Meta-learning and data augmentation for mass-generalised jet taggers

Abstract

Deep neural networks trained for jet tagging are typically specific to a narrow range of transverse momenta or jet masses. Given the large phase space that the LHC is able to probe, the potential benefit of classifiers that are effective over a wide range of masses or transverse momenta is significant. In this work we benchmark the performance of a number of methods for achieving accurate classification at masses distant from those used in training, with a focus on algorithms that leverage meta-learning. We study the discrimination of jets from boosted Z bosons against a QCD background. We find that a simple data augmentation strategy that standardises the angular scale of jets with different masses is sufficient to produce strong generalisation. The meta-learning algorithms provide only a small improvement in generalisation when combined with this augmentation. We also comment on the relationship between mass generalisation and mass decorrelation, demonstrating that those models which generalise better than the baseline also sculpt the background to a smaller degree.

What's Anomalous in LHC Jets?

“…Based on these practical successes, ML-methods for anomaly detection at the LHC have generally received a lot of attention in the context of anomalous jets [10][11][12][13][14][15][16][17], anomalous events pointing to physics beyond the Standard Model [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35], or enhancing established search strategies [36][37][38][39][40][41][42]. They include a first ATLAS analysis [43], experimental validation of some of the methods [44,45], quantum machine learning [46], applications to heavy-ion collisions [47], the DarkMachines challenge [48], and the LHC Olympics 2020 community challenge [49,50].…”

A Holistic Approach to Predicting Top Quark Kinematic Properties with the Covariant Particle Transformer

“…training the Covariant Particle Transformer with a more representative sample or possibly active decorrelation strategies [33][34][35][36][37][38][39][40][41][42][43][44][45][46], which we defer to future studies. Figure 3 shows distributions of the system-level observables constructed from individual top quark four-momenta for t tH and t tH CP-odd samples.…”

A Normalized Autoencoder for LHC Triggers

“…Motivated by their initial success, ML-methods for anomaly detection at the LHC were developed for anomalous jets [7][8][9][10][11][12][13][14][15][16], anomalous events [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35], or to enhance search strategies [36][37][38][39][40][41][42][43][44]. They include a first ATLAS analysis [45], experimental validation [46,47], quantum machine learning [48], self-supervised learning [49,50], applications to heavy-ion collisions [51], the DarkMachines community challenge [52], and the LHC Olympics 2020 community challenge [53,…”