Resonant anomaly detection without background sculpting

We introduce a new technique named Latent CATHODE (LaCATHODE) for performing "enhanced bump hunts", a type of resonant anomaly search that combines conventional one-dimensional bump hunts with a model-agnostic anomaly score in an auxiliary feature space where potential signals could also be localized. The main advantage of LaCATHODE over existing methods is that it provides an anomaly score that is well behaved when evaluating it beyond the signal region, which is essential to prevent the sculpting of background distributions in the bump hunt. LaCATHODE accomplishes this by constructing the anomaly score directly in the latent space learned by a conditional normalizing flow trained on sideband regions. We demonstrate the superior stability and comparable performance of LaCATHODE for enhanced bump hunting in an illustrative toy example as well as on the LHC Olympics R&D dataset.Comment: 11 pages, 8 figures; v2 (published version): referencing code and minor style update

Back To The Roots: Tree-Based Algorithms for Weakly Supervised Anomaly Detection

Weakly supervised methods have emerged as a powerful tool for model-agnostic anomaly detection at the Large Hadron Collider (LHC). While these methods have shown remarkable performance on specific signatures such as di-jet resonances, their application in a more model-agnostic manner requires dealing with a larger number of potentially noisy input features. In this paper, we show that using boosted decision trees as classifiers in weakly supervised anomaly detection gives superior performance compared to deep neural networks. Boosted decision trees are well known for their effectiveness in tabular data analysis. Our results show that they not only offer significantly faster training and evaluation times, but they are also robust to a large number of noisy input features. By using advanced gradient boosted decision trees in combination with ensembling techniques and an extended set of features, we significantly improve the performance of weakly supervised methods for anomaly detection at the LHC. This advance is a crucial step towards a more model-agnostic search for new physics.Comment: 11 pages, 9 figure

The Interplay of Machine Learning--based Resonant Anomaly Detection Methods

Machine learning--based anomaly detection (AD) methods are promising tools for extending the coverage of searches for physics beyond the Standard Model (BSM). One class of AD methods that has received significant attention is resonant anomaly detection, where the BSM is assumed to be localized in at least one known variable. While there have been many methods proposed to identify such a BSM signal that make use of simulated or detected data in different ways, there has not yet been a study of the methods' complementarity. To this end, we address two questions. First, in the absence of any signal, do different methods pick the same events as signal-like? If not, then we can significantly reduce the false-positive rate by comparing different methods on the same dataset. Second, if there is a signal, are different methods fully correlated? Even if their maximum performance is the same, since we do not know how much signal is present, it may be beneficial to combine approaches. Using the Large Hadron Collider (LHC) Olympics dataset, we provide quantitative answers to these questions. We find that there are significant gains possible by combining multiple methods, which will strengthen the search program at the LHC and beyond.Comment: 23 pages, 17 figure

Decorrelating jet substructure variables from the jet mass for W boson identification

Non-trivial correlations between substructure variables and the mass of a reconstructed hadronic jet can transform the mass distribution of background jets when such variables are used to identify the ori- gin of the jet. This can affect a physics analysis, e.g. the search for hypothetical heavy resonances at the CMS experiment. Several meth- ods are known today to decorrelate these variables from the mass to some extent. Following a previous study by the ATLAS Collabora- tion, this study aims at testing three such decorrelation methods on two widely used substructure variables. Comparing these methods for both variables, a significant degree of mass decorrelation, as well as an increase in tagging performance, can be achieved by all of these meth- ods, particularly the so-called fixed-efficiency k-nearest-neighbor fit. Their behavior is also shown to be robust with respect to an increased number of primary vertices. Additionally, the results are shown to be comparable to the ones obtained by the ATLAS Collaboration

Score rectification for online assessments in robot-assisted arm rehabilitation

Relative comparison of clinical scores to measure the effectiveness of neuro-rehabilitation therapy is possible through a series of discrete measurements during the rehabilitation period within specifically designed task environments. Robots allow quantitative, continuous measurement of data. Resulting robotic scores are also only comparable within similar context, e.g. type of task. We propose a method to decouple these scores from their respective context through functional orthogonalization and compensation of the compounding factors based on a data-driven sensitivity analysis of the user performance. The method was validated for the established accuracy score with variable arm weight support, provoked muscle fatigue and different task directions on 6 participants of our arm exoskeleton group on the ANYexo robot. In the best case, the standard deviation of the assessed score in changing context could be reduced by a factor of 3.2. Therewith, we paved the way to context-independent, quantitative online assessments, recorded autonomously with robots.ISSN:0178-2312ISSN:2196-677

Classifying anomalies through outer density estimation

We propose a new model-agnostic search strategy for physics beyond the standard model (BSM) at the LHC, based on a novel application of neural density estimation to anomaly detection. Our approach, which we call classifying anomalies through outer density estimation (cathode), assumes the BSM signal is localized in a signal region (defined e.g., using invariant mass). By training a conditional density estimator on a collection of additional features outside the signal region, interpolating it into the signal region, and sampling from it, we produce a collection of events that follow the background model. We can then train a classifier to distinguish the data from the events sampled from the background model, thereby approaching the optimal anomaly detector. Using the LHC Olympics R&D dataset, we demonstrate that cathode nearly saturates the best possible performance, and significantly outperforms other approaches that aim to enhance the bump hunt (cwola hunting and anode). Finally, we demonstrate that cathode is very robust against correlations between the features and maintains nearly optimal performance even in this more challenging setting

The interplay of machine learning-based resonant anomaly detection methods

Machine learning-based anomaly detection (AD) methods are promising tools for extending the coverage of searches for physics beyond the Standard Model (BSM). One class of AD methods that has received significant attention is resonant anomaly detection, where the BSM physics is assumed to be localized in at least one known variable. While there have been many methods proposed to identify such a BSM signal that make use of simulated or detected data in different ways, there has not yet been a study of the methods' complementarity. To this end, we address two questions. First, in the absence of any signal, do different methods pick the same events as signal-like? If not, then we can significantly reduce the false-positive rate by comparing different methods on the same dataset. Second, if there is a signal, are different methods fully correlated? Even if their maximum performance is the same, since we do not know how much signal is present, it may be beneficial to combine approaches. Using the Large Hadron Collider (LHC) Olympics dataset, we provide quantitative answers to these questions. We find that there are significant gains possible by combining multiple methods, which will strengthen the search program at the LHC and beyond

miRNAs, from Evolutionary Junk to Possible Prognostic Markers and Therapeutic Targets in COVID-19

The COVID-19 pandemic has been a public health issue around the world in the last few years. Currently, there is no specific antiviral treatment to fight the disease. Thus, it is essential to highlight possible prognostic predictors that could identify patients with a high risk of developing complications. Within this framework, miRNA biomolecules play a vital role in the genetic regulation of various genes, principally, those related to the pathophysiology of the disease. Here, we review the interaction of host and viral microRNAs with molecular and cellular elements that could potentiate the main pulmonary, cardiac, renal, circulatory, and neuronal complications in COVID-19 patients. miR-26a, miR-29b, miR-21, miR-372, and miR-2392, among others, have been associated with exacerbation of the inflammatory process, increasing the risk of a cytokine storm. In addition, increased expression of miR-15b, -199a, and -491 are related to the prognosis of the disease, and miR-192 and miR-323a were identified as clinical predictors of mortality in patients admitted to the intensive care unit. Finally, we address miR-29, miR-122, miR-155, and miR-200, among others, as possible therapeutic targets. However, more studies are required to confirm these findings

Measurement of the double-differential inclusive jet cross section in proton-proton collisions at s\sqrt{s} = 5.02 TeV

International audienceThe inclusive jet cross section is measured as a function of jet transverse momentum $p_\mathrm{T}$ and rapidity $y$. The measurement is performed using proton-proton collision data at $\sqrt{s}$ = 5.02 TeV, recorded by the CMS experiment at the LHC, corresponding to an integrated luminosity of 27.4 pb$^{-1}$. The jets are reconstructed with the anti-$k_\mathrm{T}$ algorithm using a distance parameter of $R$ = 0.4, within the rapidity interval $\lvert y\rvert$$\lt$ 2, and across the kinematic range 0.06 $\lt$$p_\mathrm{T}$$\lt$ 1 TeV. The jet cross section is unfolded from detector to particle level using the determined jet response and resolution. The results are compared to predictions of perturbative quantum chromodynamics, calculated at both next-to-leading order and next-to-next-to-leading order. The predictions are corrected for nonperturbative effects, and presented for a variety of parton distribution functions and choices of the renormalization/factorization scales and the strong coupling $\alpha_\mathrm{S}$

Two-particle Bose-Einstein correlations and their Lévy parameters in PbPb collisions at sNN\sqrt{s_\mathrm{NN}} = 5.02 TeV

Two-particle Bose-Einstein momentum correlation functions are studied for charged-hadron pairs in lead-lead collisions at a center-of-mass energy per nucleon pair of $\sqrt{s_\mathrm{NN}}$ = 5.02 TeV. The data sample, containing 4.27$\times10^{9}$ minimum bias events corresponding to an integrated luminosity of 0.607 nb$^{-1}$, was collected by the CMS experiment in 2018. The experimental results are discussed in terms of a L\'evy-type source distribution. The parameters of this distribution are extracted as functions of particle pair average transverse mass and collision centrality. These parameters include the L\'evy index or shape parameter ($\alpha$), the L\'evy scale parameter ($R$), and the correlation strength parameter ($\lambda$). The source shape, characterized by $\alpha$, is found to be neither Cauchy nor Gaussian, implying the need for a full L\'evy analysis. Similarly to what was previously found for systems characterized by Gaussian source radii, a hydrodynamical scaling is observed for the L\'evy $R$ parameter. The $\lambda$ parameter is studied in terms of the core-halo model.Comment: Submitted to Physical Review C. All figures and tables can be found at http://cms-results.web.cern.ch/cms-results/public-results/publications/HIN-21-011 (CMS Public Pages