Présidents de session
AI and physics conference: Day 1 Morning
- David Rousseau (IJCLab, Orsay, France)
- Jean-Roch Vlimant (California Institute of Technology (US))
AI and physics conference: Day 1 Afternoon
- Cécile Germain (LISN Université Paris-Saclay)
- Andreas Salzburger (CERN)
AI and physics conference: Day 2 morning
- François Lanusse (CEA Saclay)
- Eilam Gross (Weizmann)
AI and physics conference: Day 2 afternoon
- Savannah Thais (Princeton)
AI and physics conference: Day 3 Morning
- Pietro Vischia (UC Louvain)
- Jean-Roch Vlimant (California Institute of Technology (US))
AI and physics conference: Day 3 afternoon
- Anja Butter (ITP Heidelberg)
- Felice Pantaleo (CERN)
Documents de présentation
Data-Centric Artificial Intelligence (DCAI) was introduced by Andrew Ng in 2021. This call was launched to answer the discrepancy of research contributions which almost always focus on models while considering fixed and well engineered data. However, in practice data-scientists spend most of their time on data engineering tasks.
Through this presentation, we will start by giving a broad...
Self-Organizing-Maps (SOM) are widely used neural nets for unsupervised learning and dimensional reduction. They have not yet been applied in high energy physics. We discuss two applications of SOM in particle physics. First, the separation of physics processes in regions of the dimensionally reduced representation. Second, we obtain Monta Carlo scale factors by fitting templates to the...
Super-resolution algorithms are commonly used to enhance the granularity of an imaging system beyond what can be achieved using the measuring device.
We show the first application of super-resolution algorithms using deep learning-based methods for calorimeter reconstruction using a simplified geometry consisting of overlapping showers originated by charged and neutral pions events.
The task...
Variational autoencoders are deep generative networks widely used for a large area of tasks, such as image or text generation. They are composed of two sub-models. On the one hand, the encoder aims to infer the parameters of the approximate posterior distribution
Computing topological properties of Calabi-Yau manifolds is a challenging mathematical task. Recent years have witnessed the rising use of deep learning as a method for exploration of large sets of data, to learn their [patterns and properties][1]. This is specifically interesting when it comes to unravel complicated geometrical structures, as well as in the development of trustworthy AI...
To efficiently solve a big problem by deep learning, it is sometimes useful to decompose it into smaller blocks, enabling us to introduce our knowledge into the model by utilizing an appropriate loss function for each block.
A simple model decomposition, however, causes a performance decrease due to bottlenecks of transferred information induced by the loss definition.
We proposed a method...
The physics reach of the HL-LHC will be limited by how efficiently the experiments can use the available computing resources, i.e. affordable software and computing are essential. The development of novel methods for charged particle reconstruction at the HL-LHC incorporating machine learning techniques or based entirely on machine learning is a vibrant area of research. In the past years,...
Each proton-proton collision event at the LHC produces a myriad of particles and interactions that are recorded by specialized detectors. Trackers are designed to sample the trajectories of these particles at multiple space-points; tracking is the connecting-the-dots process of linking these signals (hits) to reconstruct particle trajectories (tracks). Tracker data is naturally represented as...
In 2012 the imagenet challenge and the discovery of the Higgs boson produced a paradigm shift in particle physics analysis. Today a new revolution awaits to be made. The possibility to map continuously the space of design solutions of even the hardest optimization problem, using differentiable programming, promises to provide us with entirely new and more performant or cheaper solutions to...
Upcoming surveys such as the Large Survey of Space and Time (LSST) and Euclid will observe the sky with unprecedented depth and area of coverage. As these surveys will detect fainter objects, the increase in object density will lead to an increased number of overlapping sources. For example, in LSST we expect around 60% of the objects to be blended. In order to better constrain Dark Energy...
The Phase-II upgrade of the LHC will increase its instantaneous luminosity by a factor of 7 leading to the High Luminosity LHC (HL-LHC). At the HL-LHC, the number of proton-proton collisions in one bunch crossing (called pileup) increases significantly, putting more stringent requirements on the LHC detectors electronics and real-time data processing capabilities.
The ATLAS Liquid Argon...
Artificial intelligence (AI) algorithms applied to HEP analyses come to the rescue in scenarios in which implementing an efficient discriminant for separating a very low-rate signal from a huge background is extremely important. In this context, we investigate the usage of several Machine Learning (ML) and Deep Learning (DL) methods via the TensorFlow open-source platform to boost the...
We study the problem of retrieving a truth distribution from noisy observed data, often referred to as unfolding in High Energy Physics, which facilitates comparisons with theoretical predictions, and thus aids the process of scientific discovery. Our simulation-based inference technique, which we call Neural Empirical Bayes (NEB), combines Empirical Bayes, also known as maximum marginal...
We derive a well-defined renormalized version of mutual information that allows us to estimate the dependence between continuous random variables in the important case when one is deterministically dependent on the other. This is the situation relevant for feature extraction, where the goal is to produce allow-dimensional effective description of a high-dimensional system. Our approach enables...
Artificial intelligence is advancing at a very rapid pace in both research and applications, and is raising societal questions that are far from being answered. But as it moves forward rapidly, it runs into what I call the five walls of AI. Any one of these five walls is capable of halting its progress, which is why it is essential to know what they are and to seek answers in order to avoid...
ALICE, one of the four big experiments at the CERN LHC, is a detector dedicated to heavy-ion physics. A high interaction rate environment causes pile-up which necessitates the use of advanced methods of data analysis.
Over the recent years machine learning (ML) has come to dominate multi-dimensional data analysis. However, it is more difficult to interpret the ML models and to evaluate...
An overarching issue of LHC experiments is the necessity to produce massive numbers of simulated collision events in very restricted regions of phase space. A commonly used approach to tackle the problem is the use of event weighting techniques where the selection cuts are replaced by event weights constructed from efficiency parametrizations. These techniques are however limited by the...
We present a novel methodology to address many ill-posed inverse problems, by providing a description of the posterior distribution, which enables us to get point estimate solutions and to quantify their associated uncertainties. Our approach combines Neural Score Matching for learning a prior distribution from physical simulations, and a novel posterior sampling method based on an annealed...
There are many problems in physics, biology, and other natural sciences in which symbolic regression can provide valuable insights and discover new laws of nature. A widespread Deep Neural Networks don’t provide interpretable solutions. Meanwhile, symbolic expressions give us a clear relation between observations and the target variable. However, at the moment, there is no dominant solution...
Global interpretations of particle physics data in the context of the Standard Model Effective Field Theory (SMEFT) rely on the combination of a wide range of physical observables from many different processes. We present ongoing work towards the integration of unbinned measurements into such global SMEFT interpretations by means of machine learning tools. We use a deep-learning...
With an upcoming generation of wide-field cosmological surveys, cosmologists are facing new and outstanding challenges at all levels of scientific analysis, from pixel-level data reduction to cosmological inference. As powerful as Deep Learning (DL) has proven to be in recent years, in most cases a DL approach alone proves to be insufficient to meet these challenges, and is typically plagued...
How to bring value into AI : Valuelessness is an inherent and proudly upheld feature of the scientific approach. It made it successful for modelling the material world and to keep our biases under control. I will argue that, at the same time, it is one of the main obstacles for developing true AI that can be seamlessly integrated in society. I will shed light on this argument through my...
Due to their undoubting importance, the systematic publication of Full Likelihood Functions (LFs) of LHC results is a very hot topic among the HEP community. Major steps have been taken towards this goal; a notable example being ATLAS release of full likelihoods with the pyhf framework. However, the publication of LFs remains a difficult challenge since they are generally complex and...
There is a growing need for anomaly detection methods that can broaden the search for new particles in a model-agnostic manner. Most proposals for new methods focus exclusively on signal sensitivity. However, it is not enough to select anomalous events—there must also be a strategy to provide context to the selected events. We propose the first complete strategy for unsupervised detection of...
Generative models (GM) are promising applications for near-term quantum computers due to the probabilistic nature of quantum mechanics. In this work, we propose comparing a classical conditional generative adversarial network (C-GAN) approach with a Born machine while addressing their strengths and limitations to generate muonic force carriers (MFCs) events. The former uses a neural network as...
The Geant4 detector simulation, using full particle tracking (FullSim), is usually the most accurate detector simulation used in HEP but it is computationally expensive. The cost of FullSim is amplified in highly segmented calorimeters where large fraction of the computations are performed to track the shower’s low-energy photons through the complex geometry. A method to limit the production...
Particle accelerators are used in a wide array of medical, industrial, and scientific applications, ranging from cancer treatment to understanding fundamental laws of physics. While each of these applications brings with them different operational requirements, a common challenge concerns how to optimally adjust controllable settings of the accelerator to obtain the desired beam...
During Run 2 of the Large Hadron Collider at CERN, the LHCb experiment has spent more than 80% of the pledged CPU time to produce simulated data samples. The upcoming upgraded version of the experiment will be able to collect larger data samples, requiring many more simulated events to analyze the data to be collected in Run 3. Simulation is a key necessity of analysis to interpret signal vs...
The basis of lattice QCD is the formulation of the QCD path integral on a Euclidean
space-time lattice, allowing for computing expectation values of observables using Monte
Carlo simulations. Despite the success of lattice QCD in determinations of many parameters
of the Standard Model, limitations on the current techniques and algorithms still exist,
such as critical slowing down or the...
