ML4Jets2025

Session

Reconstruction and Analysis

20 Aug 2025, 10:50

CHEN100 (California Institute of Technology)

Conveners

Reconstruction and Analysis

• Vinicius Massami Mikuni (Lawrence Berkeley National Lab. (US))

Reconstruction and Analysis

• Claudius Krause (HEPHY Vienna (ÖAW))

22. Neural autoregressive flows for data-driven background estimation in a search for four-top quark production in the all-hadronic final state with CMS at 13 TeV

Melissa Quinnan (Univ. of California San Diego (US))

20/08/2025, 10:50

A novel machine-learning based background estimation technique using normalizing flows to estimate background distributions from data in control regions is described in detail. This is demonstrated in four-top quark production in the all hadronic channel in Run II [1], which was facilitated by this method to estimate dominant QCD multijets and \ttbar backgrounds. The flow is able to reliably...

15. Fast and Precise Track Fitting with Machine Learning

Daniel Whiteson (University of California Irvine (US))

20/08/2025, 11:10

Accurate and efficient particle tracking is a crucial component of precise measurements of the Standard Model and searches for new physics. This task consists of two main computational steps: track finding, the identification of a subset of all hits that are due to a single particle; and track fitting, the extraction of crucial parameters such as direction and momenta. Novel solutions to...

18. MaskFormers for Reconstruction Tasks in High Energy Physics

Max Hart (University College London (GB))

20/08/2025, 11:30

Mask Transformers, or MaskFormers, have emerged as the current state of the art in a wide range of image and point cloud segmentation tasks. We present the application of this architecture to various reconstruction tasks in high energy physics with the aim of tackling both problem scale and complexity. We consider the popular track reconstruction algorithm benchmark dataset TrackML, which...

26. A Graph Neural Network Approach for General Reconstruction of Non-Helical Tracks [remote]

Levi Condren (University of California Irvine (US))

20/08/2025, 11:50

Tracking algorithms typically assume helical trajectories to simplify the task of reconstruction. However, numerous theories predict interactions which lead to non-helical tracks. Graph neural networks can split the task of finding and fitting tracks, allowing them to find non-helical tracks from physics beyond the Standard Model, such as quirks. Yet, particles could exhibit behavior beyond...

14. Machine Learning for Dark Matter searches at the LHC

Rafal Maselek

20/08/2025, 12:10

Dark Matter remains one of the most intriguing mysteries in modern physics. A promising strategy to uncover its nature is through a possible production at the Large Hadron Collider (LHC). One of the key signatures for such searches is the monojet channel, characterised by one or a few high-energy jets recoiling against the large missing transverse momentum and no isolated leptons. However, the...

2. $\texttt{DeepSub}$: Deep Learning for Thermal Background Subtraction in Heavy-Ion Collisions [remote]

Umar Sohail Qureshi (Vanderbilt University)

20/08/2025, 12:30

Jet reconstruction in an ultra-relativistic heavy-ion collision suffers from a notoriously large thermal background. Traditional background subtraction methods struggle to remove this soft background while preserving the jet's hard substructure. In this talk, we present $\texttt{DeepSub}$, the first machine learning-based approach for full-event background subtraction.
$\texttt{DeepSub}$...

36. Boosting HH(4b) beyond boosted HH(4b): a calibratable full-particle search framework

Tianyi Yang (Peking University (CN))

22/08/2025, 09:20

We demonstrate that the successful techniques developed for boosted HH(4b) analyses can be effectively extended to the resolved regime through advanced deep learning engineering. By leveraging O(100M) training samples, employing efficient state-of-the-art architectures and training frameworks, and analyzing objects containing O(100) particles, we can replicate the capabilities of Xbb taggers...

43. Going HyPER: Enhancing collider measurements with hypergraph learning

Ethan Lewis Simpson (The University of Manchester (GB))

22/08/2025, 09:40

Hypergraph learning extends traditional graph learning techniques by exploring higher-order correlations on graphs, leading to powerful and expressive representations of collider events. The HyPER model employs hypergraph learning to tackle the reconstruction of short-lived particles, and the separation of signal events from backgrounds. HyPER has been tested on top quark kinematic...

64. Machine Learning-Assisted Measurement of Lepton-Jet Azimuthal Angular Asymmetries and of the complete final state in Deep-Inelastic Scattering at HERA

Vinicius Massami Mikuni (Lawrence Berkeley National Lab. (US))

22/08/2025, 10:00

Deep-inelastic positron-proton scattering at high momentum transfer $Q^2$ is an ideal place to study QCD effects. The H1 collaboration presents two such studies based on data collected in ep collisions at $Q^2>150$ GeV$^2$. The data are unfolded (corrected for detector effects) using advanced machine learning methods. This results in parallel and unbinned measurements of several observables,...

69. Optimal Transport for $e/\pi^0$ Particle Classification in LArTPC Neutrino Experiments

Jessica N. Howard (Kavli Institute for Theoretical Physics)

22/08/2025, 10:20

The efficient classification of electromagnetic activity from $\pi^0$ and electrons remains an open problem in the reconstruction of neutrino interactions in Liquid Argon Time Projection Chamber (LArTPC) detectors. We address this problem using the mathematical framework of Optimal Transport (OT), which has been successfully employed for event classification in other HEP contexts and is...

34. Simultaneous reconstruction of boosted, resolved, and semi-boosted top-quark events with symmetry-preserving attention networks

Daniel Primosch (Univ. of California San Diego (US)), Thomas Coulter Sievert (California Institute of Technology (US))

The production of multiple top quarks at the CERN LHC provides both a rich environment to probe the Standard Model for signs of new physics and also serves as a major background in many searches. A substantial fraction of these events result in fully hadronic final states, where each top quark decays into a bottom quark and a W boson, with the latter further decaying into two light quarks. In...