Quasi anomalous knowledge: searching for new physics with embedded knowledge

Park, Sang Eon; Rankin, D.; Udrescu, Silviu-Marian; Yunus, Mikaeel; Harris, P.

doi:10.1007/jhep06(2021)030

Cited by 25 publications

(23 citation statements)

References 58 publications

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“…This is comparable to what a supervised network trained to find tops over QCD gives when tested on W vs. QCD (AUC of 0.86). These observations suggest that a path forward might be to use a semi-supervised approach [43,[87][88][89], where a network is trained with an example signal in mind, and then used for anomaly detection more broadly.…”

Section: Discussionmentioning

confidence: 99%

Challenges for Unsupervised Anomaly Detection in Particle Physics

Fraser,

Homiller,

Mishra

et al. 2021

Preprint

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Anomaly detection relies on designing a score to determine whether a particular event is uncharacteristic of a given background distribution. One way to define a score is to use autoencoders, which rely on the ability to reconstruct certain types of data (background) but not others (signals). In this paper, we study some challenges associated with variational autoencoders, such as the dependence on hyperparameters and the metric used, in the context of anomalous signal (top and W ) jets in a QCD background. We find that the hyperparameter choices strongly affect the network performance and that the optimal parameters for one signal are non-optimal for another. In exploring the networks, we uncover a connection between the latent space of a variational autoencoder trained using mean-squared-error and the optimal transport distances within the dataset. We then show that optimal transport distances to representative events in the background dataset can be used directly for anomaly detection, with performance comparable to the autoencoders. Whether using autoencoders or optimal transport distances for anomaly detection, we find that the choices that best represent the background are not necessarily best for signal identification. These challenges with unsupervised anomaly detection bolster the case for additional exploration of semi-supervised or alternative approaches.

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Section: Discussionmentioning

confidence: 99%

Challenges for Unsupervised Anomaly Detection in Particle Physics

Fraser,

Homiller,

Mishra

et al. 2021

Preprint

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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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“…Another option is to build B using pure [143][144][145] or data-augmented [146,147] simulation, which is composed of only the 0 class by construction. Simulations can also be used to add signal-like labels for A [148,149]. Hybrid approaches have also been proposed that use parameterized density estimation from the sideband to estimate the background density in the signal region [47,150,151] or autoencoders to learn the noisy labels in the first place [139].…”

Section: Weakly and Semi-supervisedmentioning

confidence: 99%

Machine Learning in the Search for New Fundamental Physics

Karagiorgi¹,

Kasieczka²,

Kravitz³

et al. 2021

Preprint

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Machine learning plays a crucial role in enhancing and accelerating the search for new fundamental physics. We review the state of machine learning methods and applications for new physics searches in the context of terrestrial high energy physics experiments, including the Large Hadron Collider, rare event searches, and neutrino experiments. While machine learning has a long history in these fields, the deep learning revolution (early 2010s) has yielded a qualitative shift in terms of the scope and ambition of research. These modern machine learning developments are the focus of the present review.

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Quasi anomalous knowledge: searching for new physics with embedded knowledge

Cited by 25 publications

References 58 publications

Challenges for Unsupervised Anomaly Detection in Particle Physics

Challenges for Unsupervised Anomaly Detection in Particle Physics

What's Anomalous in LHC Jets?

Machine Learning in the Search for New Fundamental Physics

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