Enhancing anomaly detection with topology-aware autoencoders

• URL: http://arxiv.org/abs/2502.10163v1
• Date: Fri, 14 Feb 2025 13:50:46 GMT
• Title: Enhancing anomaly detection with topology-aware autoencoders
• Authors: Vishal S. Ngairangbam, Błażej Rozwoda, Kazuki Sakurai, Michael Spannowsky,
• Abstract summary: Autoencoders provide a signal-agnostic approach but are limited by the topology of their latent space. We construct autoencoders with spherical ($Sn$), product ($S2 otimes S2$), and projective ($mathbbRP2$) latent spaces. Applying our approach to simulated hadronic top-quark decays, we show that latent spaces with appropriate topological constraints enhance sensitivity and robustness in detecting anomalous events.
• Score: 0.0
• License:
• Abstract: Anomaly detection in high-energy physics is essential for identifying new physics beyond the Standard Model. Autoencoders provide a signal-agnostic approach but are limited by the topology of their latent space. This work explores topology-aware autoencoders, embedding phase-space distributions onto compact manifolds that reflect energy-momentum conservation. We construct autoencoders with spherical ($S^n$), product ($S^2 \otimes S^2$), and projective ($\mathbb{RP}^2$) latent spaces and compare their anomaly detection performance against conventional Euclidean embeddings. Our results show that autoencoders with topological priors significantly improve anomaly separation by preserving the global structure of the data manifold and reducing spurious reconstruction errors. Applying our approach to simulated hadronic top-quark decays, we show that latent spaces with appropriate topological constraints enhance sensitivity and robustness in detecting anomalous events. This study establishes topology-aware autoencoders as a powerful tool for unsupervised searches for new physics in particle-collision data.

Related papers

• Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach [49.995833831087175]
  This work proposes a novel method for generating generic Video-temporal PAs by inpainting a masked out region of an image. In addition, we present a simple unified framework to detect real-world anomalies under the OCC setting. Our method performs on par with other existing state-of-the-art PAs generation and reconstruction based methods under the OCC setting.
  arXiv  Detail & Related papers  (2023-11-27T13:14:06Z)
• Ensuring Topological Data-Structure Preservation under Autoencoder Compression due to Latent Space Regularization in Gauss--Legendre nodes [0.0]
  We prove that regularised autoencoders ensure a one-to-one re-embedding of the initial data manifold to its latent representation. This observation extends through the classic FashionMNIST dataset up to real world encoding problems for MRI brain scans.
  arXiv  Detail & Related papers  (2023-09-15T07:58:26Z)
• Triggering Dark Showers with Conditional Dual Auto-Encoders [1.5615730862955413]
  We present a family of conditional dual auto-encoders (CoDAEs) for generic and model-independent new physics searches at colliders.
  arXiv  Detail & Related papers  (2023-06-22T15:13:18Z)
• Making Reconstruction-based Method Great Again for Video Anomaly Detection [64.19326819088563]
  Anomaly detection in videos is a significant yet challenging problem. Existing reconstruction-based methods rely on old-fashioned convolutional autoencoders. We propose a new autoencoder model for enhanced consecutive frame reconstruction.
  arXiv  Detail & Related papers  (2023-01-28T01:57:57Z)
• Enhancing Detection of Topological Order by Local Error Correction [0.5025737475817937]
  We introduce a new paradigm for quantifying topological states by combining methods of error correction with ideas of renormalization-group flow. We demonstrate the power of LED using numerical simulations of the toric code under a variety of perturbations. We then apply it to an experimental realization, providing new insights into a quantum spin liquid created on a Rydberg-atom simulator.
  arXiv  Detail & Related papers  (2022-09-26T05:31:04Z)
• Anomaly Detection with Adversarially Learned Perturbations of Latent Space [9.473040033926264]
  Anomaly detection is to identify samples that do not conform to the distribution of the normal data. In this work, we have designed an adversarial framework consisting of two competing components, an Adversarial Distorter, and an Autoencoder. The proposed method outperforms the existing state-of-the-art methods in anomaly detection on image and video datasets.
  arXiv  Detail & Related papers  (2022-07-03T19:32:00Z)
• Bayesian Autoencoders for Drift Detection in Industrial Environments [69.93875748095574]
  Autoencoders are unsupervised models which have been used for detecting anomalies in multi-sensor environments. Anomalies can come either from real changes in the environment (real drift) or from faulty sensory devices (virtual drift)
  arXiv  Detail & Related papers  (2021-07-28T10:19:58Z)
• Topological Obstructions to Autoencoding [4.2056926734482065]
  We show how the intrinsic and extrinsic topology of the dataset affects the behavior of an autoencoder. We ground this analysis in the discussion of a mock "bump hunt" in which the autoencoder fails to identify an anomalous "signal"
  arXiv  Detail & Related papers  (2021-02-16T19:00:00Z)
• ESAD: End-to-end Deep Semi-supervised Anomaly Detection [85.81138474858197]
  We propose a new objective function that measures the KL-divergence between normal and anomalous data. The proposed method significantly outperforms several state-of-the-arts on multiple benchmark datasets.
  arXiv  Detail & Related papers  (2020-12-09T08:16:35Z)
• Attribute-Guided Adversarial Training for Robustness to Natural Perturbations [64.35805267250682]
  We propose an adversarial training approach which learns to generate new samples so as to maximize exposure of the classifier to the attributes-space. Our approach enables deep neural networks to be robust against a wide range of naturally occurring perturbations.
  arXiv  Detail & Related papers  (2020-12-03T10:17:30Z)
• Unsupervised Anomaly Detection with Adversarial Mirrored AutoEncoders [51.691585766702744]
  We propose a variant of Adversarial Autoencoder which uses a mirrored Wasserstein loss in the discriminator to enforce better semantic-level reconstruction. We put forward an alternative measure of anomaly score to replace the reconstruction-based metric. Our method outperforms the current state-of-the-art methods for anomaly detection on several OOD detection benchmarks.
  arXiv  Detail & Related papers  (2020-03-24T08:26:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.