• Corpus ID: 220280155

The relationship between fine galaxy stellar morphology and star formation activity in cosmological simulations: a deep learning view.

@article{Zanisi2020TheRB,
  title={The relationship between fine galaxy stellar morphology and star formation activity in cosmological simulations: a deep learning view.},
  author={L. Zanisi and Marc Huertas-Company and François Lanusse and Connor Bottrell and Annalisa Pillepich and Dylan Nelson and Vicente Rodriguez-Gomez and Francesco Shankar and Lars Hernquist and Avishai Dekel and Berta Margalef-Bentabol and Mark Vogelsberger and Joel R. Primack},
  journal={arXiv: Astrophysics of Galaxies},
  year={2020}
}
Hydrodynamical simulations of galaxy formation and evolution attempt to fully model the physics that shapes galaxies. The agreement between the morphology of simulated and real galaxies, and the way the morphological types are distributed across galaxy scaling relations are important probes of our knowledge of galaxy formation physics. Here we propose an unsupervised deep learning approach to perform a stringent test of the fine morphological structure of galaxies coming from the Illustris and… 

References

SHOWING 1-10 OF 16 REFERENCES
The Hubble Sequence at z ∼ 0 in the IllustrisTNG simulation with deep learning
We analyse the optical morphologies of galaxies in the IllustrisTNG simulation at z ∼ 0 with a convolutional neural network trained on visual morphologies in the Sloan Digital Sky Survey. We
Ejective and preventative: the IllustrisTNG black hole feedback and its effects on the thermodynamics of the gas within and around galaxies
Supermassive black holes (SMBHs) which reside at the centres of galaxies can inject vast amounts of energy into the surrounding gas and are thought to be a viable mechanism to quench star-formation
Detecting outliers in astronomical images with deep generative networks
TLDR
This work explores the ability of deep generative networks for detecting outliers in astronomical imaging data sets by using a generative model to learn a representation of expected data defined by the training set and then looking for deviations from the learned representation by looking for the best reconstruction of a given object.
Do Deep Generative Models Know What They Don't Know?
TLDR
The density learned by flow-based models, VAEs, and PixelCNNs cannot distinguish images of common objects such as dogs, trucks, and horses from those of house numbers, and such behavior persists even when the flows are restricted to constant-volume transformations.
Likelihood Ratios for Out-of-Distribution Detection
TLDR
This work investigates deep generative model based approaches for OOD detection and observes that the likelihood score is heavily affected by population level background statistics, and proposes a likelihood ratio method forDeep generative models which effectively corrects for these confounding background statistics.
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
TLDR
This paper addresses the visualisation of image classification models, learnt using deep Convolutional Networks (ConvNets), and establishes the connection between the gradient-based ConvNet visualisation methods and deconvolutional networks.
Deep learning, galaxy structure & quenching
  • 2012
Input complexity and out-of-distribution detection with likelihood-based generative models
TLDR
This paper uses an estimate of input complexity to derive an efficient and parameter-free OOD score, which can be seen as a likelihood-ratio, akin to Bayesian model comparison, and finds such score to perform comparably to, or even better than, existing OOD detection approaches under a wide range of data sets, models, model sizes, and complexity estimates.
2017, in ICLR
  • Astronomy and Computing,
  • 2015
...
1
2
...