A method based on Generative Adversarial Networks for disentangling physical and chemical properties of stars in astronomical spectra
Data compression techniques focused on information preservation have become essential in the modern era of big data. In this work, an encoder-decoder architecture has been designed, where adversarial training, a modification of the traditional autoencoder, is used in the context of astrophysical spectral analysis. The goal of this proposal is to obtain an intermediate representation of the astronomical stellar spectra, in which the contribution to the flux of a star due to the most influential physical properties (its surface temperature and gravity) disappears and the variance reflects only the effect of the chemical composition over the spectrum. A scheme of deep learning is used with the aim of unraveling in the latent space the desired parameters of the rest of the information contained in the data. This work proposes a version of adversarial training that makes use of a discriminator per parameter to be disentangled, thus avoiding the exponential combination that occurs in the use of a single discriminator, as a result of the discretization of the values to be untangled. To test the effectiveness of the method, synthetic astronomical data are used from the APOGEE and Gaia surveys. In conjunction with the work presented, we also provide a disentangling framework (GANDALF) available to the community, which allows the replication, visualization, and extension of the method to domains of any nature.
Code (0)
등록된 구현이 없습니다.
Tasks
Data CompressionSimilar Papers 제목 키워드 기반
Disentangling stellar atmospheric parameters in astronomical spectra using Generative Adversarial Neural Networks
A method based on Generative Adversaria! Networks (GANs) is developed for disentangling the physical (effective temperature and gravity) and chemical (metallicity, overabundance of a-elements with respect to iron) atmosp…
Dimensionality ReductionNeural Face Editing with Intrinsic Image Disentangling
Traditional face editing methods often require a number of sophisticated and task specific algorithms to be applied one after the other --- a process that is tedious, fragile, and computationally intensive. In this paper…
Facial EditingGenerative Adversarial NetworkDeep Reinforcement Learning for De-Novo Drug Design
We propose a novel computational strategy for de novo design of molecules with desired properties termed ReLeaSE (Reinforcement Learning for Structural Evolution). Based on deep and reinforcement learning approaches, ReL…
Deep Reinforcement LearningDrug Designreinforcement-learningReinforcement Learning+1Quantum Generative Models for Small Molecule Drug Discovery
Existing drug discovery pipelines take 5-10 years and cost billions of dollars. Computational approaches aim to sample from regions of the whole molecular and solid-state compounds called chemical space which could be on…
Drug DiscoveryTag Disentangled Generative Adversarial Networks for Object ImageRe-rendering
In this paper, we propose a principled Tag Dis-entangled Generative Adversarial Networks (TD-GAN) for re-rendering new images for the object of interest from a single image of it by specifying multiple scene …
ObjectTAG