We propose a differentiable vertex fitting algorithm that can be used for secondary vertex fitting, and that can be seamlessly integrated into neural networks for jet flavour tagging. Vertex fitting is formulated as an optimization problem where gradients of the optimized solution vertex are defined through implicit differentiation and can be passed to upstream or downstream neural network...
We investigate the possibility to apply quantum machine learning techniques for data analysis, with particular regard to an interesting use-case in high-energy physics. We propose an anomaly detection algorithm based on a parametrized quantum circuit. This algorithm was trained on a classical computer and tested with simulations as well as on real quantum hardware. Tests on NISQ devices were...
Markov chain Monte Carlo (MCMC) simulations is a very powerful approach to tackle a large variety of problems in all computational science. The recent advances in machine learning techniques have provided new ideas in the domain of Monte Carlo simulations. The ability of artificial neural networks to model a very wide class of probability distributions through the Variational Autoregressive...
The lack of new physics discoveries at the LHC calls for an effort to to go beyond model-driven analyses. In this talk I will present the New Physics Learning Machine, a methodology powered by machine learning to perform a signal-agnostic and multivariate likelihood ratio test (arXiv:2305.14137). I will focus on an implementation based on kernel methods, which is efficient and scalable while...
Estimating unknown parameters of open quantum systems is an important task that is common to many branches of quantum technologies, from metrology to computing. When open quantum systems are monitored and a signal is continuously acquired, this signal can be used to efficiently extract information about the interactions in the system. Previous works have demonstrated a Bayesian framework for...
Detected Gravitational Waves are goldmines of information on the compact binary emitting systems. Usually MCMC techniques infer parameter's values in a 15-dimensional parameter space in an accurate way, but they are very lengthy. On the other hand, Physics-Informed Neural Networks (PINNs) are a rapidly emerging branch of Supervised Machine Learning, devoted precisely to solve physical...
Generative models, particularly normalizing flows, have recently been proposed to speed up lattice field theory sample generation. We have explored the role symmetry considerations and ML concepts like transfer learning may have, by applying novel continuous normalizing flows to a scalar field theory. Beyond that, interesting connections exist between renormalization group theory and...
Particle physics detectors introduce distortions in the observed data due to their finite resolution and other experimental factors, the task of correcting for these effects is known as unfolding. While traditional unfolding methods are restricted to binned distributions of a single observable, recently proposed ML-based methods enable unbinned, high-dimensional unfolding over the entire phase...
The early inspiral from stellar-mass black hole binaries can emit milli-Hertz gravitational wave signals, making them detectable sources for space-borne gravitational wave missions like TianQin. However, the traditional matched filtering technique poses a significant challenge for analyzing these kinds of signals, as it requires an impractically high number of templates ranging from 10^31 to...
Statistical anomaly detection empowered by AI is a subject of growing interest in high-energy physics and astrophysics. AI provides a multidimensional and highly automatized solution to enable signal-agnostic data validation, and new physics searches.
The unsupervised nature of the anomaly detection task combined with the highly complex nature of the LHC and astrophysical data give rise to a...
Tracking charged particles in high-energy physics experiments is one of the most computationally demanding steps in the data analysis pipeline.
As we approach the High Luminosity LHC era, with an estimate increase in the number of proton-proton interactions per beam collision by a factor 3-5 (from 50 to 140-200 primary interactions per collision on average), particle tracking will become even...
Nested sampling has become an important tool for inference in astronomical data analysis. However, it is often computationally expensive to run. This poses a challenge for certain applications, such as gravitational-wave inference. To address this, we previously introduced nessai, a nested sampling algorithm that incorporates normalizing flows to accelerate gravitational-wave inference by up...
Due to poor observational constraints on the low-mass end of the subhalo mass function, the detection of dark matter (DM) subhalos lacking a visible counterpart on sub-galactic scales would provide valuable information about the nature of DM. Novel indirect probes for DM substructure within the Milky Way (MW) are stellar wakes, which are perturbations of the stellar medium induced by DM...
As a new era of gravitational wave detections rapidly unfolds, the importance of having accurate models for their signals becomes increasingly important.
The best model for gravitational waves are the fully-fledged simulations of General Relativity, although their daunting cost make it prohibitive to perform data analysis. To alleviate this, the community has developed a variety of...
This presentation will highlight the impactful role of machine learning (ML) in high energy nuclear physics, particularly in studying QCD matter under extreme conditions. The presentation will focus on three key applications: analyzing heavy ion collisions, reconstructing neutron star Equation of State (EoS), and advancing lattice field theory studies.
In heavy ion collisions, ML techniques...
Tensor Networks (TNs) is a computational paradigm used for representing quantum many-body systems. Recent works show how TNs can be applied to perform Machine Learning (ML) tasks, yielding comparable results to standard supervised learning techniques. In particular [1] leveraged Tree Tensor Networks (TTNs) to achieve the classification of particle flavor state in the context of High Energy...
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. To do this, however, we need reliable ways of estimating galaxy redshifts from only photometry. I will present an overview of our pop-cosmos...
The formation mechanism of supermassive black holes is yet unknown, despite their presence in nearly every galaxy, including the Milky Way. As stellar evolution predicts that stars cannot collapse to black holes $\gtrsim 50 - 130\, \text{M}_{\odot}$ due to pair-instability, plausible formation mechanisms include the hierarchical mergers of intermediate-mass black holes (IMBHs). The direct...
Quantifying tension between different experimental efforts aiming to constrain the same physical models is essential for validating our understanding of the Universe. A commonly used metric of tension is the ratio, R, of the joint Bayesian evidence to the product of individual evidences for two experimental datasets under some common model. R can be interpreted as a measure of our relative...
The Large-Sized Telescope (LST) is one of three telescope types being built as part of the Cherenkov Telescope Array Observatory (CTAO) to cover the lower energy range between 20 GeV and 200 GeV. The Large-Sized Telescope prototype (LST-1), installed at the La Palma Observatory Roque de Los Muchachos, is currently being commissioned and has successfully taken data since November 2019. The...
Recently, machine learning has become a popular tool in lattice field theory. Here I will report on some applications of (lattice) field theory methods to further understand ML, illustrated using the Restricted Boltzmann Machine and stochastic quantisation as simple examples.
We introduce an innovative approach to combinatorial optimization problems through Physics-Informed Graph Neural Networks (GNNs). We combine the structural advantages of GNNs with physics-based algorithms, enhancing solution accuracy and computational efficiency. With respect to available literature we were able to design and train a deep graph neural network model able to solve the graph...
In this talk we propose a Physics based AI framework for precise radiometer calibration in global 21cm cosmology. These experiments aim to study formation of the first stars and galaxies by detecting the faint 21-cm radio emission from neutral hydrogen. The global or sky-averaged signal is predicted to be five orders of magnitude dimmer than the foregrounds. Therefore detection of the signal...
Resource utilization plays a crucial role for successful implementation of fast real-time inference for deep neural networks on latest generation of hardware accelerators (FPGAs, SoCs, ACAPs, GPUs). To fulfil the needs of the triggers that are in development for the upgraded LHC detectors, we have developed a multi-stage compression approach based on conventional compression strategies...
We propose a quantum version of a generative diffusion model. In this algorithm, artificial neural networks are replaced with parameterized quantum circuits, in order to directly generate quantum states. We present both a full quantum and a latent quantum version of the algorithm; we also present a conditioned version of these models. The models' performances have been evaluated using...
The ongoing search for physics beyond the Standard Model imposes a growing demand for highly sensitive anomaly detection methods. Various approaches to anomaly detection exist, and prominent techniques include semi-supervised and unsupervised training of neural networks. While semi-supervised approaches often require sophisticated methods for precise background estimation, unsupervised methods...
Traditionally, machine-learning methods have mostly focused on making predictions without providing explicit probability distributions. However, the importance of predicting probability distributions lies in its understanding of the model’s level of confidence and the range of potential outcomes. Unlike point estimates, which offer a single value, probability distributions offer a range of...
In the realm of high-energy physics, the advent of machine learning has revolutionized data analysis, especially in managing the vast volumes of data produced by particle detectors.
Facing the challenge of analyzing unlabelled, high-volume detector data, advanced machine learning solutions become indispensable.
Our research introduces a machine learning approach that effectively bridges the...
The High Luminosity upgrade for the Large Hadron Collider (HL-LHC) is due to come online in 2029. This will result in an unprecedented throughput of collision event data. Identifying and analysing meaningful signals within this information poses a formidable challenge in the search for new physics. The demand for automatic tools capable of physically-aware and data-driven inference, which can...
Off-shell effects in large LHC backgrounds are crucial for precision predictions and, at the same time, challenging to simulate. We show how a generative diffusion network learns off-shell kinematics given the much simpler on-shell process. It generates off-shell configurations fast and precisely, while reproducing even challenging on-shell features.
PolySwyft is an implementation of a sequential simulation-based nested sampler by merging two algorithms that are commonly used for Bayesian inference: PolyChord and swyft. PolySwyft uses the NRE functionality of swyft and generates a new joint training dataset with PolyChord to iteratively estimate more accurate posterior distributions. PolySwyft can be terminated using pre-defined rounds...
The Data-Directed paradigm (DDP) represents an innovative approach to efficiently investigate new physics across diverse spectra, which are in the presence of smoothly falling Standard Model (SM) backgrounds. Diverging from the conventional analysis employed in collider particle physics, DDP eliminates the necessity for a simulated or functionally derived background estimate. Instead, it...
New radio telescopes, such as the SKA, will revolutionise our understanding of the Universe. They can detect the faintest distant galaxies and provide high-resolution observations of nearby galaxies. This allows for detailed statistical studies and insights into the formation and evolution of galaxies across cosmic time. These telescopes also play a crucial role in unravelling the physical...
The Galactic centre serves as a laboratory for fundamental physics, particularly in the context of indirect dark matter searches. This study explores the potential of the James Webb Space Telescope to shed light on self-annihilating, sub-GeV dark matter candidates by examining their influence on exoplanet overheating and providing sensitivity estimates via probabilistic programming languages.
Abstract: Large-scale physics experiments generating high data rates impose significant demands on the data acquisition system (DAQ). The Deep Underground Neutrino Experiment (DUNE) is a next-generation experiment for neutrino science at the Fermi National Accelerator Laboratory in Batavia, Illinois. It will consist of a massive detector operating continually for over a decade, resulting in...
The GeV gamma-ray sky, as observed by the Fermi Large Area Telescope (Fermi LAT), harbours a plethora of localised point-like sources. At high latitudes ($|b| >30^{\circ}$), most of these sources are of extragalactic origin. The source-count distribution as a function of their flux, $\mathrm{d}N/\mathrm{d}S$, is a well-established quantity to summarise this population. We employ sequential...
The Pierre Auger Observatory, located in the Argentinian Pampa, is the world's largest cosmic-ray experiment. It offers the most precise measurements of cosmic particles at ultra-high energies by measuring their induced air showers. The centerpiece of the Observatory is the surface detector (SD) consisting of over 1,660 water-Cherenkov detectors that cover an area of 3,000 km$^2$ and measure...
We have been studying the use of deep neural networks (DNNs) to identify and locate primary vertices (PVs) in proton-proton collisions at the LHC. Previously reported results demonstrate that a hybrid architecture, using a fully connected network (FCN) as the first stage and a convolutional neural network (CNN) as the second stage provides better efficiency than the default heuristic...
Mutual information is one of the basic information-theoretic measures of correlations between different subsystems. It may carry interesting physical information about the phase of the system. It is notoriously difficult to estimate as it involves sums over all possible system and subsystem states. In this talk, I describe a direct approach to estimate the bipartite mutual information using...
Type Ia supernovae (SNae Ia) are instrumental in constraining cosmological parameters, particularly dark energy. State-of-the-art likelihood-based analyses scale poorly to future large datasets, are limited to simplified probabilistic descriptions of e.g. peculiar velocities, photometric redshift uncertainties, instrumental noise, and selection effects, and must explicitly sample a...
The efficient simulation of particle propagation and interaction within the detectors of the Large Hadron Collider (LHC) is of primary importance for precision measurements and new physics searches. The most computationally expensive step of the simulation pipeline is the generation of calorimeter showers, and will become ever more costly and high-dimensional as the LHC moves into its high...
Continuing from our prior work \citep{10.1093/mnras/stac3797}, where a single detector data of the Einstein Telescope (ET) was evaluated for the detection of binary black hole (BBHs) using deep learning (DL). In this work we explored the detection efficiency of BBHs using data combined from all the three proposed detectors of ET, with five different lower frequency cutoff ($F_{low}$): 5 Hz, 10...
Successfully and accurately inferring the properties of compact binary mergers observed by facilities including Virgo and LIGO requires accurate and fast waveform models. Direct calculation from general relativity is not currently feasible, and approximations that are used to produce tractable models necessarily induce errors.
Using Gaussian process regression (GPR), we have developed a...
Simulation is the crucial connection between particle physics theory and experiment. Our ability to simulate particle collision based on first principles allows us to analyze and understand the vast amount of data of the Large Hadron Collider (LHC) experiments. This, however, comes at a cost: A lot of computational resources are needed to simulate all necessary interactions to the required...
The next-generation ground-based gamma-ray observatory, the Cherenkov Telescope Array Observatory (CTAO), will consist of two arrays of tens of imaging atmospheric Cherenkov telescopes (IACTs) to be built in the Northern and Southern Hemispheres, aiming to improve the sensitivity of current-generation instruments by a factor of five to ten. Three different sizes of IACTs are proposed to cover...
Machine learning (ML) plays a significant role in data mining at the High Energy Physics experiments. An overview of ML applications at the ATLAS experiments will be shown, with highlights in Physics Beyond the Standard model searches using anomaly detection and active learning. Additionally, advances in the object reconstruction and improvements in simulation using ML will be shown.
In some sense, the detection of a stochastic gravitational wave background (SGWB) is one of the most subtle GW analysis challenges facing the community in the next-generation detector era. For example, at an experiment such as LISA, to extract the SGWB contributions, we must simultaneously: detect and analyse thousands of highly overlapping sources including massive binary black holes mergers...
COSMOPOWER is a state-of-the-art Machine Learning framework adopted by all major Large-Scale Structure (LSS) and Cosmic Microwave Background (CMB) international collaborations for acceleration of their cosmological inference pipelines. It achieves orders-of-magnitude acceleration by replacing the expensive computation of cosmological power spectra, traditionally performed with a Boltzmann...
In particle collider experiments, such as the ATLAS and CMS experiments at CERN, high-energy particles collide and shatter into a plethora of charged particles traversing a silicon detector and leaving energy deposits, or hits, on the detector modules. The reconstruction of charged-particle trajectories (tracks) from these hits, an integral part in any physics program at the Large Hadron...
Theory predictions for the LHC require precise numerical phase-space integration and generation of unweighted events. We combine machine-learned multi-channel weights with a normalizing flow for importance sampling to improve classical methods for numerical integration. By integrating buffered training for potentially expensive integrands, VEGAS initialization, symmetry-aware channels, and...
In-beam gamma-ray spectroscopy, particularly with high-velocity recoil nuclei, necessitates precise Doppler correction. The Advanced GAmma Tracking Array (AGATA) represents a groundbreaking development in gamma-ray spectrometers, boasting the ability to track gamma-rays within the detector. This capability leads to exceptional position resolution which ensures optimal Doppler...
Research on Universe and Matter (ErUM) at major infrastructures such as CERN or large observatories, jointly conducted with university groups, is an important driver for the digital transformation. In Germany, about 20.000 scientists are working on ErUM-related sciences and can benefit from actual methods of artificial intelligence. The central networking and transfer office ErUM-Data-Hub...
Traditional physics simulations are fundamental in the field of particle physics. Common simulation tools like Geant4, are very precise, but comparatively slow. Generative machine learning can be used to speed up such simulations.
Calorimeter data can be represented either as images or as point clouds, i.e. permutation-invariant lists of measurements.
We advance the generative models for...
This contribution presents the ERC-funded project NuRadioOpt, which aims to substantially increase the detection rate of ultra-high-energy (UHE) cosmic neutrinos for large in-ice radio arrays such as the Radio Neutrino Observatory Greenland (RNO-G, under construction) and the envisioned IceCube-Gen2 project. These detectors consist of autonomous compact detector stations with very limited...
With new astronomical surveys, we are entering a data-driven era in cosmology. Modern machine learning methods are up for the task to optimally learn the Universe from low to high redshift. In 3D, tomography of the large-scale structure (LSS) via the 21cm line of hydrogen targeted by the SKA (Square Kilometre Array) can both teach about properties of sources and gaseous media between, while...
The simulation of calorimeter showers is computationally intensive, leading to the development of generative models as substitutes. We propose a framework for designing generative models for calorimeter showers that combines the strengths of voxel and point cloud approaches to improve both accuracy and computational efficiency. Our approach employs a pyramid-shaped design, where the base of...
The diffusion model has demonstrated promising results in image generation, recently becoming mainstream and representing a notable advancement for many generative modeling tasks. Prior applications of the diffusion model for both fast event and detector simulation in high energy physics have shown exceptional performance, providing a viable solution to generate sufficient statistics within a...
PolyChord was originally advertised encouraging users to experiment with their own clustering algorithms. Identifying clusters of nested sampling live points is critical for PolyChord to perform nested sampling correctly. We have updated the Python interface of PolyChordLite to allow straightforward substitution of different clustering methods.
Recent reconstructions of the...
"Data deluge" refers to the situation where the sheer volume of new data generated overwhelms the capacity of institutions to manage it and researchers to use it[1]. Data Deluge is becoming a common problem in industry and big science facilities like the synchrotron laboratory MAX IV and the Large Hadron Collider at CERN[2].
As a novel solution to this problem, a small cross-disciplinary...
Nested sampling is a tool for posterior estimation and model comparison across a wide variety of cross-disciplinary fields, and is used in Simulation Based Inference and AI emulation. This talk explores the performance and accuracy gains to be made in high dimensional nested sampling by rescuing the discarded likelihood evaluations available in present nested sampling runs, and is thus useful...
Sampling techniques are a stalwart of reliable inference in the physical sciences, with the nested sampling paradigm emerging in the last decade as a ubiquitous tool for model fitting and comparison. Parallel developments in the field of generative machine learning have enabled advances in many applications of sampling methods in scientific inference pipelines.
This work explores the...
This study explores the inference of BSM models and their parameters from kinematic distributions of collider signals through an n-channel 1D-Convolutional Neural Network (n1D-CNN). Our approach enables simultaneous inference from distributions of any fixed number of observables. As our training data are computationally expensive simulations, we also introduce a novel data augmentation...
Quantum entanglement, a fundamental concept for understanding physics at atomic and subatomic scales, is explored in this presentation. We introduce a novel technique for computing quantum entanglement (Rényi) entropy, grounded on the replica trick and leveraging the abilities of generative neural networks for accurate partition function calculations. The approach is demonstrated on the...
Phenomenological analyses in beyond the Standard Model (BSM) theories assess the viability of BSM models by testing them against current experimental data, aiming to explain new physics signals. However, these analyses face significant challenges. The parameter space in BSM models are commonly large and high dimensional. The regions capable of accommodating a combination of experimental...
Sensitivity forecasts inform the design of experiments and the direction of theoretical efforts. To arrive at representative results, Bayesian forecasts should marginalize their conclusions over uncertain parameters and noise realizations rather than picking fiducial values. However, this is typically computationally infeasible with current methods for forecasts of an experiment’s ability to...
Analyses in HEP experiments often rely on large MC simulated datasets. These datasets are usually produced with full-simulation approaches based on Geant4, or exploiting parametric “fast” simulations introducing approximations and reducing the computational cost.
In the present work, we discuss a prototype of a fast simulation framework that we call “FlashSim” targeting analysis level data...
The matrix element method is the LHC inference method of choice for limited statistics. We present a dedicated machine learning framework, based on efficient phase-space integration, a learned acceptance and transfer function. It is based on a choice of INN and diffusion networks, and a transformer to solve jet combinatorics. We showcase this setup for the CP-phase of the top Yukawa coupling...
The Dark Matter Particle Explorer (DAMPE) is the largest calorimeter-based space-borne experiment. Since its launch in December 2015, DAMPE detects electrons, positrons and gamma rays from few GeV to 10 TeV, as well as protons and heavier nuclei from 10 GeV to 100 TeV. The study of galactic and extragalactic gamma-ray sources and diffuse emissions as well as the search for dark-matter...
Presented is a novel method for analyzing particle identification (PID) by incorporating machine learning techniques, applied to a physics case within the fixed-target program at the LHCb experiment at CERN. Typically, a PID classifier is constructed by integrating responses from specialized subdetectors, utilizing diverse techniques to ensure redundancy and broad kinematic coverage. The...
Uncertainty quantification (UQ) is crucial for reliable predictions in inverse problems, where the model parameters are inferred from limited and noisy data. Monte Carlo methods offer a powerful approach to quantifying uncertainty in inverse problems, but their effectiveness hinges on the accuracy of the input data. This talk explores the robustness of an inverse problem methodology that...
The Cherenkov Telescope Array (CTA) is entering its production phase and the upcoming data will drastically improve the point source sensitivity compared to previous imaging atmospheric Cherenkov telescopes. The Galactic Plane Survey (GPS), proposed as one of the Key Science Projects for CTA observation will focus on the observation of the inner galactic region ($|b|<6^∘$).
Here we discuss...
The problem of comparing two high-dimensional samples to test the null hypothesis that they are drawn from the same distribution is a fundamental question in statistical hypothesis testing. This study presents a comprehensive comparison of various non-parametric two-sample tests, specifically focusing on their statistical power in high-dimensional settings. The tests are built from univariate...
Generative networks are promising tools for fast event generation for the LHC, yet struggle to meet the required precision when scaling up to particles in the final state. We employ the flexibility of autoregressive transformers to tackle this challenge, focusing on Z and top quark pair production with additional jets. We demonstrate the use of classifiers in combination with the...
Within the Compact Muon Solenoid (CMS) Collaboration, various Deep Neural Networks (DNNs) and Machine Learning (MLs) approaches have been employed to investigate the production of a new massive particle that undergoes decay into Higgs Boson pairs (HH) which further decay into a pair of b-quarks and a pair of tau leptons and discriminate the HH signal from the backgrounds.
However, these...
Particle physics experiments entail the collection of large data samples of complex information. In order to produce and detect low probability processes of interest (signal), a huge number of particle collisions must be carried out. This type of experiments produces huge sets of observations where most of them are of no interest (background). For this reason, a mechanism able to differentiate...
Particle track reconstruction is a fundamental aspect of experimental analysis in high-energy particle physics. Conventional methodologies for track reconstruction are suboptimal in terms of efficiency in anticipation of the High Luminosity phase of the Large Hadron Collider. This has motivated researchers to explore the latest developments in deep learning for their scalability and potential...
The intracluster medium (ICM) holds signatures of the dynamical history of
the galaxy cluster, including the dark matter density profile, mergers with
other clusters, and energetic activity (from supernovae and supermassive
black holes) in its member galaxies. For all but the most relaxed galaxy
clusters observed at high spatial resolution by instruments such as the
Chandra and...
Supervised learning has been used successfully for jet classification and to predict a range of jet properties, such as mass and energy. Each model learns to encode jet features, resulting in a representation that is tailored to its specific task. But could the common elements underlying such tasks be combined in a single foundation model to extract features generically? To address this...
Blazars are among the most powerful extragalactic sources, emitting across the entire electromagnetic spectrum, from radio to very high energy gamma-ray bands. As powerful sources of non-thermal radiation, blazars are frequently monitored using various telescopes, leading to the accumulation of substantial multi-wavelength data over different time periods. Also, over the years, the complexity...
Adversarial deep learning techniques are based on changing input distributions (adversaries), with the goal of causing false classifications when input to a deep neural network classifier. Adversaries aim to maximize the output error while only exerting minimal perturbations to the input data. Moreover, various techniques to defend against such attacks have been developed in the past. While...
The Ring Imaging Cherenkov (RICH) detector is integral to the CBM experiment's electron identification process, aiming to distinguish electrons and suppress pions in the study of dielectronic decay channels of vector mesons. This study is crucial for exploring the phase diagram of strongly interacting matter under conditions of high net baryon densities and moderate temperatures, as...
The Advanced Virgo interferometer is a complex machine constantly monitored by a vast array of sensors, producing the auxiliary channels datastream. Many analytical tools aid in the task of navigating the information cointained in the $\sim 10^5$ channels, but the limitations of the linear algorithms can hinder their capabilities of correctly assessing the health of the instrument. In this...
A major task in particle physics is the measurement of rare signal processes. These measurements are highly dependent on the classification accuracy of these events in relation to the huge background of other Standard Model processes. Reducing the background by a few tens of percent with the same signal efficiency can already increase the sensitivity considerably.
This study demonstrates...
The quantum-chromodynamic substructure of hadrons at the smallest scales relies critically on the accurate interpretation of abundant experimental data generated by large-scale infrastructures such as the Large Hadron Collider. Comparing a multitude of measured cross sections with the latest higher-order theory predictions, we probe the validity of the standard model of particles with...
This research introduces a physics-driven graph neural network (GNN) [1] tailored for the identification and reconstruction of $\Lambda$ hyperons in the WASA-FRS [2] experiment. The reconstructed $\Lambda$ hyperons serve as calibration processes, essential for the primary objective of the experiment, namely to detect hypertritons. This GNN is based upon successfully developed machine learning...
The upcoming silicon-based sampling calorimeters, such as the high-granularity calorimeter of the CMS experiment, will have unprecedented granularity in both the lateral and longitudinal dimensions. We expect these calorimeters to greatly benefit from machine learning-based reconstruction techniques. With the novel idea of interpreting the multiple sampling layers of calorimeters in the $\eta$...
Recent experiments with high-energy heavy ion beams challenge the current understanding of light hypernuclei (sub-atomic nuclei exhibiting strangeness), particularly the hypertriton [1,2,3,4,5,6,7,8]. This perplexing situation, known as the "hypertriton puzzle," is the focal point of our European-Japanese collaboration between CSIC – Spain, GSI-FAIR – Germany and RIKEN – Japan within the...
In 2015, the first gravitational wave from a binary black hole merger was detected and since then, Ligo-Virgo-Kagra have observed many binary black hole mergers. However, identifying these cosmic events is computationally expensive. Therefore, fast data analysis will be essential in order to make future gravitational-wave observations a success. Template banks are used to identify potential...
The Alpha Magnetic Spectrometer-02 (AMS-02) experiment is a magnetic spectrometer on the International Space Station (ISS) that can measure the flux of particles from cosmic sources in a rigidity window ranging from GVs to a few TVs and up to at least Nickel (charge Z=28). High-precision measurements of fluxes of rare nuclei, such as Sc, Ti, and Mn, provide unique constraints to models of...
One of the most important challenges in High Energy Physics today is to find rare new physics signals among an abundance of Standard Model proton-proton collisions, also known as anomaly detection. Deep Learning (DL) based techniques for this anomaly detection problem are increasing in popularity [1]. One such DL technique is the Deep SVDD model [2], which shows great results when applied to...
We report progress in using transformer models to generate particle theory Lagrangians. By treating Lagrangians as complex, rule-based constructs similar to linguistic expressions, we employ transformer architectures —proven in language processing tasks— to model and predict Lagrangians. A dedicated dataset, which includes the Standard Model and a variety of its extensions featuring various...
In the LHCb experiment, during Run2, more than 90% of the computing resources available to the Collaboration were used for detector simulation. The detector and trigger upgrades introduced for Run3 allow to collect larger datasets that, in turn, will require larger simulated samples. Despite the use of a variety of fast simulation options, the demands for simulations will far exceed the...
The Compressed Baryonic Matter (CBM) experiment, located at the Facility for Antiproton and Ion Research (FAIR) accelerator complex in Darmstadt, Germany, aims to study the phase diagram of strongly interacting matter in the realm of high net baryon densities and moderate temperatures. The SIS-100 accelerator ring at FAIR produces accelerated beams up to the energies of about 30 GeV for...
With metallic-magnetic calorimeters (MMCs) - like the maXs-detector series developed within this collaboration - promising new tools for high precision x-ray spectroscopy application have become available. Because of their unique working principles, MMCs combine several advantages over conventional energy- and wavelength-dispersive photon detectors. They can reach spectral resolving powers of...
Machine Learning (ML) techniques have been employed for the high energy physics (HEP) community since the early 80s to deal with a broad spectrum of problems. This work explores the prospects of using Deep Learning techniques to estimate elliptic flow (v2) in heavy-ion collisions at the RHIC and LHC energies. A novel method is developed to process the input
observables from track-level...
The next generation of observatories such as the Vera C. Rubin Observatory and Euclid are posing a massive data challenge. An obstacle we need to overcome is the inference of accurate redshifts from photometric observations that can be limited to a handful of bands. We addressed this challenge with a forward modeling framework, pop-COSMOS, calibrated by fitting a population model to...
Strong gravitational lensing has become one of the most important tools for investigating the nature of dark matter (DM). With a technique called gravitational imaging, the number and mass of dark matter subhaloes can be measured in strong lenses, constraining the underlying DM model.
Gravitational imaging however is an expensive method and requires adaptation in astronomy's current "big...
Foundation models are increasingly prominent in various physics subfields. Moreover, the application of supervised machine learning methods in astronomy suffers from scarce training data. We explore computer vision foundation models, focusing on their application to radio astronomical image data.
Specifically, we explore the unsupervised, morphological classification of radio sources through...
Machine learning can be a powerful tool to discover new signal types in astronomical data. In our recent study, we have applied it for the first time to search for long-duration transient gravitational waves triggered by pulsar glitches, which could yield physical insight into the mostly unknown depths of the pulsar. Other methods previously applied to search for such signals rely on matched...
In this work we demonstrate that significant gains in performance and data efficiency can be achieved moving beyond the standard paradigm of sequential optimization in High Energy Physics (HEP). We conceptually connect HEP reconstruction and analysis to modern machine learning workflows such as pretraining, finetuning, domain adaptation and high-dimensional embedding spaces and quantify the...
Catalogs of sources have many sources with unknown physical nature. In particular, Fermi-LAT catalogs of gamma-ray sources have about one third of sources with unknown multi-wavelength counterparts. Some of the gamma-ray sources may be visible only in gamma rays, such as distant pulsars with radio jets not pointing at the observer. Machine learning algorithms provide a tool to perform a...
Methods for training jet taggers directly on real data are well motivated due to both the ambiguity of parton labels and the potential for mismodelled jet substructure in Monte Carlo. This talk presents a study of weakly-supervised learning applied to Z+jet and dijet events in CMS Open Data. In order to measure the discrimination power in real data, we consider three different estimates of the...
Timepix4 is a hybrid pixel detector readout ASIC developed by the Medipix4 Collaboration at CERN. It consists of a matrix of about 230\,k pixels, each equipped with amplifier, discriminator and time-to-digital converter with 195 ps bin size that allows to measure both time-of-arrival and time-over-threshold of the hits. Due to its characteristics, it can be exploited in a wide range of fields,...
Modern simulation-based inference techniques leverage neural networks to solve inverse problems efficiently. One notable strategy is neural posterior estimation (NPE), wherein a neural network parameterizes a distribution to approximate the posterior. This approach is particularly advantageous for tackling low-latency or high-volume inverse problems. However, the accuracy of NPE varies...
In any lattice QCD based study, gauge configurations have to be generated using some form of Monte Carlo simulations. These are then used to compute physical observables. In these measurements, physical observables (like the chiral condensate or baryon number density) can be expressed as a trace of a combination of products of the inverse fermion matrix. These traces are usually estimated...
Semivisible jets are a novel signature arising in Hidden Valley (HV) extensions of the SM with a confining interaction [1]. Originating from a double shower and hadronization process and containing undetectable dark bound states, semivisible jets are expected to have a substantially different radiation pattern compared to SM jets.
Unsupervised...
In the field of nuclear physics, multi-neutron detection plays a critical role in revealing specific nuclear properties(e.g. the structure of light exotic nuclei or four-neutron resonance states). However, one neutron can interact several times in different bars of neutron detector array, since it will likely pass through the detectors without losing all its energy. The phenomenon commonly...
The Bert pretraining paradigm has proven to be highly effective in many domains including natural language processing, image processing and biology. To apply the Bert paradigm the data needs to be described as a set of tokens, and each token needs to be labelled. To date the Bert paradigm has not been explored in the context of HEP. The samples that form the data used in HEP can be described...
We present a Machine Learning approach to perform fully Bayesian
inference of the neutron star equation of state given results from
parameter estimation from gravitational wave signals of binary neutron
star (BNS) mergers. The detection of gravitational waves from BNS merger
GW170817 during the second observing run of the ground based
gravitational wave detector network provided a new...
I will present an explainable deep learning framework for extracting new knowledge about the underlying physics of cosmological structure formation. I will focus on an application to dark matter halos, which form the building blocks of the cosmic large-scale structure and wherein galaxy formation takes place. The goal is to use an interpretable neural network to generate a compressed, “latent”...
The application of modern Machine Learning (ML) techniques for anomaly detection in collider physics is a very active and prolific field, with use cases that include the exploration of physics beyond the Standard Model and the detection of faults in the experimental setup. Our primary focus is on data-quality monitoring. Within large experimental collaborations, this anomaly detection task...
Strong gravitational lenses are a singular probe of the Universe's small-scale structure --- they are sensitive to the gravitational effects of low-mass ($<10^{10} M_\odot$) halos even without a luminous counterpart. Recent strong-lensing analyses of dark matter structure rely on simulation-based inference (SBI). Modern SBI methods, which leverage neural networks as density estimators, have...
We present a newly developed code, JERALD - JAX Enhanced Resolution Approximate Lagrangian Dynamics -, that builds on the Lagrangian Deep Learning method (LDL) of Dai and Seljak (2021), improving on the time and the memory requirements of the original code. JERALD takes as input DM particle positions from a low-resolution, computationally inexpensive run of the approximate N-body simulator...
The Hubble function entirely characterizes a given Friedmann-Robertson-Walker spacetime as a consequence of homogeneity and isotropy on cosmological scales. In conjunction with the gravitational field equation, it can be related to the densities of the cosmological fluids and their respective equation of state. The type Ia supernovae allow to constrain the evolution of the luminosity distance...
Weakly supervised machine learning has emerged as a powerful tool in particle physics, enabling the classification of data without relying on extensive labeled examples. This approach holds immense potential for the identification of exotic objects in the gamma-ray sky, particularly those arising from dark matter annihilation. In this contribution, we present our methodology for exploring this...
Traditionally, searches for new physics use complex computer simulations to reproduce what Standard Model processes should look like in collisions recorded by the LHC experiments. These are then compared to simulations of new-physics models (e.g. dark matter, supersymmetry, etc.).
The lack of evidence for new interactions and particles since the Higgs boson’s discovery has motivated the...
Modern machine learning will allow for simulation-based inference from reionization-era 21cm observations at the Square Kilometre Array. Our framework combines a convolutional summary network and a conditional invertible network through a physics-inspired latent representation. It allows for an optimal and extremely fast determination of the posteriors of astrophysical and cosmological...
Accelerator-based experiments in particle physics and medical experiments in neuroscience generate petabytes of data, where well-defined questions could be answered by intense computing analysis, however, new correlations may remain hidden in the huge data-sea. On the other hand, physics/neuroscience-informed AI/ML can help to discover new connections, integrating seamlessly data and...
Foundation models are multi-dataset and multi-task machine learning methods that once pre-trained can be fine-tuned for a large variety of downstream applications. The successful development of such general-purpose models for physics data would be a major breakthrough as they could improve the achievable physics performance while at the same time drastically reduce the required amount of...
Cosmic Dawn (CD) and Epoch of Reionization (EoR) are epochs of the Universe which host invaluable information about the cosmology and astrophysics of X-ray heating and hydrogen reionization. Radio interferometric observations of the 21-cm line at high redshifts have the potential to revolutionize our understanding of the Universe during this time. However, modelling the evolution of these...
A recent proposal suggests using autoregressive neural networks to approximate multi-dimensional probability distributions found in lattice field theories or statistical mechanics. Unlike Monte Carlo algorithms, these networks can serve as variational approximators to evaluate extensive properties of statistical systems, such as free energy.
In the case of two-dimensional systems, the...
Recently, conditional normalizing flows have shown promise to directly approximate the posterior distribution via amortized stochastic variational inference from raw simulation data without resorting to likelihood modelling.
In this contribution, I will discuss an open-source GitHub package, "jammy_flows", a pytorch-based project which comes with many state of the art normalizing flows out of...
Weakly supervised methods have emerged as a powerful tool for anomaly detection at the LHC. While these methods have shown remarkable performance on specific signatures, their application in an even more model-agnostic manner requires using higher dimensional feature spaces compared to the first publications on this topic. We present two directions towards more model agnosticity, either by...
Machine learning, in its conventional form, has often been criticised for being a black box, providing outputs without a clear rationale. To obtain more interpretable results we can make use of symbolic regression (SR) which, as opposed to traditional regression techniques, goes beyond curve-fitting and attempts to determine the underlying mathematical equations that best describe the data. In...
Dark energy has ushered in a golden age of astronomical galaxy surveys, allowing for the meticulous mapping of galaxy distributions to constrain models of dark energy and dark matter. The majority of these surveys rely on measuring galaxy redshifts through a limited set of observations in broad optical bands. While determining redshift is theoretically a straightforward machine learning...
We present a pipeline to infer the equation of state of neutron stars from observations based on deep neural networks. In particular, using the standard (deterministic), as well as Bayesian (probabilistic) deep networks, we explore how one can infer the interior speed of sound of the star given a set of mock observations of total stellar mass, stellar radius and tidal deformability. We...
Physics-Informed Neural Networks (PINNs) have gained significant attention in the field of deep learning for their ability to tackle physical scenarios, gaining significant interest since its inception in scientific literature. These networks optimize neural architectures by incorporating inductive biases derived from knowledge of physics. To embed the underlying physics, a suitable loss...
Whilst gravitational waves from compact binary signals are well modelled, other transient signals do not not necessarily have a clearly defined waveform. Searches for these kinds of signals are often un-modelled so do not say much about the system that produced the gravitational wave. Having a method that can extract some information on the structure and dynamics of the system could be crucial...
The Fair Universe project is building a large-compute-scale AI ecosystem for sharing datasets, training large models and hosting challenges and benchmarks. Furthermore, the project is exploiting this ecosystem for an AI challenge series focused on minimizing the effects of systematic uncertainties in High-Energy Physics (HEP), and on predicting accurate confidence intervals.
This talk will...
Non-Gaussian transient noise artifacts, commonly referred to as glitches, are one of the most challenging limitations in the study of gravitational-wave interferometer data due to their similarities with astrophysical sources signals in the time and frequency domains. Therefore, exploring novel methods to recover physical information from data corrupted by glitches is essential. In our work,...
Gauge symmetry is fundamental to describing quantum chromodynamics on a lattice. While the local nature of gauge symmetry presents challenges for machine learning due to the vast and intricate parameter space, which involves distinct group transformations at each spacetime point, it remains a fundamental and indispensable prior in physics. Lattice gauge equivariant convolutional neural...
Anomaly detection at the LHC broadens the search for BSM effects by making no assumptions about the signal hypothesis. We employ ML to perform density estimation on raw data and use the density estimate for anomaly detection. A neural network can learn the physics content of the raw data. However, the gain in sensitivity to features of interest can be hindered by redundant information already...
The LHCb experiment at the Large Hadron Collider (LHC) is designed to perform high-precision measurements of heavy-hadron decays, which requires the collection of large data samples and a good understanding and suppression of multiple background sources. Both factors are challenged by a five-fold increase in the average number of proton-proton collisions per bunch crossing, corresponding to a...
In recent years, deep learning algorithms have excelled in various domains, including Astronomy. Despite this success, few deep learning models are planned for online deployment in the O4 data collection run of the LIGO-Virgo-KAGRA collaboration. This is partly due to a lack of standardized software tools for quick implementation and deployment of novel ideas with confidence in production...
Knowledge of the primordial matter density field from which the present non-linear observations formed is of fundamental importance for cosmology, as it contains an immense wealth of information about the physics, evolution, and initial conditions of the universe. Reconstructing this density field from the galaxy survey data is a notoriously difficult task, requiring sophisticated statistical...
How can we gain physical intuition in real-world datasets using ‘black-box’ machine learning? In this talk, I will discuss how ordered component analyses can be used to seperate, identify, and understand physical signals in astronomical datasets. We introduce Information Ordered Bottlenecks (IOBs), a neural layer designed to adaptively compress data into latent variables organized by...
A dedicated experimental search for a muon electric dipole moment (EDM) is being set up in PSI. This experiment will search for a muon EDM signal with a final precision of \SI{6e-23}{e \cdot cm} using the frozen-spin technique. This will be the most stringent test of the muon EDM to date, improving the current experimental limit by 3 orders of magnitude. A crucial component of the experiment...
Investigating the properties of QCD matter at extreme temperatures and densities is a fundamental objective of high energy nuclear physics. Such matter can be created in facilities like CERN and FAIR for short periods of time through heavy-ion collisions. Particularly interesting are the intermediate energy heavy-ion collision experiments such as CBM@FAIR, STAR-FXT@RHIC and experiments at NICA...
The Multi-disciplinary Use Cases for Convergent new Approaches to AI explainability (MUCCA) project is pioneering efforts to enhance the transparency and interpretability of AI algorithms in complex scientific endeavours. The presented study focuses on the role of Explainable AI (xAI) in the domain of high-energy physics (HEP). Approaches based on Machine Learning (ML) methodologies, from...
The theory of the strong force, quantum chromodynamics, describes the proton in terms of its constituents, the quarks and gluons. A major conundrum since the formulation of QCD five decades ago has been whether heavy quarks also exist as a part of the proton wavefunction determined by non-perturbative dynamics: so-called intrinsic heavy quarks. Innumerable efforts to establish intrinsic charm...
Gravitational wave parameter estimation plays a crucial role in understanding astrophysical phenomena, yet it is often challenged by real-world noise inherent in the detection process. In this work, we use the simulation-based-inference pipeline PEREGRINE to do robust parameter estimation and tailor it to address the complexities of real noise in gravitational wave data analysis. We aim to...
Accurately reconstructing particles from detector data is a critical challenge in experimental particle physics. The detector's spatial resolution, specifically the calorimeter's granularity, plays a crucial role in determining the quality of the particle reconstruction. It also sets the upper limit for the algorithm's theoretical capabilities. Super-resolution techniques can be explored as a...
The space-time picture of hadron formation in high-energy collisions with nuclear targets is still poorly known. The tests of hadron formation was suggested for the first stage of SPD running. They will require measuring charged pion and proton spectra with the precision better than $10\%$. A research has been carried out to check feasibility of such studies at SPD. In this work,...
A key challenge in the field of AI is to make machine-assisted discovery interpretable, enabling it not only to uncover correlations but also to enhance our physical understanding of the world. A nascent branch of machine learning -- Symbolic Regression (SR) -- aims to discover the optimal functional representations of datasets, producing perfectly interpretable outputs (equations) by...
Accurate particle reconstruction from detector data is a fundamental task in experimental particle physics. Traditional methods are becoming sub-optimal in the face of the increasing demands of the High Lumi phase of the LHC, making machine learning-based approaches more relevant.
Incorporating physics knowledge into machine learning-based reconstruction can enhance performance and provide...
Large language models (LLMs) have revolutionized how we interact with knowledge, serving as a critical link that allows human to interact with large datasets through natural language. This study explores how the scientific community, especially in fundamental physics, can harness the power of LLMs to augment research processes. A significant challenge in science is keeping pace with the...
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, there is little expectation that the implementations...
We deploy an advanced Machine Learning environment, leveraging a
multi-scale cross-attention encoder for event classification. Our multi-modal network can extract information from the jet substructure and the kinematics of the final state particles through self-attention transformer layers. The diverse learned information is subsequently integrated to improve classification performance using...
Addressing the challenge of Out-of-Distribution (OOD) multi-set generation, we introduce YonedaVAE, a novel equivariant deep generative model inspired by Category Theory, motivating the Yoneda-Pooling mechanism. This approach presents a learnable Yoneda Embedding to encode the relationships between objects in a category, providing a dynamic and generalizable representation of complex...
Hamiltonian and Lagrangian equations of motion are the workhorse of Theoretical Physics. The behaviour of physical systems is analytically described by a set of, usually complex, PDEs and ODEs. Consequently, the time evolution of such systems requires numerical integrators and in the case of, e.g., black hole binary evolution, this is, in most cases, computationally expensive or even...
