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...
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)...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
