Related papers: Adversarially Learned Anomaly Detection on CMS Ope…
The search for first top-quark pair candidate events and the related background studies using data-driven techniques are reported for about 300 inverse nb of 7 TeV proton-proton collision data delivered by the Large Hadron Collider (LHC)…
Anomaly detection is a promising, model-agnostic strategy to find physics beyond the Standard Model. State-of-the-art machine learning methods offer impressive performance on anomaly detection tasks, but interpretability, resource, and…
There is a growing need for machine learning-based anomaly detection strategies to broaden the search for Beyond-the-Standard-Model (BSM) physics at the Large Hadron Collider (LHC) and elsewhere. The first step of any anomaly detection…
This report summarizes recent results of differential top quark cross section measurements performed by the ATLAS and CMS experiments. The $t\bar{t}$ process is studied as well as the production of single (anti-)top quarks and the…
Time-series anomaly detectors are commonly compared on workstation-class hardware under unconstrained execution. In-vehicle monitoring, however, requires predictable latency and stable behavior under limited CPU parallelism. Accuracy-only…
Selected results from searches for new physics with unconventional signatures using the ATLAS and CMS detectors are presented. Such signatures include emerging jets, heavy charged particles, displaced or delayed objects, and disappearing…
This note presents an overview of current and potential future applications of machine-learning-based techniques in the study of the top quark. The research community has developed a diverse set of ideas and tools, including algorithms for…
This paper presents studies of the performance of several jet-substructure techniques, which are used to identify hadronically decaying top quarks with high transverse momentum contained in large-radius jets. The efficiency of identifying…
For anomaly detection (AD), early approaches often train separate models for individual classes, yielding high performance but posing challenges in scalability and resource management. Recent efforts have shifted toward training a single…
The online Data Quality Monitoring system (DQM) of the CMS electromagnetic calorimeter (ECAL) is a crucial operational tool that allows ECAL experts to quickly identify, localize, and diagnose a broad range of detector issues that would…
Recent inclusive and differential cross section measurements of the associated production of top quark pairs with gauge bosons or heavy-flavor jets are reported. A search for physics beyond the standard model in the top quark sector is also…
Detecting anomalies in large complex systems is a critical and challenging task. The difficulties arise from several aspects. First, collecting ground truth labels or prior knowledge for anomalies is hard in real-world systems, which often…
Modern cyberattacks in cyber-physical systems (CPS) rapidly evolve and cannot be deterred effectively with most current methods which focused on characterizing past threats. Adaptive anomaly detection (AAD) is among the most promising…
Anomaly detection is a fundamental problem in computer vision area with many real-world applications. Given a wide range of images belonging to the normal class, emerging from some distribution, the objective of this task is to construct…
Anomaly detection is crucial for ensuring the stability and reliability of web service systems. Logs and metrics contain multiple information that can reflect the system's operational state and potential anomalies. Thus, existing anomaly…
We develop a distribution-free, unsupervised anomaly detection method called ECAD, which wraps around any regression algorithm and sequentially detects anomalies. Rooted in conformal prediction, ECAD does not require data exchangeability…
In anomaly detection (AD), one seeks to identify whether a test sample is abnormal, given a data set of normal samples. A recent and promising approach to AD relies on deep generative models, such as variational autoencoders (VAEs), for…
During the operation of industrial robots, unusual events may endanger the safety of humans and the quality of production. When collecting data to detect such cases, it is not ensured that data from all potentially occurring errors is…
The Large Hadron Collider has delivered exceptionally large proton-proton collision datasets at centre-of-mass energies of up to 13.6 TeV. These datasets, collected by the ATLAS and CMS detectors, enable precision tests of theoretical…
Petabytes of data are generated at the Atlas experiment at the Large Hadron Collider however not all of it is necessarily interesting, so what do we do with all of this data and how do we find these interesting needles in an uninteresting…