相关论文: Unsupervised Domain Adaptation for Constraining St…

I review some recent progress made in our understanding of galaxy evolution and the cosmic history of star formation. The Hubble Deep Field (HDF) imaging survey has achieved the sensitivity to capture the bulk of the extragalactic…

This paper presents the spatially resolved star formation history (SFH) of nearby galaxies with the aim of furthering our understanding of the different processes involved in the formation and evolution of galaxies. To this end, we apply…

Machine learning systems must adapt to data distributions that evolve over time, in applications ranging from sensor networks and self-driving car perception modules to brain-machine interfaces. We consider gradual domain adaptation, where…

JWST have revealed temporarily-quenched and ultraviolet-luminous galaxies in the early universe, suggesting enhanced star formation stochasticity. Verifying this hypothesis is critical, yet challenging; outshining, wherein light from young…

While unsupervised change detection using contrastive learning has been significantly improved the performance of literature techniques, at present, it only focuses on the bi-temporal change detection scenario. Previous state-of-the-art…

We present a classification of galaxies in the Pan-STARRS1 (PS1) 3$\pi$ survey based on their recent star formation history and morphology. Specifically, we train and test two Random Forest (RF) classifiers using photometric features…

In the first two years of operation JWST has delivered key new insights into the formation and evolution of galaxies in the early Universe. By combining imaging with spectroscopy, we discovered and characterised the first generation of…

Domain adaptation on time series data is an important but challenging task. Most of the existing works in this area are based on the learning of the domain-invariant representation of the data with the help of restrictions like MMD.…

Domain shift, characterized by degraded model performance during transition from labeled source domains to unlabeled target domains, poses a persistent challenge for deploying deep learning systems. Current unsupervised domain adaptation…

Hierarchical models of galaxy formation now provide a much closer match to observations than they did a few years ago. The progress has been achieved by adjusting the description of baryonic processes such as star formation and…

Clues to the identity of dark matter have remained surprisingly elusive, given the scope of experimental programs aimed at its identification. While terrestrial experiments may be able to nail down a model, an alternative, and equally…

The phenomenon of data distribution evolving over time has been observed in a range of applications, calling the needs of adaptive learning algorithms. We thus study the problem of supervised gradual domain adaptation, where labeled data…

Galaxies evolve via a complex interaction of numerous different physical processes, scales and components. In spite of this, overall trends often appear. Simplified models for galaxy histories can be used to search for and capture such…

One of the ways to understand the genesis and evolution of the universe is to know how galaxies have formed and evolved. In this regard, the study of star formation history (SFH) plays an important role in the accurate understanding of…

We study the evolution of 82302 star-forming (SF) galaxies from the SDSS. Our main goals are to explore new ways of handling star formation histories (SFH) obtained with our publicly available spectral synthesis code STARLIGHT, and apply…

Self-supervised prediction is a powerful mechanism to learn representations that capture the underlying structure of the data. Despite recent progress, the self-supervised video prediction task is still challenging. One of the critical…

Artificial intelligence methods show great promise in increasing the quality and speed of work with large astronomical datasets, but the high complexity of these methods leads to the extraction of dataset-specific, non-robust features.…

Astronomical observations already produce vast amounts of data through a new generation of telescopes that cannot be analyzed manually. Next-generation telescopes such as the Large Synoptic Survey Telescope and the Square Kilometer Array…

Star formation happens in a clustered way which is why the star cluster population of a particular galaxy is closely related to the star formation history of this galaxy. From the probabilistic nature of a mass function follows that the…

Deep learning-based models in medical imaging often struggle to generalize effectively to new scans due to data heterogeneity arising from differences in hardware, acquisition parameters, population, and artifacts. This limitation presents…