Unsupervised clustering for collider physics

Mikuni, V. M.; Canelli, F.

doi:10.1103/physrevd.103.092007

Cited by 33 publications

(21 citation statements)

References 28 publications

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“…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].…”

Section: What Is Anomalous?mentioning

confidence: 99%

What's Anomalous in LHC Jets?

Buss¹,

Dillon²,

Finke³

et al. 2022

Preprint

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Searches for anomalies are the main motivation for the LHC and define key analysis steps, including triggers. We discuss how LHC anomalies can be defined through probability density estimates, evaluated in a physics space or in an appropriate neural network latent space. We illustrate this for classical k-means clustering, a Dirichlet variational autoencoder, and invertible neural networks. For two especially challenging scenarios of jets from a dark sector we evaluate the strengths and limitations of each method.

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Section: What Is Anomalous?mentioning

confidence: 99%

What's Anomalous in LHC Jets?

Buss¹,

Dillon²,

Finke³

et al. 2022

Preprint

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“…At the LHC, it is possible to use this latent space representation of the jets for classification [6][7][8], and even anomaly detection. The use of machine learning methods for anomaly detection has received a lot of attention in recent years [9][10][11][12][13][14][15][16][17][18][19][20][21], including a first ATLAS analysis [22], and the LHC Olympics 2020 community challenge has seen many groups submit their results on a di-jet anomaly search using black-box data-sets [23]. Additional papers studying similar unsupervised LHC problems include Ref.…”

Section: Introductionmentioning

confidence: 99%

Better latent spaces for better autoencoders

et al. 2021

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Autoencoders as tools behind anomaly searches at the LHC have the structural problem that they only work in one direction, extracting jets with higher complexity but not the other way around. To address this, we derive classifiers from the latent space of (variational) autoencoders, specifically in Gaussian mixture and Dirichlet latent spaces. In particular, the Dirichlet setup solves the problem and improves both the performance and the interpretability of the networks.

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“…[17], and also to exploiting the Lund-plane representation of splittings [18,19]. GNNs have also been studied in various scenarios [20][21][22][23][24] at the LHC. Moreover, they have also shown promising performances for use in real-time triggers [25].…”

Section: Introductionmentioning

confidence: 99%

Anomaly detection with convolutional Graph Neural Networks

Atkinson

Bhardwaj

Englert

et al. 2021

J. High Energ. Phys.

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We devise an autoencoder based strategy to facilitate anomaly detection for boosted jets, employing Graph Neural Networks (GNNs) to do so. To overcome known limitations of GNN autoencoders, we design a symmetric decoder capable of simultaneously reconstructing edge features and node features. Focusing on latent space based discriminators, we find that such setups provide a promising avenue to isolate new physics and competing SM signatures from sensitivity-limiting QCD jet contributions. We demonstrate the flexibility and broad applicability of this approach using examples of W bosons, top quarks, and exotic hadronically-decaying exotic scalar bosons.

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Unsupervised clustering for collider physics

Cited by 33 publications

References 28 publications

What's Anomalous in LHC Jets?

What's Anomalous in LHC Jets?

Better latent spaces for better autoencoders

Anomaly detection with convolutional Graph Neural Networks

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