My work in experimental particle physics has contributed to a wide range of publications — from precision measurements within the ATLAS Collaboration to method-oriented studies on machine learning and anomaly detection. These papers reflect the diversity of my research, spanning collider phenomenology, event reconstruction, and statistical modeling.
As a member of the ATLAS Collaboration, I have co-authored over 700 publications, while contributing directly to a focused set of key analyses, particularly in top-quark physics, event reconstruction, and searches for new physics. Beyond ATLAS, I have published multiple independent projects on machine learning and data analysis techniques in high-energy physics.
📚 Complete publication list available via:
Google Scholar |
InspireHEP |
ORCID.
ATLAS Publications
These papers are part of my collaborative work within the ATLAS experiment at CERN. While many are large-scale efforts, I’ve made substantial personal contributions to several key publications — especially those tied to my research on rare top-quark processes and searches for new physics signatures.
Search for Displaced Vertices and Muons
Search for massive, long-lived particles that produce displaced vertices and displaced muons, using data collected with ATLAS between 2022 and 2024 (LHC Run 3).
Read more about this search.
Observation of tt̄ Threshold Enhancement
Observation of a cross-section enhancement near the tt̄ production threshold in 13 TeV pp collisions, providing evidence for potential quasi-bound state effects in the top-quark pair production threshold region.
Read more about the toponium observation.
Dijet Anomaly Detection with ATLAS
A weakly supervised anomaly detection approach identifies potential new physics signals in dijet events, using machine learning to search for deviations from Standard Model predictions.
tt̄ + charm Production Measurement
First dedicated ATLAS measurement of tt̄ + charm production, featuring a novel b/c-tagger for jet identification and providing crucial background insights for Higgs searches.
Read more about the tt̄ + charm measurement.
Top+X Roadmap for Run 3 and beyond
Roadmap outlining future combinations and Effective Field Theory interpretations of top+X processes in ATLAS, emphasizing enhanced sensitivity through combined analyses.
tt̅ɣ and tWɣ Precision Measurements in the eμ Channel
First ATLAS measurement of combined tt̅ɣ and tWɣ production in the eμ channel, providing precise cross-sections for electroweak processes.
Read more about the tt̅ɣ measurement.
tt̅ɣ Production in 1L and 2L Final States
Precision measurements of tt̅ɣ production cross-sections in leptonic final states using 13 TeV ATLAS data, providing insights into top quark electromagnetic interactions.
tt̅Z and tt̅W Cross-Sections in Run 2
First measurement of tt̅Z and tt̅W cross-sections at 13 TeV using ATLAS data, focusing on leptonic decays to test Standard Model predictions and electroweak interactions.
Independent and Methodological Work
This section features peer-reviewed publications and open resources I’ve led or co-led outside the ATLAS collaboration. These projects focus on machine learning, event reconstruction, open datasets, and anomaly detection, bridging physics with broader computational challenges.
RODEM Jet Datasets for Machine Learning
RODEM Jet Datasets provides a comprehensive collection of simulated jet events for developing and benchmarking machine learning algorithms in particle physics research.
PC-JeDi: Particle Cloud Jet Diffusion
PC-JeDi introduces a diffusion model for generating particle clouds in high-energy physics, enabling improved simulation and analysis of jet formation and particle interactions.
ν²-Flows: Multi-Neutrino Reconstruction
ν²-Flows extends ν-Flows to reconstruct multiple neutrinos in complex collider events, significantly enhancing speed and accuracy for dileptonic final states in top quark analyses.
Read more about ν²-Flows.
Topographs: Graph Neural Networks for Event Reconstruction
Topographs introduces a graph neural network approach solving combinatorial challenges in event reconstruction with linear scaling, improving efficiency from 72.7% to 81.7%.
Read more about Topographs.
ν‐Flows: Neutrino Regression with Normalizing Flows
ν‐Flows introduces a machine learning framework using conditional normalizing flows for neutrino reconstruction, providing probabilistic estimates that enhance precision in top quark analyses.
Read more about ν‐Flows.
These publications illustrate the range and impact of my research — from high-precision collider measurements to innovative methodological work. For context on how these efforts fit into my broader program, see the Research page.
Interested in collaboration or have questions about a specific paper? Feel free to get in touch.
