Sensitivity Estimation for Dark Matter Subhalos in Synthetic Gaia DR2 using Deep Learning

• URL: http://arxiv.org/abs/2203.08161v1
• Date: Tue, 15 Mar 2022 18:00:02 GMT
• Title: Sensitivity Estimation for Dark Matter Subhalos in Synthetic Gaia DR2 using Deep Learning
• Authors: Abdullah Bazarov, Mar\'ia Benito, Gert H\"utsi, Rain Kipper, Joosep Pata and Sven P\~oder
• Abstract summary: We describe the challenges of detecting stars whose phase-space distribution may be perturbed by the passage of dark matter subhalos using a machine learning approach. We first quantify the magnitude of the perturbations in the simulated galaxies using an anomaly detection algorithm. We find that a classification algorithm optimized on about half a billion synthetic star observables exhibits mild but nonzero sensitivity.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The abundance of dark matter subhalos orbiting a host galaxy is a generic prediction of the cosmological framework. It is a promising way to constrain the nature of dark matter. Here we describe the challenges of detecting stars whose phase-space distribution may be perturbed by the passage of dark matter subhalos using a machine learning approach. The training data are three Milky Way-like galaxies and nine synthetic Gaia DR2 surveys derived from these. We first quantify the magnitude of the perturbations in the simulated galaxies using an anomaly detection algorithm. We also estimate the feasibility of this approach in the Gaia DR2-like catalogues by comparing the anomaly detection based approach with a supervised classification. We find that a classification algorithm optimized on about half a billion synthetic star observables exhibits mild but nonzero sensitivity. This classification-based approach is not sufficiently sensitive to pinpoint the exact locations of subhalos in the simulation, as would be expected from the very limited number of subhalos in the detectable region. The enormous size of the Gaia dataset motivates the further development of scalable and accurate computational methods that could be used to select potential regions of interest for dark matter searches to ultimately constrain the Milky Way's subhalo mass function.

Related papers

• A deep-learning algorithm to disentangle self-interacting dark matter and AGN feedback models [0.0]
  We present a Machine Learning method that ''learns'' how the impact of dark matter self-interactions differs from that of astrophysical feedback. We train a Convolutional Neural Network on images of galaxy clusters from hydro-dynamic simulations.
  arXiv  Detail & Related papers  (2024-05-27T18:00:49Z)
• Field-level simulation-based inference with galaxy catalogs: the impact of systematic effects [0.6271213328710472]
  We train and test our models on galaxy catalogs, created from thousands of state-of-the-art hydrodynamic simulations run with different codes from the CAMELS project. Although the presence of these effects degrades the precision and accuracy of the models, the fraction of galaxy catalogs where the model performs well is over 90 %.
  arXiv  Detail & Related papers  (2023-10-23T18:00:07Z)
• Weakly-Supervised Anomaly Detection in the Milky Way [1.3375143521862154]
  We use Classification Without Labels (CWoLa) to identify cold stellar streams within the more than one billion Milky Way stars observed by the Gaia satellite. CWoLa operates without the use of labeled streams or knowledge of astrophysical principles. This technique may have broad applicability within astrophysics as well as other domains interested in identifying localized anomalies.
  arXiv  Detail & Related papers  (2023-05-05T18:00:09Z)
• Cosmology from Galaxy Redshift Surveys with PointNet [65.89809800010927]
  In cosmology, galaxy redshift surveys resemble such a permutation invariant collection of positions in space. We employ a textitPointNet-like neural network to regress the values of the cosmological parameters directly from point cloud data. Our implementation of PointNets can analyse inputs of $mathcalO(104) - mathcalO(105)$ galaxies at a time, which improves upon earlier work for this application by roughly two orders of magnitude.
  arXiv  Detail & Related papers  (2022-11-22T15:35:05Z)
• 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)
• Inferring Structural Parameters of Low-Surface-Brightness-Galaxies with Uncertainty Quantification using Bayesian Neural Networks [70.80563014913676]
  We show that a Bayesian Neural Network (BNN) can be used for the inference, with uncertainty, of such parameters from simulated low-surface-brightness galaxy images. Compared to traditional profile-fitting methods, we show that the uncertainties obtained using BNNs are comparable in magnitude, well-calibrated, and the point estimates of the parameters are closer to the true values.
  arXiv  Detail & Related papers  (2022-07-07T17:55:26Z)
• 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)
• Pure Exploration in Kernel and Neural Bandits [90.23165420559664]
  We study pure exploration in bandits, where the dimension of the feature representation can be much larger than the number of arms. To overcome the curse of dimensionality, we propose to adaptively embed the feature representation of each arm into a lower-dimensional space.
  arXiv  Detail & Related papers  (2021-06-22T19:51:59Z)
• DeepShadows: Separating Low Surface Brightness Galaxies from Artifacts using Deep Learning [70.80563014913676]
  We investigate the use of convolutional neural networks (CNNs) for the problem of separating low-surface-brightness galaxies from artifacts in survey images. We show that CNNs offer a very promising path in the quest to study the low-surface-brightness universe.
  arXiv  Detail & Related papers  (2020-11-24T22:51:08Z)
• In search of the weirdest galaxies in the Universe [0.0]
  Weird galaxies are outliers that have either unknown or very uncommon features making them different from the normal sample. In this work, we look for the weird outlying galaxies using two different outlier detection techniques. We find that both unsupervised methods extract important features from the data and can be used to find many different types of outliers.
  arXiv  Detail & Related papers  (2020-07-16T18:00:01Z)

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.