Uncertainty-Aware Blob Detection with an Application to Integrated-Light Stellar Population Recoveries

• URL: http://arxiv.org/abs/2208.05881v1
• Date: Thu, 11 Aug 2022 15:34:27 GMT
• Title: Uncertainty-Aware Blob Detection with an Application to Integrated-Light Stellar Population Recoveries
• Authors: Prashin Jethwa, Fabian Parzer, Otmar Scherzer, Glenn van de Ven
• Abstract summary: We develop an uncertainty-aware version of the classic Laplacian-of-Gaussians method for blob detection, which we call ULoG. We apply ULoG to the inferred M54 age/metallicity distributions, identifying between 2 or 3 significant, distinct populations amongst its stars.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Context. Blob detection is a common problem in astronomy. One example is in stellar population modelling, where the distribution of stellar ages and metallicities in a galaxy is inferred from observations. In this context, blobs may correspond to stars born in-situ versus those accreted from satellites, and the task of blob detection is to disentangle these components. A difficulty arises when the distributions come with significant uncertainties, as is the case for stellar population recoveries inferred from modelling spectra of unresolved stellar systems. There is currently no satisfactory method for blob detection with uncertainties. Aims. We introduce a method for uncertainty-aware blob detection developed in the context of stellar population modelling of integrated-light spectra of stellar systems. Methods. We develop theory and computational tools for an uncertainty-aware version of the classic Laplacian-of-Gaussians method for blob detection, which we call ULoG. This identifies significant blobs considering a variety of scales. As a prerequisite to apply ULoG to stellar population modelling, we introduce a method for efficient computation of uncertainties for spectral modelling. This method is based on the truncated Singular Value Decomposition and Markov Chain Monte Carlo sampling (SVD-MCMC). Results. We apply the methods to data of the star cluster M54. We show that the SVD-MCMC inferences match those from standard MCMC, but are a factor 5-10 faster to compute. We apply ULoG to the inferred M54 age/metallicity distributions, identifying between 2 or 3 significant, distinct populations amongst its stars.

Related papers

• One-for-More: Continual Diffusion Model for Anomaly Detection [61.12622458367425]
  Anomaly detection methods utilize diffusion models to generate or reconstruct normal samples when given arbitrary anomaly images. Our study found that the diffusion model suffers from severe faithfulness hallucination'' and catastrophic forgetting'' We propose a continual diffusion model that uses gradient projection to achieve stable continual learning.
  arXiv  Detail & Related papers  (2025-02-27T07:47:27Z)
• Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular Balls [65.44462297594308]
  Outlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data. Most unsupervised outlier detection methods are carefully designed to detect specified outliers. We propose a fuzzy rough sets-based multi-scale outlier detection method to identify various types of outliers.
  arXiv  Detail & Related papers  (2025-01-06T12:35:51Z)
• A Novel Application of Conditional Normalizing Flows: Stellar Age Inference with Gyrochronology [0.0]
  We show that a data-driven approach can constrain gyrochronological ages with a precision comparable to other standard techniques. This work demonstrates the potential of a probabilistic data-driven solution to widen the applicability of gyrochronological stellar dating.
  arXiv  Detail & Related papers  (2023-07-17T18:00:19Z)
• Monte Carlo Neural PDE Solver for Learning PDEs via Probabilistic Representation [59.45669299295436]
  We propose a Monte Carlo PDE solver for training unsupervised neural solvers. We use the PDEs' probabilistic representation, which regards macroscopic phenomena as ensembles of random particles. Our experiments on convection-diffusion, Allen-Cahn, and Navier-Stokes equations demonstrate significant improvements in accuracy and efficiency.
  arXiv  Detail & Related papers  (2023-02-10T08:05:19Z)
• Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection [57.85347204640585]
  We develop a Universal Domain Adaptation method DeepAstroUDA. It can be applied to datasets with different types of class overlap. For the first time, we demonstrate the successful use of domain adaptation on two very different observational datasets.
  arXiv  Detail & Related papers  (2022-11-01T18:07:21Z)
• Deep Learning Models of the Discrete Component of the Galactic Interstellar Gamma-Ray Emission [61.26321023273399]
  A significant point-like component from the small scale (or discrete) structure in the H2 interstellar gas might be present in the Fermi-LAT data. We show that deep learning may be effectively employed to model the gamma-ray emission traced by these rare H2 proxies within statistical significance in data-rich regions.
  arXiv  Detail & Related papers  (2022-06-06T18:00:07Z)
• Detection of extragalactic Ultra-Compact Dwarfs and Globular Clusters using Explainable AI techniques [1.3764085113103222]
  Compact stellar systems such as Ultra-compact dwarfs (UCDs) and Globular Clusters (GCs) around galaxies are known to be the tracers of the merger events that have been forming these galaxies. Here, we train a machine learning model to separate these objects from the foreground stars and background galaxies using the multi-wavelength imaging data of the Fornax galaxy cluster in 6 filters. We are able to identify UCDs/GCs with a precision and a recall of >93 percent and provide relevances that reflect the importance of each feature dimension %(colors and angular sizes)
  arXiv  Detail & Related papers  (2022-01-05T13:37:55Z)
• A neural simulation-based inference approach for characterizing the Galactic Center $\gamma$-ray excess [9.101294179203794]
  The Fermi gamma-ray Galactic Center Excess (GCE) has remained a persistent mystery for over a decade. We use recent advancements in the field of simulation-based inference to characterize the contribution of modeled components to the GCE.
  arXiv  Detail & Related papers  (2021-10-13T18:00:00Z)
• Determination of the critical exponents in dissipative phase transitions: Coherent anomaly approach [51.819912248960804]
  We propose a generalization of the coherent anomaly method to extract the critical exponents of a phase transition occurring in the steady-state of an open quantum many-body system.
  arXiv  Detail & Related papers  (2021-03-12T13:16:18Z)
• Variational Inference for Deblending Crowded Starfields [0.8471366736328809]
  We propose StarNet, a Bayesian method to deblend sources in astronomical images of crowded star fields. In experiments with SDSS images of the M2 globular cluster, StarNet is substantially more accurate than two competing methods.
  arXiv  Detail & Related papers  (2021-02-04T04:36:58Z)
• Sampling in Combinatorial Spaces with SurVAE Flow Augmented MCMC [83.48593305367523]
  Hybrid Monte Carlo is a powerful Markov Chain Monte Carlo method for sampling from complex continuous distributions. We introduce a new approach based on augmenting Monte Carlo methods with SurVAE Flows to sample from discrete distributions. We demonstrate the efficacy of our algorithm on a range of examples from statistics, computational physics and machine learning, and observe improvements compared to alternative algorithms.
  arXiv  Detail & Related papers  (2021-02-04T02:21:08Z)
• Quasar Detection using Linear Support Vector Machine with Learning From Mistakes Methodology [0.0]
  Linear Support Vector Machine (LSVM) is explored to detect Quasars, which are extremely bright objects in which a supermassive black hole is surrounded by a luminous accretion disk. It was observed that LSVM along with Ensemble Bagged Trees (EBT) achieved a 10x reduction in the False Negative Rate.
  arXiv  Detail & Related papers  (2020-10-01T13:41:51Z)
• Manifolds for Unsupervised Visual Anomaly Detection [79.22051549519989]
  Unsupervised learning methods that don't necessarily encounter anomalies in training would be immensely useful. We develop a novel hyperspherical Variational Auto-Encoder (VAE) via stereographic projections with a gyroplane layer. We present state-of-the-art results on visual anomaly benchmarks in precision manufacturing and inspection, demonstrating real-world utility in industrial AI scenarios.
  arXiv  Detail & Related papers  (2020-06-19T20:41:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.