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Juan Jose Juan Castella

Publications and source records attributed to Juan Jose Juan Castella.

2 recordsLinked to original sources

Recent results in $b \rightarrow s\,\ell\ell$ transitions at LHCb

Rare decays of $B$ hadrons, such as $b\rightarrow s\ell \ell$ processes, provide great avenues to search for the influence of new physics (NP). They are highly suppressed in the Standard Model (SM) and therefore sensitive to NP contributions. In recent years, experimental measurements have shown tension with SM predictions. It is still up for debate whether these stem from genuine NP or an incomplete understanding of the theory. These proceedings focus on the recent efforts of the LHCb collaboration to advance this field. Some of the results covered show that tensions in these processes, like in $B^0 \rightarrow K^{*0}\, μ^+ μ^-$, still persist upon higher precision. Others aim to shed some light on the theoretical picture through amplitude analyses of decays like $B^0 \rightarrow K^{*0}\, μ^+ μ^-$ and $B^+ \rightarrow K^+ \, μ^+ μ^-$. We also present two innovative measurements: the branching fraction of $B^0 \rightarrow K^+ π^- μ^+ μ^-$ at large dihadron mass; and the first hadron collider measurement of the lepton flavour universality ratio of $B^0 \rightarrow K^{*0} \, \ell^+\ell^-$ at high $q^2$ (dilepton mass squared).

hep-ex↗

Capturing Cardiac Cyclicity through Phase-Equivariant Self-Supervised Learning

The cyclic structure of physiological processes offers a natural prior for self-supervised representation learning, and the cardiac cycle provides a particularly well-defined setting in which to exploit it. We derive a phase-equivariant self-supervised objective and introduce Winder, a joint-embedding architecture that organises representations into phase-invariant coordinates and phase-rotating harmonic subspaces. Its transport operator is fixed and closed-form, derived from the cycle's geometry rather than learned, and adds no parameters. Evaluated on PTB-XL under a frozen linear-probe protocol, Winder attains diagnostic accuracy within the range reported by state-of-the-art self-supervised methods at a ~1 M parameter footprint, while exhibiting phase-equivariant latent geometry. These findings demonstrate that explicitly encoding cardiac-phase symmetry can preserve diagnostically useful information while yielding a latent geometry that is legible, parameter-efficient, and directly tied to a measurable physiological quantity.

cs.LG↗