arXiv subjects

Giulia Manco

Publications and source records attributed to Giulia Manco.

3 recordsLinked to original sources

arXiv2026

Measurement of multi-jets and vector boson plus jets production in ATLAS

The production of multiple jets or vector bosons in association with jets at the LHC provides a unique testing ground for Quantum Chromodynamics (QCD) in the high-energy regime. With the increasing precision of the ATLAS measurements, detailed studies have become possible on observables that probe different aspects of QCD, such as the topological configurations between vector bosons and jets, jet substructure features, and heavy-flavor jet contributions. These measurements also play a key role in improving the precision of the determination of the strong coupling constant. Recent ATLAS results in these areas are presented, offering new insights into QCD dynamics and the performance of state-of-the-art theoretical predictions.

hep-ex↗

arXiv2023

Deep Anatomical Federated Network (Dafne): An open client-server framework for the continuous, collaborative improvement of deep learning-based medical image segmentation

Purpose: To present and evaluate Dafne (deep anatomical federated network), a freely available decentralized, collaborative deep learning system for the semantic segmentation of radiological images through federated incremental learning. Materials and Methods: Dafne is free software with a client-server architecture. The client side is an advanced user interface that applies the deep learning models stored on the server to the user's data and allows the user to check and refine the prediction. Incremental learning is then performed at the client's side and sent back to the server, where it is integrated into the root model. Dafne was evaluated locally, by assessing the performance gain across model generations on 38 MRI datasets of the lower legs, and through the analysis of real-world usage statistics (n = 639 use-cases). Results: Dafne demonstrated a statistically improvement in the accuracy of semantic segmentation over time (average increase of the Dice Similarity Coefficient by 0.007 points/generation on the local validation set, p < 0.001). Qualitatively, the models showed enhanced performance on various radiologic image types, including those not present in the initial training sets, indicating good model generalizability. Conclusion: Dafne showed improvement in segmentation quality over time, demonstrating potential for learning and generalization.

eess.IV↗

arXiv2021

Tagging the Higgs boson decay to bottom quarks with colour-sensitive observables and the Lund jet plane

We study the problem of distinguishing $b$-jets stemming from the decay of a colour singlet, such as the Higgs boson, from those originating from the abundant QCD background. In particular, as a case study, we focus on associate production of a vector boson and a Higgs boson decaying into a pair of $b$-jets, which has been recently observed at the LHC. We consider the combination of several theory-driven observables proposed in the literature, together with Lund jet plane images, in order to design an original $Hbb$ tagger. The observables are combined by means of standard machine learning algorithms, which are trained on events obtained with fast detector simulation techniques. We find that the combination of high-level single-variable observables with the Lund jet plane provides an excellent discrimination performance. We also study the dependence of the tagger on the invariant mass of the decaying particles, in order to assess the extension to a generic $Xbb$ tagger.

hep-ph↗