With the availability of the huge amounts of data produced by current and future large multi-band photometric surveys, photometric redshifts have become a crucial tool for extragalactic astronomy and cosmology. In this paper we present a novel method, called Weak Gated Experts (WGE), which allows to derive photometric …
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…
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.
In this paper we explore the applicability of the unsupervised machine learning technique of Self Organizing Maps (SOM) to estimate galaxy photometric redshift probability density functions (PDFs). This technique takes a spectroscopic training set, and maps the photometric attributes, but not the redshifts, to a two di…
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…
FlexCode converts high-dimensional regression to high-dimensional CDE.
problem Complex conditional distributions in high dimensions.
method Reformulates CDE as a non-parametric orthogonal series problem.
result Efficiently estimates conditional densities in high dimensions.
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.
PICZL improves photometric redshifts for AGN in all-sky surveys.
problem Challenges in accurately computing photo-z for AGN due to interplay of SMBH and host galaxy emissions.
method PICZL uses an ensemble of CNNs with cross-channel integration of image and catalog data, leveraging Gaussian mixture models.
result PICZL achieves a photo-z variance of 4.5% and outlier fraction of 5.6% on a validation sample of 8098 AGN, outperforming previous methods.
Tabular foundation models outperform other methods in conditional density estimation across various datasets.
problem Estimating the full conditional distribution of a response given tabular covariates, especially in settings with heteroscedasticity, multimodality, or asymmetric uncertainty.
method Benchmarked three tabular foundation model variants (TabPFN and TabICL) against six CDE baselines on 39 real-world datasets.
result Tabular foundation models achieve the best CDE loss, log-likelihood, and CRPS across all sample sizes, outperforming other methods.
LADaR framework calibrates machine learning models for instance-wise predictions.
problem Challenges in assessing and calibrating predictive distributions for complex inputs.
method Local Amortized Diagnostics and Reshaping of Conditional Densities (LADaR) framework and extttCal−PIT algorithm. result Achieves better instance-wise calibration than existing methods in galaxy distance estimation.
Proposes a method to improve learning when training data is not representative.
problem Improving supervised learning when training data is not representative (covariate shift).
method Conditioning on propensity scores to balance covariates within strata.
result Significantly improved target prediction and AUC (0.958) on supernovae classification challenge.
Machine learning detects type Ia supernovae from photometric data.
problem Detecting type Ia supernovae accurately from photometric data.
method Machine learning approach using only real observation data.
result Good results on real data from the Open Supernovae Catalog.
New method calibrates photometric redshift PDFs more accurately.
problem Inaccurate photometric redshift uncertainties lead to systematic errors.
method Local re-calibration using feature-space regression of Probability Integral Transform (PIT) distributions.
result Calibrated PDFs are more accurate at all locations in feature space.
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 photometric stereo method using dictionary learning for better normal vector reconstruction.
problem Photometric stereo's reliance on diffuse surface model limits its effectiveness for complex reflectance patterns.
method Developed two formulations of dictionary learning for photometric stereo: one for Lambertian and one for non-Lambertian objects.
result State-of-the-art performance compared to existing robust photometric stereo methods on synthetic and real datasets.
New photometric stereo method using learned dictionaries for robustness.
problem Estimating object normals from varying lighting conditions.
method Adaptive dictionary learning for image preprocessing and direct regularization of normal vectors.
result State-of-the-art performance in noisy conditions.
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.
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 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.
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.
Automated classification of astronomical light curves for LSST.
problem Handling massive astronomical data from LSST.
method Gradient boosting of decision trees, feature extraction and selection, augmentation.
result Achieved one of the top results in the PLAsTiCC challenge.
GANs simulate realistic galaxy images.
problem Simulate complex astronomical images efficiently.
method Progressive GANs with Wasserstein cost function.
result Generates naturalistic galaxy images.
Machine learning models classify celestial objects like pulsars and black holes.
problem Classifying high-energy celestial objects using photometric data.
method Applied tree-based models and RNN to classify pulsars and black holes.
result RNN showed potential for real-time object discrimination and classification.
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.
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.
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 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.
Observations of astrophysical objects such as galaxies are limited by various sources of random and systematic noise from the sky background, the optical system of the telescope and the detector used to record the data. Conventional deconvolution techniques are limited in their ability to recover features in imaging da…
Paper presents a method for robust surface reconstruction from noisy gradients using adaptive dictionary learning.
problem Reconstructing surfaces from noisy photometric stereo normal vector maps.
method Adaptive dictionary learning to sparsely represent spatial patches of the surface, enforcing smoothness constraints.
result The method effectively learns the underlying surface structure and is robust to noise.
The Baire metric induces an ultrametric on a dataset and is of linear computational complexity, contrasted with the standard quadratic time agglomerative hierarchical clustering algorithm. We apply the Baire distance to spectrometric and photometric redshifts from the Sloan Digital Sky Survey using, in this work, about…
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.
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.
Large-scale surveys make huge amounts of photometric data available. Because of the sheer amount of objects, spectral data cannot be obtained for all of them. Therefore it is important to devise techniques for reliably estimating physical properties of objects from photometric information alone. These estimates are nee…
We invoke a Gaussian mixture model (GMM) to jointly analyse two traditional emission-line classification schemes of galaxy ionization sources: the Baldwin-Phillips-Terlevich (BPT) and WHα vs. [NII]/Hα (WHAN) diagrams, using spectroscopic data from the Sloan Digital Sky Survey Data Release 7 and SEAGal/STARLI…
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.
Phylogenetic approaches are finding more and more applications outside the field of biology. Astrophysics is no exception since an overwhelming amount of multivariate data has appeared in the last twenty years or so. In particular, the diversification of galaxies throughout the evolution of the Universe quite naturally…
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.
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.
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.
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.
Understanding the nature of dark energy, the mysterious force driving the accelerated expansion of the Universe, is a major challenge of modern cosmology. The next generation of cosmological surveys, specifically designed to address this issue, rely on accurate measurements of the apparent shapes of distant galaxies. H…
This study benchmarks data augmentation schemes to improve CNN performance.
problem Lack of training data for deep learning models.
method Various geometric and photometric data augmentation schemes evaluated on a CNN.
result Cropping in geometric augmentation significantly improves CNN task performance.
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 Baire metric induces an ultrametric on a dataset and is of linear computational complexity, contrasted with the standard quadratic time agglomerative hierarchical clustering algorithm. In this work we evaluate empirically this new approach to hierarchical clustering. We compare hierarchical clustering based on the …
Designing a photometric system to best fulfil a set of scientific goals is a complex task, demanding a compromise between conflicting requirements and subject to various constraints. A specific example is the determination of stellar astrophysical parameters (APs) - effective temperature, metallicity etc. - across a wi…
The paper introduces tools for nonparametric conditional density estimation in astronomy.
problem Estimating photometric redshifts and likelihood-free cosmological inference with uncertainty quantification.
method Nonparametric conditional density estimation (CDE) tools in Python and R.
result Comprehensive statistical tools and software for CDE in astronomy.
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.
We apply a novel spectral graph technique, that of locally-biased semi-supervised eigenvectors, to study the diversity of galaxies. This technique permits us to characterize empirically the natural variations in observed spectra data, and we illustrate how this approach can be used in an exploratory manner to highlight…