In the deep learning era, improving the neural network performance in jet physics is a rewarding task as it directly contributes to more accurate physics measurements at the LHC. Recent research has proposed various network designs in consideration of the full Lorentz symmetry, but its benefit is still not systematically asserted, given that there remain many successful networks without taking...
During Run2 of the Large Hadron Collider (LHC), deep-learning-based algorithms were established and led to a significantly improved heavy flavor (b and c) jet tagging performance. In the scope of large-radius boosted jets like top-quark jets, Graph Neural Network (GNN) based models, e.g. ParticleNet, have reached state-of-the-art performance. As a step further, we present Particle Transformer...
Precise reconstruction of top quark properties is a challenging task at the Large Hadron Collider due to combinatorial backgrounds and missing information. We introduce a physics-informed neural network architecture called the Covariant Particle Transformer (CPT) for directly predicting the top quark kinematic properties from reconstructed final state objects. This approach is permutation...
A lot of attention has been paid to the applications of common machine learning methods in physics experiments and theory. However, much less attention is paid to the methods themselves and their viability as physics modeling tools. One of the most fundamental aspects of modeling physical phenomena is the identification of the symmetries that govern them. Incorporating symmetries into a model...
Collider searches face the challenge of defining a representation of high-dimensional data such that physical symmetries are manifest, the discriminating features are retained, and the choice of representation is new-physics agnostic. We introduce JetCLR to solve the mapping from low-level data to optimized observables though self-supervised contrastive learning. As an example, we construct a...
In high-energy heavy-ion collisions, the unconfined state of partons known as the Quark Gluon Plasma (QGP), is known to suppress the yield of jets with respect to proton-proton collision, as well as modify the structure of jets that transverse it. Nonetheless, samples of heavy-ion jets, even at the highest centralities, will contain a significant fraction of jets that, for one reason or the...
We introduce a novel framework to capture the inherent topological structure of collider events. Using persistence homology, the evolution of various topological features across scales is recorded graphically in a persistence diagram, and further encoded as scalars and vectors amenable to machine learning classifiers, showing excellent performance on both jet tagging and event classification...
High-multiplicity signatures at particle colliders can arise in Standard Model processes and beyond. With such signatures, difficulties often arise from the large dimensionality of the kinematic space. For final states containing a single type of particle signature, this results in a combinatorial problem that hides underlying kinematic information. We explore using a neural network that...
With current and future high-energy collider experiments' vast data collecting capabilities comes an increasing demand for computationally efficient simulations. Generative machine learning models allow fast event generation, yet so far are largely constrained to fixed data and detector geometries.
We introduce a Deep Sets based permutation equivariant generative adversarial network (GAN)...
Particle Cloud Generation
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...
Machine-learning-based data generation has become a major topic in particle physics, as the current Monte Carlo simulation approach is computationally challenging for future colliders, which will have a significantly higher luminosity. The generation of particles poses difficult problems similar as is the case for point clouds. We propose that a transformer setup is well fitted to this task....
High-precision theory predictions require the numerical integration of high-dimensional phase-space integrals and the simultaneous generation of unweighted events to feed the full simulation chain and subsequent analyses. While current methods are based on first principles and are mathematically guaranteed to converge to the correct answer, the computational cost to decrease the numerical...
I will give an overview of recent progress in less-than-supervised methods for new physics searches at the LHC.
An application of unsupervised machine learning-based anomaly detection to a generic dijet resonance is presented using the full LHC Run 2 dataset collected by ATLAS. A novel variational recurrent neural network (VRNN) is trained over data, specifically large-radius jets that are modeled using a sequence of constituent four-vectors and substructure variables, to identify anomalous jets based...
Anomaly Detection algorithms are crucial tools for identifying unusual decays from proton collisions at the LHC and are efficient methods for seeking out the possibility of new physics. These detection algorithms should be robust against nuisance kinematic variables and detector conditions. To achieve this robustness, popular detection models built via autoencoders, for example, have to go...
I discuss several approaches to anomaly detection in collider physics, including using variational autoencoders, which rely on the ability to reconstruct certain types of data (background) but not others (signals), and optimal transport distances, which which measures how easily one pT distribution can be changed into another. I discuss advantages and challenges associated with each approach....
"ML connections between industry and HEP"
AtlFast3 is the new, high-precision fast simulation in ATLAS that was deployed by the collaboration to replace AtlFastII, the fast simulation tool that was successfully used for most of Run2. AtlFast3 combines a parametrization-based Fast Calorimeter Simulation and a new machine-learning-based Fast Calorimeter Simulation based on Generative Adversarial Networks (GANs). The new fast simulation...
Simulating particle detector response is the single most computationally expensive step in the Large Hadron Collider computational pipeline. Recently it was shown that normalizing flows can accelerate this process while achieving unprecedented levels of accuracy (CaloFlow).
Applying CaloFlow to the photon and charged pion GEANT4 showers of Dataset 1 of the Fast Calorimeter Simulation...
Score-based generative models are a new class of generative algorithms that have been shown to produce realistic images even in high dimensional spaces, currently surpassing other state-of-the-art models for different benchmark categories and applications. In this work we introduce CaloScore, a score-based generative model for collider physics applied to calorimeter shower generation. Three...
The efficient simulation of particle propagation and interaction within the detectors of the Large Hadron Collider is of primary importance for precision measurements and new physics searches. The most computationally expensive simulations involve calorimeter showers, which will become ever more costly and high-dimensional as the Large Hadron Collider moves into its High Luminosity era....
Simulation in High Energy Physics (HEP) places a heavy burden on the available computing resources and is expected to become a major bottleneck for the upcoming high luminosity phase of the LHC and for future Higgs factories, motivating a concerted effort to develop computationally efficient solutions. Methods based on generative machine learning methods hold promise to alleviate the...
Simulation of calorimeter response is important for modern high energy physics experiments. With the increasingly large and high granularity design of calorimeters, the computational cost of conventional MC-based simulation of each particle-material interaction is becoming a major bottleneck. We propose a new generative model based on a Vector-Quantized Variational Autoencoder (VQ-VAE) to...
A realistic detector simulation is an essential component of experimental particle physics. However, it is currently very inefficient computationally since large amounts of resources are required to produce, store, and distribute simulation data. Deep generative models allow for more cost-efficient and faster simulations. Nevertheless, generating detector responses is a highly non-trivial task...
A study of different jet observables in high $Q^{2}$ Deep-Inelastic Scattering events close to the Born kinematics is presented. Differential and multi-differential cross-sections are presented as a function of the jet’s charged constituent multiplicity, momentum dispersion, jet charge, as well as three values of jet angularities. Results are split into multiple $Q^{2}$ intervals, probing the...
CMS has a wide search program making use of ML for jet tagging and event reconstruction. This talk will report recent usage of ML in searches for heavy resonances involving boosted W, Z, H and top quark jets.
This talk will present the performance of constituent-based jet taggers on large radius boosted top quark jets reconstructed from optimized jet input objects in simulated collisions at s√=13 TeV. Several taggers which consider all the information contained in the kinematic information of the jet constituents are tested, and compared to a tagger which relies on high-level summary quantities...
Machine learning (ML) plays a significant role in the physics analyses at the CMS experiment. Many different techniques and strategies have been deployed to a wide range of applications. In this presentation we will illustrate the most advanced techniques used in top quark physics measurements, such as using ML algorithms to improve the extraction of effective field theory contributions, and...
Deep learning is a standard tool in high-energy physics, facilitating identification of physics objects. In particular, complex neural network architectures play a major role for jet flavor tagging. However, these methods are reliant on accurate simulations and a calibration is required to treat non-negligible performance differences with respect to data. In order to reduce residual...
The unfolding of detector effects impacting experimental measurements is crucial for the comparison of data to theory predictions. While traditional methods were limited to low dimensional data, machine learning has enabled new tech- niques to unfold high-dimensional data. Generative networks like conditional Invertible Neural Networks (cINN) enable a probabilistic unfolding, which map...
In high-energy physics experiments, estimating the efficiency of a process using selection cuts is a widely used technique. However, this method is limited by the number of events that could be simulated in the required analysis phase space. A way to improve this sensitivity is to use efficiency weights instead of selecting events by selection cuts. This method of efficiency measurements is...
We present a new algorithm that identifies reconstructed jets originating from hadronic decays of tau leptons against those from quarks or gluons. No tau lepton reconstruction algorithm is used. Instead, the algorithm represents jets as heterogeneous graphs using the associated low-level objects such as tracks and energy clusters and trains a Graph Neural Network (GNN) to identify hadronically...
The matrix element method is widely considered the perfect approach to LHC inference, but computationally expensive. We show how a combination of two conditional Invertible Neural Networks can be used to learn the transfer function between parton level and reconstructed objects, and to make integrating out the partonic phase space numerically tractable. We illustrate our approach for the...
QCD factorization allows us to model the jet energy-loss in A-A collisions as a convolution between the jet cross section in p-p collisions and an energy loss distribution. Meanwhile, Bayesian inference provides a data-driven way of constraining the energy loss distribution parameterization. Only a few efforts have been made in this direction, and solely using untagged jets. However, gluon and...
Tau leptons are a key ingredient to perform many Standard Model measurements and searches for new physics at LHC. The CMS experiment has released a new algorithm to discriminate hadronic tau lepton decays against jets, electrons, and muons. The algorithm is based on a deep neural network and combines fully connected and convolutional layers. It combines information from all individual...
Uncertainty estimation is a crucial issue when considering the application of deep neural network to problems in high energy physics such as jet energy calibrations.
We introduce and benchmark a novel algorithm that quantifies uncertainties by Monte Carlo sampling from the models Gibbs posterior distribution. Unlike the established 'Bayes By Backpropagation' training regime, it does not...
New physics searches are usually done by training a supervised classifier to separate a signal model from a background model. However, even when the signal model is correct, systematic errors in the background model can influence supervised classifiers and might adversely affect the signal detection procedure. To tackle this problem, one approach is to find a classifier constrained to be...
Uncertainty quantification is crucial for data analysis and hypothesis testing. Many machine learning algorithms were not designed to provide information about the reliability of their predictions, and the methods for estimating uncertainties from these algorithms can lack transparency. In this talk we demonstrate the Bayesian network framework, which was developed using a rigorous formalism...
We study the benefits of jet- and event-level deep learning methods in distinguishing vector boson fusion (VBF) from gluon-gluon fusion (GGF) Higgs production at the LHC. We show that a variety of classifiers (CNNs, attention-based networks) trained on the complete low-level inputs of the full event achieve significant performance gains over shallow machine learning methods (BDTs) trained on...
In this talk, we explore machine learning-based event and jet identification at the future Electron-Ion Collider (EIC). We study the effectiveness of machine learning-based classifiers at the relatively low EIC energies, focusing on (i) identifying the flavor of the jet, in terms of both quark flavor tagging and quark vs. gluon tagging, and (ii) identifying the hard-scattering process, using...
Jets in heavy ion collisions contain contributions from a background of soft-particles. The kinematic reach into low jet momentum is largely driven by the precision of the method used to subtract this background. This precision is also a significant contribution to uncertainties of jet measurements. Previous studies have suggested that deep neural networks can improve momentum resolution at...
The dominant neutrino-nucleon interaction above 100 GeV is Deep Inelastic Scattering (DIS) in which an incoming neutrino scatters off a quark in the nucleon by exchanging a weak boson, producing an outgoing lepton accompanied by a hadron shower. Two sub-dominant processes are expected to produce two high energy charged leptons in the final state. The first one is a subset of DIS where a...
We introduce a new model independent technique for constructing background data templates for use in searches for new physics processes at the LHC.
This method, called CURTAINs, uses invertible neural networks to parametrise the distribution of side band data as a function of the resonant observable. The network learns a transformation to map any data point from its value of the resonant...
Machine learning-based anomaly detection techniques offer exciting possibilities to significantly extend the search for new physics at the Large Hadron Collider (LHC) and elsewhere by reducing the model dependence. In this work, we focus on resonant anomaly detection, where generative models can be trained in sideband regions and interpolated into a signal region to provide an estimate of the...
Resonant anomaly detection is a promising framework for model-independent searches for new particles. Weakly supervised resonant anomaly detection methods compare data with a potential signal against a template of the Standard Model (SM) background inferred from sideband regions. We propose a means to generate this background template that uses a normalizing flow to create a mapping between...
We introduce a new technique named Latent CATHODE (LaCATHODE) for performing "enhanced bump hunts", a type of resonant anomaly search that combines conventional one-dimensional bump hunts with a model-agnostic anomaly score in an auxiliary feature space where potential signals could also be localized. The main advantage of LaCATHODE over existing methods is that it provides an anomaly score...
At an increasing number of interferometer sites with constantly-changing detector conditions, AI can play an important role in real-time and offline data processing. In this talk, we develop novel algorithms and training schemes that sift through noise and instrumental glitches to detect gravitational waves (GW) from compact binary coalescences (CBCs). For real-time processing, we create...
The Gaia space telescope measures the position and proper motion of a billion stars in the neighborhood of the Sun. This dataset contains stellar streams, tidal debris, and other structures that can cast light on the structure of the Galaxy, its merger history, and its dark matter component. I review the machine learning approaches -- including classifiers, normalizing flows, and anomaly...
I will give an overview of recent progress in ML applications to Astro/Cosmo.
I will give an overview of ML applications to Neutrino Physics.
Hadronic signals of new-physics origin at the Large Hadron Collider can remain hidden within the copiously produced hadronic jets. Unveiling such signatures require highly performant deep-learning algorithms. We construct a class of Graph Neural Networks (GNN) in the message-passing formalism that makes the network output infra-red and collinear (IRC) safe, an important criterion satisfied...
The particle-flow (PF) algorithm is of central importance to event reconstruction at the CMS detector, and has been a focus of developments in light of planned Phase-2 running conditions with an increased pileup and detector granularity. Current rule-based implementations rely on extrapolating tracks to the calorimeters, correlating them with calorimeter clusters, subtracting charged energy...
The reconstruction and calibration of hadronic final states in the ATLAS detector present complex experimental challenges. For isolated pions in particular, classifying $\pi^0$ versus $\pi^{\pm}$ and calibrating pion energy deposits in the ATLAS calorimeters are key steps in the hadronic reconstruction process. The baseline methods for local hadronic calibration were optimized early in the...
Discriminating quark-initiated from gluon-initiated jets is an extremely challenging yet important task in high-energy physics. Recent studies have shown that the discriminating features between quark and gluon jets produced by the Monte Carlo generator Pythia differ significantly from the features produced by Herwig. To understand this simulation-dependent discrepancy, we propose a Bayesian...
Besides modern architectures designed via geometric deep learning achieving high accuracies via Lorentz group invariance, this process involves high amounts of computation. Moreover, the framework is restricted to a particular classification scheme and lacks interpretability.
To tackle this issue, we present BIP, an efficient and computationally cheap framework to build rotational,...
Particle reconstruction is a task underlying virtually all analyses of collider-detector data. Recently, the application of deep learning algorithms on graph-structured low-level features has suggested new possibilities beyond the scope of traditional parametric approaches. In particular, we explore the possibility to reconstruct and classify individual neutral particles in a collimated...
We train a network to identify jets with fractional dark decay (semi-visible jets) using the pattern of their low-level jet constituents, and explore the nature of the information used by the network by mapping it to a space of jet substructure observables. Semi-visible jets arise from dark matter particles which decay into a mixture of dark sector (invisible) and Standard Model (visible)...
Hadronic jets and missing transverse energy are key experimental probes when searching for new physics or performing standard model precision measurements in collision events at the LHC. In this work, we propose a graph neural network algorithm for obtaining a global event description that demonstrates greatly improved resolution in the aforementioned objects obtained with a fast simulation of...
We present ν-Flows, a novel method for restricting the likelihood space of neutrino kinematics in high energy collider experiments using conditional normalizing flows and deep invertible neural networks.
This method allows the recovery of the full neutrino momentum, which is usually left as a free parameter, and permits one to sample neutrino values under a learned conditional likelihood...
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. Earlier work focused on finding primary vertices in simulated LHCb data using a hybrid approach that started with kernel density estimators (KDEs) derived from the ensemble of charged track parameters and predicted “target histograms” from which...
Dimensionality reduction is a crucial aspect of data analysis in high energy physics, even if accompanied by information loss. Several methods, including histogram- and kernel-based analyses, are only computationally feasible for low-dimensional data. Furthermore, simulation models used in HEP can often only be validated for low-dimensional data. We provide several blueprints for using machine...
In a decade from now, the Upgrade II of LHCb experiment will face an instantaneous luminosity ten times higher than in the current Run 3 conditions. This will bring LHCb to a new era, with huge event sizes and typically several signal heavy-hadron decays per event. The trigger scope will shift from deciding ‘which events are interesting?’ to ‘which parts of the event are interesting?’. To...
Calorimetric muon energy estimation in high-energy physics is an example of a likelihood-free inference (LFI) problem, where simulators that implicitly encode the likelihood function are used to mimic complex particle interactions at different values of the physical parameters. Recently, Kieseler et al. (2022) exploited simulated measurements from a dense, finely segmented calorimeter to infer...
We use unlabeled collision data from CMS and weakly-supervised learning to train models which can distinguish prompt muons from non-prompt muons using patterns of low-level particle activity in vicinity of the muon, and interpret the models in the space of energy flow polynomials. Particle activity associated with muons is a valuable tool for identifying prompt muons, those due to heavy boson...
We develop a nearest neighbor algorithm for regressor for the problem of estimating the energy of multi-TeV muons in a high-granularity calorimeter, exploiting the pattern of soft photon deposits around the muon track. The algorithm is heavily overparametrized by assigning weights and biases to the training events. Parameters are learnt by batch gradient descent. The performance compares...
We describe a new scale-invariant jet clustering algorithm which does not impose a fixed cone size on the event. The proposed construction unifies fat-jet finding, substructure axis-finding, and recursive filtering of soft wide-angle radiation into a single procedure. The sequential clustering measure history facilitates high-performance substructure tagging with a boosted decision tree. ...
We propose a novel neural architecture that enforces an exact upper bound on the Lipschitz constant of the model by constraining the norm of its weights. This architecture was useful in developing new algorithms for the LHCb trigger which have robustness guarantees as well as powerful inductive biases leveraging the neural network’s ability to be monotonic in any subset of features. A new and...
Following the previous work of leveraging Standard Model jet classifiers as generic anomalous jet taggers (https://arxiv.org/abs/2201.07199), we present an analysis of regularized SM jet classifiers serving as anti-QCD taggers. In the second part of the presentation, from the perspective of interdisciplinary research, we initiate a discussion on the opportunities and challenges involved in the...
We apply the artificial event variable technique, a deep neural network with an information bottleneck, to strongly coupled hidden sector models. These models of physics beyond the standard model predict collider production of invisible, composite dark matter candidates mixed with regular hadrons in the form of semivisible jets. We explore different resonant production mechanisms to determine...
There is a growing recent interest in endowing the space of collider events with a metric structure calculated directly in the space of its inputs. For quarks and gluons, the recently developed energy mover's distance has allowed for a quantification of what is different between physical events. However, the large number of particles within jets makes using metrics and interpreting these...
We present a novel computational approach for extracting weak signals, whose exact location and width may be unknown, from complex background distributions with an arbitrary functional form. We focus on datasets that can be naturally presented as binned integer counts, demonstrating our approach on the datasets from the Large Hadron Collider. Our approach is based on Gaussian Process (GP)...
Measuring the density profile of dark matter in the Solar neighborhood has important implications for both dark matter theory and experiment. In this work, we apply masked autoregressive flows to stars from a realistic simulation of a Milky Way-type galaxy to learn -- in an unsupervised way -- the stellar phase space density and its derivatives. With these as inputs we calculate the...
