Combining multiple collider events improves machine learning performance.
problem Improving machine learning for collider physics.
method Study of single-event vs multi-event classifiers under IID assumption.
result Training single-event classifiers is more effective than multi-event classifiers.
LOT framework speeds up event distance computation in collider physics.
problem Computational inefficiency in quantifying event distances.
method Linearized Optimal Transport (LOT) for efficient computation.
result LOT significantly reduces computational cost without sacrificing accuracy.
Efficient event generation for collider phenomenology using parallel Langevin sampling and learned Stein diagnostics.
problem Event generation for precision collider phenomenology.
method Parallel Langevin sampling with learned Stein diagnostics.
result Relaxation time is estimated using a data-driven approach.
Improved particle-flow event reconstruction for future colliders using scalable neural networks.
problem Efficient and accurate particle reconstruction in future particle detectors.
method Comparative study of scalable machine learning models (graph neural network and kernel-based transformer) for event reconstruction.
result Graph neural network model improves jet transverse momentum resolution by up to 50%.
Deep Sets improve jet discrimination in particle physics.
problem Representing and learning from collider events with variable-length particle sets.
method Energy Flow Networks and Particle Flow Networks, based on Deep Sets framework.
result Improved or similar performance in discriminating quark jets from gluon jets compared to existing methods.
FAT-GAN simulates electron-proton scattering without theoretical assumptions.
problem Efficiently training GANs to simulate complex particle distributions.
method Developed FAT-GAN using transformed and augmented features to improve GAN performance.
result FAT-GAN accurately reproduces electron momenta distributions in electron-proton scattering.
Machine learning boosts physics research, especially at high energy experiments.
problem Finding new fundamental physics in high energy experiments.
method Review of machine learning methods and applications in high energy physics.
result Modern machine learning techniques have expanded the scope of physics research.
For data sets populated by a very well modeled process and by another process of unknown probability density function (PDF), a desired feature when manipulating the fraction of the unknown process (either for enhancing it or suppressing it) consists in avoiding to modify the kinematic distributions of the well modeled …
Versatile model for High Energy Physics events.
problem Modeling complex interactions in high-energy physics data.
method Energy-based probabilistic model with multi-purpose architecture.
result Achieves success in diverse applications like simulation, anomaly detection, and particle identification.
Tensor networks improve b-jet classification in high-energy physics.
problem Classifying jets from b-quarks in proton-proton collisions.
method Quantum-inspired machine learning using tensor networks.
result Optimized classification of b-jets with improved precision and speed.
Challenge uses unsupervised learning to detect new physics signals at LHC.
problem Detecting new physics signals at the LHC using unsupervised machine learning.
method Developed and evaluated anomaly detection algorithms on a large dataset.
result Benchmark dataset of >1 Billion simulated LHC events for future studies.
Collider regression improves predictive performance in regression tasks.
problem Discarding prior causal knowledge in regression tasks.
method Collider regression framework incorporating probabilistic causal knowledge from collider structures.
result Proves positive generalization benefit and provides closed-form estimators.
The interpretation of Large Hadron Collider (LHC) data in the framework of Beyond the Standard Model (BSM) theories is hampered by the need to run computationally expensive event generators and detector simulators. Performing statistically convergent scans of high-dimensional BSM theories is consequently challenging, a…
New neural method calculates EMD for particle physics data.
problem Metric for particle collider events based on Wasserstein metric.
method Neural network architecture estimating EMD using Kantorovich-Rubinstein duality.
result Differentiable way to calculate EMD for geometric fitting.
New algorithm learns any part of a Bayesian network structure efficiently.
problem Learning specific parts of a Bayesian network structure is computationally inefficient.
method APS-L, a new algorithm that divides V-structures into collider and non-collider types and recursively finds them in Markov blankets.
result The APSL algorithm efficiently and accurately learns any part of a Bayesian network structure.
Deep learning predicts lung cancer prognosis by controlling collider effects.
problem Unbiased survival predictions for lung cancer patients.
method Dual task network predicting both outcome and collider, enforcing independence with OLS.
result Unbiased individual prognosis predictions are possible with deep learning.
The paper presents efficient methods for identifying causal graphs with latent variables.
problem Recovering causal graphs with latent variables while minimizing intervention costs.
method Two intervention cost models (linear and identity) are considered. Algorithms are provided for both models.
result Upper bounds on the number of interventions needed for recovery, and approximation factors for the linear cost model.
RL algorithm optimizes jet grooming for collider experiments.
problem Improving jet mass resolution in boosted objects.
method Deep RL agent trained on signal and background samples.
result RL-derived grooming algorithm matches state-of-the-art techniques.
Enhanced latent spaces improve collider simulation precision.
problem Improving the precision of collider physics simulations.
method Machine learning techniques including reweighting, pre-processing, and latent space refinement.
result Sub-percent precision across various phase spaces achieved.
Tensor networks improve anomaly detection at LHC for new physics.
problem Identifying new phenomena in proton collision events at LHC.
method Tensor network-based anomaly detection using Matrix Product State with an isometric feature map.
result Tensor networks outperform established quantum methods in identifying new phenomena.
OmniFold simultaneously unfolds all observables using machine learning.
problem Traditional unfolding methods are limited to individual observables and do not incorporate all detector information.
method OmniFold iteratively reweights a simulated dataset using machine learning to handle all available information.
result OmniFold enables the simultaneous measurement of all observables, including those not yet invented.
Efficiently searches ancestral graphs using multivariate information.
problem Discovering causal relationships in graphs with latent variables.
method Greedy search-and-score algorithm with two-step approach.
result Outperforms existing methods on benchmark datasets.
Machine learning has been applied to several problems in particle physics research, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event identification and reconstruction in the 2010s. In this document we discuss promising futu…
Study interprets deep learning for LHC jet tagging.
problem Understanding deep learning models in LHC jet tagging.
method Recursive neural networks, comparative study of jet tagging tasks.
result Interesting observations on the latent space of jet tagging models.
MLPF uses graph neural networks to improve particle-flow reconstruction in high-pileup conditions.
problem Improving particle-flow reconstruction in high-pileup conditions at high-luminosity LHC.
method End-to-end trainable machine-learned particle-flow algorithm based on graph neural networks.
result MLPF improves physics response and demonstrates scalable reconstruction in high-pileup environments.
New method uses cluster shapes to improve track finding in particle collisions.
problem Combining timing and additional detector information for efficient track finding.
method Neural networks to analyze cluster shapes for track seeding.
result Cluster shapes reduce fake combinatorial backgrounds while maintaining high track efficiency.
Foundation models trained on collider data improve jet generation tasks.
problem Improving foundation models for jet generation tasks.
method Pre-training OmniJet-α model on AspenOpenJets dataset. result Pre-trained model improves performance on jet generation tasks with domain shift.
We introduce jet topics: a framework to identify underlying classes of jets from collider data. Because of a close mathematical relationship between distributions of observables in jets and emergent themes in sets of documents, we can apply recent techniques in "topic modeling" to extract jet topics from data with mini…
Neyman-Pearson testing improves goodness of fit in detecting new physics.
problem Detecting small anomalies in data distributions.
method Employing Neyman-Pearson strategy with a rich parametrized family of models.
result Neyman-Pearson testing is more sensitive to small departures and unbiased towards specific anomalies.
Modern machine learning techniques can be used to construct powerful models for difficult collider physics problems. In many applications, however, these models are trained on imperfect simulations due to a lack of truth-level information in the data, which risks the model learning artifacts of the simulation. In this …
PHAZE framework uses zkML and hashing for fast, verifiable LHC trigger decisions.
problem Inefficient inference on large machine learning models for LHC trigger performance.
method Cryptographic techniques like hashing and zkML for low latency, certifiable inference.
result Achieves nanosecond-order latency for LHC triggers, enabling dynamic low-level triggers.
Develops a robust training framework to detect backdoor attacks in DNNs.
problem Vulnerability of DNNs to backdoor attacks by poisoned training data.
method Collider framework selects prominent samples based on geometric structures and coreset selection objective.
result Significantly reduces backdoor success rate in various poisoned datasets.
Can you fill R^n with a froth of "soap bubbles" that meet at most n at a time? Not if they have bounded diameter, as follows from Lebesgue's Covering Theorem. We provide some related results and conjectures.
New SV models calibrated to market instruments using Schrodinger bridge approach.
problem Creating calibrated Stochastic Volatility Models to market instruments.
method Building a new class of SV models using Schrodinger bridge approach, with instantaneous volatility not modified.
result Models differ from local SV models and can be interpreted as martingale Schrodinger bridges.
Modern machine learning techniques, such as convolutional, recurrent and recursive neural networks, have shown promise for jet substructure at the Large Hadron Collider. For example, they have demonstrated effectiveness at boosted top or W boson identification or for quark/gluon discrimination. We explore these methods…
High-precision machine learning reduces particle physics simulations by orders of magnitude.
problem Reducing computational burden in particle physics simulations.
method Developed optimal training strategies and tuned machine learning regressors, including Deep Neural Networks with skip connections and boosted decision trees.
result Significantly reduced computational time by factors of 10^3 to 10^6 over first-principles simulations.
Proposes a meta-algorithm for classification with overlapping classes in high-energy physics.
problem Challenges of class overlap in binary classification.
method Combines bagging and boosting techniques with a randomization trick.
result Improves statistical significance of Higgs discovery.
We study the asymptotics of the determinant of Laplacian on a translation surface (a compact Riemann surface equipped with a conformal flat conical metric with trivial holonomy) of genus g with 2g-2 conical points of angle 4πas two conical points collide.
EagleEye detects localized density anomalies in multivariate data.
problem Identifying signal events, regime changes, or model mismatch in scientific data.
method EagleEye pinpoints local over- and under-densities by assigning anomaly scores based on binary membership sequences and binomial null models.
result EagleEye can detect genuine local anomalies and estimate background purity.
Recent results at the Large Hadron Collider (LHC) have pointed to enhanced physics capabilities through the improvement of the real-time event processing techniques. Machine learning methods are ubiquitous and have proven to be very powerful in LHC physics, and particle physics as a whole. However, exploration of the u…
NSBI approach detects Higgs trilinear coupling with high luminosity upgrade constraints.
problem Determining the Higgs trilinear self-coupling via off-shell Higgs production.
method Hybrid neural simulation-based inference (NSBI) incorporating SMEFT and quantum interference effects.
result NSBI achieves sensitivity close to theoretical optimum for Higgs trilinear self-coupling.
A multi-player bandit system resists adversarial attacks with near-optimal regret.
problem Adversaries attempt to manipulate rewards in a multi-player multi-armed bandit game.
method Players communicate a single bit to resist attacks, achieving near-optimal regret.
result Achieves near-optimal regret of O(log1+δT+W), where W is the total time of adversarial attacks. Study moduli space of quadratic differentials with new geometric insights.
problem Understanding the structure of moduli spaces of quadratic differentials.
method Using decorated marked surfaces, Abel-Jacobi map, and 3-Calabi-Yau categories.
result Fundamental group of moduli space equals kernel of Abel-Jacobi map.
Deep Gaussian Processes model functions on DAGs with partially observed data.
problem Reconstructing and inferring from partially observed functions on DAGs with noisy measurements.
method Place priors over functions on DAGs, theoretically study prior-collapse behavior, and offer a structured variational approximation.
result Almost-sure lower bounds on the preservation of input distinctions and interpretability of simulator hierarchies.
Quantum GNNs outperform classical GNNs in jet tagging.
problem Classifying partons initiating jets from high-energy particle collisions.
method Comparison of classical and quantum GNNs and their equivariant counterparts.
result Quantum GNNs outperformed classical GNNs in binary classification tasks.
Boosted decision trees improved for particle identification in high-energy physics.
problem Overfitting in boosted decision trees hampers their performance in particle identification.
method Meta-learning techniques of boosting and bagging to mitigate overfitting.
result The proposed algorithm achieves performance close to that of deep neural networks on a benchmark data set.
New method uses information theory to uncover causal relationships in complex systems.
problem Discovering causal relationships in multivariate systems, especially in Bayesian networks and hypergraphs.
method Partial Information Decomposition (PID) to explicitly model higher-order interactions.
result PID components reveal direct causal neighbors and collider relationships in Bayesian networks and multi-tail hyperedges in causal hypergraphs.
A class of conserved models of wealth distributions are studied where wealth (or money) is assumed to be exchanged between a pair of agents in a population like the elastically colliding molecules of a gas exchanging energy. All sorts of distributions from exponential (Boltzmann-Gibbs) to something like Gamma distribut…