arXiv subjects

Blaž Bortolato

Publications and source records attributed to Blaž Bortolato.

5 recordsLinked to original sources

arXiv2024

Ultra High Energy Cosmic Rays versus Models of High Energy Hadronic Interactions

We evaluate the consistency of hadronic interaction models in the CORSIKA simulation package with publicly available fluorescence telescope data from the Pierre Auger Observatory. By comparing the first few central moments of the extended air shower depth maximum distributions, as extracted from measured events, to those predicted by the best-fit inferred compositions, we derive a statistical measure of the consistency of a given hadronic model with data. To mitigate possible systematic biases, we include all primaries up to iron, compensate for the differences between the measured and simulated energy spectra of cosmic rays and account for other known systematic effects. Additionally, we study the effects of including higher central moments in the fit and project our results to larger statistics.

astro-ph.HE↗

arXiv2024

Resolution of (Heavy) Primaries in Ultra High Energy Cosmic Rays

Measurements of Ultra-High Energy Cosmic Rays (UHECR) suggest a complex composition with significant contributions from heavy nuclei at the highest energies. We systematically explore how the selection and number of primary nuclei included in the analysis impact the inferred UHECR mass composition. Introducing a distance measure in the space of $X_{\rm max}$ distribution moments, we demonstrate that limiting the analysis to a few primaries can introduce significant biases, particularly as observational data improves. We provide lists of primaries approximately equidistant in the new measure, which guaranty unbiased results at given statistical confidence. Additionally, we explore consistent inclusion of nuclei heavier than iron and up to plutonium, deriving first observational upper bounds on their contributions to UHECR with the Pierre Auger Open Data.

astro-ph.HE↗

arXiv2023

Improving the Composition of Ultra High Energy Cosmic Rays with Ground Detector Data

We show that the maximum shower depth distributions of Ultra-High Energy Cosmic Rays (UHECRs), as measured by fluorescence telescopes, can be augmented by building a mapping to observables collected by surface detectors. The resulting statistical improvement of such augmented dataset depends in a universal way on the strength of the correlation exhibited by the mapping. Building upon the publicly available data on "golden hybrid" events from the Pierre Auger Observatory we project possible improvements in the inferred composition of UHECRs for a range of possible mappings with varying correlation strengths.

astro-ph.HE↗

arXiv2022

Bump Hunting in Latent Space

Unsupervised anomaly detection could be crucial in future analyses searching for rare phenomena in large datasets, as for example collected at the LHC. To this end, we introduce a physics inspired variational autoencoder (VAE) architecture which performs competitively and robustly on the LHC Olympics Machine Learning Challenge datasets. We demonstrate how embedding some physical observables directly into the VAE latent space, while at the same time keeping the classifier manifestly agnostic to them, can help to identify and characterise features in measured spectra as caused by the presence of anomalies in a dataset.

hep-ph↗

arXiv2021

Optimized probes of $CP$-odd effects in the $t \bar{t} h$ process at hadron colliders

We use machine learning (ML) and non-ML techniques to study optimized $CP$-odd observables, directly and maximally sensitive to the $CP$-odd $i \tilde κ\bar t γ^5 t h$ interaction at the LHC and prospective future hadron colliders using the final state with a Higgs boson and a top quark pair, $pp\to t\bar t h$, followed by semileptonic $t$ decays. We perform phase-space optimization of manifestly $CP$-odd observables ($\boldsymbol ω$), sensitive to the sign of $\tilde κ$, and constructed from experimentally accessible final state momenta. We identify a simple optimized linear combination $\boldsymbol α\cdot \boldsymbolω$ that gives similar sensitivity as the studied fully fledged ML models. Using $\boldsymbolα\cdot \boldsymbolω$ we project the expected sensitivities to $\tilde κ$ at HL-LHC, HE-LHC, and FCC-hh.

hep-ph↗