# Long Short-Term Memory (LSTM) networks with jet constituents for boosted top tagging at the LHC

arXiv: https://arxiv.org/abs/1711.09059 · HTML: https://mlsanity.com/paper/1711.09059/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2017.

## Built on by (most cited first, 13 of 13)

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- [Scaling Particle Collision Data Analysis](https://mlsanity.com/paper/2412.00129/) (2024)
- [Comparing Classical and Quantum Machine Learning for Regression in High Energy Physics Collision Data](https://mlsanity.com/paper/2608.28084/) (2026)

## Abstract (arXiv)

Multivariate techniques based on engineered features have found wide adoption in the identification of jets resulting from hadronic top decays at the Large Hadron Collider (LHC). Recent Deep Learning developments in this area include the treatment of the calorimeter activation as an image or supplying a list of jet constituent momenta to a fully connected network. This latter approach lends itself well to the use of Recurrent Neural Networks. In this work the applicability of architectures incorporating Long Short-Term Memory (LSTM) networks is explored. Several network architectures, methods of ordering of jet constituents, and input pre-processing are studied. The best performing LSTM network achieves a background rejection of 100 for 50% signal efficiency. This represents more than a factor of two improvement over a fully connected Deep Neural Network (DNN) trained on similar types of inputs.