Novel spectral graph technique highlights global and local structure in SDSS galaxy data.
problem Characterize natural variations in galaxy spectra data.
method Locally-biased semi-supervised eigenvectors applied to Sloan Digital Sky Survey (SDSS) data.
result Method reveals fine local structure and strong correlations with star formation rate.
New method speeds up galaxy analysis from hours to seconds.
problem Infeasibility of state-of-the-art SED analyses for large surveys.
method Amortized Neural Posterior Estimation (ANPE) for scalable Bayesian inference.
result Posterior distributions of 12 model parameters estimated in seconds per galaxy.
CNN identifies AGN host galaxies from Sloan Digital Sky Survey data.
problem Identifying AGN host galaxies using traditional methods is time-consuming.
method Trained a convolutional neural network on 210,000 galaxies.
result CNN can distinguish AGN host galaxies from non-active galaxies.
New GAN model deblends galaxy images with high accuracy and speed.
problem Deblending blended galaxy images in dense regions of the universe.
method Branched generative adversarial network (GAN) to produce images of deblended galaxies.
result High peak signal-to-noise ratio and structural similarity scores compared to ground truth images.
Machine learning analysis of galaxy catalogues finds only one class separable.
problem Difficulty in classifying galaxies using visual inspection schemes.
method Generalized Relevance Matrix Learning Vector Quantization and Random Forests.
result Only one class, Little Blue Spheroids, is consistently separable.
We propose to describe the variety of galaxies from SDSS by using only one affine parameter. To this aim, we build the Principal Curve (P-curve) passing through the spine of the data point cloud, considering the eigenspace derived from Principal Component Analysis of morphological, physical and photometric galaxy prope…
Automates galaxy morphology classification with less human labelling.
problem Insufficient human-labeled galaxy images for accurate classification.
method Developed a VAE with equivariant transformer layers and a classifier network.
result Improves accuracy with fewer labels and unlabelled data.
New model reduces bias in cosmic shear measurements.
problem Bias in cosmic shear measurements due to non-well-defined ellipticity.
method Hybrid physical and deep learning Hierarchical Bayesian Model.
result Unbiased estimate of shear on realistic galaxies.
CasVAE outperforms supervised methods for star-galaxy classification.
problem Challenges in machine learning for astronomy data.
method Cascade Variational Auto-Encoder (CasVAE) for unsupervised star-galaxy classification.
result CasVAE outperforms baseline models in accuracy and stability.
We present a large catalog of optically selected galaxy clusters from the application of a new Gaussian Mixture Brightest Cluster Galaxy (GMBCG) algorithm to SDSS Data Release 7 data. The algorithm detects clusters by identifying the red sequence plus Brightest Cluster Galaxy (BCG) feature, which is unique for galaxy c…
A new method models galaxies as points in space for better analysis.
problem Limitations of binning and voxelization in galaxy surveys.
method A diffusion-based generative model for galaxy point clouds.
result Demonstrated on dark matter haloes in Quijote simulations.
Deep learning classifies galaxies from Dark Energy Survey in just 8 minutes.
problem Classifying galaxies from large-scale surveys.
method Transfer learning from pre-trained neural networks.
result Achieved state-of-the-art accuracy of 99.6% in galaxy classification.
Study uses evolutionary deep learning to identify galaxies obscured by star densities.
problem Identifying galaxies in the Zone of Avoidance due to high star densities and extinction.
method Evolutionary algorithm to optimize CNN architecture for near-infrared images.
result Best evolved CNN outperforms other variants in identifying galaxies in the Zone of Avoidance.
Deep learning improves galaxy image deconvolution in surveys.
problem Deconvolving large survey images with space-variant PSFs.
method Employed a U-Net DNN architecture for supervised galaxy image processing. Two strategies: Tikhonov deconvolution and ADMM-based iterative deconvolution.
result Deep learning approaches outperform standard convex optimization techniques in galaxy image reconstruction and shape recovery.
ICA identifies nine independent components for galaxy classification.
problem Subjective galaxy classification limits understanding of galaxy formation and evolution.
method Independent Component Analysis (ICA) followed by K-means clustering.
result Galaxies can be grouped into ten distinct and homogeneous categories.
Measuring the morphological parameters of galaxies is a key requirement for studying their formation and evolution. Surveys such as the Sloan Digital Sky Survey (SDSS) have resulted in the availability of very large collections of images, which have permitted population-wide analyses of galaxy morphology. Morphological…
Method identifies galaxies with recent star formation variations.
problem Identify galaxies with recent star formation variations.
method Approximate Bayesian Computation (ABC) with machine learning.
result Flexible star formation histories are needed for accurate modeling.
GANs improve galaxy image recovery beyond deconvolution limits.
problem Limited recovery of galaxy features from noisy, low-resolution images.
method Training a GAN on galaxy images to recover features from degraded data.
result GANs can recover features from degraded images better than simple deconvolution.
GANs simulate realistic galaxy images.
problem Simulate complex astronomical images efficiently.
method Progressive GANs with Wasserstein cost function.
result Generates naturalistic galaxy images.
Generative models help calibrate galaxy images for dark energy studies.
problem Calibrating galaxy shape measurements for dark energy studies.
method Applied deep conditional generative models, including variational autoencoders and adversarial training.
result Generative models can generate realistic galaxy images for calibration data.
Method generates joint posterior samples of source and foreground mass distributions for gravitational lensing.
problem Challenging inference problem for high-resolution, high signal-to-noise ratio gravitational lensing.
method Combines diffusion-based generative modeling and recurrent inference machines.
result Can model realistic gravitational lensing simulations down to the noise level.
A probabilistic approach classifies galaxies using spectroscopic data.
problem Classifying galaxies based on emission lines.
method Gaussian mixture model (GMM) applied to spectroscopic data.
result Four Gaussian components explain up to 97% of data variance.
Astrophysics uses phylogenetic methods to classify galaxies, overcoming limitations of traditional Hubble classification.
problem Classifying distant galaxies and understanding their evolutionary relationships.
method Maximum Parsimony and cladistics applied to multivariate astrophysical data.
result Maximum Parsimony provides useful astrophysical results for large datasets of galaxies.
GANs create realistic galaxy images for astronomy.
problem Handling large astronomical datasets.
method Chained generative adversarial networks (GANs).
result GAN-generated galaxy images closely match real galaxies.
Bayesian method finds voids in galaxy surveys with deep neural networks.
problem Finding genuine matter underdensities in sparse galaxy surveys is underconstrained.
method Deep graph neural network evolves 'test particles' to sample from stochastic void definitions.
result Trained model performs well and finds Bayes-optimal void mappings.
Generative models help explore astrophysical phenomena like galaxy evolution.
problem Exploring hypotheses in astrophysics and other areas using data-driven methods.
method Using a neural network to learn a latent space representation of data and generate artificial data to test hypotheses.
result Demonstrated the ability to independently manipulate physical attributes in artificial data.
This study examines cores within superclusters, highlighting their transitional nature and dynamical state.
problem Understanding the morphology and dynamical properties of cores within superclusters.
method Projected and radial velocity distributions of galaxies, morphological analysis, entropy and mass estimates.
result Cores are transitional structures that evolve towards virialisation but remain gravitationally bound.
Machine learning improves accuracy in estimating cosmological parameters from dark matter distribution.
problem Accurately estimating cosmological parameters from the dark matter distribution.
method Application of deep 3D convolutional networks and distribution regression framework to volumetric dark-matter simulations.
result Machine learning techniques can estimate cosmological parameters with comparable or higher accuracy than maximum-likelihood methods.
Machine learning improves cosmic shear measurements by compensating for feature noise.
problem Accurately measuring cosmic shear from galaxy images in the presence of various nuisance effects.
method Supervised machine learning with artificial neural networks trained on simulated data.
result Demonstrated competitive low shear biases in Euclid-like images.
The incredible variety of galaxy shapes cannot be summarized by human defined discrete classes of shapes without causing a possibly large loss of information. Dictionary learning and sparse coding allow us to reduce the high dimensional space of shapes into a manageable low dimensional continuous vector space. Statisti…
Python package for manifold learning of millions of points.
problem Scalability of manifold learning algorithms for high-dimensional data.
method Modular, scalable implementation with fast approximate neighbors and sparse eigendecompositions.
result Embeds millions of data points in minutes, including a large dataset of galaxy spectra.
Hybrid model speeds up galaxy simulations by incorporating baryonic properties.
problem Inaccurate baryonic properties in dark matter-only simulations.
method Combining analytic models and machine learning for faster, more accurate simulations.
result Hybrid model outperforms machine learning alone for some baryonic properties.
Astrophysics uses phylogenetic tools to classify evolving objects.
problem Classifying evolving astrophysical entities like galaxies and clusters.
method Phylogenetic approach, addressing challenges of hierarchical diversity and continuous parameters.
result Conceptual and practical difficulties have been solved with limited samples.
New study finds environment significantly suppresses star formation in galaxies, contrary to previous beliefs.
problem Understanding the role of environment in galaxy formation and evolution.
method Applied causal inference framework to IllustrisTNG simulations.
result Environment suppresses star formation by a factor of ~100, contrary to previous beliefs.
Paper uses machine learning to detect dark matter subhalos in simulated Gaia DR2 data.
problem Detecting dark matter subhalos in simulated Gaia DR2 data.
method Proposed anomaly detection and classification-based approaches.
result Anomaly detection algorithm is sensitive to DM subhalos, but classification-based approach is not.
High-dimensional, large-sample astrophysical databases of galaxy clusters, such as the Chandra Deep Field South COMBO-17 database, provide measurements on many variables for thousands of galaxies and a range of redshifts. Current understanding of galaxy formation and evolution rests sensitively on relationships between…
Bayesian neural networks improve cosmic parameter estimation from modified gravity simulations.
problem Estimating cosmological parameters from large-scale structure data with modified gravity.
method Implement Bayesian neural networks (BNNs) with two cases: single BLL and FullB, trained on dark matter only particle mesh N-body simulations. result BNNs yield well-calibrated uncertainty estimates and accurately predict cosmological parameters for Ωm and σ8. I-MAD detects malware with high accuracy and interpretable results.
problem Detecting new malware samples and providing interpretable results.
method Galaxy Transformer network and interpretable feed-forward neural network.
result Significantly outperforms existing static malware detection models.
Introduces new limit spaces for degenerating Calabi-Yau families.
problem Understanding degenerating Calabi-Yau families and their limit structures.
method Introduces galaxy spaces as dense subspace of infinite open Calabi-Yau varieties.
result Galaxy spaces are projective limits of toroidal compactifications.
New method uses spectral series for fast, reliable inferences on complex data.
problem Making fast and reliable inferences for complex, high-dimensional data.
method Orthogonal series estimator based on kernel machine learning and Fourier methods.
result The spectral series approach adapts to the intrinsic geometry and dimension of the data.
In this paper we study the financial repercussions of the destruction of two fully armed and operational moon-sized battle stations ("Death Stars") in a 4-year period and the dissolution of the galactic government in Star Wars. The emphasis of this work is to calibrate and simulate a model of the banking and financial …
Study bridge spectra of 2-bridge knots and their cables.
problem Computing bridge spectra for 2-bridge knots and their cables.
method Computed bridge spectra of cables of 2-bridge knots.
result Results on bridge spectra and distance of Montesinos knots.
Investigates point spectra of vector fields and their properties.
problem Understanding the point spectra of vector fields.
method Define and study point spectra, prove properties under isometries, and analyze compactly supported fields.
result Point spectra are well-behaved under isometries and trivial for compactly supported fields.
Khovanov spectra are shown to be functorial under certain conditions.
problem Understanding functoriality of Khovanov spectra.
method Proving functoriality up to homotopy and sign for Khovanov spectra.
result Khovanov spectra are functorial under specific conditions.
Generative Adversarial Network creates realistic halo merger trees.
problem Comparing galaxy formation theories with observations using halo merger trees.
method Treated halo merger tree construction as a matrix generation problem, using Generative Adversarial Network.
result Generated halo merger trees are of high quality and realistic.
Improved generative models using flexible convolutions.
problem Generating high-quality images efficiently.
method Generalized 1 x 1 convolutions to d x d convolutions, chaining autoregressive and periodic convolutions.
result Flexible d x d convolutions significantly improve generative flow models' performance.
We give a simple sufficient condition for Quinn's "bordism-type spectra" to be weakly equivalent to strictly associative ring spectra. We also show that Poincare bordism and symmetric L-theory are naturally weakly equivalent to monoidal functors. Part of the proof of these statements involves showing that Quinn's funct…
Method calculates spectra of Rarita-Schwinger operator on symmetric spaces.
problem Calculating spectra of the Rarita-Schwinger operator on compact symmetric spaces.
method Using Weitzenböck formulas, Laplace operator, Casimir operator, Freudenthal's formula, and branching rules.
result Obtained spectra on the sphere, complex projective space, and quaternionic projective space.