Search Results for author:
Found 15 papers, 9 papers with code
NuCLR: Nuclear Co-Learned Representations
We introduce Nuclear Co-Learned Representations (NuCLR), a deep learning model that predicts various nuclear observables, including binding and decay energies, and nuclear charge radii.
Finding NEEMo: Geometric Fitting using Neural Estimation of the Energy Mover's Distance
We present a new and interesting direction for this architecture: estimation of the Wasserstein metric (Earth Mover's Distance) in optimal transport by employing the Kantorovich-Rubinstein duality to enable its use in geometric fitting applications.
Towards Understanding Grokking: An Effective Theory of Representation Learning
We aim to understand grokking, a phenomenon where models generalize long after overfitting their training set.
Robust and Provably Monotonic Networks
The Lipschitz constant of the map between the input and output space represented by a neural network is a natural metric for assessing the robustness of the model.
Progress in developing a hybrid deep learning algorithm for identifying and locating primary vertices
The locations of proton-proton collision points in LHC experiments are called primary vertices (PVs).
High Energy Physics - Experiment Data Analysis, Statistics and Probability
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
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
Ground-state energy estimation of the water molecule on a trapped ion quantum computer
Quantum computing leverages the quantum resources of superposition and entanglement to efficiently solve computational problems considered intractable for classical computers.
Quantum Physics Emerging Technologies
Serendipity in dark photon searches
Searches for dark photons provide serendipitous discovery potential for other types of vector particles.
High Energy Physics - Phenomenology High Energy Physics - Experiment
A Roadmap for HEP Software and Computing R&D for the 2020s
Particle physics has an ambitious and broad experimental programme for the coming decades.
Computational Physics High Energy Physics - Experiment
A novel approach to the bias-variance problem in bump hunting
This study explores various data-driven methods for performing background-model selection, and for assigning uncertainty on the signal-strength estimator that arises due to the choice of background model.
High Energy Physics - Experiment Nuclear Experiment Data Analysis, Statistics and Probability
Event generator tuning using Bayesian optimization
In this article, we show that Monte Carlo event generator parameters can be accurately obtained using Bayesian optimization and minimal expert-level physics knowledge.
Data Analysis, Statistics and Probability High Energy Physics - Experiment Nuclear Experiment
New approaches for boosting to uniformity
The use of multivariate classifiers has become commonplace in particle physics.
High Energy Physics - Experiment
uBoost: A boosting method for producing uniform selection efficiencies from multivariate classifiers
The use of multivariate classifiers, especially neural networks and decision trees, has become commonplace in particle physics.
Nuclear Experiment High Energy Physics - Experiment
Efficient, reliable and fast high-level triggering using a bonsai boosted decision tree
High-level triggering is a vital component in many modern particle physics experiments.
Instrumentation and Detectors High Energy Physics - Experiment Data Analysis, Statistics and Probability
