BIP: Boost Invariant Polynomials for Efficient Jet Tagging
Deep Learning approaches are becoming the go-to methods for data analysis in High Energy Physics (HEP). Nonetheless, most physics-inspired modern architectures are computationally inefficient and lack interpretability. This is especially the case with jet tagging algorithms, where computational efficiency is crucial considering the large amounts of data produced by modern particle detectors. In this work, we present a novel, versatile and transparent framework for jet representation; invariant to Lorentz group boosts, which achieves high accuracy on jet tagging benchmarks while being orders of magnitudes faster to train and evaluate than other modern approaches for both supervised and unsupervised schemes.
Code (1)
Tasks
Computational EfficiencyJet TaggingSimilar Papers 제목 키워드 기반
Retrieval of Boost Invariant Symbolic Observables via Feature Importance
Deep learning approaches for jet tagging in high-energy physics are characterized as black boxes that process a large amount of information from which it is difficult to extract key distinctive observables. In this proce…
Deep LearningFeature ImportanceJet TaggingRetrievalInvariant polynomials and machine learning
We present an application of invariant polynomials in machine learning. Using the methods developed in previous work, we obtain two types of generators of the Lorentz- and permutation-invariant polynomials in particle mo…
Bayesian InferenceBIG-bench Machine LearningAsymmetric Feature Maps with Application to Sketch Based Retrieval
We propose a novel concept of asymmetric feature maps (AFM), which allows to evaluate multiple kernels between a query and database entries without increasing the memory requirements. To demonstrate the advantages of the…
Image RetrievalRetrievalSketch-Based Image RetrievalTranslationFeature Selection with Distance Correlation
Choosing which properties of the data to use as input to multivariate decision algorithms -- a.k.a. feature selection -- is an important step in solving any problem with machine learning. While there is a clear trend tow…
Automated Feature EngineeringFeature Engineeringfeature selectionPolynomial-based rotation invariant features
One of basic difficulties of machine learning is handling unknown rotations of objects, for example in image recognition. A related problem is evaluation of similarity of shapes, for example of two chemical molecules, fo…
BIG-bench Machine Learning