Abstract: A coupled-channel approach is applied to the charged tetraquark state T-cc(+). recently discovered by the LHCb Collaboration. The parameters of the interaction are fixed by a fit to the observed line shape in the three-body (DD0)-D-0 pi(+) channel. Special attention is paid to the three-body dynamics in the T-cc(+) due to the finite life time of the D*. An approach to the T-cc(+) is argued to be self-consistent only if both manifestations of the three-body dynamics, the pion exchange between the D and D* mesons and the finite D* width, are taken into account simultaneously to ensure that three-body unitarity is preserved. This is especially important to precisely extract the pole position in the complex energy plane whose imaginary part is very sensitive to the details of the coupled-channel scheme employed. The (DD0)-D-0 and (DD+)-D-0 invariant mass distributions, predicted based on this analysis, are in good agreement with the LHCb data. The low-energy expansion of the D* D scattering amplitude is performed and the low-energy constants (the scattering length and effective range) are extracted. The compositeness parameter of the T-cc(+) is found to be close to unity, which implies that the T-cc(+) is a hadronic molecule generated by the interactions in the D*D-+(0) and D*D-0(+) channels. Employing heavy-quark spin symmetry, an isoscalar D* D* molecular partner of the T-cc(+) with J(P) = 1(+ )is predicted under the assumption that the DD* -D* D* coupled-channel effects can be neglected.

Abstract: The dark matter sector remains completely unknown. It is therefore crucial to keep an open mind regarding its nature and possible interactions. Focusing on the case of Weakly Interacting Massive Particles, in this work we make this general philosophy more concrete by applying modern machine learning techniques to dark matter direct detection. We do this by encoding and decoding the graphical representation of background events in the XENONnT experiment with a convolutional variational autoencoder. We describe a methodology that utilizes the `anomaly score' derived from the reconstruction loss of the convolutional variational autoencoder as well as a pre-trained standard convolutional neural network, in a semi-supervised fashion. Indeed, we observe that optimum results are obtained only when both unsupervised and supervised anomaly scores are considered together. A data set that has a higher proportion of anomaly score is deemed anomalous and deserves further investigation. Contrary to classical analyses, in principle all information about the events is used, preventing unnecessary information loss. Lastly, we demonstrate the reach of learning-focused anomaly detection in this context by comparing results with classical inference, observing that, if tuned properly, these techniques have the potential to outperform likelihood-based methods.