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1. Quasi anomalous knowledge: searching for new physics with embedded knowledge

   https://doi.org/https://doi.org/10.1007/JHEP06(2021)030  

   Park, S. ; Rankin, D. ; Udrescu, SM ; Yunus, M. ; Harris, P. ( June 2021 , The Journal of high energy physics)

   Discoveries of new phenomena often involve a dedicated search for a hypothetical physics signature. Recently, novel deep learning techniques have emerged for anomaly detection in the absence of a signal prior. However, by ignoring signal priors, the sensitivity of these approaches is significantly reduced. We present a new strategy dubbed Quasi Anomalous Knowledge (QUAK), whereby we introduce alternative signal priors that capture some of the salient features of new physics signatures, allowing for the recovery of sensitivity even when the alternative signal is incorrect. This approach can be applied to a broad range of physics models and neural network architectures.more »In this paper, we apply QUAK to anomaly detection of new physics events at the CERN Large Hadron Collider utilizing variational autoencoders with normalizing flow.« less
2. The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider

   https://doi.org/10.21468/SciPostPhys.12.1.043  

   Aarrestad, Thea ; van Beekveld, Melissa ; Bona, Marcella ; Boveia, Antonio ; Caron, Sascha ; Davies, Joe ; de Simone, Andrea ; Doglioni, Caterina ; Duarte, Javier ; Farbin, Amir ; et al ( January 2022 , SciPost Physics)

   We describe the outcome of a data challenge conducted as part of the Dark Machines (https://www.darkmachines.org) initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenged aims to detect signals of new physics at the Large Hadron Collider (LHC) using unsupervised machine learning algorithms. First, we propose how an anomaly score could be implemented to define model-independent signal regions in LHC searches. We define and describe a large benchmark dataset, consisting of >1 billion simulated LHC events corresponding to 10\, fb^{-1} 10 f b − 1 of proton-proton collisions at a center-of-mass energy of 13 TeV.more »We then review a wide range of anomaly detection and density estimation algorithms, developed in the context of the data challenge, and we measure their performance in a set of realistic analysis environments. We draw a number of useful conclusions that will aid the development of unsupervised new physics searches during the third run of the LHC, and provide our benchmark dataset for future studies at https://www.phenoMLdata.org. Code to reproduce the analysis is provided at https://github.com/bostdiek/DarkMachines-UnsupervisedChallenge.« less
3. Challenges for unsupervised anomaly detection in particle physics

   https://doi.org/10.1007/JHEP03(2022)066  

   Fraser, Katherine ; Homiller, Samuel ; Mishra, Rashmish K. ; Ostdiek, Bryan ; Schwartz, Matthew D. ( March 2022 , Journal of High Energy Physics)

   A bstract 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 parametersmore »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.« less
4. Topological obstructions to autoencoding

   https://doi.org/10.1007/JHEP04(2021)280  

   Batson, Joshua ; Haaf, C. Grace ; Kahn, Yonatan ; Roberts, Daniel A. ( April 2021 , Journal of High Energy Physics)

   A bstract Autoencoders have been proposed as a powerful tool for model-independent anomaly detection in high-energy physics. The operating principle is that events which do not belong to the space of training data will be reconstructed poorly, thus flagging them as anomalies. We point out that in a variety of examples of interest, the connection between large reconstruction error and anomalies is not so clear. In particular, for data sets with nontrivial topology, there will always be points that erroneously seem anomalous due to global issues. Conversely, neural networks typically have an inductive bias or prior to locally interpolate suchmore »that undersampled or rare events may be reconstructed with small error, despite actually being the desired anomalies. Taken together, these facts are in tension with the simple picture of the autoencoder as an anomaly detector. Using a series of illustrative low-dimensional examples, we show explicitly how the intrinsic and extrinsic topology of the dataset affects the behavior of an autoencoder and how this topology is manifested in the latent space representation during training. We ground this analysis in the discussion of a mock “bump hunt” in which the autoencoder fails to identify an anomalous “signal” for reasons tied to the intrinsic topology of n -particle phase space.« less
5. The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics

   Amram, Oz ; Andreassen, Anders ; Benkendorfer, Kees ; Bortolato, Blaz ; Brooijmans, Gustaaf ; Canelli, Florencia ; Collins, Jack ; Dai, Biwei ; De Freitas, Felipe ; Dillon, Barry ; et al ( January 2021 , ArXivorg)

   Kasieczka, Gregor ; Nachman, Benjamin ; Shih, David (Ed.)

   A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. In order to develop and benchmark new anomaly detection methods within this framework, it is essential to have standard datasets. To this end, we have created the LHC Olympics 2020, a community challenge accompanied by a set of simulated collider events. Participants in these Olympics have developed their methods using an R&D dataset and then tested them on black boxes: datasets with an unknown anomaly (or not). This paper will review the LHC Olympics 2020more »challenge, including an overview of the competition, a description of methods deployed in the competition, lessons learned from the experience, and implications for data analyses with future datasets as well as future colliders.« less