The Energy Flow Network (EFN) is a neural network architecture that represents jets as point clouds and enforces infrared and collinear (IRC) safety on its outputs. In this talk, I will introduce a new variant of the EFN architecture based on the Deep Sets formalism, incorporating permutation-equivariant layers. I will discuss the conditions under which IRC safety can be maintained in the new...
One of the most ubiquitous challenges in analyses at the LHC is event reconstruction, whereby heavy resonance particles (such as top quarks, Higgs bosons, or vector bosons) must be reconstructed from the detector signatures left behind by their decay products. This is particularly challenging when all decay products have similar or identical signatures, such as all-jet events. Existing methods...
We introduce the Particle Convolution Network (PCN), a new type of equivariant neural network layer suitable for many tasks in jet physics. The particle convolution layer can be viewed as an extension of Deep Sets and Energy Flow network architectures, in which the permutation-invariant operator is promoted to a group convolution. While the PCN can be implemented for various kinds of...
Optimal Transport has been applied to jet physics for the computation of distance between collider events. Here we generalize the Energy Mover’s Distance to include both the balanced Wasserstein-2 (W2) distance and the unbalanced Hellinger-Kantorovich (HK) distance. Whereas the W2 distance only allows for mass to be transported, the HK distance allows mass to be transported, created and...
Secondary vertex reconstruction is a key intermediate step in building powerful jet classifiers. We use a neural network to perform vertex finding inside jets in order to improve classification performance. This can be thought of as a supervised attention mechanism - directing the classifier towards the relevant information inside the jet. We show supervised attention outperforms an identical...
In high energy heavy-ion collisions the substructure of jets is modified compared to that in proton-proton collisions due to the presence of the quark-gluon plasma (QGP). This modification of jets in the QGP is called ''jet quenching''. We employ machine learning techniques to quantify how much information about this process is within the substructure observables. We formulate the question as...
Experiments at a future $e^{+}e^{-}$ collider will be able to search for new particles with masses below the nominal centre-of-mass energy by analyzing collisions with initial-state radiation (radiative return). We show that machine learning methods based on semisupervised and weakly supervised learning can achieve model-independent sensitivity to the production of new particles in radiative...
Invertible Neural Networks (INNs) are an extremely versatile class of generative models. Their invertibility allows for exact modelling of proability densities, computation of information-theoretic quanities, interpretable and disentangled features, among other things. Due to these properties, INNs have seen growing adoption in recent years, especially in natural sciences and engineering...
We propose Classifier-based Anomaly detection THrough Outer Density Estimation (CATHODE), a new approach to search for resonant new physics at the LHC in a model-agnostic way. In CATHODE, we train a conditional density estimator on additional features in the sideband region, interpolate it into the signal region, and sample from it. This produces in a data-driven way events that follow the SM...
We explore the robustness of the CATHODE (Classifier-based Anomaly detection THrough Outer Density Estimation) method against correlation in the input features. We also compare CATHODE to other related approaches, specifically ANODE and CWoLa Hunting. Using the LHCO R&D dataset, we will demonstrate that in the absence of feature correlations, CATHODE outperforms both ANODE and CWoLa Hunting,...
As the use of Machine Learning techniques become more widespread within High Energy Physics it is important to consider how the results from Neural Networks can be applied within hypothesis testing. We show how a Log-Likelihood Ratio test can be performed using the the output of Neural Network classifiers trained on different physical datasets to yield a detection significance between two...
A unsupervised learning tool that searches for localized, overdense regions of the copula space of a multidimensional feature space is discussed. The algorithm, named RanBox, exists in two versions - one which searches multiple times in random subspaces (typically of 8 to 12 dimensions) of the feature space, and a second one (RanBoxIter) which iteratively adds dimensions to the searched space....
The Energy Movers Distance was recently proposed as an advantageous metric to distinguish certain types of signals at the LHC. We explore generalizations of this distance to multiple families of signals and find similar performance anomaly detection through variational autoencoders. We investigate this connection by exploring the correlation of event distances with distances in the latent...
We present the machine learning methodology that is the backbone of the new release of the NNPDF family of parton distribution functions. The new methodology introduces state of the art machine learning techniques such as stochastic gradient descent for neural network training which results in a major reduction in computational costs, and an automated optimization of the hyperparameters which...
We devise an autoencoder based strategy to facilitate anomaly detection for boosted jets, employ-
ing Graph Neural Networks (GNNs) to do so. To overcome known limitations of GNN autoencoders,
we design a symmetric decoder capable of simultaneously reconstructing edge features and node fea-
tures. Focusing on latent space based discriminators, we find that such setups provide a...
A central challenge in jet physics is that the evolution of the jet is an unobserved, latent process. In a semi-classical parton shower, this corresponds to a sequence of 1-to-2 splittings that form a tree-like showering history. Framing jet physics in probabilistic terms is attractive as it provides a principled framework to think about tasks as diverse as clustering, classification, parton...
Tuning parton shower models to data is an important task for HEP experiments. We are performing exploratory research for what tuning the parton shower might look like if the parton shower were described by a generative model with a tractable likelihood, which might be implemented with a hybrid of theoretically-motivated components or generic neural network components. For this work we consider...
We describe the outcome of a data challenge conducted as part of the Dark Machines Initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenge aims at detecting signals of new physics at the LHC using unsupervised learning algorithms. We define and describe a large benchmark dataset, consisting of > 1 Billion simulated LHC events. We then review a wide range of...
We show how an anomaly detection algorithm could be integrated in a typical search for new physics in events with jets at the CERN Large Hadron Collider (LHC). We assume that an anomaly detection algorithm is given, trained to identify rare jet types, such as jets originating from the decay of a highly boosted massive particle. We demonstrate how this algorithm could be integrated in a search...
We study unbinned multivariate analysis techniques, based on Statistical Learning, for indirect new physics searches at the LHC in the Effective Field Theory framework. We focus in particular on high-energy ZW production with fully leptonic decays, modeled at different degrees of refinement up to NLO in QCD. We show that a considerable gain in sensitivity is possible compared with current...
QCD splittings are among the most fundamental theory concepts at the LHC. In this talk, I will show how they can be studied systematically with the help of invertible neural networks. These networks work with sub-jet information to extract fundamental parameters from jet samples. Our approach expands the LEP measurements of QCD Casimirs to a systematic test of QCD properties based on low-level...
Recently, jet measurements in DIS events close to Born kinematics have been proposed as a new probe to study transverse-momentum-dependent (TMD) PDFs, TMD fragmentation functions, and TMD evolution. We report measurements of lepton-jet momentum imbalance and hadron-in-jet correlations in high-$Q^2$ DIS events collected with the H1 detector at HERA. The jets are reconstructed with the kT...
Models with dark showers represent one of the most challenging possibilities for new physics at the LHC. One of the most difficult examples is a novel collider signature called a Soft Unclustered Energy Pattern (SUEP), which can arise in certain BSM models with a hidden valley sector that is both pseudo-conformal and strongly coupled over a large range of energy scales. Large-angle emissions...
As an alternative approach (w.r.t. deep generative models) for detecting out-of-distribution samples, we explore the possibility of employing jet classifiers as anomalous jet taggers. We also discuss the advantages and limitations of different approaches.
Measurements at colliders are often done by fitting data to simulations, which depend on many physical and unphysical parameters. One example is the top-quark mass, where parameters in simulation must be profiled when fitting the top-quark mass parameter. In particular, the dependence of top-quark mass fits on simulation parameters contributes to the error in the best measurements of the...
Deep generative models are becoming widely used across science and industry for a variety of purposes. A common challenge is achieving a precise implicit or explicit representation of the data probability density. Recent proposals have suggested using classifier weights to refine the learned density of deep generative models. We extend this idea to all types of generative models and show how...
Data compression plays a major role in the field of Machine Learning and recent works based on generative models such as Generative Adversarial Networks (GANs) have shown that deep-learning-based compression can outperform state-of-the-art classical compression methodologies. Such techniques can be adapted and applied to various areas in high energy physics, in particular to the study of the...
Due to the expected increase in LHC data from the HL upgrade it is important to work on the efficiency of MC Event Generators in order to make theoretical predictions with the necessary precision accessible. One part of the calculation that could benefit from improvements is the generation of unweighted parton-level events. While adaptive multi-channel importance sampling combined with the...
Symmetries are ubiquitous and essential in physics, and the framework to describe symmetries is group theory. The symmetry described by the Lorentz group is essential in the dynamics of all particle physics experiments. A Lorentz-group-equivariant deep neural network framework, called the Lorentz group network (LGN), has been introduced by Bogatskiy et al. and tested for performance in...
Ensemble learning is a technique where multiple component learners are combined through a protocol. In this talk, we will present an Ensemble Neural Network (ENN) that uses the combined latent-feature space of multiple neural network classifiers to improve the representation of the network hypothesis. We apply this approach to construct an ENN from Convolutional and Recurrent Neural Networks...
The identification of boosted heavy particles such as top quarks or vector bosons is one of the key problems arising in experimental studies at the Large Hadron Collider. In this article, we introduce LundNet, a novel jet tagging method which relies on graph neural networks and an efficient description of the radiation patterns within a jet to optimally disentangle signatures of boosted...
In this talk we will present a a procedure to separate boosted Higgs bosons decaying into hadrons, from the background due to strong interactions. We employ the Lund jet plane to obtain a theoretically well-motivated representation of the jets of interest and we use the resulting images as the input to a convolutional neural network. In particular, we consider two different decay modes of the...
With the great promise of deep learning, discoveries of new particles at the Large Hadron Collider (LHC) may be imminent. Following the discovery of a new Beyond the Standard model particle in an all-hadronic channel, deep learning can also be used to identify its quantum numbers. Convolutional neural networks (CNNs) using jet-images can significantly improve upon existing techniques to...
We introduce a morphological analysis based on a neural network analyzing the Minkowski Functionals (MFs) of pixellated jet images. The MFs describe the geometric measures of binary images, and their changes by dilation encode the jet constituents' geometric structures that appear at various angular scales. We explicitly show that this morphological analysis can be considered a constrained...
Identification of hadronic decays of highly Lorentz-boosted W/Z/Higgs bosons and top quarks provides powerful handles to a wide range of new physics searches and Standard Model measurements at the LHC. This talk presents recent advances in boosted jet tagging algorithms in CMS. The application of novel machine-learning techniques has substantially improved the tagging performance and led to a...
We train a Convolutional Neural Network to classify longitudinally and transversely polarized hadronic $W^\pm$ using the images of boosted $W^{\pm}$ jets as input. The images capture angular and energy information from the jet constituents that is faithful to the properties of the original quark/anti-quark $W^{\pm}$ decay products without the need for invasive substructure cuts. We find that...
It is widely known that predictions for jet substructure features vary significantly between Monte Carlo generators. This is especially true for the output of deep neural networks (NN) trained with high-dimensional feature spaces to tag the origin of a jet. However, even though the spectra of a given NN varies between generators, it could be that the function learned by different generators...
We introduce CaloFlow, a fast detector simulation framework based on normalizing flows. For the first time, we demonstrate that normalizing flows can reproduce high-granularity calorimeter simulations with extremely high fidelity, providing a fresh alternative to computationally expensive GEANT4 simulations, as well as other state-of-the-art fast simulation frameworks based on GANs and VAEs....
In High Energy Physics experiments Particle Flow (PFlow) algorithms are designed to provide an optimal reconstruction of the nature and kinematic properties of the particles produced within the detector acceptance during collisions. At the heart of PFlow algorithms is the ability to distinguish the calorimeter energy deposits of neutral particles from those of charged particles, using the...
Reconstructing the jet transverse momentum ($p_{\rm T}$)is a challenging task, particularly in heavy-ion collisions due to the large fluctuating background from the underlying event. In the recent years, ALICE has developed a novel method to correct jets for this large background using machine learning techniques. This analysis intentionally does not utilize deep learning methods and instead...
Modern high energy physics crucially relies on simulation to connect experimental observations to underlying theory. While traditional methods relying on Monte Carlo techniques produce powerful simulation tools, they prove to be computationally expensive. This is particularly true when they are applied to calorimeter shower simulation, where many particle interactions occur. The strain on...
Generative machine learning models are a promising way to efficiently amplify classical Monte Carlo generators' statistics for event simulation and generation in particle physics. The high computational cost of the simulation and the expected increase in data in the high-precision era of the LHC and at future colliders indicate that we urgently need such fast surrogate simulators. We present a...
AtlFast3 is the next generation of high precision fast simulation in ATLAS that is being deployed by the collaboration and will replace AtlFastII, the fast simulation tool that was successfully used until now. AtlFast3 combines a parametrization-based Fast Calorimeter Simulation and a new machine-learning based Fast Calorimeter Simulation based on Generative Adversarial Networks (GANs). The...
Typically, high-energy physics (HEP) data analysis heavily relies on the production and the storage of large datasets of simulated events. At the LHC, the end-to-end simulation workflow can require up to 50% of the available computing resources of an experiment. Speeding up the simulation process would be crucial to save resources that could be otherwise utilized.
In our study, we investigate...
We introduce a novel strategy for machine-learning-based fast simulators, which is the first that can be trained in an unsupervised manner using observed data samples to learn a predictive model of detector response and other difficult-to-model transformations. Across the physical sciences, a barrier to interpreting observed data is the lack of knowledge of a detector's imperfect resolution,...
The performance demands of future particle-physics experiments investigating the high-energy frontier pose a number of new challenges, forcing us to find new solutions for the detection, identification, and measurement of final-state particles in subnuclear collisions. One such challenge is the precise measurement of muon momenta at very high energy, where the curvature provided by conceivable...
Jet interactions in a hot QCD medium created in heavy-ion collisions are conventionally assessed by measuring the modification of the distributions of jet observables with respect to the proton-proton baseline. However, the steeply falling production spectrum introduces a strong bias toward small energy losses that obfuscates a direct interpretation of the impact of medium effects in the...
There has been significant development recently in generative models for accelerating LHC simulations. Work on simulating jets has primarily used image-based representations, which tend to be sparse and of limited resolution. We advocate for the more natural ‘particle cloud’ representation of jets, i.e. as a set of particles in momentum space, and discuss four physics- and...
Generating large numbers of events efficiently is a major bottleneck for ML projects. As a first step towards a full-fledged event generator for modern GPUs, we investigated different recursive strategies. The GPU implementations are compared to the state-of-the-art CPU codes, showing promise for using these in other pipelines. Finally, we propose baseline implementations for the development...
We present an implementation of an explainable and physics-aware machine learning model capable of inferring the underlying physics of high-energy particle collisions using the information encoded in the energy-momentum four-vectors of the final state particles. We demonstrate the proof-of-concept of our White Box AI approach using a Generative Adversarial Network (GAN) which learns from a...
Event generation with neural networks has seen significant progress recently. The big open question is still how such new methods will accelerate LHC simulations to the level required by upcoming LHC runs. We target a known bottleneck of standard simulations and show how their unweighting procedure can be improved by generative networks. This can, potentially, lead to a very significant gain...
We investigate the possibility of using Deep Learning algorithms for jet identification in the L1 trigger at HL-LHC. We perform a survey of architectures (MLP, CNN, Graph Networks) and benchmark their performance and resource consumption on FPGAs using a QKeras+hls4ml compression-aware training procedure. We use the HLS4ML jet dataset to compare the results obtained in this study to previous...
The high collision rates at the Large Hadron Collider (LHC) make it impossible to store every single observed interaction. For this reason, only a small subset that passes so-called triggers — which select potentially interesting events — are saved while the remainder is discarded. This makes it difficult to perform searches in regions that are usually ignored by trigger setups, for example at...
A key aspect for the study of particle collisions is the comparison of the experiments data with those resulting from computer simulations, mainly obtained using Monte Carlo-based generators. However the amount of data required in simulations makes this task very time consuming. One approach to avoid this issue is by using machine learning techniques to speed up this process.
In this work,...
QCD-jets at the LHC are described by simple physics principles. We show how super-resolution generative networks can learn the underlying structures and use them to improve the resolution of jet images. We test this approach on massless QCD-jets and on fat top-jets and find that the network reproduces their main features even without training on pure samples. In addition, we show how a slim...
We introduce a collection of datasets from fundamental physics research including particle physics, astroparticle physics, hadron, and nuclear physics for supervised machine learning studies. These datasets, containing hadronic top quarks, cosmic air showers, phase transitions in the hadronic matter, and generator-level histories, are combined and made public to simplify future work on...
Unsupervised anomaly detection could be crucial in future analyses searching for rare phenomena in large datasets, as for example collected at the LHC. To this end, we introduce a physics inspired variational autoencoder (VAE) architecture which performs competitively and robustly on the LHC Olympics Machine Learning Challenge datasets. We demonstrate how embedding some physical observables...
Symmetries are a fundamental property of functions applied to datasets. A key function for any dataset is the probability density, and the corresponding symmetries are often referred to as the symmetries of the dataset itself. We provide a rigorous statistical notion of symmetry for a dataset, which involves reference datasets that we call ...
Fundamental laws of physics introduce specific topological features in the phase-space of n-body processes in collider events. We introduce a new analysis approach relying on analyzing such global topological properties of the manifold over the distribution of events. One specific property of potential interest is the dimensionality of the phase space. It can, for example, be used for...
We build a simple probabilistic model for collider events represented by a pattern of points in a space of high-level observables. The model is based on three assumptions for the point data: the measurements in individual events are discrete, exchangeable, and generated from a mixture of latent distributions, or 'themes'. The result is a mixed-membership model known as Latent Dirichlet...
Deep neural networks (DNNs) are essential tools in particle physics targeting various use cases ranging from reconstruction of particles up to event classification and anomaly detection. Whereas DNNs for event classification are primarily trained on quantities deduced from the kinematic properties of the particles in the final state (high-level observables), we present an alternative approach...
Autoencoders as tools behind anomaly searches at the LHC have the structural problem that they only work in one direction, extracting jets with higher complexity but not the other way around. To address this, we derive classifiers from the latent space of (variational) autoencoders, specifically in Gaussian mixture and Dirichlet latent spaces. In particular, the Dirichlet setup solves the...
Given the increasing data collection capabilities and limited computing resources of future collider experiments, interest in using generative neural networks for the fast simulation of collider events is growing. In our previous study, the Bounded Information Bottleneck Autoencoder (BIB-AE) architecture for generating photon showers in a high-granularity calorimeter showed a high accuracy...
We introduce persistent Betti numbers to characterize topological structure of jets. These topological invariants measure multiplicity and connectivity of jet branches at a given scale threshold, while their persistence records evolution of each topological feature as this threshold varies. With this knowledge, in particular, we are able to reconstruct branch phylogenetic tree of each jet....
Neural Stochastic Differential Equations model a dynamical environment with neural nets assigned to their drift and diffusion terms. The high expressive power of their nonlinearity comes at the expense of instability in the identification of the large set of free parameters. This worok presents a recipe to improve the prediction accuracy of such models in three steps: i) accounting for...
Recently, Generative Adversarial Networks (GANs) trained on samples of traditionally simulated collider events have been proposed as a way of generating larger simulated datasets at a reduced computational cost. In this talk we will present an argument cautioning against the usage of this method to meet the simulation requirements of an experiment, namely that data generated by a GAN cannot...
We show how Bayesian neural networks can be used to estimate uncertainties associated with regression, classification, and now also generative networks. For generative INNs, the combination of the learned density and uncertainty maps also provide insights into how these networks learn. These results show that criticizing the use of neural networks in LHC physics as black boxes is a...
Machine learning techniques are becoming an integral component of data analysis in High Energy Physics (HEP). These tools provide a significant improvement in sensitivity over traditional analyses by exploiting subtle patterns in high-dimensional feature spaces. These subtle patterns may not be well-modeled by the simulations used for training machine learning methods, resulting in an enhanced...
Nearly five years ago we introduced tree-based recursive NN models for jet physics, which intuitively reflected the sequence of 1-to-2 splittings found in a parton shower. Subsequently, tree-based models like JUNIPR were developed as (probabilistic) generative models that could be used for classification and reweighing. One result that somewhat undermined the narrative of the connection...
A framework is presented to extract and understand decision-making information from a deep neural network classifier of jet substructure tagging techniques. The general method studied is to provide expert variables that augment inputs (“eXpert AUGmented” variables, or XAUG variables), then apply layerwise relevance propagation (LRP) to networks that have been provided XAUG variables and those...
Four-tops (and its backgrounds) is very hard to model at the LHC, it represents a unique window for detecting top-philic NP, and its current measurements have some tension with theory and predictions. We find that simple, clean and powerful Bayesian Inference can be applied on the data to infer signal and background true distributions. We propose that these results could be used in a novel...
Machine learning (ML) is pushing through boundaries in computational physics.
Jet physics, with it's large and detailed dataset, is particularly well suited.
In this talk I will discuss the application of an unusual ML technique, Spectral Clustering, to jet formation.
Spectral clustering differers from much of ML as it has no "black-box" elements.
Instead, it is based on a simple,...
The choice of optimal event variables is crucial for achieving the maximal sensitivity of experimental analyses, and suitable kinematic variables for many well-motivated event topologies have been developed in collider physics. Here we propose a deep-learning-based algorithm to design good event variables that are sensitive to a wide range of the unknown model parameter values. We demonstrate...
To obtain information on the still unknown sources of ultra-high-energy cosmic rays (UHECRs), a combined fit of the observed energy spectrum and depths of the shower maximum can be used, which constrains characteristic parameters of the sources. During propagation from the sources to Earth, UHECRs can experience numerous stochastic processes such that no explicit inverse function, which would...
I describe a new machine learning algorithm, Via Machinae, to identify cold stellar streams in data from the Gaia telescope. Via Machinae is based on ANODE, a general method that uses conditional density estimation and sideband interpolation to detect local overdensities in the data in a model agnostic way. By applying ANODE to the positions, proper motions, and photometry of stars observed by...
I will give a very brief (and incomplete) review on quantum machine learning techniques and focus then on novel quantum computing approaches for the task of finding a solution to an optimisation problem. I will then give explicit examples how quantum machine learning techniques can be used for classification tasks and to calculate solutions to nonperturbative problems in quantum field theory.
Anomaly detection techniques offer exciting possibilities to significantly extend the search for new physics at the Large Hadron Collider (LHC) in a model-agnostic approach. We study how Generative Adversarial Networks could be used for this purpose, using the LHC Olympics 2020 dataset as an example.
