arXiv subjects

Dennis Noll

Publications and source records attributed to Dennis Noll.

7 recordsLinked to original sources

arXiv2026

Towards anomaly detection searches for new physics signatures including Higgs bosons with weakly supervised machine learning

The Higgs boson, with its universal coupling to mass, provides a broadly applicable portal to sectors beyond the Standard Model and is therefore a natural anchor for anomaly detection (AD) at collider experiments. The Higgs And X Anomaly Detection (HAXAD) strategy offers a principled approach to searching for such anomalies occurring in association with a Higgs boson by combining machine-learning-based feature embedding, background estimation, and weakly supervised classification. This work extends the previous HAXAD approach towards the level of maturity required for application to recorded collider data. A major addition is the introduction and comparison of two new embedding strategies, which in turn shape the background estimation and classification. In addition, a new inference framework is developed, yielding signal-agnostic and signal-specific cross section limits and thereby completing the statistical machinery needed for future AD analyses built on HAXAD. The set of investigated signal models is also significantly expanded, allowing for the evaluation of sensitivity on a much broader phase space. Improvements to the method increase signal sensitivity with respect to the original method, and when benchmarked against an example cut-based search on the same final state, HAXAD matches or exceeds the best individual cut-based limits for a wide variety of considered signal models. These developments strengthen the case for HAXAD as a viable and compelling AD-based search strategy with novel discovery potential at colliders.

hep-ex↗

arXiv2026

A Scientific Human-Agent Reproduction Pipeline

Reproducing scientific analyses is essential for preserving knowledge, building extensible codebases, and deepening researcher understanding - yet the effort often outweighs its academic recognition. We argue that the reproduction of scientific data analyses is fundamentally a translation task: converting human-readable knowledge (papers, documentation) into machine-readable analysis code. This makes it uniquely well-suited for AI agents. We present SHARP (Scientific Human-Agent Reproduction Pipeline), a structured framework for reproducing scientific analyses through human-agent collaboration. SHARP decomposes a reproduction task into discrete steps, which an AI agent executes autonomously using specialized subagents for code generation, testing, and quality assurance. At defined checkpoints, the researcher reviews progress, provides feedback, and steers the analysis - keeping the human firmly in control of scientific judgment while the agent handles implementation. We demonstrate SHARP by reproducing a jet classification task in particle physics from a published paper. We evaluate the reproduction along three axes: analysis performance against the original results, code quality and faithfulness, and the nature of the human-agent conversation. The latter is evaluated with a novel framework for characterizing human-agent interactions. Our work highlights a practical model for AI-assisted scientific reproduction where the researcher's role shifts from writing code to understanding, evaluating, and directing - elevating human understanding rather than replacing it.

hep-ph↗

arXiv2026

Signal-Aware Contrastive Latent Spaces for Anomaly Detection

High-dimensional feature spaces in particle physics events pose a fundamental challenge to density-estimation-based weakly supervised anomaly detection, whose fidelity degrades rapidly with an increasing number of dimensions. We propose a signal-aware latent space construction using supervised contrastive learning trained on simulated Standard Model backgrounds and a diverse set of hypothesized Beyond the Standard Model (BSM) signals. The resulting latent space is low-dimensional, regularized, and signal-sensitive, enabling high-fidelity density estimation for downstream weakly supervised anomaly detection. We demonstrate the approach in a diphoton final state, testing sensitivity across a broad range of BSM scenarios including supersymmetry models, extended Higgs sectors, heavy neutral resonances, and flavor-changing neutral currents. For signals represented in the contrastive training data, the method can elevate discovery sensitivity from previously inaccessible levels to the discovery regime. Critically, the approach retains sensitivity to BSM models not present during training: interpolation and extrapolation to unseen signal topologies yield substantial improvements in expected significance compared to a background-only baseline. By bridging supervised latent space embedding with weakly supervised anomaly detection, this strategy offers a viable path toward anomaly detection in high-dimensional feature spaces at the LHC and beyond.

hep-ph↗

arXiv2025

Weakly Supervised Anomaly Detection in Events with a Higgs Boson and Exotic Physics

We present a machine learning-based anomaly detection strategy designed to identify anomalous physics in events containing resonant Standard Model physics and demonstrate this method on the final state of a Higgs boson decaying to two photons. The demonstration targets high-dimensional deviations in the region of phase space containing the Higgs mass peak in a fully signal-agnostic manner. A latent-space embedding, learned from event kinematics, enables the use of a large set of potentially sensitive features. Backgrounds are estimated using a hybrid approach that combines machine learning-based generative modelling with traditional simulation, and a discriminator is trained in the latent space to distinguish data from background estimates. After applying a selection on the classifier output, the invariant mass distribution of the diphoton system is examined for localized excesses above the simulated Higgs peak. We benchmark the sensitivity of this strategy using simplified simulated proton-proton collisions corresponding to data recorded during Run 2 of the LHC, and show that the method can provide significant improvements in sensitivity, even for small signal injections that could remain undetected in an inclusive analysis. These results demonstrate that the proposed strategy is a promising and viable approach for future searches and should be applied to recorded collider data.

hep-ex↗

arXiv2025

FlexCAST: Enabling Flexible Scientific Data Analyses

The development of scientific data analyses is a resource-intensive process that often yields results with untapped potential for reuse and reinterpretation. In many cases, a developed analysis can be used to measure more than it was designed for, by changing its input data or parametrization. Existing reinterpretation frameworks, such as RECAST, enable analysis reinterpretation by preserving the analysis implementation to allow for changes of particular parts of the input data. We introduce FlexCAST, which generalizes this concept by preserving the analysis design itself, supporting changes to the entire input data and analysis parametrization. FlexCAST is based on three core principles: modularity, validity, and robustness. Modularity enables a change of the input data and parametrization, while validity ensures that the obtained results remain meaningful, and robustness ensures that as many configurations as possible yield meaningful results. While not being limited to data-driven machine learning techniques, FlexCAST is particularly valuable for the reinterpretation of analyses in this context, where changes in the input data can significantly impact the parametrization of the analysis. Using a state-of-the-art anomaly detection analysis on LHC-like data, we showcase FlexCAST's core principles and demonstrate how it can expand the reach of scientific data analysis through flexible reuse and reinterpretation.

hep-ex↗

arXiv2025

Stay Positive: Neural Refinement of Sample Weights

Monte Carlo simulations are an essential tool in particle physics data analysis. Events are typically generated alongside weights that redistribute the cross section of the simulated process across the phase space. These weights can be negative, and several post-hoc methods have been developed to eliminate or mitigate the negative values. All of these methods share the common strategy of approximating the average weight as a function of phase space. We introduce an alternative approach, which, instead of reweighting to the average, refines the initial weights with a scaling transformation, utilizing a phase space-dependent factor. Since this new refinement method does not need to model the full weight distribution, it can be more accurate. High-dimensional and unbinned phase space is processed using neural networks for the refinement method. In addition to the refinement method, we introduce a new resampling protocol, which can be used in conjunction with any weight transformation to not only preserve the average weight but also the statistical uncertainties of the initial distribution. Using both realistic and synthetic examples, we show that the new neural refinement method is able to match or exceed the accuracy of similar weight transformations and that the new resampling protocol is simpler in implementation than previous methods while exhibiting equivalent statistical properties.

hep-ph↗

arXiv2022

Fast Columnar Physics Analyses of Terabyte-Scale LHC Data on a Cache-Aware Dask Cluster

The development of an LHC physics analysis involves numerous investigations that require the repeated processing of terabytes of data. Thus, a rapid completion of each of these analysis cycles is central to mastering the science project. We present a solution to efficiently handle and accelerate physics analyses on small-size institute clusters. Our solution is based on three key concepts: Vectorized processing of collision events, the "MapReduce" paradigm for scaling out on computing clusters, and efficiently utilized SSD caching to reduce latencies in IO operations. Using simulations from a Higgs pair production physics analysis as an example, we achieve an improvement factor of $6.3$ in runtime after one cycle and even an overall speedup of a factor of $14.9$ after $10$ cycles.

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