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

arXiv: https://arxiv.org/abs/2503.04342 · HTML: https://mlsanity.com/paper/2503.04342/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 32 of its 130 references that are in the map.
- First posted on arXiv 6 Mar 2025.

## Builds on (most cited first, 30 of 32)

- [Anomaly Detection with Density Estimation](https://mlsanity.com/paper/2001.04990/) (2020)
- [Anomaly Detection With Conditional Variational Autoencoders](https://mlsanity.com/paper/2010.05531/) (2020)
- [Classification without labels: Learning from mixed samples in high energy physics](https://mlsanity.com/paper/1708.02949/) (2017)
- [Variational Autoencoders for New Physics Mining at the Large Hadron Collider](https://mlsanity.com/paper/1811.10276/) (2018)
- [Variational Autoencoders for Anomalous Jet Tagging](https://mlsanity.com/paper/2007.01850/) (2020)
- [Autoencoders for unsupervised anomaly detection in high energy physics](https://mlsanity.com/paper/2104.09051/) (2021)
- [The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider](https://mlsanity.com/paper/2105.14027/) (2021)
- [Simulation Assisted Likelihood-free Anomaly Detection](https://mlsanity.com/paper/2001.05001/) (2020)
- [Better Latent Spaces for Better Autoencoders](https://mlsanity.com/paper/2104.08291/) (2021)
- [Learning new physics efficiently with nonparametric methods](https://mlsanity.com/paper/2204.02317/) (2022)
- [Topological Obstructions to Autoencoding](https://mlsanity.com/paper/2102.08380/) (2021)
- [Bump Hunting in Latent Space](https://mlsanity.com/paper/2103.06595/) (2021)
- [Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quark](https://mlsanity.com/paper/2005.01598/) (2020)
- [Challenges for Unsupervised Anomaly Detection in Particle Physics](https://mlsanity.com/paper/2110.06948/) (2021)
- [Online-compatible Unsupervised Non-resonant Anomaly Detection](https://mlsanity.com/paper/2111.06417/) (2021)
- [Simulation-Assisted Decorrelation for Resonant Anomaly Detection](https://mlsanity.com/paper/2009.02205/) (2020)
- [Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detection](https://mlsanity.com/paper/2104.02092/) (2021)
- [Unsupervised in-distribution anomaly detection of new physics through conditional density estimation](https://mlsanity.com/paper/2012.11638/) (2020)
- [High-dimensional and Permutation Invariant Anomaly Detection](https://mlsanity.com/paper/2306.03933/) (2023)
- [Anomaly Awareness](https://mlsanity.com/paper/2007.14462/) (2020)
- [CURTAINs Flows For Flows: Constructing Unobserved Regions with Maximum Likelihood Estimation](https://mlsanity.com/paper/2305.04646/) (2023)
- [Improving new physics searches with diffusion models for event observables and jet constituents](https://mlsanity.com/paper/2312.10130/) (2023)
- [Autoencoders for Semivisible Jet Detection](https://mlsanity.com/paper/2112.02864/) (2021)
- [Unravelling physics beyond the standard model with classical and quantum anomaly detection](https://mlsanity.com/paper/2301.10787/) (2023)
- [Neural Embedding: Learning the Embedding of the Manifold of Physics Data](https://mlsanity.com/paper/2208.05484/) (2022)
- ["Flux+Mutability": A Conditional Generative Approach to One-Class Classification and Anomaly Detection](https://mlsanity.com/paper/2204.08609/) (2022)
- [Null Hypothesis Test for Anomaly Detection](https://mlsanity.com/paper/2210.02226/) (2022)
- [Quantum-probabilistic Hamiltonian learning for generative modelling & anomaly detection](https://mlsanity.com/paper/2211.03803/) (2022)
- [New Methods and Datasets for Group Anomaly Detection From Fundamental Physics](https://mlsanity.com/paper/2107.02821/) (2021)
- [Fast Particle-based Anomaly Detection Algorithm with Variational Autoencoder](https://mlsanity.com/paper/2311.17162/) (2023)

## Abstract (arXiv)

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.