With the upcoming High-Luminosity Large Hadron Collider (HL-LHC) and the corresponding increase in collision rates and pile-up, a significant surge in data quantity and complexity is expected. In response, substantial R&D efforts in artificial intelligence (AI) and machine learning (ML) have been initiated by the community in recent years to develop faster and more efficient algorithms capable...
Model misspecification analysis strategies, such as anomaly detection, model validation, and model comparison are a key component of scientific model development. Over the last few years, there has been a rapid rise in the use of simulation-based inference (SBI) techniques for Bayesian parameter estimation, applied to increasingly complex forward models. To move towards fully simulation-based...
The increasing volume of gamma-ray data from space-borne telescopes, like Fermi-LAT, and the upcoming ground-based telescopes, like the Cherenkov Telescope Array Observatory (CTAO), presents us with both opportunities and challenges. Traditional analysis methods based on likelihood analysis are often used for gamma-ray source detection and further characterization tasks. A key challenge to...
The search for resonant mass bumps in invariant-mass histograms is a fundamental approach for uncovering Beyond the Standard Model (BSM) physics at the Large Hadron Collider (LHC). Traditional, model-dependent analyses that utilize this technique, such as those conducted using data from the ATLAS detector at CERN, often require substantial resources, which prevent many final states from being...
Machine Learning (ML) is driving a revolution in the way scientists design, develop, and deploy data-intensive software. However, the adoption of ML presents new challenges for the computing infrastructure, particularly in terms of provisioning and orchestrating access to hardware accelerators for development, testing, and production. The INFN-funded project AI_INFN ("Artificial Intelligence...
High-energy physics experiments at the Large Hadron Collider (LHC) at CERN rely on simulations to model particle interactions and understand experimental data. These simulations, crucial for reconstructing collision events, are traditionally performed using Monte Carlo-based methods, which are highly computationally demanding. With hundreds of thousands of CPU cores dedicated to these tasks...
The application of machine learning techniques in particle physics has accelerated the development of methodologies for exploring physics beyond the Standard Model. This talk will present an overview of anomaly detection and its potential to enhance the detection of new physics. The talk will discuss the adaptation and real-time deployment of anomaly detection algorithms. Additionally, a novel...
Astrophysical sources vary across vast timescales, providing insight into extreme dynamical phenomena, from solar outbursts to distant AGNs and GRBs. These time-varying processes are often complex, nonlinear, and non-Gaussian, making it difficult to disentangle underlying causal mechanisms, which may act simultaneously or sequentially. Using solar variability and AGNs as examples, we...
Ensuring reliable data collection in large-scale particle physics experiments demands Data Quality Monitoring (DQM) procedures to detect possible detector malfunctions and preserve data integrity. Traditionally, this resource-intensive task has been handled by human shifters who may struggle with frequent changes in operational conditions. Instead, to simplify and automate the shifters' work,...
The TimeSPOT project has developed innovative sensors optimized for precise space and time measurements of minimum-ionizing particles in high-radiation environments. These sensors demonstrate exceptional spatial resolution (around 10 µm) and time resolution (around 10 ps), while withstanding high fluences (> 10¹⁷ 1 MeV n_eq/cm²). Tests on small-scale structures confirm their potential for...
Adversarial machine learning is a collection of techniques used to study attacks on machine learning algorithms. It is commonly used in cybersecurity and still has few applications in fundamental physics. Since systematic effects in detector data, often absent in Monte Carlo simulations, challenge the performance of machine learning models in particle physics, in this work we model the...
The first detection of the gravitational wave event GW150914 in 2015 opened the doors to the gravitational astronomy. Since then, hundreds of such events have been detected. Some of these have been particularly significant, such as GW170817, the first binary neutron star merger. This detection enabled a measurement of electromagnetic counterpart marking the beginning of the multi-messenger...
Our work identifies the sources of 11 interconnected machine learning (ML) biases that hinder the generalisation of supervised learning models in the context of gravitational wave (GW) detection. We use GW domain knowledge to propose a set of mitigation tactics and training strategies for ML algorithms that aim to address these biases concurrently and improve detection sensitivity. We...
We discuss about applications of hybrid quantum-classical computing and present Qiboml, an open-source software library for Quantum Machine Learning (QML) integrated with the Qibo quantum computing framework. Qiboml interfaces most used classical Machine Learning frameworks such as TensorFlow, PyTorch and Jax with Qibo. This combination enables users to construct quantum or hybrid...
Neural network emulators or surrogates are widely used in astrophysics and cosmology to approximate expensive simulations, accelerating both likelihood-based inference and training for simulation-based inference. However, emulator accuracy requirements are often justified heuristically rather than with rigorous theoretical bounds. We derive a principled upper limit on the information loss...
At CERN’s Large Hadron Collider (LHC), hardware trigger systems are crucial in the first stages of data processing: they select a tiny fraction of the 40 million collision events per second for further analysis, within a few microseconds.
Machine Learning (ML) techniques being used more and more frequently to enable the efficient selection of extremely rare events.
These ML algorithms are...
The graph coloring problem is an optimization problem involving the assignment of one of q colors to each vertex of a graph such that no two adjacent vertices share the same color. This problem is computationally challenging and arises in several practical applications. We present a novel algorithm that leverages graph neural networks to tackle the problem efficiently, particularly for large...
Understanding the population properties of double white dwarfs (DWDs) in the Milky Way is a key science goal for the upcoming gravitational wave detector, LISA. However, the vast number of galactic binaries (~$30 \times 10^6$) and the large data size (~$6 \times 10^6$) pose significant challenges for traditional Bayesian samplers. In this talk, I present a simulation-based inference framework...
The Transformer Machine Learning architecture has been gaining considerable momentum in recent years. Computational High-Energy Physics tasks such as jet tagging and particle track reconstruction (tracking), have either achieved proper solutions, or reached considerable milestones using Transformers. On the other hand, the use of specialised hardware accelerators, especially FPGAs, is an...
We study the application of a neural network architecture for identifying charged particle trajectories via unsupervised learning of delays and synaptic weights using a spike-time-dependent plasticity rule. In the considered model the neurons receive time-encoded information on the position of particle hits in a tracking detector for a particle collider, modeled according to the geometry of...
Reservoir computing (RC) has emerged as a powerful paradigm for processing temporal data and pattern recognition, leveraging the intrinsic dynamics of complexcsystems to perform high-dimensional nonlinear transformations without the need for training highly sophisticated networks. Our recent achievements show that colloidal systems — specifically engineered suspensions of nanoparticles in...
The identification of burst gravitational wave signals can be challenging due to the lack of well-defined waveform models for various source types. In this study, we propose a novel approach to understanding the mass dynamics of the system that produced the burst signal by reconstructing the possible motions of masses that could generate the detected waveform within certain constraints.
Our...
Experimental studies of 𝑏-hadron decays face significant challenges due to a wide range of backgrounds arising from the numerous possible decay channels with similar final states. For a particular signal decay, the process for ascertaining the most relevant background processes necessitates a detailed analysis of final state particles, potential misidentifications, and kinematic overlaps...
We introduce a framework based on Short-time Fourier Transforms (SFTs)
to analyze long-duration gravitational wave signals from compact binaries.
Targeted systems include binary neutron stars observed by third-generation
ground-based detectors and massive black-hole binaries observed by the LISA
space mission, for which we present a pilot application. Leveraging differentiable
and...
The Large Hadron Collider (LHC) at CERN generates vast amounts of data from high-energy particle collisions, requiring advanced machine learning techniques for effective analysis. While Graph Neural Networks (GNNs) have demonstrated strong predictive capabilities in high-energy physics (HEP) applications, their "black box" nature often limits interpretability. To address this challenge, we...
Gravitational wave astronomy in the era of third-generation (3G) detectors will pose significant computational challenges. While standard parameter estimation methods may remain technically feasible, the demand for more efficient inference algorithms is on the rise. We present a sequential neural simulation-based inference algorithm that merges neural ratio estimation (NRE) with nested...
Pulse pile-up is a common issue in nuclear spectroscopy and nuclear reaction studies, degrading energy and timing accuracy in particle identification. This work presents a novel method for reconstructing pile-up events using a one-dimensional convolutional autoencoder (1D-CAE). The method effectively separates and reconstructs overlapping pulses, enabling acceptance of these events and...
Magnetic Resonance Spectroscopy is a powerful, non-invasive tool for in vivo biochemical and metabolic tissue analysis, yet its widespread clinical application remains hindered by susceptibility to motion artifacts. Traditional retrospective corrections struggle with real-time constraints, limiting diagnostic precision in key medical scenarios such as neurodegenerative disease monitoring.
The...
Deep learning is playing an increasingly important role in particle physics, offering powerful tools to tackle complex challenges in data analysis. This talk presents a range of advanced deep-learning techniques applied to neutrino physics, with a particular focus on the T2K experiment. The discussion includes the use of cutting-edge models such as transformers, domain adaptation strategies...
In this conference contribution, we present our findings on applying Artificial Neural Networks (ANNs) to enhance off-vertex topology recognition using data from the HADES experiment at GSI, Darmstadt. Our focus is on decays of $\Lambda$ and K$^0_{\text{S}}$ particles produced in heavy ion as well as elementary reactions. We demonstrate how ANNs can enhance the separation of weak decays from...
In this work we simulate hadrons impinging on a homogeneous lead-tungstate (PbWO4) calorimeter to investigate how the resulting light yield and its temporal structure, as detected by an array of light-sensitive sensors, can be processed by a neuromorphic computing system. Our model encodes temporal photon distributions in the form of spike trains and employs a fully connected spiking neural...
Super-Kamiokande is a 50-kton Water Cherenkov detector, operating since 1996 in the Kamioka mine, Japan, whose broad scientific program spans from neutrino physics to baryon number violating processes, such as proton decay. In this preliminary study I show the development of a Deep Learning model, based on Convolutional Neural Networks (CNN) and Residual Neural Networks (ResNet), for event...
We develop AI agents based on instruction-finetuned large language models (LLMs) to assist in the engineering and operation of the Cherenkov Telescope Array Observatory (CTAO) Control and Data Acquisition Software (ACADA). These agents align with project-specific documentation and codebases, understand contextual information, interact with external APIs, and communicate with users in natural...
Molecular dynamics (MD) simulations are a fundamental tool for investigating the atomistic behavior of complex systems, offering deep insights into reaction mechanisms, phase transitions, and emergent properties in both condensed and soft matter. Recent advances in machine learning (ML) have determined a paradigm shift in atomistic simulations, allowing the development of force-fields that...
There are currently many Cherenkov neutrino telescopes being deployed and designed across the world. These detectors are exploring new optical sensors and geometric configurations to maximize their physics goals. Alongside detector R&D, machine learning (ML) has become established as a promising avenue for reconstructions in these detectors; however, there has not been a consistent comparison...
Arrays of imaging atmospheric Cherenkov telescopes (IACTs) are exceptional instruments for probing the very-high-energy gamma-ray sky. These telescopes focus Cherenkov light, emitted from air showers initiated by very-high-energy gamma rays and cosmic rays, onto the camera plane. A high-speed camera then digitizes the longitudinal development of the air shower, capturing its spatial, temporal,...
One of the main challenges in solving quantum many-body (MB) problems is the exponential growth of the Hilbert space with system size.
In this regard, a new promising alternative are neural-network quantum states (NQS).
This approach leverages the parameterization of the wave function with neural-network architectures.
Compared to other variational methods, NQS are highly scalable with...
Software code generation using Large Language Models (LLMs) is one the most successful application of the modern AI. Foundational models are very efficient when applied to popular frameworks and libraries, which benefit from documentation, code examples, and strong community support. However, many specialized scientific libraries lack these resources and often have unstable programming...
The Forward Wall (FW) detector in the HADES experiment at GSI/FAIR relies on accurate photomultiplier tube (PMT) gain tuning to ensure precise energy measurements and correct energy measurement range. Traditional calibration methods depend on iterative manual adjustments using cosmic muons, making them time-consuming and susceptible to systematic variations caused by PMT aging and...
The SHiP experiment is a proposed fixed-target experiment at the CERN SPS aimed at searching for feebly interacting particles beyond the Standard Model. One of its main challenges is reducing the large number of muons produced in the beam dump, which would otherwise create significant background in the detector. The muon shield, a system of magnets designed to deflect muons away from the...
This work presents an open framework for generating synthetic transactional datasets, addressing the twin challenges of data scarcity and privacy in fraud research. Conducted as an industry secondment at IBM France Lab Saclay within the SMARTHEP Network—a European project fostering collaboration between High Energy Physics and Industry—our approach leverages Generative AI Agents to simulate...
Traditional gradient-based optimization and statistical inference methods often rely on differentiable models, making it challenging to optimize models with non-differentiable components. In this talk, I’ll introduce Learning the Universe by Learning to Optimize (LULO), a novel deep learning-based framework designed to fit non-differentiable simulators at non-linear scales to data. By...
Cherenkov rings play a crucial role in identifying charged particles in high-energy physics (HEP) experiments. The size of the light cone depends directly on the mass and momentum of the particle that produced it. Most Cherenkov ring pattern reconstruction algorithms currently used in HEP experiments rely on a likelihood fit to the photo-detector response, which often consumes a significant...
The storage, transmission and processing of data is a major challenge across many fields of physics and industry. Traditional generic data compression techniques are lossless, but are limited in performance and require additional computation.
BALER [1,2] is an open-source autoencoder-based framework for the development of tailored lossy data compression models suitable for data from...
We present a novel Bayesian anomaly detection framework, applied to supernova analysis, that exploits a custom-built, differentiable, and highly parallelisable JAX implementation of the commonly used SNcosmo framework. In our framework, each supernova’s flux is modelled via the SALT3 formalism, with the core computation—integrating the model flux over observational bandpasses—being fully...
We extend the Particle-flow Neural Assisted Simulations (Parnassus) framework of fast simulation and reconstruction to entire collider events. In particular, we use two generative Artificial Intelligence (genAI) tools, conditional flow matching and diffusion models, to create a set of reconstructed particle-flow objects conditioned on stable truth-level particles from CMS Open Simulations....
The Square Kilometer Array (SKA) will bring about a new era of radio astronomy by allowing 3D imaging of the Universe during Cosmic Dawn and Reionization. Machine learning promises to be a powerful tool to analyze the highly structured and complex signal, however accurate training datasets are expensive to simulate and supervised learning may not generalize. We introduce SKATR, a...
Learning from heterogeneous data is one of the major challenges for AI in the coming years, particularly for what are called 'foundation models'. In fundamental physics, the heterogeneity of data can come from the instruments used to acquire them (the subsystems of large detectors in particle physics or the crossing of modalities in multi-messenger, multi-instrument systems in astrophysics,...
Modern radio telescopes are detecting a large number of radio sources that will be impossible to analyze individually. In particular, the morphological classification of radio galaxies remains a difficult computational challenge.
In this study, we use contrastive learning to classify radio galaxies from the LOFAR Two-meter Sky Survey Data Release 2 (LoTSS-DR2) and propose a new classification...
The Pixel Vertex Detector (PXD) is the innermost detector of the Belle II experiment. Information from the PXD, together with data from other detectors, allows to have a very precise vertex reconstruction. The effect of beam background on reconstruction is studied by adding measured or simulated background hit patterns to hits produced by simulated signal particles. This requires a huge sample...
The sheer volume and complexity of data from high-energy physics experiments makes neural networks particularly attractive for the implementation of trigger systems. On the other hand, a large amount of classified and labelled data is required to train a network and this can be a complex task, especially if the experimental data were in the form of images. In this contribution we discuss the...
Multimessenger astrophysics relies on multiple observational data channels, necessitating efficient methods for analyzing events of astrophysical origin. With the continuous increase in both volume and complexity of data from modern observatories, advanced Machine Learning techniques have become very useful for identifying and classifying signals effectively.
My project aim at developing a...
KM3NeT is a new research infrastructure housing the next generation of neutrino telescopes in the Mediterranean deep sea. This facility comprises two detectors: KM3NeT/ARCA and KM3NeT/ORCA, consisting of vertically-arranged detection units, 230 and 115, respectively, each equipped with 18 digital optical modules. The photomultipliers within each optical module detect Cherenkov light emitted by...
We present a novel method for pile-up removal of pp interactions using variational inference with diffusion models, called Vipr. Instead of using classification methods to identify which particles are from the primary collision, a generative model is trained to predict the constituents of the hard-scatter particle jets with pile-up removed. This results in an estimate of the full posterior...
High-purity germanium (HPGe) detectors play a critical role in nuclear physics experiments, including searches for neutrinoless double-beta decay. Traditional pulse shape discrimination (PSD) methods help distinguish signal from background events in such detectors. However, the performance of these traditional PSD methods declines at lower energies (500 ≲ keV). This low-energy regime is...
As searches at the LHC probe increasingly rare signals against an overwhelming background of Standard Model events, progressively tighter selection criteria are applied to enhance signal-rich regions. Simulated background samples serve as the basis for hypothesis testing, enabling comparisons between observed data and expected Standard Model backgrounds. However, this approach becomes...
The LEGEND experiment aims to push the sensitivity of neutrinoless double beta decay (0νββ) searches by minimizing backgrounds while leveraging the exceptional energy resolution of high-purity germanium (HPGe) detectors. A key challenge is improving background rejection, particularly through pulse shape discrimination (PSD). Machine learning provides powerful tools to enhance this effort.
We...
In experimental particle physics, the development of analyses depends heavily on the accurate simulation of background processes, including both the particle collisions/decays and their subsequent interactions with the detector. However, for any specific analysis, a large fraction of these simulated events is discarded by a selection tailored to identifying interesting events to study. At...
Anomaly detection — identifying deviations from Standard Model predictions — is a key challenge at the Large Hadron Collider due to the size and complexity of its datasets. This is typically addressed by transforming high-dimensional detector data into lower-dimensional, physically meaningful features. We tackle feature extraction for anomaly detection by learning powerful low-dimensional...
Most of the antimatter in cosmic rays is produced by collisions of high energy particles with the interstellar medium while they propagate through it. The detection of an antimatter component over the collisional background can be used to investigate new sources, as the presence of dark matter annihilations in the halo. A possible smoking gun for dark matter is given by the detection of...
Current Imaging Atmospheric Cherenkov Telescopes (IACT) use combined analog and digital electronics for their trigger systems, implementing simple but fast algorithms. Such trigger techniques are needed in order to cope with the extremely high data rates and strict timing requirements. In recent years, in the context of the Advanced camera design for the Large-Sized Telescopes (LSTs) of the...
Current studies of the hadron spectrum are limited by the accuracy and consistency of datasets. Information derived from theory models often requires fits to measurements taken at specific values of kinematic variables, which needs interpolation between such points. In sparse datasets the quantification of uncertainties is problematic. Machine Learning is a powerful tool that can be used to...
The real-time data processing of next-generation experiments at FAIR requires precise event topology reconstruction, which in turn depends on accurate in-situ calibration procedures. Machine learning techniques offer a promising approach to achieving fast and reliable calibrations using continuously available environmental data. In this study, we investigate a neural network-based method for...
In varying action parameters in a lattice gauge theory towards a critical point, such as the continuum limit, generic Markov chain Monte Carlo algorithms incur dramatic sampling penalties. Proof-of-principle studies in applying flow-based generative models to lattice gauge theories have suggested that such methods can mitigate against critical slowing down and topological freezing. There...
Deep generative models have become powerful tools for alleviating the computational burden of traditional Monte Carlo generators in producing high-dimensional synthetic data. However, validating these models remains challenging, especially in scientific domains requiring high precision, such as particle physics. Two-sample hypothesis testing offers a principled framework to address this task....
Monte-Carlo (MC) simulations are essential for designing particle physics experiments, as they enable us to evaluate and optimize key objectives—such as enhancing experimental sensitivity and performance. Since exhaustively sampling the full parameter space of experimental configurations is computationally prohibitive, sample-efficient methods to identify promising configurations are...
The simulation of calorimeter showers is computationally expensive, leading to the development of generative models as an alternative. Many of these models face challenges in balancing generation quality and speed. A key issue damaging the simulation quality is the inaccurate modeling of distribution tails. Normalizing flow (NF) models offer a trade-off between accuracy and speed, making them...
The Fair Universe project organised the HiggsML Uncertainty Challenge, which took place from Sep 2024 to 14th March 2025. This groundbreaking competition in high-energy physics (HEP) and machine learning was the first to place a strong emphasis on uncertainties, focusing on mastering both the uncertainties in the input training data and providing credible confidence intervals in the...
The density matrix of a quantum system provides complete information about its entanglement. Using generative autoregressive networks, we show how to estimate the matrix elements for the small quantum spin chain. Using a density matrix, we calculate Renyi entanglement entropies as well as Shanon entropy at zero temperature.
The traditional design of gravitational wave detectors follows a human-centric, rational approach based on domain expertise and ingenuity. However, the vast space encompassing all possible experimental configurations suggests that some powerful and unconventional detection strategies lay outside the reach of such human-driven design. An AI-based approach that scales with increasing computation...
Over the past 16 years, the Fermi Large Area Telescope (LAT) has significantly advanced our view of the GeV gamma-ray sky, yet several key questions remain - such as the nature of the isotropic diffuse background, the properties of the Galactic pulsar population, and the origin of the GeV excess towards the Galactic Centre. Addressing these challenges requires sophisticated astrophysical...
Recent advances in generative models have demonstrated the potential of normalizing flows for lattice field theory, particularly in mitigating critical slowing down and improving sampling efficiency. In this talk, I will discuss the role of continuous normalizing flows (CNF) or neural ODEs in learning field theories, highlighting their advantages and challenges compared to discrete flow...
The upcoming High Luminosity phase of the Large Hadron Collider will require significant advancements in real-time data processing to handle the increased event rates and maintain high-efficiency trigger decisions. In this work, we extend our previous studies on deploying compressed deep neural networks on FPGAs for high-energy physics applications by exploring the acceleration of graph neural...
We present a case for the use of Reinforcement Learning (RL) for the design of physics instrument as an alternative to gradient-based instrument-optimization methods (arXiv:2412.10237). It's applicability is demonstrated using two empirical studies. One is longitudinal segmentation of calorimeters and the second is both transverse segmentation as well longitudinal placement of trackers in a...
Neural simulation-based inference is a powerful class of machine-learning-based methods for statistical inference that naturally handles high-dimensional parameter estimation without the need to bin data into low-dimensional summary histograms. Such methods are promising for a range of measurements, including at the Large Hadron Collider, where no single observable may be optimal to scan over...
Setup design is a critical aspect of experiment development, particularly in high-energy physics, where decisions influence research trajectories for decades. Within the MODE Collaboration, we aim to generalize Machine Learning methodologies to construct a fully differentiable pipeline for optimizing the geometry of the Muon Collider Electromagnetic Calorimeter.
Our approach leverages...
The HL-LHC project is driving significant upgrades to the ATLAS experiment to enhance data processing and maintain its discovery potential under high-luminosity conditions. A key aspect of this upgrade is the replacement of the readout electronics for the ATLAS Tile Hadronic Calorimeter. The new Tile PreProcessor (TilePPr) system, equipped with Kintex Ultrascale FPGAs, serves as the interface...
The Einstein Telescope (ET) will be a key instrument for detecting gravitational waves (GWs) in the coming decades. However, analyzing the data and estimating source parameters will be challenging, especially given the large number of expected detections—between $10^4$ and $10^5$ per year—which makes current methods based on stochastic sampling impractical. In this work, we use DingoIS to...
Graph Neural Networks (GNNs) have emerged as powerful tools for particle reconstruction in high-energy physics experiments, particularly in calorimeters with irregular geometries, such as those used in the ATLAS experiment. In this work, we present a GNN-based approach to reconstruct particle showers, improve energy resolution, spatial localization, and particle identification. We discuss the...
Fast radio bursts (FRBs) are extremely brief and bright flashes of radio waves originating from distant galaxies. Localizing FRBs to or within a host galaxy is key to exploring their physical origin(s) and using them as cosmological probes. However, poor uv-coverage of interferometric arrays and susceptibility to calibration errors can make FRBs exceptionally hard to localize accurately. I...
Nested Sampling is a Monte Carlo method that performs parameter estimation and model comparison robustly for a variety of high dimension and complicated distributions. It has seen widespread usage in the physical sciences, however in recent years increasingly it is viewed as part of a legacy code base, with GPU native paradigms such as neural simulation based inference coming to the fore. In...
A(i)DAPT is a program which aims to utilize AI techniques, in particular generative modeling, to support Nuclear and High Energy Physics experiments. Its purpose is to extract physics directly from data in the most complete manner possible. Generative models such GANs are employed to capture the full correlations between particles in the final state of nuclear reactions. This many-fold program...
The LHCf experiment aims to study forward neutral particle production at the LHC, providing crucial data for improving hadronic interaction models used in cosmic ray physics. A key challenge in this context is the reconstruction of events containing (K^0) mesons, which often involve multiple calorimetric hits.
To address this, we developed a machine learning pipeline that employs multiple...
One of the most outstanding questions in modern cosmology concerns the physical processes governing the primordial universe and the origin of cosmic structure. The detection and measurement of (local) primordial non-Gaussianity would provide insights into the shape of the potential of the inflaton field, the hypothetical particle driving cosmic inflation. In the coming years, the next...
The integration of advanced artificial intelligence techniques into astroparticle experiments marks a transformative step in data analysis and experimental design. As space missions grow increasingly complex, the adoption of AI technologies becomes critical to optimizing performance and achieving robust scientific outcomes.
This study focuses on the development of innovative AI-driven...
The ATLAS detector at the LHC has comprehensive data quality monitoring procedures for ensuring high quality physics analysis data. This contribution introduces a long short-term memory (LSTM) autoencoder-based algorithm designed to identify detector anomalies in ATLAS liquid argon calorimeter data. The data is represented as a multidimensional time series, corresponding to statistical moments...
The formation of the first galaxies was a pivotal period in cosmic history that ended the cosmic dark ages and paved the way for present-day galaxies such as our Milky Way. This period, characterised by distinct conditions—such as the absence of crucial metals necessary for efficient gas cooling—poses a frontier in cosmology and astrophysics, offering opportunities to discover novel physics....
Projects such as the imminent Vera C. Rubin Observatory are critical tools for understanding cosmological questions like the nature of dark energy. By observing huge numbers of galaxies, they enable us to map the large scale structure of the Universe. However, this is only possible if we are able to accurately model our photometric observations of the galaxies, and thus infer their redshifts...
Upcoming galaxy surveys promise to greatly inform our models of the Universe’s composition and history. Leveraging this wealth of data requires simulations that are accurate and computationally efficient. While N-body simulations set the standard for precision, their computational cost makes them impractical for large-scale data analysis. In this talk, I will present a neural network-based...
Simulations play a crucial role in understanding the complex dynamics of particle collisions at CERN’s Large Hadron Collider (LHC). Traditionally, Monte Carlo-based simulations have been the primary tool for modeling these interactions, but their high computational cost presents significant challenges. Recently, generative machine learning models have emerged as an efficient alternative,...
GINGER data analysis is based on the experience gained with GINGERINO data analysis, the general analysis scheme will be described.
The reconstruction of the beat frequency of a laser gyroscope signal in the shortest possible time is a non-trivial challenge. Advancements in artificial intelligence are used to develop a DAQ system capable of determining the beat signal frequency with higher...
Extreme mass ratio inspirals are a key target for next generation space-based gravitational wave detectors because they have a rich phenomenology that could offer new astrophysics and fundamental physics insights. However, their dynamics are complicated to model, and they will be buried amongst a large population of other sources in the milliHertz frequency band, with a background of...
(Sub-)millimeter single-dish telescopes offer two key advantages compared to interferometers: they can efficiently map larger portions of the sky, and they can recover larger spatial scales. Nonetheless, fluctuations in the atmosphere, the dominant noise source in ground-based observations, limit the accurate retrieval of signals from astronomical sources. We introduce maria...
The computational costs of gravitational wave inference is expected to exponentially rise with the next generation of detectors: both the complexity and the amount of data itself will be much higher, requiring a complete rethinking of current parameter estimation methods to produce accurate science without prohibitive resources usage.
This work will present a novel way of dramatically...
Generative models based on diffusion processes have recently emerged as powerful tools in artificial intelligence, enabling high-quality sampling in a variety of domains. In this work, we propose a novel hybrid quantum-classical diffusion model, where artificial neural networks are replaced with parameterized quantum circuits to directly generate quantum states. To overcome the limitations of...
Simulation-based inference (SBI) has emerged as a powerful tool for parameter estimation, particularly in complex scenarios where traditional Bayesian methods are computationally intractable. In this work, we build upon a previous application of SBI, based on truncated neural posterior estimation (TNPE), to estimate the parameters of a gravitational wave post-merger signal, using real data...
The Laser Interferometer Space Antenna (LISA) will provide an unprecedented window into the gravitational wave sky. However, it also presents a serious data analysis challenge to separate and classify various classes of deterministic sources, instrumental noise, and potential stochastic backgrounds. This "global fit" problem presents an extremely high-dimensional inference task that sits right...
We discuss data compression methods and evidence of learned structure in using Normalising Flows to perform the conditional mapping of nuclear equation of state data given observed parameters from gravitational wave signals of binary neutron star mergers. We use a convolutional autoencoder to compress unified high density equations of state - including data from the neutron star crust - to a...
We show that the Lorentz-Equivariant Geometric Algebra Transformer (L-GATr) yields
state-of-the-art performance for a wide range of machine learning tasks at the Large
Hadron Collider. L-GATr represents data in a geometric algebra over space-time and is
equivariant under Lorentz transformations. The underlying architecture is a versatile
and scalable transformer, which is able to break...
Third-generation (3G) gravitational wave (GW) observatories will unveil a cosmic orchestra, detecting thousands of sources annually. However, their increased detection rate poses a major challenge for data analysis. Existing, widely used techniques to obtain the source parameters are prohibitively expensive, creating a bottleneck for extracting scientific insights from 3G detector data. We...
Searches for new physics at the LHC traditionally use advanced simulations to model Standard Model (SM) processes in high-energy collisions. These are then compared with predictions from new-physics theories like dark matter and supersymmetry. However, despite extensive research, no definitive signs of physics beyond the Standard Model (BSM) have been found since the Higgs boson's...
Many scientific and engineering problems are fundamentally linked to geometry, for example, designing a part to maximise strength or modelling fluid flow around an airplane wing. Thus, there is substantial interest in developing machine learning models that can not only operate on or output geometric data, but generate new geometries. Such models have the potential to revolutionise advanced...
Bayesian inference is essential for understanding the compact binaries that produce gravitational waves detected by the LIGO-Virgo-KAGRA collaboration. Performing this inference is computationally expensive and often has to be repeated multiple times with different models, e.g. different approximations of General Relativity. These repeated analyses always start from scratch, which is highly...
This contribution discusses an anomaly detection search for narrow-width resonances beyond the Standard Model that decay into a pair of jets. Using 139 fb−1 of proton-proton collision data at sqrt(s) = 13 TeV, recorded from 2015 to 2018 with the ATLAS detector at the Large Hadron Collider, we aim to identify new physics without relying on a specific signal model. The analysis employs two...
Recent cosmological surveys have opened a new window onto the nature of dark energy. In our work we reconstruct the dark energy equation of state using a “flexknot” parameterisation that represents $w(a)$ as a linear spline with free–moving nodes. By combining the latest DESI Baryonic Acoustic Oscillation measurements with Pantheon+ supernovae data—and cross–checking our results with an...
The Standard Model of particle physics has been successful in describing fundamental particles and their interactions, yet it fails to explain concepts like dark matter or the hierarchy problem, motivating the search for physics beyond the Standard Model. Despite an extensive search program at the LHC, no hints for new physics have been found so far. Anomaly detection has been introduced as a...
Jet constituents provide a more detailed description of the radiation pattern within a jet compared to observables summarizing global jet properties. In Run 2 analyses at the LHC using the ATLAS detector, transformer-based taggers leveraging low-level variables outperformed traditional approaches based on high-level variables and conventional neural networks in distinguishing quark- and...
Gravitational waves provide a powerful means to perform null tests of strong-gravity physics. Statistical methods based on hierarchical inference, adapted from population studies, have been developed to confidently identify potential signatures of new physics. While these methods are well-suited for detection, they provide limited insight into how exotic physics depends on standard degrees of...
The Pierre Auger Observatory is a cosmic-ray detector that uses multiple systems to simultaneously observe extensive air showers (EAS). EAS are particle cascades initiated by ultra-high-energy cosmic rays (UHECRs) interacting with the atmosphere of the Earth. Determining the sources of UHECRs requires precise knowledge of their mass composition. One key observable for estimating the mass of an...
In this work we consider the problem of determining the identity of hadrons at high energies based on the topology of their energy depositions in dense matter, along with the time of the interactions. Using GEANT4 simulations of a homogeneous lead tungstate calorimeter with high transverse and longitudinal segmentation, we investigated the discrimination of protons, positive pions, and...
The results of the ARDE project will be presented, aiming to develop innovative algorithms based on neural network architectures to discriminate between signals induced by electrons and γ-rays in semiconductor detectors, specifically in Si(Li) and HPGe. The algorithm performances for internal conversion electron spectroscopy measurements in an energy range from ∼300 keV to ∼1-2 MeV will be...
The resolution of any detector is finite, leading to distortions in the measured distributions. Within physics research, the indispensable correction of these distortions is know as Unfolding. Machine learning research uses a different term for this very task: Quantification Learning. For the past two decades, this difference in terminology (and some differences in notation) have prevented...
In recent years, disparities have emerged within the context of the concordance model regarding the estimated value of the Hubble constant H0 [1907.10625] using Cosmic Microwave Background (CMB) and Supernovae data (commonly referred to as the Hubble tension), the clustering σ8 [1610.04606] using CMB and weak lensing, and the curvature ΩK [1908.09139, 1911.02087] using CMB and lensing/BAO, and...
We introduce a SeismoGPT, a foundation model for seismology that leverages transformer-based architectures to model seismic waveforms. Inspired by natural language processing techniques. This approach tokenizes continuous seismograms by dividing them into fixed-length patches, where each patch represents a sequence of waveform samples. These patches serve as input tokens to the transformer...
Machine learning techniques are used to predict theoretical constraints—such as unitarity, boundedness from below, and the potential minimum—in multi-scalar models. This approach has been demonstrated to be effective when applied to various extensions of the Standard Model that incorporate additional scalar multiplets. A high level of predictivity is achieved through appropriate neural network...
We present a machine learning approach using normalizing flows for inferring cosmological parameters
from gravitational wave events. Our methodology is general to any type of compact binary coalescence
event and cosmological model and relies on the generation of training data representing distributions of
gravitational wave event parameters. These parameters are conditional on the...
Measurements of neutral, oscillating mesons are a gateway to quantum mechanics and give access to the fundamental interactions of elementary particles. For example, precise measurements of $CP$ violation in neutral $B$ mesons can be taken in order to test the Standard Model of particle physics. These measurements require knowledge of the $B$-meson flavour at the time of its production, which...
Simulation-based inference (SBI) has seen remarkable development in recent years and has found widespread application across a range of physical sciences. A defining characteristic of SBI is its ability to perform likelihood-free inference, relying on simulators rather than explicit likelihood functions. Several representative methods have emerged within this framework, such as Approximate...
Femtoscopy probes the strong interaction between hadrons via two-particle correlation functions. The ALICE collaboration has recently measured these functions with unprecedented precision, including those involving strange (Λ, Ξ, Ω) and charm (D±) quarks. Extracting the final-state interactions requires solving the Schrödinger equation, with the accurate modeling of the source...
The application of advanced artificial intelligence (AI) techniques in astroparticle experiments represents a groundbreaking advancement in data analysis and experimental design. As space missions become increasingly complex, integrating AI technologies is essential for optimizing their performance and enhancing their scientific outcomes. In this study, we propose a fully custom-designed...
Background: In High Energy Physics (HEP), jet tagging is a fundamental classification task that has been extensively studied using deep learning techniques. Among these, transformer networks have gained significant popularity due to their strong performance and intrinsic attention mechanisms. Furthermore, pre-trained transformer models are available for a wide range of classification...
The 21 cm signal from neutral hydrogen is a key probe of the Epoch of Reionization (EoR), marking the universe’s transition from a cold, neutral state to a predominantly hot, ionized one, driven by the formation of the first stars and galaxies. Extracting this faint 21 cm signal from radio interferometric data requires precise gain calibration. However, traditional calibration methods are...
Extracting continuum properties from discretized quantum field theories is significantly hindered by lattice artifacts. Fixed-point (FP) actions, defined via renormalization group transformations, offer an elegant solution by suppressing these artifacts even on coarse lattices. In this work, we employ gauge-covariant convolutional neural networks to parameterize an FP action for...
In the next decade, the third generation of ground-based gravitational wave detectors, such as the european Einstein Telescope, is expected to revolutionize our understanding of compact binary mergers. With a 10 factor improvement in sensitivity and an extended range towards lower frequencies, Einstein Telescope will enable the detection of longer-duration signals from binary black hole and...
In 2020, the German ministry of education and research launched an action plan to advance digitization in basic research. Alongside the plan a line of funding (ErUM-Data) was established to support interdisciplinary joint consortia that focus on progressing this process with concrete research projects. At the same time funding was allocated for the ErUM-Data HUB, as a networking and transfer...
A major challenge in both simulation and inference within astrophysics is the lack of a reliable prior model for galaxy morphology. Existing galaxy catalogs are heterogeneous and provide an impure representation of underlying galaxy structures due to instrument noise, blending, and other observational limitations. Consequently, priors on galaxy morphology typically rely on either simplistic...
This work explores ideas and provides a potential roadmap for the development and evaluation of physics-specific large-scale AI models, which we call Large Physics Models (LPMs). These models, based on foundation models such as Large Language Models (LLMs) - trained on broad data - are tailored to address the demands of physics research. LPMs can function independently or as part of an...
While cross sections are the fundamental experimental observables in scattering processes, the full quantum dynamics of the interactions are encoded in the complex-valued scattering amplitude. Since cross sections depend only on the squared modulus of the amplitude, reconstructing the complete information from nuclear and particle physics experiments becomes a challenging inverse problem. In...
The cosmic dawn (CD) of the first luminous objects and eventual reionisation (EoR) of the intergalactic medium (IGM) remain among the greatest mysteries in modern cosmology. The 21-cm line is one of the most powerful probes of these crucial moments in the history of the Universe, providing a clean window into both cosmology and astrophysics. Current 21-cm observations are upper limits on the...
In physics, Lagrangians provide a systematic way to describe laws governing physical systems. In the context of particle physics, they encode the interactions and behavior of the fundamental building blocks of our universe. By treating Lagrangians as complex, rule-based constructs similar to linguistic expressions, we trained a transformer model-- proven to be effective in natural language...
Understanding hadron structure requires the extraction of Quantum Correlation Functions (QCFs), such as parton distribution functions and fragmentation functions, from experimental data. The extraction of QCFs involves solving an inversion problem, which is ill-posed due to errors and limitations in the experimental data.
To address this challenge, we propose a novel method for extracting...
Modeling the distribution of neutral hydrogen is essential for understanding the physics of structure formation and the nature of dark matter, but accurate numerical simulations are computationally expensive. We describe a novel Variational Diffusion Model (VDM), built on a 3D CNN attention U-Net architecture, which we use in concert with the CAMELS simulation suite to generate accurate 21 cm...
Advances in Machine Learning, particularly Large Language Models (LLMs), enable more efficient interaction with complex datasets through tokenization and next-token prediction strategies. This talk presents and compares various approaches to structuring particle physics data as token sequences, allowing LLM-inspired models to learn event distributions and detect anomalies via next-token (or...
Understanding the properties of galaxy populations and their evolution is directly linked to the success of large-scale surveys such as The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST). Galaxy spectral energy densities (SEDs) encode these properties, but SED observations for a broad wavelength range via spectroscopy is a time consuming practice. LSST will perform...
The rapid increase in astrophysical data and scientific literature poses a significant challenge for researchers seeking to efficiently process, analyze, and extract meaningful insights. While traditional Large Language Models (LLMs) primarily focus on text-based tasks, there is a pressing need for advanced AI-driven frameworks that seamlessly integrate literature review, data retrieval, and...
Alpha Magnetic Spectrometer (AMS-02) is a precision high-energy cosmic-ray experiment on the ISS operating since 2011 and has collected more than 228 billion particles. Among them, positrons are important to understand the particle nature of dark matter. Separating the positrons from cosmic background protons is challenging above 1 TeV. Therefore, we use state-of-the-art convolutional and...
The adoption of AI-based techniques in theoretical research is often slower than in other fields due to the perception that AI-based methods lack rigorous validation against theoretical counterparts. In this talk, we introduce COEmuNet, a surrogate model designed to emulate carbon monoxide (CO) line radiation transport in stellar atmospheres.
COEmuNet is based on a three-dimensional...
The LEGEND experiment aims to detect neutrinoless double-beta ($0\nu\beta\beta$) decay using high-purity germanium detectors (HPGes) enriched in $^{76}$Ge, immersed in instrumented liquid argon (LAr). Atmospheric LAr contains the cosmogenically activated isotope $^{42}$Ar, whose decay progeny, $^{42}$K, can undergo beta decay ($Q_{\beta} = 3.5$ MeV) on the HPGe surface. Without the baseline...
OmniJet-alpha, released in 2024, is the first cross-task foundation model for particle physics, demonstrating transfer learning between an unsupervised problem (jet generation) and a classic supervised task (jet tagging). This talk will present current developments and expansions of the model. We will for example show how we are able to utilize real, unlabeled CMS data to pretrain the model....
This work explores the application of Reinforcement Learning (RL) to the control of a Fabry-Perot (FP) optical cavity, a key component in interferometric gravitational-wave detectors. By leveraging RL’s inherent ability to handle high-dimensional non-linear systems, the project aims to achieve robust and autonomous cavity locking—a process typically hindered by elevated finesse values, mirror...
Graph neural networks (GNNs) have become state-of-the-art tools across diverse scientific disciplines due to their ability to model complex relationships in datasets that lack simple spatial or sequential structures. In this talk, we present recent advancements in the deep full event interpretation (DFEI) framework [García Pardiñas, J., et al. Comput. Softw. Big Sci. 7 (2023) 1, 12]. The DFEI...
The Matrix-Element Method (MEM) has long been a cornerstone of data analysis in high-energy physics. It leverages theoretical knowledge of parton-level processes and symmetries to evaluate the likelihood of observed events. We combine MEM-inspired symmetry considerations with equivariant neural network design for particle physics analysis. Even though Lorentz invariance and permutation...
Simulating showers of particles in highly-granular detectors is a key frontier in the application of machine learning to particle physics. Achieving high accuracy and speed with generative machine learning models can enable them to augment traditional simulations and alleviate a major computing constraint.
Recent developments have shown how diffusion based generative shower simulation...
Analyzing irregular and sparse time-series is a widespread problem in fundamental physics, astronomy, climate science and many other fields. This talk presents the Rotary Bidirectional Transformer Encoder (RoBiTE), a novel Transformer-based architecture for multi-dimensional irregular time-series and sparse data, designed as a foundation model for general time-series interpolation and object...
Simulation-based inference (SBI) allows amortized Bayesian inference for simulators with implicit likelihoods. However, some explicit likelihoods cannot easily be reformulated as simulators, hindering its integration into combined analyses within the SBI framework. One key example in cosmology is given by the Planck CMB likelihoods. In this talk, I will present a simple method to construct an...
The ATLAS detector at CERN’s Large Hadron Collider (LHC) is a complex system composed of multiple subdetectors, each designed to capture complementary aspects of particle interactions. Thus, accurate understanding of the physical phenomena under study requires effectively combining information from these components.
This work focuses on the key challenge of associating data from the inner...
https://arxiv.org/abs/2501.03921
Simulation-based inference is undergoing a renaissance in statistics and machine learning. With several packages implementing the state-of-the-art in expressive AI [mackelab/sbi] [undark-lab/swyft], it is now being effectively applied to a wide range of problems in the physical sciences, biology, and beyond.
Given the rapid pace of AI/ML, there is little...
The next generation of tracking detectors at upcoming and future high luminosity hadron colliders will be operating under extreme radiation levels with an unprecedented number of track hits per proton-proton collision that can only be processed if precise timing information is made available together with state-of-the-art spatial resolution. 3D Diamond pixel sensors are considered as a...
Identifying products of ultrarelativistic collisions delivered by the LHC and RHIC colliders is one of the crucial objectives of experiments such as ALICE and STAR, which are specifically designed for this task. They allow for a precise Particle Identification (PID) over a broad momentum range.
Traditionally, PID methods rely on hand-crafted selections, which compare the recorded signal of...
Black holes represent some of the most extreme environments in the universe, spanning vast ranges in mass, size, and energy output. Observations from the Event Horizon Telescope (EHT) have provided an unprecedented opportunity to directly image black holes, with future plans aiming to create time-resolved movies of their evolution. To fully leverage these observations, we need theoretical...
It was recently demonstrated [1] that brain networks and the cosmic web share key structural features—such as degree distributions, path lengths, modularity, and information density. Inspired by this work, we apply AI-based methods to study the geometry of neuronal networks formed by isolated brain neurons in culture, with a focus on the spontaneous formation of dendritic lattices and noted...
