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.
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.
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.
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.
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 …
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.
Using deep neural networks for identifying physics objects at the Large Hadron Collider (LHC) has become a powerful alternative approach in recent years. After successful training of deep neural networks, examining the trained networks not only helps us understand the behaviour of neural networks, but also helps improv…
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.
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.
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.
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.
A key question for machine learning approaches in particle physics is how to best represent and learn from collider events. As an event is intrinsically a variable-length unordered set of particles, we build upon recent machine learning efforts to learn directly from sets of features or "point clouds". Adapting and spe…
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.
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 …
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…
Visualizes futures markets using particle physics tools.
problem Understanding high-velocity data in futures markets.
method Uses ROOT, an open-source data-analysis tool, to reconstruct and visualize message-based data.
result Allows stakeholders to gain a better understanding of markets and monitor effectively.
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.
Transformers predict scattering amplitudes in theoretical physics.
problem Computing exact coefficients of scattering amplitudes in N = 4 SYM theory.
method Applied Transformers to predict integer coefficients of scattering amplitudes.
result Transformers achieve high (> 98%) accuracy on predicting scattering amplitudes.
Reliable data quality monitoring is a key asset in delivering collision data suitable for physics analysis in any modern large-scale High Energy Physics experiment. This paper focuses on the use of artificial neural networks for supervised and semi-supervised problems related to the identification of anomalies in the d…
New method unfolds distribution moments directly from data without binning.
problem Deconvolving detector distortions in particle physics.
method Uses machine learning, inspired by GANs, to unfold moments directly.
result More precise than bin-based approaches and comparable to unbinned methods.
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.
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.
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%.
Paper presents FPGA implementation for efficient recurrent neural networks.
problem Implementing recurrent neural networks on FPGAs for low latency.
method Developed hls4ml framework to implement LSTM and GRU layers.
result Demonstrated effective designs for both small and large models.
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.
Turbo-Sim generates models from physics principles, improving interpretability and flexibility.
problem Transforming particle properties from theory to observation in collider physics.
method Maximizes mutual information between input and output, setting loss term weights.
result Mathematically interpretable and flexible generative models.
A new machine learning method handles nuisance parameters for better unfolding in particle physics.
problem Improving statistical correction of cross sections in complex particle physics detectors.
method Profile OmniFold, a machine learning-based Expectation-Maximization procedure that incorporates nuisance parameters.
result Demonstrated the effectiveness of Profile OmniFold on both simulated and real data.
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.
DVAEs speed up calorimeter simulation for LHC data.
problem Slow calorimeter simulation in LHC experiments.
method Discrete Variational Autoencoders (DVAEs).
result Significantly faster calorimeter shower simulation.
Applications of machine learning tools to problems of physical interest are often criticized for producing sensitivity at the expense of transparency. To address this concern, we explore a data planing procedure for identifying combinations of variables -- aided by physical intuition -- that can discriminate signal fro…
BCAE-2D compresses 3D data from a time projection chamber at high speed.
problem Compressing high-speed, sparse 3D data from a time projection chamber.
method 2D Bicephalous Convolutional Autoencoder (BCAE-2D) approach.
result 3x speedup in compression throughput with improved reconstruction accuracy.
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.
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.
Artificial intelligence offers the potential to automate challenging data-processing tasks in collider physics. To establish its prospects, we explore to what extent deep learning with convolutional neural networks can discriminate quark and gluon jets better than observables designed by physicists. Our approach builds…
Study compares MCMC and nested sampling for high-dimensional physics problems.
problem Efficiently sampling high-dimensional Bayesian posterior distributions in particle physics and cosmology.
method Review and comparison of MCMC and nested sampling techniques on high-dimensional test functions and real physics examples.
result Modern MCMC algorithms can outperform nested sampling in certain cases, highlighting implementation details.
Optimal transport calibrates machine learning models for particle physics simulations.
problem Discrepancies between simulation and experimental data limit machine learning effectiveness.
method A model calibration approach based on optimal transport applied to high-dimensional simulations.
result Calibrated high-dimensional representations enable proper calibration of various downstream quantities.
Two methods use simulation to improve anomaly detection in particle physics.
problem Artificial bumps in invariant mass spectra from machine learning classifiers.
method Simulation-assisted decorrelation techniques.
result Both methods are robust to correlations in the data and improve anomaly detection.
Collimated streams of particles produced in high energy physics experiments are organized using clustering algorithms to form jets. To construct jets, the experimental collaborations based at the Large Hadron Collider (LHC) primarily use agglomerative hierarchical clustering schemes known as sequential recombination. W…
Study compares unsupervised and weakly-supervised methods for anomaly detection at the LHC.
problem Detecting new physics signals at the LHC with model-agnostic techniques.
method Compared unsupervised autoencoder (AE) and weakly-supervised Classification Without Labels (CWoLa) methods.
result Both methods complement each other, providing sensitivity to different types of signals.
Identifying the flavour of neutral B mesons production is one of the most important components needed in the study of time-dependent CP violation. The harsh environment of the Large Hadron Collider makes it particularly hard to succeed in this task. We present an inclusive flavour-tagging algorithm as an upgrade of…
We develop, discuss, and compare several inference techniques to constrain theory parameters in collider experiments. By harnessing the latent-space structure of particle physics processes, we extract extra information from the simulator. This augmented data can be used to train neural networks that precisely estimate …
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.
Researchers develop PAIN to improve self-driving safety through adversarial training.
problem Overfitting and poor generalizability of neural networks in self-driving vehicles.
method PAIN combines adversarial training in CARLA simulation to generate edge cases.
result Trained self-driving vehicles are more resilient to environmental uncertainty and less prone to collisions.
Deep learning has shown remarkable results for image analysis and is expected to aid individual treatment decisions in health care. To achieve this, deep learning methods need to be promoted from the level of mere associations to being able to answer causal questions. We present a scenario with real-world medical image…
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…
Precise scientific analysis in collider-based particle physics is possible because of complex simulations that connect fundamental theories to observable quantities. The significant computational cost of these programs limits the scope, precision, and accuracy of Standard Model measurements and searches for new phenome…
Three-dimensional N=2 superconformal field theories are constructed by compactifying M5-branes on three-manifolds. In the infrared the branes recombine, and the physics is captured by a single M5-brane on a branched cover of the original ultraviolet geometry. The branch locus is a tangle, a one-dimensional knotted subm…