LHC studies will start using unsupervised neural networks (autoencoder) for anomaly detection to increase sensitivity to BSM models and remove trivial backgrounds. Such neural networks are trained using data, but how to compare theoretical BSM models with the limits from such neural networks? Theorists need to know acceptances for their BSM models. See, for example https://arxiv.org/abs/2307.01612. The TensorFlow model trained (on data) is available from HEPDATA, but to use such neural networks is quite complicated.

This web service is designed to calculate acceptance corrections for any BSM scenario using HEPMC, PROMC, LHE files. It converts truth-level data (from ProMC, HEPMC or LHE files) into identifiable objects such as isolated jets, b-jets, electrons, and muons, which are then processed by publicly available TensorFlow models.

For ATLAS/CMS : ATLAS and CMS can also use HEPMC and LHE files. ATLAS can use PHYS_LIGHT and DAOD inputs. These formats have not been implemented yet.

Currently only ProMC input files and slimmed Delphes ROOT files with the size less than 150MB are supported. These files can be downloaded from HepSim repository.

References

S.V. Chekanov, W. Islam, R. Zhang, N. Luongo, "ADFilter -- A Web Tool for New Physics Searches With Autoencoder-Based Anomaly Detection Using Deep Unsupervised Neural Networks", (September 2024) ANL-HEP-190964, arXiv:2409.03065

If you are an ATLAS Collaboration member, you can use this internal note that describes more features of this tool (multiple trigger streams):

Chekanov, Sergei; Islam, Wasikul; Zhang, Rui; Mohiuddin, Saad; Luongo, Nicholas; Weik, Edison James; Allen, Derrick Ray; "ADFilter - a web tool for anomaly detection using autoencoders based on deep unsupervised neural networks", ATL-COM-PHYS-2024-469, August 2024, CERN, ATL-COM-PHYS-2024-469