Interpretable deep learning models for the inference and classification of LHC data

The Shower Deconstruction methodology is pivotal in distinguishing signal and background jets, leveraging the detailed information from perturbative parton showers. Rooted in the Neyman-Pearson lemma, this method is theoretically designed to differentiate between signal and background processes optimally in high-energy physics experiments. A key challenge, however, arises from the combinatorial growth associated with increasing jet constituents, which hampers its computational feasibility. We address this by demonstrating that the likelihood derived from comparing the most probable signal and background shower histories is equally effective for discrimination as the conventional approach of summing over all potential histories in top quark versus Quantum Chromodynamics (QCD) scenarios. We propose a novel approach by conceptualising the identification of the most probable shower history as a Markov Decision Process (MDP). Utilising a sophisticated modular point-transformer architecture, our method efficiently learns the optimal policy for this task. The developed neural agent excels in constructing the most likely shower history and demonstrates robust generalisation capabilities on unencountered test data. Remarkably, our approach mitigates the complexity inherent in the inference process, achieving a linear scaling relationship with the number of jet constituents. This offers a computationally viable and theoretically sound method for signal-background differentiation, paving the way for more effective data analysis in particle physics

Hypergraphs in LHC phenomenology — the next frontier of IRC-safe feature extraction

In this study, we critically evaluate the approximation capabilities of existing infra-red and collinear (IRC) safe feature extraction algorithms, namely Energy Flow Networks (EFNs) and Energy-weighted Message Passing Networks (EMPNs). Our analysis reveals that these algorithms fall short in extracting features from any N-point correlation that isn’t a power of two, based on the complete basis of IRC safe observables, specifically C-correlators. To address this limitation, we introduce the Hypergraph Energy-weighted Message Passing Networks (H-EMPNs), designed to capture any N-point correlation among particles efficiently. Using the case study of top vs. QCD jets, which holds significant information in its 3-point correlations, we demonstrate that H-EMPNs targeting up to N=3 correlations exhibit superior performance compared to EMPNs focusing on up to N=4 correlations within jet constituents

Alternatively activated macrophages promote pancreatic fibrosis in chronic pancreatitis.

Chronic pancreatitis (CP) is a progressive and irreversible inflammatory and fibrotic disease with no cure. Unlike acute pancreatitis (AP), we find that alternatively activated macrophages (AAMs) are dominant in mouse and human CP. AAMs are dependent on interleukin (IL)-4 and IL-13 signalling, and we show that mice lacking IL-4Rα, myeloid-specific IL-4Rα and IL-4/IL-13 were less susceptible to pancreatic fibrosis. Furthermore, we demonstrate that mouse and human pancreatic stellate cells (PSCs) are a source of IL-4/IL-13. Notably, we show that pharmacologic inhibition of IL-4/IL-13 in human ex vivo studies as well as in established mouse CP decreases pancreatic AAMs and fibrosis. We identify a critical role for macrophages in pancreatic fibrosis and in turn PSCs as important inducers of macrophage-alternative activation. Our study challenges and identifies pathways involved in crosstalk between macrophages and PSCs that can be targeted to reverse or halt pancreatic fibrosis progression

IRC-Safe Graph Autoencoder for Unsupervised Anomaly Detection

Anomaly detection through employing machine learning techniques has emerged as a novel powerful tool in the search for new physics beyond the Standard Model. Historically similar to the development of jet observables, theoretical consistency has not always assumed a central role in the fast development of algorithms and neural network architectures. In this work, we construct an infrared and collinear safe autoencoder based on graph neural networks by employing energy-weighted message passing. We demonstrate that whilst this approach has theoretically favorable properties, it also exhibits formidable sensitivity to non-QCD structures