optimal performance for physics analyses, and of requiring a careful calibration on data.
We present an inclusive flavour tagger that builds on set transformers (to model particle interactions via set attention) and on domain-adversarial training (to mitigate differences between data sources). These foundations allow the tagger to learn intermediate data representations that are both interaction-aware and domain-invariant, i.e., they capture the interactions between tracks and do not allow for an overfitting to simulations. In our benchmark, we increase the statistical power of flavour-tagged samples by 10% with respect to the usage of deep sets, thus demonstrating the value of interaction-aware and domain-invariant representation learning.
| AI keywords | set transformers; unsupervised domain adaptation; multiple-instance learning; dataset shift; representation learning |
|---|
