| Anomaly detection with Convolutional Graph Neural Networks O Atkinson, A Bhardwaj, C Englert, VS Ngairangbam, M Spannowsky Journal of High Energy Physics 2021 (8), 1-19, 2021 | 73 | 2021 |
| Anomaly detection in high-energy physics using a quantum autoencoder VS Ngairangbam, M Spannowsky, M Takeuchi Physical Review D 105 (9), 095004, 2022 | 58 | 2022 |
| Energy-weighted message passing: an infra-red and collinear safe graph neural network algorithm P Konar, VS Ngairangbam, M Spannowsky Journal of High Energy Physics 2022 (2), 1-30, 2022 | 32 | 2022 |
| IRC-safe Graph Autoencoder for unsupervised anomaly detection A Bhardwaj, O Atkinson, C Englert, P Konar, VS Ngairangbam, ... Frontiers in Artificial Intelligence, 2022 | 21* | 2022 |
| Invisible Higgs search through Vector Boson Fusion: A deep learning approach VS Ngairangbam, A Bhardwaj, P Konar, AK Nayak The European Physical Journal C 80, 1-25, 2020 | 17 | 2020 |
| Influence of QCD parton showers in deep learning invisible Higgs bosons through vector boson fusion P Konar, VS Ngairangbam Physical Review D 105 (11), 113003, 2022 | 4 | 2022 |
| LLPNet: Graph Autoencoder for Triggering Light Long-Lived Particles at HL-LHC B Bhattacherjee, P Konar, VS Ngairangbam, P Solanki arXiv preprint arXiv:2308.13611, 2023 | 2 | 2023 |
| Hypergraphs in LHC phenomenology—the next frontier of IRC-safe feature extraction P Konar, VS Ngairangbam, M Spannowsky Journal of High Energy Physics 2024 (1), 1-22, 2024 | 1 | 2024 |
| Equivariant, Safe and Sensitive $\unicode {x2013} $ Graph Networks for New Physics A Bhardwaj, C Englert, W Naskar, VS Ngairangbam, M Spannowsky arXiv preprint arXiv:2402.12449, 2024 | | 2024 |
| Interpretable deep learning models for the inference and classification of LHC data VS Ngairangbam, M Spannowsky arXiv preprint arXiv:2312.12330, 2023 | | 2023 |
