Renhao Ye

Renhao Ye

Looking at the Universe.

Publications

From DECaLS to BASS+MzLS: Galaxy Morphological Classification with unsupervised domain adaption

Published in MNRAS?, 2024

The morphological classification of galaxies plays a important role in our understanding of galaxy formation and evolution. We present a catalog of detailed morphology classification in the DESI BASS+MzLS footprint of 214,600 galaxies (with š‘§ < 0.15 and š‘šš‘Ÿ < 17.77). Leveraging a Bayesian CNN initially trained on Galaxy Zoo DECaLS labels, we successfully adapted our model for the BASS+MzLS footprint through source-free unsupervised domain adaptation without collecting new labels. This domain adaptation addresses the covariate shift between the DECaLS and BASS, MzLS datasets due to the different survey parameters. Our model can predict the posterior of each question-answer pair in the GZD-5 decision tree that is related to morphology. Benchmarking against previous methodologies, our approach demonstrates superior performance, particularly in effectively handling domain adaptation verified through the overlapping footprints of DECaLS and BASS+MzLS. Our code, along with the detailed morphological classifications for a total of 214,600 galaxies, is publicly accessible.

Recommended citation: Renhao Ye. (2024). "Paper Title Number 2." Journal 1. 1(2). http://academicpages.github.io/files/paper2.pdf

Constraints on Triton atmospheric evolution from occultations: 1989-2022

Published in Journal 1, 2023

Context. Around the year 2000, Tritonā€™s south pole experienced an extreme summer solstice that occurs every āˆ¼650 years, when the subsolar latitude reached about 50ā—¦S. Bracketing this epoch, a few occultations probed Tritonā€™s atmosphere in 1989, 1995, 1997, 2008 and 2017. A recent ground-based stellar occultation observed on 6 October 2022 provides a new measurement of Tritonā€™s atmospheric pressure which is presented here. Aims. The goal is to constrain the Volatile Transport Models (VTMs) of Tritonā€™s atmosphere that is basically in vapor pressure equilibrium with the nitrogen ice at its surface. Methods. Fits to the occultation light curves yield Tritonā€™s atmospheric pressure at the reference radius 1400 km, from which the surface pressure is induced. Results. The fits provide a pressure p1400 = 1.211 Ā± 0.044 Ī¼bar at radius1400 km (47-km altitude), from which a surface pressure of psurf = 14.54 Ā± 0.53 Ī¼bar is induced (1Ļƒ error bars). To within error bars, this is identical to the pressure derived from the previous occultation of 5 October 2017, p1400 = 1.18 Ā± 0.03 Ī¼bar and psurf = 14.1 Ā± 0.4 Ī¼bar, respectively. Based on recent models of Tritonā€™s volatile cycles, the overall evolution over the last 30 years of the surface pressure is consistent with N2 condensation taking place in the northern hemisphere. However, models typically predict a steady decrease in surface pressure for the period 2005-2060, which is not confirmed by this observation. Complex surface-atmosphere interactions, such as ice albedo runaway and formation of local N2 frosts in the equatorial regions of Triton could explain the relatively constant pressure between 2017 and 2022

Recommended citation: B. Sicardy. (2023). "Paper Title Number 2." Astronomy & Astrophysics. 1(2). http://academicpages.github.io/files/paper2.pdf

From images to features: unbiased morphology classification via variational auto-encoders and domain adaptation

Published in MNRAS, 2023

We present a novel approach for the dimensionality reduction of galaxy images by leveraging a combination of variational auto-encoders (VAEs) and domain adaptation (DA). We demonstrate the effectiveness of this approach using a sample of low-redshift galaxies with detailed morphological type labels from the Galaxy Zoo Dark Energy Camera Legacy Survey (DECaLS) project. We show that 40-dimensional latent variables can effectively reproduce most morphological features in galaxy images. To further validate the effectiveness of our approach, we utilized a classical random forest classifier on the 40-dimensional latent variables to make detailed morphology feature classifications. This approach performs similar to a direct neural network application on galaxy images. We further enhance our model by tuning the VAE network via DA using galaxies in the overlapping footprint of DECaLS and Beijing-Arizona Sky Survey + Mayall z-band Legacy Survey, enabling the unbiased application of our model to galaxy images in both surveys. We observed that DA led to even better morphological feature extraction and classification performance. Overall, this combination of VAE and DA can be applied to achieve image dimensionality reduction, defect image identification, and morphology classification in large optical surveys.

Recommended citation: Your Name, You. (2009). "Paper Title Number 1." Journal 1. 1(1). https://academic.oup.com/mnras/article/526/4/6391/7320326