Related papers: Unsupervised learning for variability detection wi…
Context: Many if not most planetary nebulae (PNe) are now thought to be the outcome of binary evolutionary scenarios. However only a few percent of PNe in the Milky Way are known to host binary systems. The high precision repeated observing…
Anomalies are samples that significantly deviate from the rest of the data and their detection plays a major role in building machine learning models that can be reliably used in applications such as data-driven design and novelty…
Generative modeling and self-supervised learning have in recent years made great strides towards learning from data in a completely unsupervised way. There is still however an open area of investigation into guiding a neural network to…
Gaia is undertaking a deep synoptic survey of the Galaxy, but photometry from individual epochs has, as of yet, only been released for a minimal number of sources. We show that it is possible to identify variable stars in Gaia Data Release…
Multiple stellar systems are ubiquitous in the Milky Way, but are often unresolved and seen as single objects in spectroscopic, photometric, and astrometric surveys. Yet, modeling them is essential for developing a full understanding of…
Context. Understanding the demographics of white dwarf - main sequence (WDMS) binaries is key to uncovering the formation of various stellar exotica and refining the details of binary stellar evolution. Despite several dedicated efforts to…
The data volume generated by astronomical surveys is growing rapidly. Traditional analysis techniques in spectroscopy either demand intensive human interaction or are computationally expensive. In this scenario, machine learning, and…
We propose an unsupervised classification method using a limited number of coded acquisitions from a DD-CASSI hyperspectral imager. Based on a simple model of intra-class spectral variability, this approach allow to identify classes and…
We learn, in an unsupervised way, an embedding from sequences of radar images that is suitable for solving the place recognition problem with complex radar data. Our method is based on invariant instance feature learning but is tailored for…
The peripheral regions of globular clusters (GCs) are extremely challenging to study due to their low surface brightness nature and the dominance of Milky Way contaminant populations along their sightlines. We have developed a probabilistic…
Universal anomaly detection still remains a challenging problem in machine learning and medical image analysis. It is possible to learn an expected distribution from a single class of normative samples, e.g., through epistemic uncertainty…
The Gaia DR2 sample of short-timescale variable candidates results from the investigation of the first 22 months of Gaia photometry for a subsample of sources at the Gaia faint end. For this exercise, we limited ourselves to the case of…
The intermediate-mass pre-main sequence Herbig Ae/Be stars are key to understanding the differences in formation mechanisms between low- and high-mass stars. The study of the general properties of these objects is hampered by the fact that…
Based on the astrometry and photometry in Gaia DR3, we identified new nearby white dwarfs and validated those that had been missed from recent white dwarf catalogues despite being previously documented. To ensure the reliability of their…
Unsupervised anomaly detection (UAD) learns one-class classifiers exclusively with normal (i.e., healthy) images to detect any abnormal (i.e., unhealthy) samples that do not conform to the expected normal patterns. UAD has two main…
Unsupervised approaches to learning in neural networks are of substantial interest for furthering artificial intelligence, both because they would enable the training of networks without the need for large numbers of expensive annotations,…
We provide the first results from the complete SNAD adaptive learning pipeline in the context of a broad scope of data from large-scale astronomical surveys. The main goal of this work is to explore the potential of adaptive learning…
This paper addresses the problem of unsupervised clustering which remains one of the most fundamental challenges in machine learning and artificial intelligence. We propose the clustered generator model for clustering which contains both…
The Galactic center (GC) is the densest region of the Milky Way. Variability surveys towards the GC potentially provide the largest number of variable stars per square degree within the Galaxy. However, high stellar density is also a…
Classification will be an important first step for upcoming surveys that will detect billions of new sources such as LSST and Euclid, as well as DESI, 4MOST and MOONS. The application of traditional methods of model fitting and…