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Found 64 papers, 37 papers with code
Discriminative versus Generative Approaches to Simulation-based Inference
We find that both the direct likelihood and likelihood ratio estimation are able to effectively extract parameters with reasonable uncertainties.
Generative Unfolding with Distribution Mapping
Machine learning enables unbinned, highly-differential cross section measurements.
CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation
We present the results of the "Fast Calorimeter Simulation Challenge 2022" - the CaloChallenge.
FAIR Universe HiggsML Uncertainty Challenge Competition
The FAIR Universe -- HiggsML Uncertainty Challenge focuses on measuring the physics properties of elementary particles with imperfect simulators due to differences in modelling systematic errors.
Multidimensional Deconvolution with Profiling
In many experimental contexts, it is necessary to statistically remove the impact of instrumental effects in order to physically interpret measurements.
Moment Unfolding
Deconvolving ("unfolding'') detector distortions is a critical step in the comparison of cross section measurements with theoretical predictions in particle and nuclear physics.
Constraining the Higgs Potential with Neural Simulation-based Inference for Di-Higgs Production
Determining the form of the Higgs potential is one of the most exciting challenges of modern particle physics.
Unifying Simulation and Inference with Normalizing Flows
There have been many applications of deep neural networks to detector calibrations and a growing number of studies that propose deep generative models as automated fast detector simulators.
The Landscape of Unfolding with Machine Learning
Recent innovations from machine learning allow for data unfolding, without binning and including correlations across many dimensions.
Designing Observables for Measurements with Deep Learning
Many analyses in particle and nuclear physics use simulations to infer fundamental, effective, or phenomenological parameters of the underlying physics models.
The Optimal use of Segmentation for Sampling Calorimeters
One of the key design choices of any sampling calorimeter is how fine to make the longitudinal and transverse segmentation.
Flows for Flows: Morphing one Dataset into another with Maximum Likelihood Estimation
We propose a protocol called flows for flows for training normalizing flows to morph one dataset into another even if the underlying probability density of neither dataset is known explicitly.
Improving Generative Model-based Unfolding with Schrödinger Bridges
Machine learning-based unfolding has enabled unbinned and high-dimensional differential cross section measurements.
Comparison of Point Cloud and Image-based Models for Calorimeter Fast Simulation
Score based generative models are a new class of generative models that have been shown to accurately generate high dimensional calorimeter datasets.
High-dimensional and Permutation Invariant Anomaly Detection
Methods for anomaly detection of new physics processes are often limited to low-dimensional spaces due to the difficulty of learning high-dimensional probability densities.
Learning Likelihood Ratios with Neural Network Classifiers
The likelihood ratio is a crucial quantity for statistical inference in science that enables hypothesis testing, construction of confidence intervals, reweighting of distributions, and more.
ELSA -- Enhanced latent spaces for improved collider simulations
We explore various approaches for enhancing the precision of simulations using machine learning, including interventions at the end of the simulation chain (reweighting), at the beginning of the simulation chain (pre-processing), and connections between the end and beginning (latent space refinement).
Weakly-Supervised Anomaly Detection in the Milky Way
Large-scale astrophysics datasets present an opportunity for new machine learning techniques to identify regions of interest that might otherwise be overlooked by traditional searches.
Supervised Anomaly Detection
Weakly-supervised Anomaly Detection
Unbinned Profiled Unfolding
We propose a new machine learning-based unfolding method that results in an unbinned differential cross section and can profile nuisance parameters.
Resonant Anomaly Detection with Multiple Reference Datasets
An important class of techniques for resonant anomaly detection in high energy physics builds models that can distinguish between reference and target datasets, where only the latter has appreciable signal.
Machine-Learning Compression for Particle Physics Discoveries
In collider-based particle and nuclear physics experiments, data are produced at such extreme rates that only a subset can be recorded for later analysis.
Score-based Generative Models for Calorimeter Shower 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.
Bias and Priors in Machine Learning Calibrations for High Energy Physics
Machine learning offers an exciting opportunity to improve the calibration of nearly all reconstructed objects in high-energy physics detectors.
BIG-bench Machine Learning
Vocal Bursts Intensity Prediction
Exploring the Universality of Hadronic Jet Classification
The modeling of jet substructure significantly differs between Parton Shower Monte Carlo (PSMC) programs.
New directions for surrogate models and differentiable programming for High Energy Physics detector simulation
The computational cost for high energy physics detector simulation in future experimental facilities is going to exceed the current available resources.
Machine Learning in the Search for New Fundamental Physics
Machine learning plays a crucial role in enhancing and accelerating the search for new fundamental physics.
Learning from learning machines: a new generation of AI technology to meet the needs of science
We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery.
Online-compatible Unsupervised Non-resonant Anomaly Detection
There is a growing need for anomaly detection methods that can broaden the search for new particles in a model-agnostic manner.
A Cautionary Tale of Decorrelating Theory Uncertainties
A variety of techniques have been proposed to train machine learning classifiers that are independent of a given feature.
New Methods and Datasets for Group Anomaly Detection From Fundamental Physics
The identification of anomalous overdensities in data - group or collective anomaly detection - is a rich problem with a large number of real world applications.
Latent Space Refinement for Deep Generative Models
Deep generative models are becoming widely used across science and industry for a variety of purposes.
Scaffolding Simulations with Deep Learning for High-dimensional Deconvolution
A common setting for scientific inference is the ability to sample from a high-fidelity forward model (simulation) without having an explicit probability density of the data.
Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detection
We examine the ability of the two methods to identify a new physics signal at different cross sections in a fully hadronic resonance search.
Quantum Gate Pattern Recognition and Circuit Optimization for Scientific Applications
The first ingredient is a technique to recognize repeated patterns of quantum gates, opening up the possibility of future hardware co-optimization.
Quantum Physics
A Living Review of Machine Learning for Particle Physics
Modern machine learning techniques, including deep learning, are rapidly being applied, adapted, and developed for high energy physics.
E Pluribus Unum Ex Machina: Learning from Many Collider Events at Once
There have been a number of recent proposals to enhance the performance of machine learning strategies for collider physics by combining many distinct events into a single ensemble feature.
Beyond 4D Tracking: Using Cluster Shapes for Track Seeding
The shape of clusters provides an additional dimension for track seeding that can significantly reduce the combinatorial challenge of track finding.
Enhancing searches for resonances with machine learning and moment decomposition
A key challenge in searches for resonant new physics is that classifiers trained to enhance potential signals must not induce localized structures.
High Energy Physics - Phenomenology High Energy Physics - Experiment Data Analysis, Statistics and Probability
Readout Rebalancing for Near Term Quantum Computers
These X gates are placed so that the expected number of qubits in the 1 state is minimized.
Quantum Physics
Parameter Estimation using Neural Networks in the Presence of Detector Effects
Histogram-based template fits are the main technique used for estimating parameters of high energy physics Monte Carlo generators.
Simulation-Assisted Decorrelation for Resonant Anomaly Detection
A growing number of weak- and unsupervised machine learning approaches to anomaly detection are being proposed to significantly extend the search program at the Large Hadron Collider and elsewhere.
DCTRGAN: Improving the Precision of Generative Models with Reweighting
We introduce a post-hoc correction to deep generative models to further improve their fidelity, based on the Deep neural networks using the Classification for Tuning and Reweighting (DCTR) protocol.
GANplifying Event Samples
A critical question concerning generative networks applied to event generation in particle physics is if the generated events add statistical precision beyond the training sample.
Supervised Jet Clustering with Graph Neural Networks for Lorentz Boosted Bosons
By labeling simulated final state hadrons as descending from an uncolored particle, it is possible to train a supervised learning method to create boson jets.
High Energy Physics - Phenomenology High Energy Physics - Experiment Data Analysis, Statistics and Probability
Simulation Assisted Likelihood-free Anomaly Detection
For potential signals that are resonant in one known feature, this new method first learns a parameterized reweighting function to morph a given simulation to match the data in sidebands.
Anomaly Detection with Density Estimation
By estimating the probability density of the data in a signal region and in sidebands, and interpolating the latter into the signal region, a likelihood ratio of data vs. background can be constructed.
OmniFold: A Method to Simultaneously Unfold All Observables
Collider data must be corrected for detector effects ("unfolded") to be compared with many theoretical calculations and measurements from other experiments.
Expression of Interest for the CODEX-b Detector
A design overview is presented for the CODEX-$\beta$ demonstrator detector, which will enable background calibration and detector design studies.
High Energy Physics - Experiment High Energy Physics - Phenomenology
AI Safety for High Energy Physics
The field of high-energy physics (HEP), along with many scientific disciplines, is currently experiencing a dramatic influx of new methodologies powered by modern machine learning techniques.
Error detection on quantum computers improves accuracy of chemical calculations
A major milestone of quantum error correction is to achieve the fault-tolerance threshold beyond which quantum computers can be made arbitrarily accurate.
Quantum Physics Chemical Physics
Neural Networks for Full Phase-space Reweighting and Parameter Tuning
We therefore introduce Deep neural networks using Classification for Tuning and Reweighting (DCTR), a neural network-based approach to reweight and fit simulations using all kinematic and flavor information -- the full phase space.
Anomaly Detection for Resonant New Physics with Machine Learning
Despite extensive theoretical motivation for physics beyond the Standard Model (BSM) of particle physics, searches at the Large Hadron Collider (LHC) have found no significant evidence for BSM physics.
High Energy Physics - Phenomenology High Energy Physics - Experiment
The Optimal Use of Silicon Pixel Charge Information for Particle Identification
Particle identification using the energy loss in silicon detectors is a powerful technique for probing the Standard Model (SM) as well as searching for new particles beyond the SM.
Instrumentation and Detectors
Learning to Classify from Impure Samples with High-Dimensional Data
In particle physics, this challenge is surmounted by the use of simulations.
CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks
The precise modeling of subatomic particle interactions and propagation through matter is paramount for the advancement of nuclear and particle physics searches and precision measurements.
Controlling Physical Attributes in GAN-Accelerated Simulation of Electromagnetic Calorimeters
High-precision modeling of subatomic particle interactions is critical for many fields within the physical sciences, such as nuclear physics and high energy particle physics.
Classification without labels: Learning from mixed samples in high energy physics
In this paper, we introduce the paradigm of classification without labels (CWoLa) in which a classifier is trained to distinguish statistical mixtures of classes, which are common in collider physics.
Pileup Mitigation with Machine Learning (PUMML)
Pileup involves the contamination of the energy distribution arising from the primary collision of interest (leading vertex) by radiation from soft collisions (pileup).
Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeters
Physicists at the Large Hadron Collider (LHC) rely on detailed simulations of particle collisions to build expectations of what experimental data may look like under different theory modeling assumptions.
Weakly Supervised Classification in High Energy Physics
As machine learning algorithms become increasingly sophisticated to exploit subtle features of the data, they often become more dependent on simulations.
Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis
We provide a bridge between generative modeling in the Machine Learning community and simulated physical processes in High Energy Particle Physics by applying a novel Generative Adversarial Network (GAN) architecture to the production of jet images -- 2D representations of energy depositions from particles interacting with a calorimeter.
Jet-Images -- Deep Learning Edition
Building on the notion of a particle physics detector as a camera and the collimated streams of high energy particles, or jets, it measures as an image, we investigate the potential of machine learning techniques based on deep learning architectures to identify highly boosted W bosons.
Fuzzy Jets
Collimated streams of particles produced in high energy physics experiments are organized using clustering algorithms to form jets.
Bisection-based asymmetric MT2 computation: a higher precision calculator than existing symmetric methods
An MT2 calculation algorithm is described.
High Energy Physics - Phenomenology
