arXiv subjects

Santiago Tanco

Publications and source records attributed to Santiago Tanco.

3 recordsLinked to original sources

arXiv2026

Probing electroweak pair production of heavy neutral leptons with displaced vertices at the LHC

We study the sensitivity of displaced vertex searches at the LHC to heavy neutral leptons (also known as sterile neutrinos) that are produced in pairs with an electroweak-size cross section. We work within the context of a supersymmetric model in which the sterile neutrino is produced along with Standard Model particles in higgsino decays. By making use of model-independent reconstruction efficiencies provided by the ATLAS collaboration in their search for displaced vertices with multiple jets, we obtain constraints on this model from $139$ fb$^{-1}$ of data collected by ATLAS during the LHC Run~2, and assess the discovery reach of Run~3 and of the high-luminosity LHC (HL-LHC). Depending on the higgsino mass parameter, sterile neutrino masses between $20~\mathrm{GeV}$ and $230~\mathrm{GeV}$ and active-sterile neutrino mixings in the range $4 \times 10^{-14} \lesssim V^2_N \lesssim 3 \times 10^{-10}$ can be excluded. At the HL-LHC, discovery-level significances could be reached for sterile neutrinos masses up to $295~\mathrm{GeV}$ and values of $V^2_N$ down to $3 \times 10^{-14}$. Finally, moving away from the supersymmetric scenario, we study to which extent these results can be generalized to a broader class of models in which the sterile neutrinos are produced in the decays of heavier particles that are themselves pair-produced with an electroweak-size cross section.

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arXiv2025

Di-Higgs to 4b with Bayesian inference: improving simulation estimates

Measuring di-Higgs production in the four-bottom channel is challenged by overwhelming QCD backgrounds and imperfect simulations. We develop a Bayesian mixture model that simultaneously infers signal and background fractions and their individual shapes directly in the signal region. The likelihood is a nuanced combination of a one-dimensional kinematic discriminator and per-jet flavour scores; with their correlations incorporated via kinematic bins. Monte Carlo informs weak Dirichlet priors, while the posterior adjusts to the interplay of the model, priors and observed data. Using pseudo-data simulated with standard tools and with controlled mismatches, we show that the method corrects biased priors, delivers calibrated 68-95% credible intervals for the signal count, and improves dataset-level ROC/AUC relative to simple cut-and-count baselines. This study highlights how Bayesian inference can harvest information present in the signal region and self-calibrate model parameters, providing a robust route to increased sensitivity in di-Higgs searches.

hep-ph↗

arXiv2025

Inferring correlated distributions: boosted top jets

Improving the understanding of signal and background distributions in signal-region is a valuable key to enhance any analysis in collider physics. This is usually a difficult task because -- among others -- signal and backgrounds are hard to discriminate in signal-region, simulations may reach a limit of reliability if they need to model non-perturbative QCD, and distributions are multi-dimensional and many times may be correlated within each class. Bayesian density estimation is a technique that leverages prior knowledge and data correlations to effectively extract information from data in signal-region. In this work we extend previous works on data-driven mixture models for meaningful unsupervised signal extraction in collider physics to incorporate correlations between features. Using a standard dataset of top and QCD jets, we show how simulators, despite having an expected bias, can be used to inject sufficient inductive nuance into an inference model in terms of priors to then be corrected by data and estimate the true correlated distributions between features within each class. We compare the model with and without correlations to show how the signal extraction is sensitive to their inclusion and we quantify the improvement due to the inclusion of correlations using both supervised and unsupervised metrics.

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