相关论文: Machine Learning for automatic identification of n…
Machine learning, satellites or local sensors are key factors for a sustainable and resource-saving optimisation of agriculture and proved its values for the management of agricultural land. Up to now, the main focus was on the enlargement…
Self-supervised learning (SSL) applied to natural images has demonstrated a remarkable ability to learn meaningful, low-dimension representations without labels, resulting in models that are adaptable to many different tasks. Until now,…
The Transiting Exoplanet Survey Satellite (TESS) has a goal of detecting small planets orbiting stars bright enough for mass determination via ground-based radial velocity observations. Here we present estimates of how many exoplanets the…
Exoplanet detection with precise radial velocity (RV) observations is currently limited by spurious RV signals introduced by stellar activity. We show that machine learning techniques such as linear regression and neural networks can…
State-of-the-art techniques to identify H\alpha emission line sources in narrow-band photometric surveys consist of searching for H\alpha excess with reference to nearby objects in the sky (position-based selection). However, while this…
The performance of object detection, to a great extent, depends on the availability of large annotated datasets. To alleviate the annotation cost, the research community has explored a number of ways to exploit unlabeled or weakly labeled…
It has recently been demonstrated that deep learning has significant potential to automate parts of the exoplanet detection pipeline using light curve data from satellites such as Kepler \cite{borucki2010kepler} \cite{koch2010kepler} and…
Transit photometry is currently the most efficient and sensitive method for detecting extrasolar planets (exoplanets) and a large majority of confirmed exoplanets have been detected with this method. The substantial success of space-based…
Classifying and summarizing large data sets from different sky survey projects is essential for various subsequent scientific research. By combining data from 4XMM-DR13, SDSS DR18, and CatWISE, we formed an XMM-WISE-SDSS sample that…
The current archives of LAMOST multi-object spectrograph contain millions of fully reduced spectra, from which the automatic pipelines have produced catalogues of many parameters of individual objects, including their approximate spectral…
Crater cataloging is an important yet time-consuming part of geological mapping. We present an automated Crater Detection Algorithm (CDA) that is competitive with expert-human researchers and hundreds of times faster. The CDA uses multiple…
Planetary rover systems need to perform terrain segmentation to identify drivable areas as well as identify specific types of soil for sample collection. The latest Martian terrain segmentation methods rely on supervised learning which is…
We apply the ${\tt weirddetector}$, a nonparametric signal detection algorithm based on phase dispersion minimization, in a search for low duty-cycle periodic signals in the Transiting Exoplanet Survey Satellite (${\it TESS}$) photometry.…
Design of new drugs is a challenging process: a candidate molecule should satisfy multiple conditions to act properly and make the least side-effect -- perfect candidates selectively attach to and influence only targets, leaving off-targets…
In this series of papers, we employ several machine learning (ML) methods to classify the point-like sources from the miniJPAS catalogue, and identify quasar candidates. Since no representative sample of spectroscopically confirmed sources…
We present a new space mission concept that is capable of finding, detecting, and tracking 90% of near-Earth objects (NEO) with H magnitude of $\rm H\leq22$ (i.e., $\sim$140 m in size) that are potentially hazardous to the Earth. The new…
Automatic source detection and classification tools based on machine learning (ML) algorithms are growing in popularity due to their efficiency when dealing with large amounts of data simultaneously and their ability to work in…
Large-scale astrophysics datasets present an opportunity for new machine learning techniques to identify regions of interest that might otherwise be overlooked by traditional searches. To this end, we use Classification Without Labels…
We present the results of the first-ever visible spectroscopic survey fully dedicated to the small (absolute magnitude H>20) near-Earth asteroid (NEA) population. Observations have been performed at the New Technology Telescope (NTT) of the…
A machine-learning-based method is developed to identify objects with unusual stellar spectra. The method employs an autoencoder, a neural network trained to compress spectral data into a low-dimensional representation and subsequently…