TRANSIT your events into a new mass: Fast background interpolation for weakly-supervised anomaly searches

• URL: http://arxiv.org/abs/2503.04342v1
• Date: Thu, 06 Mar 2025 11:39:07 GMT
• Title: TRANSIT your events into a new mass: Fast background interpolation for weakly-supervised anomaly searches
• Authors: Ivan Oleksiyuk, Svyatoslav Voloshynovskiy, Tobias Golling,
• Abstract summary: We introduce a new model for conditional and continuous data morphing called TRansport Adversarial Network for Smooth InTerpolation (TRANSIT)<n>We apply it to create a background data template for weakly-supervised searches at the LHC.<n>The model captures non-linear mass correlations of features and produces a template that offers a competitive anomaly sensitivity compared to state-of-the-art transport-based template generators.
• Score: 1.024113475677323
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a new model for conditional and continuous data morphing called TRansport Adversarial Network for Smooth InTerpolation (TRANSIT). We apply it to create a background data template for weakly-supervised searches at the LHC. The method smoothly transforms sideband events to match signal region mass distributions. We demonstrate the performance of TRANSIT using the LHC Olympics R\&D dataset. The model captures non-linear mass correlations of features and produces a template that offers a competitive anomaly sensitivity compared to state-of-the-art transport-based template generators. Moreover, the computational training time required for TRANSIT is an order of magnitude lower than that of competing deep learning methods. This makes it ideal for analyses that iterate over many signal regions and signal models. Unlike generative models, which must learn a full probability density distribution, i.e., the correlations between all the variables, the proposed transport model only has to learn a smooth conditional shift of the distribution. This allows for a simpler, more efficient residual architecture, enabling mass uncorrelated features to pass the network unchanged while the mass correlated features are adjusted accordingly. Furthermore, we show that the latent space of the model provides a set of mass decorrelated features useful for anomaly detection without background sculpting.

Related papers

• Constrained Diffusion Models via Dual Training [80.03953599062365]
  Diffusion processes are prone to generating samples that reflect biases in a training dataset. We develop constrained diffusion models by imposing diffusion constraints based on desired distributions. We show that our constrained diffusion models generate new data from a mixture data distribution that achieves the optimal trade-off among objective and constraints.
  arXiv  Detail & Related papers  (2024-08-27T14:25:42Z)
• SGM-PINN: Sampling Graphical Models for Faster Training of Physics-Informed Neural Networks [4.262342157729123]
  SGM-PINN is a graph-based importance sampling framework to improve the training efficacy of Physics-Informed Neural Networks (PINNs) Experiments demonstrate the advantages of the proposed framework, achieving $3times$ faster convergence compared to prior state-of-the-art sampling methods.
  arXiv  Detail & Related papers  (2024-07-10T04:31:50Z)
• DA-Flow: Dual Attention Normalizing Flow for Skeleton-based Video Anomaly Detection [52.74152717667157]
  We propose a lightweight module called Dual Attention Module (DAM) for capturing cross-dimension interaction relationships in-temporal skeletal data. It employs the frame attention mechanism to identify the most significant frames and the skeleton attention mechanism to capture broader relationships across fixed partitions with minimal parameters and flops.
  arXiv  Detail & Related papers  (2024-06-05T06:18:03Z)
• On the Trajectory Regularity of ODE-based Diffusion Sampling [79.17334230868693]
  Diffusion-based generative models use differential equations to establish a smooth connection between a complex data distribution and a tractable prior distribution. In this paper, we identify several intriguing trajectory properties in the ODE-based sampling process of diffusion models.
  arXiv  Detail & Related papers  (2024-05-18T15:59:41Z)
• Diffusion-Model-Assisted Supervised Learning of Generative Models for Density Estimation [10.793646707711442]
  We present a framework for training generative models for density estimation. We use the score-based diffusion model to generate labeled data. Once the labeled data are generated, we can train a simple fully connected neural network to learn the generative model in the supervised manner.
  arXiv  Detail & Related papers  (2023-10-22T23:56:19Z)
• One-Step Detection Paradigm for Hyperspectral Anomaly Detection via Spectral Deviation Relationship Learning [17.590080772567678]
  Hyperspectral anomaly detection involves identifying the targets that deviate spectrally from their surroundings. The current deep detection models are optimized to complete a proxy task, such as background reconstruction or generation. In this paper, an unsupervised transferred direct detection model is proposed, which is optimized directly for the anomaly detection task.
  arXiv  Detail & Related papers  (2023-03-22T06:41:09Z)
• OST: Efficient One-stream Network for 3D Single Object Tracking in Point Clouds [6.661881950861012]
  We propose a novel one-stream network with the strength of the instance-level encoding, which avoids the correlation operations occurring in previous Siamese network. The proposed method has achieved considerable performance not only for class-specific tracking but also for class-agnostic tracking with less computation and higher efficiency.
  arXiv  Detail & Related papers  (2022-10-16T12:31:59Z)
• Hierarchical Latent Structure for Multi-Modal Vehicle Trajectory Forecasting [0.0]
  We introduce a hierarchical latent structure into a VAE-based trajectory forecasting model. Our model is capable of generating clear multi-modal trajectory distributions and outperforms the state-of-the-art (SOTA) models in terms of prediction accuracy.
  arXiv  Detail & Related papers  (2022-07-11T04:52:28Z)
• Truncated tensor Schatten p-norm based approach for spatiotemporal traffic data imputation with complicated missing patterns [77.34726150561087]
  We introduce four complicated missing patterns, including missing and three fiber-like missing cases according to the mode-drivenn fibers. Despite nonity of the objective function in our model, we derive the optimal solutions by integrating alternating data-mputation method of multipliers.
  arXiv  Detail & Related papers  (2022-05-19T08:37:56Z)
• A Generative Learning Approach for Spatio-temporal Modeling in Connected Vehicular Network [55.852401381113786]
  This paper proposes LaMI (Latency Model Inpainting), a novel framework to generate a comprehensive-temporal quality framework for wireless access latency of connected vehicles. LaMI adopts the idea from image inpainting and synthesizing and can reconstruct the missing latency samples by a two-step procedure. In particular, it first discovers the spatial correlation between samples collected in various regions using a patching-based approach and then feeds the original and highly correlated samples into a Varienational Autocoder (VAE)
  arXiv  Detail & Related papers  (2020-03-16T03:43:59Z)
• Convolutional Tensor-Train LSTM for Spatio-temporal Learning [116.24172387469994]
  We propose a higher-order LSTM model that can efficiently learn long-term correlations in the video sequence. This is accomplished through a novel tensor train module that performs prediction by combining convolutional features across time. Our results achieve state-of-the-art performance-art in a wide range of applications and datasets.
  arXiv  Detail & Related papers  (2020-02-21T05:00:01Z)

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.