Simulation-assisted decorrelation for resonant anomaly detection

Benkendorfer, Kees; Pottier, Luc Le; Nachman, B. P.

doi:10.1103/physrevd.104.035003

Cited by 46 publications

(24 citation statements)

References 59 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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“…But is still possible to classify and rank different methodologies by their "degree of model-independence" as we did for Anomaly Detection, by trying to figure out which type of new physics signals they might or might not be sensitive to. From this viewpoint one would rank BumpHunter [56] and similar strategies [55,[57][58][59][60][61][62][63], which target resonant signals in a pre-specified variable, lower than methods with a broader target [1,2,[38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][64][65][66][67]. On the other hand, one should not employ these generic considerations to tell which one is the "right" strategy to pursue.…”

Section: Discussionmentioning

confidence: 99%

Learning New Physics from an Imperfect Machine

D’Agnolo¹,

Grosso²,

Pierini³

et al. 2021

Preprint

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We show how to deal with uncertainties on the Standard Model predictions in an agnostic new physics search strategy that exploits artificial neural networks. Our approach builds directly on the specific Maximum Likelihood ratio treatment of uncertainties as nuisance parameters for hypothesis testing that is routinely employed in high-energy physics. After presenting the conceptual foundations of our method, we first illustrate all aspects of its implementation and extensively study its performances on a toy one-dimensional problem. We then show how to implement it in a multivariate setup by studying the impact of two typical sources of experimental uncertainties in two-body final states at the LHC.

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“…[141,142] proposed using a region near the potential signal to construct A and a sideband region is used to define B. 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].…”

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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Simulation-assisted decorrelation for resonant anomaly detection

Cited by 46 publications

References 59 publications

What's Anomalous in LHC Jets?

What's Anomalous in LHC Jets?

Learning New Physics from an Imperfect Machine

Machine Learning in the Search for New Fundamental Physics

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