Physics & Astronomy

62 verified cases, highest-rated first.

Industries: quantum computing 7, astronomy 7, gravitational-wave astronomy 5, computational chemistry 5, particle physics 4, space weather 4, high-energy physics 3, radio astronomy 3, cosmology 2, space weather forecasting 2, astrophysics 2, gravitational wave astrophysics 1

• Learning high-accuracy error decoding for quantum processors

  Building large-scale quantum computers requires accurately decoding noisy syndrome information from quantum error-correction codes such as the surface code.

  Physics & Astronomy · 2024 · partially solved · 7/10 importance

• Simultaneous discovery of quantum error correction codes and encoders with a noise-aware reinforcement learning agent

  Discovering quantum error correction codes and encoding circuits tailored to specific qubit hardware is critical and hard to do manually; prior reinforcement learning approaches were severely limited in scalability.

  Physics & Astronomy · 2024 · partially solved · 7/10 importance

• Improved particle-flow event reconstruction with scalable neural networks for current and future particle detectors

  Reconstructing particles efficiently and accurately in the highly granular detectors expected at the High-Luminosity LHC and Future Circular Collider is computationally challenging.

  Physics & Astronomy · 2024 · ai assisted · 7/10 importance

• Quantum Error Correction with Quantum Autoencoders

  Quantum processors require active error correction, and designing effective strategies for detecting and correcting varied errors (including correlated errors and qubit loss) is difficult.

  Physics & Astronomy · 2023 · ai assisted · 7/10 importance

• Space-based gravitational wave signal detection and extraction with deep neural network

  Matched filtering for space-based gravitational wave detectors would require a computationally unaffordable template bank, so a feasible detection and extraction method was needed.

  Physics & Astronomy · 2023 · partially solved · 7/10 importance

• Magnetic control of tokamak plasmas through deep reinforcement learning

  Shaping and maintaining high-temperature plasma in a tokamak requires high-dimensional, high-frequency magnetic control that is difficult to design.

  Physics & Astronomy · 2022 · solved · 7/10 importance

• Machine learning for continuous quantum error correction on superconducting qubits

  Continuous quantum error correction must detect bit-flip errors from noisy, non-ideal syndrome measurements on superconducting qubits.

  Physics & Astronomy · 2022 · ai assisted · 7/10 importance

• Detecting an axion-like particle with machine learning at the LHC

  Light axion-like particles (ALP mass MeV-GeV) are hard to detect because cosmological and astrophysical bounds on them are weak.

  Physics & Astronomy · 2021 · ai assisted · 7/10 importance

• Deep Learning for Gravitational-Wave Data Analysis: A Resampling White-Box Approach

  Detecting weak gravitational-wave signals from compact binary coalescences in noisy single-interferometer LIGO detector data.

  Physics & Astronomy · 2021 · partially solved · 7/10 importance

• Optimizing Quantum Error Correction Codes with Reinforcement Learning

  Determining the most suited quantum error correction encoding for unknown error channels or specific laboratory setups is highly challenging.

  Physics & Astronomy · 2019 · partially solved · 7/10 importance

• Machine-learning-assisted correction of correlated qubit errors in a topological code

  Fault-tolerant quantum computation requires efficient detection and correction of errors in encoded quantum information, and standard decoders miss correlations between bit-flip and phase-flip errors.

  Physics & Astronomy · 2018 · ai assisted · 7/10 importance

• Solving the quantum many-body problem with artificial neural networks

  The exponential complexity of the many-body wave function makes describing interacting quantum systems computationally intractable.

  Physics & Astronomy · 2017 · partially solved · 7/10 importance

• GSpyNetTree: a signal-vs-glitch classifier for gravitational-wave event candidates

  LIGO-Virgo detector data contain non-Gaussian transient noise 'glitches' that can mimic real gravitational-wave signals, and the higher expected O4 event rate requires automated candidate validation.

  Physics & Astronomy · 2024 · ai assisted · 6/10 importance

• CONNECT: a neural network based framework for emulating cosmological observables and cosmological parameter inference

  Bayesian cosmological parameter inference requires 10^5-10^6 theoretical model evaluations, each taking tens of CPU core-seconds, making the process very computationally expensive.

  Physics & Astronomy · 2023 · solved · 6/10 importance

• An unsupervised deep learning model for dense velocity field reconstruction in particle image velocimetry (PIV) measurements

  Reconstructing dense velocity fields from PIV particle images without requiring synthetic training data, while handling particle occlusion and boundary motion.

  Physics & Astronomy · 2023 · ai assisted · 6/10 importance

• Machine Learning for Particle Flow Reconstruction at CMS

  Reconstructing stable particles in the CMS detector from combined tracks and calorimeter clusters (particle flow) to improve jets and missing transverse energy.

  Physics & Astronomy · 2023 · ai assisted · 6/10 importance

• Solar Flare Forecast Using 3D Convolutional Neural Networks

  Forecasting whether an active region on the Sun will erupt a C- or M-class solar flare within the next 24 hours.

  Physics & Astronomy · 2022 · ai assisted · 6/10 importance

• Complete parameter inference for GW150914 using deep learning

  Iterative MCMC sampling for gravitational-wave parameter inference is computationally costly as detection rates grow and fast multimessenger alerts are needed.

  Physics & Astronomy · 2021 · ai assisted · 6/10 importance

• Quantum-inspired machine learning on high-energy physics data

  Classifying b-jets from proton-proton collisions in the LHCb experiment at CERN, a challenging big data problem in high-energy physics.

  Physics & Astronomy · 2021 · partially solved · 6/10 importance

• Deep Learning for Line Intensity Mapping Observations: Information Extraction from Noisy Maps

  Line intensity mapping observations are contaminated by foreground/background sources and noise, hindering study of large-scale structure and galaxy evolution.

  Physics & Astronomy · 2021 · ai assisted · 6/10 importance

• Exploring gravitational-wave detection and parameter inference using deep learning methods

  Detecting gravitational-wave signals from binary black hole mergers in noisy detector data and inferring their parameters.

  Physics & Astronomy · 2021 · ai assisted · 6/10 importance

• Galaxy morphological classification in deep-wide surveys via unsupervised machine learning

  Upcoming big-data surveys will produce galaxy images at volumes that make visual inspection and supervised machine learning impractical for morphological classification.

  Physics & Astronomy · 2019 · ai assisted · 6/10 importance

• Image-based deep learning for classification of noise transients in gravitational wave detectors

  Glitches (transient noise events) degrade data quality in gravitational wave detectors like Advanced LIGO and Virgo and need automatic classification.

  Physics & Astronomy · 2018 · ai assisted · 6/10 importance

• Scientific Domain Knowledge Improves Exoplanet Transit Classification with Deep Learning

  Space missions like Kepler and TESS produce large datasets of transit signals that must be efficiently and systematically classified as exoplanets or false positives.

  Physics & Astronomy · 2018 · ai assisted · 6/10 importance

• PHOTOMETRIC SUPERNOVA CLASSIFICATION WITH MACHINE LEARNING

  Upcoming imaging surveys will discover too many supernovae for spectroscopic type confirmation, so automated photometric classification is needed.

  Physics & Astronomy · 2016 · ai assisted · 6/10 importance

• SymbolNet: neural symbolic regression with adaptive dynamic pruning for compression

  Finding compact symbolic expressions for high-dimensional datasets is hard for genetic-programming-based symbolic regression, which matters for efficient inference on resource-constrained hardware such as FPGAs at the CERN Large Hadron Collider.

  Physics & Astronomy · 2025 · ai assisted · 5/10 importance

• The future of cosmological likelihood-based inference: accelerated high-dimensional parameter estimation and model comparison

  Bayesian parameter estimation and model comparison in cosmology becomes computationally slow in high-dimensional settings, limiting analyses.

  Physics & Astronomy · 2024 · ai assisted · 5/10 importance

• Amplitude scintillation detection with geodetic GNSS receivers leveraging machine learning decision tree

  Amplitude scintillation detection normally requires dedicated, costly ionospheric scintillation monitoring receivers (ISMRs).

  Physics & Astronomy · 2024 · solved · 5/10 importance

• Application of Machine Learning Ensemble Methods to ASTRI Mini-Array Cherenkov Event Reconstruction

  Imaging Atmospheric Cherenkov Telescope data is overwhelmingly dominated by cosmic-ray background, requiring efficient gamma/hadron separation and energy reconstruction.

  Physics & Astronomy · 2023 · ai assisted · 5/10 importance

• Probabilistic Solar Proxy Forecasting With Neural Network Ensembles

  Forecasting the F10.7cm solar radio flux proxy, which drives thermosphere density forecasts affecting low-Earth-orbit objects through atmospheric drag.

  Physics & Astronomy · 2023 · ai assisted · 5/10 importance

• Learning to simulate high energy particle collisions from unlabeled data

  Simulating how theoretical particle physics models map to experimental collision data is extremely computationally expensive.

  Physics & Astronomy · 2022 · ai assisted · 5/10 importance

• Photometric Classification of Early-time Supernova Light Curves with SCONE

  Astronomers need to identify a supernova's type as early as possible from photometric light-curve data so surveys can decide which events merit follow-up.

  Physics & Astronomy · 2022 · solved · 5/10 importance

• GlitchNet: A Glitch Detection and Removal System for SEIS Records Based on Deep Learning

  Glitches, a common type of noise, contaminate continuous seismic data recorded by the SEIS instrument on Mars during the InSight mission.

  Physics & Astronomy · 2022 · solved · 5/10 importance

• Recurrent neural network-based volumetric fluorescence microscopy

  Volumetric fluorescence microscopy normally requires many axial scans of a sample, making 3D imaging slow. The paper reconstructs a full 3D volume from only a few sparsely captured 2D images.

  Physics & Astronomy · 2021 · solved · 5/10 importance

• AI Feynman: a physics-inspired method for symbolic regression

  Discovering the equations governing physical systems from raw data, without knowing the formulas in advance.

  Physics & Astronomy · 2020 · ai assisted · 5/10 importance

• Neural network based model for global Total Electron Content forecasting

  Forecasting the global Total Electron Content (TEC) of the ionosphere 24 hours in advance, including during disturbed geomagnetic conditions.

  Physics & Astronomy · 2020 · ai assisted · 5/10 importance

• Using principal component analysis for neural network high-dimensional potential energy surface

  Computing high-dimensional potential energy surfaces for chemical reactions remains difficult for most relevant systems with electronic structure methods.

  Physics & Astronomy · 2020 · ai assisted · 5/10 importance

• Quantum error correction for the toric code using deep reinforcement learning

  Correcting bit-flip errors on the topological toric quantum code requires an effective decoder.

  Physics & Astronomy · 2019 · partially solved · 5/10 importance

• Forecasting the <i>Dst</i> index using a swarm‐optimized neural network

  Forecasting the disturbance storm time (Dst) index, a measure of geomagnetic disturbance, 1 to 6 hours ahead.

  Physics & Astronomy · 2017 · ai assisted · 5/10 importance

• Evaluating machine learning models for supernova gravitational wave signal classification

  Determining the equation of state of nuclear matter from gravitational waves of rotating core-collapse supernovae requires classifying signals generated by different equations of state.

  Physics & Astronomy · 2024 · ai assisted · 4/10 importance

• Classification of Low Earth Orbit (LEO) Resident Space Objects’ (RSO) Light Curves Using a Support Vector Machine (SVM) and Long Short-Term Memory (LSTM)

  Classifying short light curves of low Earth orbit resident space objects to infer object type, attitude and shape.

  Physics & Astronomy · 2023 · ai assisted · 4/10 importance

• Uncertainty Quantification for Machine Learning‐Based Ionosphere and Space Weather Forecasting: Ensemble, Bayesian Neural Network, and Quantile Gradient Boosting

  ML-based ionosphere and space weather forecasts lacked uncertainty quantification, so the confidence and reliability of their results were unknown.

  Physics & Astronomy · 2023 · ai assisted · 4/10 importance

• Unsupervised Machine Learning for Exploratory Data Analysis of Exoplanet Transmission Spectra

  Decoding the chemical composition of exoplanet atmospheres from high-dimensional transmission spectral data.

  Physics & Astronomy · 2022 · ai assisted · 4/10 importance

• Quantum machine learning for particle physics using a variational quantum classifier

  Classifying resonance signals in di-top final states in particle physics, where data-driven classifiers must work with limited data.

  Physics & Astronomy · 2021 · ai assisted · 4/10 importance

• Particle reconstruction of volumetric particle image velocimetry with the strategy of machine learning

  3D particle reconstruction from limited 2D projections in volumetric PIV is an under-determined inverse problem that iterative methods solve only approximately.

  Physics & Astronomy · 2021 · solved · 4/10 importance

• Exoplanet detection using machine learning

  Conventional algorithm-based approaches for detecting exoplanets via the transit method needed improvement.

  Physics & Astronomy · 2021 · ai assisted · 4/10 importance

• Using machine learning for transient classification in searches for gravitational-wave counterparts

  Large gravitational-wave sky localization regions produce many transient candidates that must be prioritized given restricted telescope time.

  Physics & Astronomy · 2020 · ai assisted · 4/10 importance

• Classification of gravitational-wave glitches via dictionary learning

  Gravitational-wave interferometers like advanced LIGO produce transient noise signals (glitches) that must be classified and separated from real astrophysical signals.

  Physics & Astronomy · 2019 · partially solved · 4/10 importance

• Identifying Exoplanets with Deep Learning: A Five-planet Resonant Chain around Kepler-80 and an Eighth Planet around Kepler-90

  Weak Kepler signals at low signal-to-noise are hard to distinguish from false positives, blocking accurate occurrence rates of Earth-sized planets.

  Physics & Astronomy · 2018 · solved · 4/10 importance

• Permutation invariant polynomial neural network approach to fitting potential energy surfaces. IV. Coupled diabatic potential energy matrices

  Accurately representing coupled diabatic potential energy matrices of molecules, needed to model non-adiabatic photodissociation dynamics.

  Physics & Astronomy · 2018 · solved · 4/10 importance

• A Machine Learning Classifier for Fast Radio Burst Detection at the VLBA

  Fast transient searches like V-FASTR on the Very Long Baseline Array produce huge numbers of candidate detections that burden astronomers with manual review time.

  Physics & Astronomy · 2016 · ai assisted · 4/10 importance

• UNSUPERVISED CLUSTERING OF TYPE II SUPERNOVA LIGHT CURVES

  Astronomers needed a systematic, unbiased way to classify Type II supernova light curves ahead of upcoming large transient surveys.

  Physics & Astronomy · 2016 · ai assisted · 4/10 importance

• Permutation invariant polynomial neural network approach to fitting potential energy surfaces. III. Molecule-surface interactions

  Constructing highly accurate potential energy surfaces for molecule-surface interactions was difficult, hindering simulation of reactions at surfaces.

  Physics & Astronomy · 2014 · solved · 4/10 importance

• Comparison of neural network and support vector machine methods for Kp forecasting

  Near-real-time forecasting of the Kp geomagnetic activity index was addressed by comparing four forecast models based on neural networks and support vector machines using solar wind data and preliminary Kp values.

  Physics & Astronomy · 2013 · partially solved · 4/10 importance

• Explaining deep learning of galaxy morphology with saliency mapping

  Measuring galactic bar lengths at scale requires automation beyond human visual classification of galaxy morphology.

  Physics & Astronomy · 2022 · solved · 3/10 importance

• Quantifying uncertainty in deep learning approaches to radio galaxy classification

  Deep learning classifiers of radio galaxies provide predictions without reliable uncertainty estimates, making their outputs hard to trust.

  Physics & Astronomy · 2022 · partially solved · 3/10 importance

• Experimental velocity data estimation for imperfect particle images using machine learning

  Experimental particle image velocimetry often has missing regions in images due to experimental limitations, leaving gaps in measured velocity fields.

  Physics & Astronomy · 2021 · partially solved · 3/10 importance

• Neural network potential energy surface for the low temperature ring polymer molecular dynamics of the H2CO + OH reaction

  Accurate low-temperature dynamics for the H2CO + OH reaction requires a potential energy surface, but existing symmetry-adapted neural network schemes suffer spurious long-range interactions.

  Physics & Astronomy · 2021 · solved · 3/10 importance

• Radio Galaxy Zoo: giant radio galaxy classification using multidomain deep learning

  Identifying rare giant radio galaxies in very large radio survey datasets is difficult, and will become harder with next-generation telescopes like the SKA.

  Physics & Astronomy · 2021 · ai assisted · 3/10 importance

• Bars formed in galaxy merging and their classification with deep learning

  Astronomers lacked understanding of how stellar bars form in galaxy merging and whether barred galaxies can be classified by their formation mechanism.

  Physics & Astronomy · 2020 · ai assisted · 3/10 importance

• Machine Learning Techniques for Stellar Light Curve Classification

  Predicting and classifying stellar properties from noisy, sparse real Kepler light-curve time-series data, a task previously studied mostly on simulated data.

  Physics & Astronomy · 2018 · partially solved · 3/10 importance

• Vibrational energies for HFCO using a neural network sum of exponentials potential energy surface

  Accurately computing the vibrational energies of formyl fluoride (HFCO) requires an accurate six-dimensional potential energy surface fitted to expensive ab initio data.

  Physics & Astronomy · 2016 · solved · 2/10 importance

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