arXiv subjects

Ilias Tsaklidis

Publications and source records attributed to Ilias Tsaklidis.

3 recordsLinked to original sources

arXiv2026

SysVar: Consistent and scalable treatment of systematic uncertainties in template-based analyses

Propagating systematic uncertainties from correction weights into templates generated from simulated data while preserving correlations in the signal extraction variables becomes increasingly challenging as analyses scale in size. SysVar's lightweight API hides most of the bookkeeping complexity of applying correction weights, building systematic variations, and histogramming nominal and varied templates. This streamlines workflows that are otherwise error-prone but essential for high-precision measurements. Correlations are preserved by propagating the full covariance across the analysis and compressing it into a small set of orthogonal eigenvariations, each defining an ordinary shape nuisance parameter. In this work, we demonstrate SysVar's workflow and impact by combining two independent pseudo-measurements that benefit from consistently correlated systematic uncertainties encoded in the template shapes. SysVar is open-source, pip-installable, and experiment-agnostic.

hep-ex↗

arXiv2026

Joint LHCb--Belle II Prospects to Constrain New Physics in $B\to D^{(*)}τν$

Semileptonic $b\to cτ\barν_τ$ decays are powerful probes of non-Standard-Model effects within an effective-field-theory (EFT) framework, but fully exploiting them in current and future data demands combinations that maximise sensitivity while controlling biases from Standard-Model-based modelling and from theory inputs that are shared, and therefore correlated, across analyses in different experiments. We present a first sensitivity study of a combined extraction of Wilson coefficients in $\bar{B}\to D^{(*)}τ\barν_τ$ decays using LHCb- and Belle~II-like analysis configurations. Detector simulations for signal and backgrounds are typically generated under Standard Model assumptions; if non-SM contributions are present, this can bias the fitted Wilson coefficients. In addition, hadronic inputs such as form-factor parameters of signal and background components are common across analyses, requiring a consistent treatment of fully correlated effects in combinations. To avoid repeating large-scale detector simulation for each EFT hypothesis, we use event-by-event reweighting to map simulated samples to arbitrary combinations of Wilson coefficients. We then compare a simultaneous fit across multiple $\bar{B}\to D^{(*)}τ\barν_τ$ channels and datasets with a combination based on post-fit averages. Sharing Wilson coefficients and common form-factor parameters in the simultaneous fit reduces model-induced biases and improves sensitivity relative to independent fits, providing a robust and scalable strategy for precision EFT constraints in $b\to cτ\barν_τ$ transitions using forthcoming LHCb and Belle~II datasets.

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↗