question_id string | paper_id string | question string | option_a string | option_b string | correct_option string | difficulty string |
|---|---|---|---|---|---|---|
Q0001 | 2024MNRAS.535.1844L | How can one obtain accurate photometric redshift estimates for high-redshift galaxies using only flux measurements from different observational filters as input? | MLP | LSTM | B | hard |
Q0002 | 2024ApJ...977...19D | How can the global 21-cm signal be reliably modeled from a specified set of cosmological and astrophysical parameters? | Two-layer LSTM | VAE | A | easy |
Q0003 | 2024ApJ...977...19D | How can the global 21-cm signal be reliably modeled from a specified set of cosmological and astrophysical parameters? | Two-layer LSTM | Single MLP | A | easy |
Q0004 | 2024ApJ...977...19D | How can the global 21-cm signal be reliably modeled from a specified set of cosmological and astrophysical parameters? | Two-layer LSTM | Multi-MLP + PCA + Decision Tree | A | easy |
Q0005 | 2024AJ....168..242A | How can the near-surface gravity field of an irregular, nonuniform small body be accurately reconstructed from sampled simulated gravity-field data? | MLP | Neural density field | B | easy |
Q0006 | 2024AJ....168..242A | How can the near-surface gravity field of an irregular, nonuniform small body be accurately reconstructed from sampled simulated gravity-field data? | vanilla PINN | Neural density field | B | easy |
Q0007 | 2024AJ....168..242A | How can the near-surface gravity field of an irregular, nonuniform small body be accurately reconstructed from sampled simulated gravity-field data? | Transformer-PINN | Neural density field | B | easy |
Q0008 | 2024AJ....168..242A | How can the near-surface gravity field of an irregular, nonuniform small body be accurately reconstructed from sampled simulated gravity-field data? | Neural density field | Self-adaptive PINN | A | hard |
Q0009 | 2024AJ....168..242A | How can the near-surface gravity field of an irregular, nonuniform small body be accurately reconstructed from sampled simulated gravity-field data? | Self-adaptive PINN | MLP | A | easy |
Q0010 | 2024AJ....168..242A | How can the near-surface gravity field of an irregular, nonuniform small body be accurately reconstructed from sampled simulated gravity-field data? | Self-adaptive PINN | vanilla PINN | A | easy |
Q0011 | 2024AJ....168..242A | How can the near-surface gravity field of an irregular, nonuniform small body be accurately reconstructed from sampled simulated gravity-field data? | Self-adaptive PINN | Transformer-PINN | A | easy |
Q0012 | 2024AJ....168..242A | How can the near-surface gravity field of an irregular, nonuniform small body be accurately reconstructed from sampled simulated gravity-field data? | MLP | Transformer-PINN | B | easy |
Q0013 | 2024AJ....168..242A | How can the near-surface gravity field of an irregular, nonuniform small body be accurately reconstructed from sampled simulated gravity-field data? | Transformer-PINN | vanilla PINN | A | easy |
Q0014 | 2024AJ....168..242A | How can the near-surface gravity field of an irregular, nonuniform small body be accurately reconstructed from sampled simulated gravity-field data? | vanilla PINN | MLP | A | easy |
Q0015 | 2024A&A...692A.132H | How can we accurately and precisely measure time delays from simulated microlensed light curves of strongly lensed Type Ia supernovae? | Random Forest | LSTM + fully connected neural network | B | easy |
Q0016 | 2024A&A...692A..72C | For identifying galaxy-scale strong lenses in multiband survey images, which CNN architecture gives higher recall with very few false positives? | CNN | ResNet | B | easy |
Q0017 | 2024A&A...692A..72C | For identifying galaxy-scale strong lenses in multiband survey images, which CNN architecture gives higher recall with very few false positives? | Group-equivariant CNN | CNN | A | easy |
Q0018 | 2024A&A...692A..72C | How can we identify galaxy-scale strong gravitational lenses in multi-band survey images while maximizing recall at zero or very low contamination? | Group-equivariant CNN | ResNet | B | hard |
Q0019 | 2024ApJ...976..143T | For quasar spectra, how can the unabsorbed continuum in the Lyα forest be more accurately predicted from only spectral information redward of Lyα? | PCA | CNN | B | easy |
Q0020 | 2024ApJ...976..143T | For quasar spectra, how can the unabsorbed continuum in the Lyα forest be more accurately predicted from only spectral information redward of Lyα? | CNN | Non-Negative Matrix Factorization | A | easy |
Q0021 | 2024ApJ...976...37W | In cosmological hydrodynamical simulations, how can galaxy stellar masses be more accurately predicted from dark-matter subhalo properties and large-scale environment? | Explainable Boosting Machines | E(3)-invariant GNN | B | easy |
Q0022 | 2024A&A...691A.338C | For Solar System survey tracklets, how can interstellar objects be more reliably distinguished from other Solar System objects while minimizing false positives and false negatives? | Neural Network | Gradient Boosting Machines | B | easy |
Q0023 | 2024A&A...691A.338C | For Solar System survey tracklets, how can interstellar objects be more reliably distinguished from other Solar System objects while minimizing false positives and false negatives? | Random Forest | Neural Network | A | hard |
Q0024 | 2024A&A...691A.223V | Using low-resolution optical spectra, how can hot subdwarf binaries be more accurately identified against single hot subdwarfs? | CNN | Self-Organizing Map | A | easy |
Q0025 | 2024A&A...691A.223V | Using optical photometry and low-resolution spectrophotometric observations, which approach more accurately identifies hot subdwarf binaries against single hot subdwarfs? | weighted SVM | CNN | B | easy |
Q0026 | 2024ApJ...974..159J | Using five-band galaxy images and photometry, how can photometric redshifts be estimated more reliably for weak-lensing cosmology? | Bayesian CNN | XGBoost | A | easy |
Q0027 | 2024ApJ...974..159J | Using five-band galaxy images and photometry, how can photometric redshifts be estimated more reliably for weak-lensing cosmology? | Bayesian CNN | Bayesian MLP | A | hard |
Q0028 | 2024ApJ...974..159J | Using five-band galaxy images and photometry, how can photometric redshifts be estimated more reliably for weak-lensing cosmology? | Bayesian CNN | MLP | A | easy |
Q0029 | 2024ApJ...974..159J | Using five-band galaxy images and photometry, how can photometric redshifts be estimated more reliably for weak-lensing cosmology? | SVM | Bayesian CNN | B | easy |
Q0030 | 2024ApJ...974..159J | Using five-band galaxy images and photometry, how can photometric redshifts be estimated more reliably for weak-lensing cosmology? | XGBoost | CNN | B | easy |
Q0031 | 2024ApJ...974..159J | Using five-band galaxy images and photometry, how can photometric redshifts be estimated more reliably for weak-lensing cosmology? | MLP | CNN | B | easy |
Q0032 | 2024ApJ...974..159J | Using five-band galaxy images and photometry, how can photometric redshifts be estimated more reliably for weak-lensing cosmology? | SVM | CNN | B | easy |
Q0033 | 2024ApJ...974...96S | For astrometric candidate pairs, how can true astrometric binaries be distinguished from visual alignments with high discriminating power using proper motions, parallaxes, and angular and physical separations? | Logistic regression | Artificial neural network | B | easy |
Q0034 | 2024ApJ...973..163Y | For compact-binary phase-space trajectories learned from numerical simulations, how can orbital evolution be predicted more accurately and efficiently over longer prediction horizons? | Temporal Convolutional Network | LSTM | B | hard |
Q0035 | 2024ApJ...973..154H | How can stellar evolutionary tracks and observables be emulated more accurately from grids of stellar evolution models? | Conditional Normalizing Flows | Nearest-Neighbor Interpolation | A | easy |
Q0036 | 2024A&A...690A.224L | How can QSO classifications based on broadband colors achieve higher precision in a joint star-galaxy-QSO classification task? | hierarchical density-based clustering | sharpened-dimensionality-reduction + k-nearest-neighbor classification | B | easy |
Q0037 | 2024RAA....24i5023F | For stellar spectra represented as wavelet images, how can spectral types be classified more accurately? | Vision Transformer | feature-pyramid ConvNeXt-V2 CNN | B | easy |
Q0038 | 2024RAA....24i5023F | For stellar spectra represented as wavelet images, how can spectral types be classified more accurately? | Random Forest | feature-pyramid ConvNeXt-V2 CNN | B | easy |
Q0039 | 2024RAA....24i5023F | For stellar spectra represented as wavelet images, how can spectral types be classified more accurately? | stochastic-gradient-descent linear classifier | feature-pyramid ConvNeXt-V2 CNN | B | easy |
Q0040 | 2024RAA....24i5022M | For sunspot groups observed in white-light and magnetogram images, how can their magnetic classes be classified more reliably when edge dimming weakens image features? | dual-stream ResNet50 | dual-stream ResNet50 with channel-and-spatial attention | B | easy |
Q0041 | 2024RAA....24i5022M | For sunspot groups observed in white-light and magnetogram images, how can their magnetic classes be classified more reliably when edge dimming weakens image features? | dual-stream ResNet50 with squeeze-and-excitation attention | dual-stream ResNet50 with channel-and-spatial attention | B | easy |
Q0042 | 2024MNRAS.533..525M | Across combined real-and-simulated strong-lens test sets, which neural-network design gives more stable overall lens-classification performance? | five-layer classical CNN | ResNet-based CNN | A | hard |
Q0043 | 2024MNRAS.533..525M | For strong-lens searches evaluated on real lens candidates and real non-lenses, which neural-network design gives a better precision-recall trade-off at the adopted survey-search threshold? | ResNet-based CNN | block-structured CNN with batch normalization | A | hard |
Q0044 | 2024ApJS..274....7M | For low-to-high-quality galaxy-image super-resolution, which approach produces more visually realistic galaxy reconstructions? | SRGAN | EDSR | A | easy |
Q0045 | 2024ApJS..274....7M | For low-to-high-quality galaxy-image super-resolution, which approach produces more visually realistic galaxy reconstructions? | SRGAN | RCAN | A | easy |
Q0046 | 2024ApJS..274....7M | For low-to-high-quality galaxy-image super-resolution, which approach produces more visually realistic galaxy reconstructions? | SRGAN | efficient nonlocal contrastive attention network | A | easy |
Q0047 | 2024ApJS..274....7M | For low-to-high-quality galaxy-image super-resolution, which approach better matches high-resolution reference images in pixel-level and structural fidelity? | efficient nonlocal contrastive attention network | SRGAN | A | hard |
Q0048 | 2024ApJ...973...41Z | For Bayesian inference of reionization parameters from pure-signal 3D 21 cm light-cone images, which summary-compression approach yields tighter posterior constraints? | solid harmonic wavelet scattering transform + DELFI | 3D CNN + DELFI | A | easy |
Q0049 | 2024ApJ...973...41Z | For Bayesian inference of reionization parameters from 3D 21 cm light-cone images, which summary-compression approach yields tighter posterior constraints? | power spectrum + DELFI | solid harmonic wavelet scattering transform + DELFI | B | easy |
Q0050 | 2024ApJ...972....7R | For bright-transient survey candidates, which architecture better supports automated spectroscopic follow-up when source-level completeness and purity are prioritized? | multi-modal CNN | fully connected neural network | A | hard |
Q0051 | 2024ApJ...972....7R | For identifying bright extragalactic transients in survey alerts, which ML pipeline gives higher classification accuracy? | CNN-MLP fusion of images with photometric and catalogue features | CNN using images | A | easy |
Q0052 | 2024ApJ...972....7R | For classifying individual bright-transient survey alert packets before source-level follow-up policies are applied, which unimodal architecture gives higher alert-level accuracy and ROC-AUC? | fully connected neural network | uni-modal CNN | A | hard |
Q0053 | 2024A&A...689A.181M | For mock galaxy-cluster velocity-dispersion maps, which U-Net variant better recovers the radial ICL-fraction structure while reducing spurious small-scale ICL features? | Attention U-Net | U-Net | A | easy |
Q0054 | 2024A&A...689A.166C | For emission-line galaxy target selection across the tested photometric configurations, which classifier more consistently maintains higher target precision at high target recall? | Self-Organizing Map | MLP | B | easy |
Q0055 | 2024A&A...689A.166C | For emission-line galaxy target selection across the tested photometric configurations, which classifier more consistently maintains higher target precision at high target recall? | Self-Organizing Map | SVM | B | easy |
Q0056 | 2024A&A...689A.166C | For emission-line galaxy target selection across the tested photometric configurations, which classifier more consistently maintains higher target precision at high target recall? | Self-Organizing Map | Extra Trees Classifier | B | hard |
Q0057 | 2024A&A...689A.166C | For emission-line galaxy target selection across the tested photometric configurations, which classifier more consistently maintains higher target precision at high target recall? | Random Forest | Self-Organizing Map | A | hard |
Q0058 | 2024A&A...689A.166C | For emission-line galaxy target selection across the tested photometric configurations, which classifier more consistently maintains higher target precision at high target recall? | AdaBoost | MLP | B | hard |
Q0059 | 2024A&A...689A.166C | For emission-line galaxy target selection across the tested photometric configurations, which classifier more consistently maintains higher target precision at high target recall? | SVM | AdaBoost | A | hard |
Q0060 | 2024A&A...689A.143G | For faint companion spectra cross-correlated with multiple molecular templates, which classifier detects planets more sensitively at low false discovery rate when atmospheric characteristics are uncertain? | 1D CNN | Perceptron | A | hard |
Q0061 | 2024MNRAS.532.3322A | For identifying multi-component radio sources, which ML approach gives higher classification accuracy? | CNN using radio images | CNN-ANN fusion of radio images and catalogue features | B | easy |
Q0062 | 2024MNRAS.532.3322A | For identifying multi-component radio sources, which ML approach gives higher classification accuracy? | ANN using catalogue features | CNN-ANN fusion of radio images and catalogue features | B | easy |
Q0063 | 2024ApJS..273...27S | For reconstructing the cosmic distance ladder from Type Ia supernova apparent magnitudes with full covariance information, which method better predicts distances while preserving a smooth and monotonic redshift-distance relation? | Support Vector Regression | covariance-aware LSTM | B | hard |
Q0064 | 2024ApJS..273...27S | For reconstructing the cosmic distance ladder from Type Ia supernova apparent magnitudes with full covariance information, which method better predicts distances while preserving a smooth and monotonic redshift-distance relation? | MLP | covariance-aware LSTM | B | easy |
Q0065 | 2024ApJS..273...27S | For reconstructing the cosmic distance ladder from Type Ia supernova apparent magnitudes with full covariance information, which method better predicts distances while preserving a smooth and monotonic redshift-distance relation? | kNN regression | covariance-aware LSTM | B | easy |
Q0066 | 2024ApJ...970..120D | For extracting sunspot components from heterogeneous historical sunspot drawings when labels are available from one station and other stations are unlabeled, which segmentation approach gives more accurate cross-station segmentation? | Hybrid Task Cascade R-CNN | semisupervised dynamic mutual training with boundary-guided segmentation | B | easy |
Q0067 | 2024ApJ...970..120D | For extracting sunspot components from heterogeneous historical sunspot drawings when labels are available from one station and other stations are unlabeled, which segmentation approach gives more accurate cross-station segmentation? | semisupervised dynamic mutual training with boundary-guided segmentation | scale-aware attention Mask R-CNN with PointRend | A | easy |
Q0068 | 2024ApJ...970..120D | For extracting sunspot components from heterogeneous historical sunspot drawings when labels are available from one station and other stations are unlabeled, which segmentation approach gives more accurate cross-station segmentation? | semisupervised dynamic mutual training with boundary-guided segmentation | dynamic mutual training with DeepLab-v3 | A | easy |
Q0069 | 2024A&A...688A.199P | For compressing cosmic 21 cm lightcones for astrophysical parameter inference across the prior volume, which summary approach yields the tightest typical Fisher-forecast constraints? | 2D power spectrum + information-maximizing neural network | RNN | A | easy |
Q0070 | 2024A&A...688A.199P | Among neural-network summaries of cosmic 21 cm lightcones, which approach gives tighter typical astrophysical parameter constraints across the prior volume? | Information-maximizing neural network | RNN | A | hard |
Q0071 | 2024MNRAS.531.4363S | For separating accreted from in-situ stars in Milky Way-like galaxies when a classifier trained on one cosmological simulation suite must generalize to another, which method is less sensitive to domain shift? | MLP | XGBoost | A | hard |
Q0072 | 2024ApJS..273....3A | For simulated high-dynamic-range radio-interferometric imaging, how can images be reconstructed with higher SNR/logSNR while suppressing hallucinated structure? | U-Net | residual-to-residual DNN series | B | easy |
Q0073 | 2024ApJS..273....3A | For simulated radio-interferometric imaging, which learned reconstruction approach better preserves image-domain data fidelity while maintaining substantially higher reconstruction quality? | U-Net | plug-and-play learned-denoiser reconstruction | B | hard |
Q0074 | 2024MNRAS.531.1454K | When assigning optical emission-line equivalent widths to mock galaxy spectra, which empirical mapping is less tied to assumptions from a separate stellar-population model while giving comparable or slightly better EW predictions? | feed-forward neural network | gradient-boosted decision trees | A | hard |
Q0075 | 2024MNRAS.533.1976M | For classifying 1D optical spectra into quasars, galaxies, stars, and BAL quasars, which neural-network architecture gives higher classification accuracy when trained on the same spectra? | Transformer | 1D residual CNN | B | hard |
Q0076 | 2024A&A...687A.202W | For estimating wavefront aberrations from non-modulated pyramid wavefront-sensor images in adaptive optics, which neural-network architecture gives lower wavefront-estimation error? | global-context vision Transformer | ConvNeXt CNN | A | easy |
Q0077 | 2024A&A...687A.202W | For estimating wavefront aberrations from non-modulated pyramid wavefront-sensor images in adaptive optics, which neural-network architecture gives lower wavefront-estimation error? | wavefront-sensing CNN | global-context vision Transformer | B | easy |
Q0078 | 2024MNRAS.528.5890P | For estimating stellar surface gravity from stellar light curves, which neural-network architecture gives lower prediction error? | convolution-augmented Transformer | ResNet CNN | A | easy |
Q0079 | 2024MNRAS.528.5890P | For estimating stellar surface gravity from stellar light curves, which neural-network architecture gives lower prediction error? | convolution-augmented Transformer | convolution-only network | A | easy |
Q0080 | 2024MNRAS.528.5890P | For estimating stellar surface gravity from stellar light curves, which neural-network architecture gives lower prediction error? | convolution-augmented Transformer | self-attention-only Transformer | A | easy |
Q0081 | 2024MNRAS.528.5890P | For estimating stellar surface gravity from stellar light curves, which neural-network architecture gives lower prediction error? | convolution-only network | self-attention-only Transformer | B | easy |
Q0082 | 2024MNRAS.528.5890P | For estimating stellar surface gravity across the red-giant branch from stellar light-curve information, which machine-learning approach gives lower RMSE and fewer outlier predictions? | Transformer | k-nearest-neighbor regression | A | hard |
Q0083 | 2024AJ....167..118R | For compressing Solar System object feature vectors used in anomaly searches, which dimensionality-reduction method preserves the features more accurately? | principal component analysis | deep autoencoder | B | easy |
Q0084 | 2024AJ....167..118R | For compressing Solar System object feature vectors used in anomaly searches, which dimensionality-reduction method preserves the features more accurately? | non-negative matrix factorization | deep autoencoder | B | easy |
Q0085 | 2024ApJS..272...14H | Within a large survey variable-source classification system, for identifying RR Lyrae ab variable stars from light-curve features, which supervised ML method gives higher precision and recall? | XGBoost gradient-boosted decision trees | convolutional/dense neural network | A | hard |
Q0086 | 2024ApJS..272...14H | Within a large survey variable-source classification system, for identifying Delta Scuti variable stars from light-curve features, which supervised ML method gives higher precision and recall? | XGBoost gradient-boosted decision trees | convolutional/dense neural network | A | hard |
Q0087 | 2024ApJS..272...14H | Within a large survey variable-source classification system, for identifying active galactic nuclei from light-curve features, which supervised ML method gives higher precision and recall? | convolutional/dense neural network | XGBoost gradient-boosted decision trees | B | hard |
Q0088 | 2024A&A...681A..68E | For identifying galaxy-galaxy strong-lensing systems in high-resolution space-like images when less prominent lensing features are included, which CNN architecture gives higher classification accuracy and AUC? | residual CNN | inception CNN | B | hard |
Q0089 | 2024A&A...692A.260R | For AGN photometric-redshift estimation in wide-area survey data where host-galaxy and nuclear emission can be spatially mixed, which ML approach gives lower scatter and outlier fraction on an independent blind sample? | image-based CNN ensemble | fully connected neural network using catalog multi-aperture photometry | A | hard |
Q0090 | 2024AJ....168..234L | For identifying true stellar flares from flare-candidate profile features, which supervised classifier is more reliable? | XGBoost gradient-boosted decision trees | fully connected deep neural network | A | easy |
Q0091 | 2024AJ....168..234L | Among tree-based classifiers for identifying true stellar flares from flare-candidate profile features, which method better suppresses false positives without losing flare recovery? | XGBoost gradient-boosted decision trees | Random Forest | A | easy |
Q0092 | 2024A&A...690A.326L | For identifying galaxy mergers in high-resolution galaxy images, which CNN-based consensus classifier gives a better purity-completeness balance on visually classified real-galaxy test sets? | dual-CNN consensus classifier with a single decision cutoff | dual-CNN consensus classifier with ambiguous-case rejection | B | hard |
Q0093 | 2024MNRAS.532.1902R | For mitigating imaging systematics in photometric galaxy-clustering measurements used to constrain local primordial non-Gaussianity, which data-driven regression treatment better passes residual-systematics tests? | linear multivariate regression | feedforward neural-network regression | B | hard |
Q0094 | 2024RAA....24d5025L | For identifying cloudy-contaminated frames in light-curve preprocessing, when a classifier is trained on cloudy-frame data from one observing night and evaluated on frames from another observing night, which supervised classifier achieves higher classification accuracy? | support-vector machine | LightGBM | A | hard |
Q0095 | 2024ApJ...967..152A | For predicting galaxy star formation rates from broad-band galaxy images, which ResNet50-based architecture achieves higher test-set prediction accuracy? | sequential ResNet50 CNN chain | standalone ResNet50 CNN | A | hard |
Q0096 | 2024MNRAS.527.1240H | For inferring plane-of-sky magnetic-field orientation from spectroscopic velocity-channel maps under comparable molecular-cloud turbulence conditions, which CNN architecture gives lower median angular uncertainty? | 2D convolution-pooling CNN | 3D residual U-Net autoencoder | A | hard |
Q0097 | 2024ApJ...964..130J | For photometric redshift estimation in cosmology, which machine-learning pipeline gives lower RMS error and fewer outlier predictions? | Random Forest | Bayesian neural network with uncertainty-based rejection | B | easy |
Q0098 | 2024ApJ...964..130J | For photometric redshift estimation in cosmology, which machine-learning pipeline gives lower RMS error and fewer outlier predictions? | standard neural network | Bayesian neural network with uncertainty-based rejection | B | easy |
Q0099 | 2024ApJ...964..130J | For photometric redshift estimation in cosmology, which machine-learning pipeline gives lower RMS error and fewer outlier predictions? | XGBoost | Bayesian neural network with uncertainty-based rejection | B | easy |
Q0100 | 2024A&A...686A.144B | For filtering radio single-pulse candidates in magnetar observations before applying source-specific RFI rejection, which neural-network classification strategy gives the higher F-score? | single neural-network classifier | three-network voting ensemble | B | easy |
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