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tommaso dorigo (INFN Sezione di Padova)04/03/2024 11:10
We describe RanBox, a tool for the discovery of overdensities in a standardized multi-dimensional space. The search is performed by reducing the feature space to the unit hypercube (copula), and searching for an excess of events within small multidimensional intervals exploiting the prediction of the background in multi-dimensional sideband around them. The algorithm is shown to compare...
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Sabine Kraml (LPSC Granoble)04/03/2024 11:35
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David Rousseau (IJCLab, CNRS/IN2P3, Université Paris-Saclay)04/03/2024 12:00
The AISSAI workshop on AI and the Uncertainty Challenge in Fundamental Physics https://indico.in2p3.fr/event/30589/ took place in Paris and Orsay 27-Nov to 1st Dec 2023. The following themes were covered Uncertainty Quantification, Explainable AI, Simulation-Based Inference, Data frugal approaches, Data-centric AI , Benchmarks dataset and challenges, Unfolding (or de-biasing, de-blurring)...
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Priscila Pessi (Oskar Klein Centre)04/03/2024 14:45
Almost all astronomical transients are hosted by a galaxy. Hostless transients are rare and have been associated with events that probe the extremes of physical mechanisms. Early identification based on their hostless characteristics would allow rapid follow up and consequently better datasets for modelling and interpretation. Most apparently hostless events are not in fact hostless but their...
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Dr JIECHIEU KAMENI Florentin Flambeau (CNRS-LIMOS)04/03/2024 16:05
DASMA is a research project that aims at designing and building a system for real-time anomaly detection and explanation. The novelty is the abiltiy of the system to process a multivariate and numerical datastream in order to provide real-time explanations to anomalies detected by highlighting the variables mainly responsible for the anomaly. The prototype described in this work consists of a...
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Artur Lobanov (Universität Hamburg)04/03/2024 16:30
We present the preparation, deployment, and testing of an autoencoder trained for unbiased detection of new physics signatures in the CMS experiment Global Trigger test crate FPGAs during LHC Run 3. The Global Trigger makes the final decision whether to readout or discard the data from each LHC collision, which occur at a rate of 40 MHz, within a 50 ns latency. The Neural Network makes a...
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Marco Letizia (MaLGa Center, University of Genoa and INFN)04/03/2024 16:55
The New Physics Learning Machine is a methodology to perform a model-independent and multivariate likelihood ratio test powered by machine learning (arXiv:2305.14137). I will present its implementation based on kernel methods, which is extremely efficient while maintaining high flexibility (arXiv:2204.02317). After outlining the general framework, I will discuss recent results on model...
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Jean-Michel Becu (ACOEM)05/03/2024 10:15
Abstract (20 lines max) Anomaly detection is a key issue in many fields of application. In condition-based maintenance, we are navigating between two key issues: the health status assessment and the identification of the default (diagnosis itself). The first can be answered by an "unsupervised" approach, which seeks to detect changes in the behavior of complex industrial equipment, in order...
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Paula Sanchez Saez (European Southern Observatory (ESO))05/03/2024 11:10
The classic classification scheme for Active Galactic Nuclei (AGNs) was challenged by the discovery of the changing-state AGNs (CSAGNs). The physical mechanism behind this phenomenon is still a matter of open debate and the samples are too small and of serendipitous nature to provide robust answers. In this talk I will present an anomaly detection (AD) technique designed to identify AGN light...
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Dr Gabriella Contardo (SISSA)05/03/2024 11:35
Anomalies are usually framed as “rare objects”, lying in a low-density region of the feature space. However, finding them in practice under this broad definition can come with limitations: density estimation can be hard to perform reliably for high dimensional, noisy or complex (non-rectangular) data. Additionally, not all low-density points are interesting anomalies. In many cases, we are...
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Mlle Aleksandra Avdeeva (Institute of Astronomy RAS)05/03/2024 12:00
Selection of extreme objects in the data from large-scale sky surveys is a powerful tool for the detection of new classes of astrophysical objects or rare stages of their evolution. The cross-matching of catalogues and analysis of the color indices of their objects is a usual approach for this problem which has already provided a lot of interesting results. However, the analysis of objects...
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Dr Louis Vaslin (QUP/KEK)05/03/2024 14:00
With the rapid development of advanced Machine Learning techniques, many new and efficient Anomaly Detection algorithms have been released. In the field of experimental High Energy Physics, interest for such Anomaly Detection algorithms is growing. In the production of new detectors, one of the critical aspects is to ensure the functioning of each component through extensive Quality Control...
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Stefano Cavuoti (INAF - Astronomical Observatory of Capodimonte Napoli)05/03/2024 14:25
We present a novel method for detecting outliers in astronomical time series based on the combination of a deep neural network and a k-nearest neighbor algorithm. We use an EfficientNet network pre-trained on ImageNet as a feature extractor, and then perform a k-nearest neighbor search in the resulting feature space to measure the distance from the first neighbor for each image. If the...
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Mlle Lilianne Nakazono (University of Sao Paulo)06/03/2024 12:00
In this talk I will present the Southern Photometric Local Universe and its fourth data release (DR4) and briefly show our past and ongoing machine learning projects using this data. S-PLUS will cover ~9300 square degrees of the southern sky with an 80-cm telescope (T80-South) located in the Cerro Tololo Inter-American Observatory. The observations are taken in 12 bands: 5 sloan-like broad...
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Pietro Vischia (Universidad de Oviedo and Instituto de Ciencias y Tecnologías Espaciales de Asturias (ICTEA))06/03/2024 14:45
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36. Model-agnostic search for dijet resonances with anomalous jet substructure with the CMS detectorLouis Moureaux (Universität Hamburg)06/03/2024 15:10
We present a model-agnostic search for new physics in the dijet final state using five different novel machine-learning techniques. Other than the requirement of a narrow dijet resonance, minimal additional assumptions are placed on the signal hypothesis. Signal regions are obtained utilizing multivariate machine learning methods to select jets with anomalous substructure. A collection of...
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Dr Roberto Seidita (ETH Zürich)06/03/2024 16:05
Semivisible jets are an intriguing signature predicted to arise at hadron colliders when the Standard Model /(SM) of particle physics is extended with a new, hidden sector, governed by a confining interaction. Made of a mixture of SM particles and undetectable bound states of new particles, semivisible jets present a unique radiation pattern. Exploiting the resulting differences in jet...
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Louis Vaslin (LPC Clermont)06/03/2024 16:30
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Florian Josselin (SHL Medical)07/03/2024 10:15
Injection molding, especially in medical device manufacturing, faces significant costs associated with manual quality control, largely due to regulatory requirements. Standard approaches using Design of Experiments combined with manual control are limited by performance, high cost and delayed detection, while other approaches like Statistical Process Control are limited by extensive need for...
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Anastasiia Voloshina (SNAD team)07/03/2024 11:10
M-dwarf stars make up a vast majority of stars in the Milky Way galaxy. As low-mass, fully convective stars, they exhibit frequent flaring events caused by powerful magnetic reconnection processes in their atmospheres. The study of M-dwarf flares gives key insights into stellar magnetism, high-energy phenomena, and the impacts on potential habitable planets orbiting these stars. In this work...
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Emmanuel Gangler (LPC)07/03/2024 11:35
Machine learning is often viewed as a black box when it comes to understanding its output, be it a decision or a score. Automatic anomaly detection is no exception to this rule, and quite often the human expert is left to independently analyze the data in order to understand why it is tagged as an anomaly. Worst, the expert may end up scrutinizing over and over the same kind of rare phenomena...
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Etienne Russeil07/03/2024 12:00
Feature extraction is one of the crucial stages in trying to apply machine learning to real scientific cases. Because of their potentially non homogeneous sampling, feature extraction of time series represents an additional challenge. One of the standard methods to tackle it requires to use a parametric equation which should be versatile enough to describe all the diversity of your dataset....
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Shikma Bressler (Weizmann Institute of Science)07/03/2024 14:00
No stone can be left unturned in the search for new physics beyond the standard model (BSM). Since no indication of new physics was found yet, and the resources in hand are limited, we must devise novel avenues for discovery. We propose a Data-Directed Paradigm (DDP), whose principal objective is to direct dedicated analysis efforts towards regions of data which hold the highest potential for...
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Mlle Eva Mayer (Université Clermont Auvergne)07/03/2024 14:25
The Data-Directed paradigm (DDP) is a new physics search strategy for efficiently detecting anomalies in a large number of spectra with smoothly-falling SM backgrounds. Unlike the traditional analysis strategy, DDP avoids the need for a simulated or functional-form based background estimate by directly predicting the statistical significance using a convolutional neural network trained to...
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Dr Federica Maria Simone (Università and INFN, Bari, Italy)07/03/2024 14:50
Ensuring the quality of data in large HEP experiments like CMS at the LHC is of primary importance to ensure solid physics results. Well established Data Quality Monitoring (DQM) and Data Certification (DC) procedures at CMS presently rely on the visual inspection of a set of reference histograms providing a concise overview of the detector status and performance. Besides the required...
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Jean-Michel Becu (ACOEM)
Anomaly detection is a key issue in many fields of application. In condition-based maintenance, we are navigating between two key issues: the health status assessment and the identification of the default (diagnosis itself). The first can be answered by an "unsupervised" approach, which seeks to detect changes in the behavior of complex industrial equipment, in order to detect faults or...
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Mme Anastasiia Voloshina (SNAD team)
M-dwarf stars make up a vast majority of stars in the Milky Way galaxy. As low-mass, fully convective stars, they exhibit frequent flaring events caused by powerful magnetic reconnection processes in their atmospheres. The study of M-dwarf flares gives key insights into stellar magnetism, high-energy phenomena, and the impacts on potential habitable planets orbiting these stars. In this work...
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Etienne Russeil
Feature extraction is one of the crucial stages in trying to apply machine learning to real scientific cases. Because of their potentially non homogeneous sampling, feature extraction of time series represents an additional challenge. One of the standard methods to tackle it requires to use a parametric equation which should be versatile enough to describe all the diversity of your dataset....
Go to contribution page -
Etienne Russeil
Feature extraction is one of the crucial stages in trying to apply machine learning to real scientific cases. Because of their potentially non homogeneous sampling, feature extraction of time series represents an additional challenge. One of the standard methods to tackle it requires to use a parametric equation which should be versatile enough to describe all the diversity of your dataset....
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Julien Mellaerts (Eviden)
Quantum-inspired anomaly detection, a QUBO formulation
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Abstract (20 lines max) Anomaly detection is a crucial task in machine learning that involves identifying unusual patterns or events in data. It has numerous applications in various domains such as finance, healthcare, and cybersecurity. With the advent of quantum computing, there has been a growing interest in developing quantum... -
Artur Lobanov (Universität Hamburg)
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Adrian Rubio Jimenez (Instituto de Fisica Corpuscular - CERN)
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