Robust Jet Classifiers through Distance Correlation

A key challenge in searches for resonant new physics is that classifiers trained to enhance potential signals must not induce localized structures. Such structures could result in a false signal when the background is estimated from data using sideband methods. A variety of techniques have been developed to construct classifiers which are independent from the resonant feature (often a mass). Such strategies are sufficient to avoid localized structures, but are not necessary. We develop a new set of tools using a novel moment loss function (Moment Decomposition or MoDe) which relax the assumption of independence without creating structures in the background. By allowing classifiers to be more flexible, we enhance the sensitivity to new physics without compromising the fidelity of the background estimation.

“…We use a simple 3-layer neural network with a similar architecture to that described in ref. [47]. However, unlike refs.…”

Section: Classifier Detailsmentioning

confidence: 68%

Section: Example Resultsmentioning

confidence: 99%

“…• Distance Correlation (DisCo) [47,54]: w(m) = 1, λ > 0, and the last term in eq. (2.2) is the distance correlation [74][75][76][77] between f (x) and m for the background.…”

Section: Existing Decorrelation Methodsmentioning

confidence: 99%

“…The simulated samples used in this section are the same as in ref. [47] (intended to emulate the study in ref. [46]) and are briefly summarized here.…”

Section: Jhep04(2021)070mentioning

confidence: 99%

See 2 more Smart Citations

Enhancing searches for resonances with machine learning and moment decomposition

Kitouni

Nachman

Weisser

et al. 2021

“…This means that there are background events that populate the signal window and that are used in the training with a dijet mass in the signal-window, whereas in the CWoLa hunting approach all background events are in dijet mass sidebands. The other advantage is that by training a classifier for each jet separately, one can try to explicitly decorrelate the classifier's dependence on jet p T through one of the techniques that have been used in supervised jet classification [63][64][65][66][67]. 6 We explored reweighting events in the background-rich sample to have the same p T distribution as the signal-rich region, but as there was not much mass sculpting to begin with, there were no significant differences in the mass sculpting or classification performance.…”

Section: Jhep01(2021)153mentioning

confidence: 99%

Tag N’ Train: a technique to train improved classifiers on unlabeled data

Amram

Mantilla

2021

There has been substantial progress in applying machine learning techniques to classification problems in collider and jet physics. But as these techniques grow in sophistication, they are becoming more sensitive to subtle features of jets that may not be well modeled in simulation. Therefore, relying on simulations for training will lead to sub-optimal performance in data, but the lack of true class labels makes it difficult to train on real data. To address this challenge we introduce a new approach, called Tag N’ Train (TNT), that can be applied to unlabeled data that has two distinct sub-objects. The technique uses a weak classifier for one of the objects to tag signal-rich and background-rich samples. These samples are then used to train a stronger classifier for the other object. We demonstrate the power of this method by applying it to a dijet resonance search. By starting with autoencoders trained directly on data as the weak classifiers, we use TNT to train substantially improved classifiers. We show that Tag N’ Train can be a powerful tool in model-agnostic searches and discuss other potential applications.

Quasi anomalous knowledge: searching for new physics with embedded knowledge

Park

Rankin

Udrescu

et al. 2021

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. 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.