ABSTRACT Calibrating the photometric redshifts of 109 galaxies for upcoming weak lensing cosmology experiments is a major challenge for the astrophysics community. The path to obtaining the required ...spectroscopic redshifts for training and calibration is daunting, given the anticipated depths of the surveys and the difficulty in obtaining secure redshifts for some faint galaxy populations. Here we present an analysis of the problem based on the self-organizing map, a method of mapping the distribution of data in a high-dimensional space and projecting it onto a lower-dimensional representation. We apply this method to existing photometric data from the COSMOS survey selected to approximate the anticipated Euclid weak lensing sample, enabling us to robustly map the empirical distribution of galaxies in the multidimensional color space defined by the expected Euclid filters. Mapping this multicolor distribution lets us determine where-in galaxy color space-redshifts from current spectroscopic surveys exist and where they are systematically missing. Crucially, the method lets us determine whether a spectroscopic training sample is representative of the full photometric space occupied by the galaxies in a survey. We explore optimal sampling techniques and estimate the additional spectroscopy needed to map out the color-redshift relation, finding that sampling the galaxy distribution in color space in a systematic way can efficiently meet the calibration requirements. While the analysis presented here focuses on the Euclid survey, similar analysis can be applied to other surveys facing the same calibration challenge, such as DES, LSST, and WFIRST.
Several approaches were proposed to describe the geomorphology of drainage networks and the abiotic/biotic factors determining their morphology. There is an intrinsic complexity of the explicit ...qualification of the morphological variations in response to various types of control factors and the difficulty of expressing the cause-effect links. Traditional methods of drainage network classification are based on the manual extraction of key characteristics, then applied as pattern recognition schemes. These approaches, however, have low predictive and uniform ability. We present a different approach, based on the data-driven supervised learning by images, extended also to extraterrestrial cases. With deep learning models, the extraction and classification phase is integrated within a more objective, analytical, and automatic framework. Despite the initial difficulties, due to the small number of training images available, and the similarity between the different shapes of the drainage samples, we obtained successful results, concluding that deep learning is a valid way for data exploration in geomorphology and related fields.
The importance of the current role of data-driven science is constantly increasing within Astrophysics, due to the huge amount of multi-wavelength data collected every day, characterized by complex ...and high-volume information requiring efficient and, as much as possible, automated exploration tools. Furthermore, to accomplish main and legacy science objectives of future or incoming large and deep survey projects, such as James Webb Space Telescope (JWST), James Webb Space Telescope (LSST), and Euclid, a crucial role is played by an accurate estimation of photometric redshifts, whose knowledge would permit the detection and analysis of extended and peculiar sources by disentangling low-z from high-z sources and would contribute to solve the modern cosmological discrepancies. The recent photometric redshift data challenges, organized within several survey projects, like LSST and Euclid, pushed the exploitation of the observed multi-wavelength and multi-dimensional data or
ad hoc
simulated data to improve and optimize the photometric redshifts prediction and statistical characterization based on both Spectral Energy Distribution (SED) template fitting and machine learning methodologies. They also provided a new impetus in the investigation of hybrid and deep learning techniques, aimed at conjugating the positive peculiarities of different methodologies, thus optimizing the estimation accuracy and maximizing the photometric range coverage, which are particularly important in the high-z regime, where the spectroscopic ground truth is poorly available. In such a context, we summarize what was learned and proposed in more than a decade of research.
Astronomy, as many other scientific disciplines, is facing a true data deluge which is bound to change both the praxis and the methodology of every day research work. The emerging field of ...astroinformatics, while on the one end appears crucial to face the technological challenges, on the other is opening new exciting perspectives for new astronomical discoveries through the implementation of advanced data mining procedures. The complexity of astronomical data and the variety of scientific problems, however, call for innovative algorithms and methods as well as for an extreme usage of ICT technologies.
ABSTRACT
In order to answer the open questions of modern cosmology and galaxy evolution theory, robust algorithms for calculating photometric redshifts (photo-z) for very large samples of galaxies ...are needed. Correct estimation of the various photo-z algorithms’ performance requires attention to both the performance metrics and the data used for the estimation. In this work, we use the supervised machine learning algorithm MLPQNA (Multi-Layer Perceptron with Quasi-Newton Algorithm) to calculate photometric redshifts for the galaxies in the COSMOS2015 catalogue and the unsupervised Self-Organizing Maps (SOM) to determine the reliability of the resulting estimates. We find that for zspec < 1.2, MLPQNA photo-z predictions are on the same level of quality as spectral energy distribution fitting photo-z. We show that the SOM successfully detects unreliable zspec that cause biases in the estimation of the photo-z algorithms’ performance. Additionally, we use SOM to select the objects with reliable photo-z predictions. Our cleaning procedures allow us to extract the subset of objects for which the quality of the final photo-z catalogues is improved by a factor of 2, compared to the overall statistics.
Context. The Kilo-Degree Survey (KiDS) is an optical wide-field imaging survey carried out with the VLT Survey Telescope and the OmegaCAM camera. KiDS will image 1500 square degrees in four filters ...(ugri), and together with its near-infrared counterpart VIKING will produce deep photometry in nine bands. Designed for weak lensing shape and photometric redshift measurements, its core science driver is mapping the large-scale matter distribution in the Universe back to a redshift of ~0.5. Secondary science cases include galaxy evolution, Milky Way structure, and the detection of high-redshift clusters and quasars. Aims. KiDS is an ESO Public Survey and dedicated to serving the astronomical community with high-quality data products derived from the survey data. Public data releases, the first two of which are presented here, are crucial for enabling independent confirmation of the survey’s scientific value. The achieved data quality and initial scientific utilization are reviewed in order to validate the survey data. Methods. A dedicated pipeline and data management system based on Astro-WISE, combined with newly developed masking and source classification tools, is used for the production of the data products described here. Science projects based on these data products and preliminary results are outlined. Results. For 148 survey tiles (≈160 sq.deg.) stacked ugri images have been released, accompanied by weight maps, masks, source lists, and a multi-band source catalogue. Limiting magnitudes are typically 24.3, 25.1, 24.9, 23.8 (5σ in a 2′′ aperture) in ugri, respectively, and the typical r-band PSF size is less than 0.7′′. The photometry prior to global homogenization is stable at the ~2% (4%) level in gri (u) with some outliers due to non-photometric conditions, while the astrometry shows a typical 2D rms of 0.03′′. Early scientific results include the detection of nine high-z QSOs, fifteen candidate strong gravitational lenses, high-quality photometric redshifts and structural parameters for hundreds of thousands of galaxies.
Aims . We present a novel method for detecting outliers in astronomical time series based on the combination of a deep neural network and a k-nearest neighbor algorithm with the aim of identifying ...and removing problematic epochs in the light curves of astronomical objects. Methods . We used an EfficientNet network pretrained on ImageNet as a feature extractor and performed a k-nearest neighbor search in the resulting feature space to measure the distance from the first neighbor for each image. If the distance was above the one obtained for a stacked image, we flagged the image as a potential outlier. Results . We applied our method to a time series obtained from the VLT Survey Telescope monitoring campaign of the Deep Drilling Fields of the Vera C. Rubin Legacy Survey of Space and Time. We show that our method can effectively identify and remove artifacts from the VST time series and improve the quality and reliability of the data. This approach may prove very useful in light of the amount of data that will be provided by the LSST, which will prevent the inspection of individual light curves. We also discuss the advantages and limitations of our method and suggest possible directions for future work.
Context. The Kilo-Degree Survey (KiDS) is an ongoing optical wide-field imaging survey with the OmegaCAM camera at the VLT Survey Telescope. It aims to image 1500 square degrees in four filters ...(ugri). The core science driver is mapping the large-scale matter distribution in the Universe, using weak lensing shear and photometric redshift measurements. Further science cases include galaxy evolution, Milky Way structure, detection of high-redshift clusters, and finding rare sources such as strong lenses and quasars. Aims. Here we present the third public data release and several associated data products, adding further area, homogenized photometric calibration, photometric redshifts and weak lensing shear measurements to the first two releases. Methods. A dedicated pipeline embedded in the Astro-WISE information system is used for the production of the main release. Modifications with respect to earlier releases are described in detail. Photometric redshifts have been derived using both Bayesian template fitting, and machine-learning techniques. For the weak lensing measurements, optimized procedures based on the THELI data reduction and lensfit shear measurement packages are used. Results. In this third data release an additional 292 new survey tiles (≈300 deg2) stacked ugri images are made available, accompanied by weight maps, masks, and source lists. The multi-band catalogue, including homogenized photometry and photometric redshifts, covers the combined DR1, DR2 and DR3 footprint of 440 survey tiles (44 deg2). Limiting magnitudes are typically 24.3, 25.1, 24.9, 23.8 (5σ in a 2′′ aperture) in ugri, respectively, and the typical r-band PSF size is less than 0.7′′. The photometric homogenization scheme ensures accurate colours and an absolute calibration stable to ≈2% for gri and ≈3% in u. Separately released for the combined area of all KiDS releases to date are a weak lensing shear catalogue and photometric redshifts based on two different machine-learning techniques.
Context.
Modern sky surveys are producing ever larger amounts of observational data, which makes the application of classical approaches for the classification and analysis of objects challenging and ...time consuming. However, this issue may be significantly mitigated by the application of automatic machine and deep learning methods.
Aims.
We propose
ulisse
, a new deep learning tool that, starting from a single prototype object, is capable of identifying objects that share common morphological and photometric properties, and hence of creating a list of candidate lookalikes. In this work, we focus on applying our method to the detection of active galactic nuclei (AGN) candidates in a Sloan Digital Sky Survey galaxy sample, because the identification and classification of AGN in the optical band still remains a challenging task in extragalactic astronomy.
Methods.
Intended for the initial exploration of large sky surveys,
ulisse
directly uses features extracted from the ImageNet dataset to perform a similarity search. The method is capable of rapidly identifying a list of candidates, starting from only a single image of a given prototype, without the need for any time-consuming neural network training.
Results.
Our experiments show
ulisse
is able to identify AGN candidates based on a combination of host galaxy morphology, color, and the presence of a central nuclear source, with a retrieval efficiency ranging from 21% to 65% (including composite sources) depending on the prototype, where the random guess baseline is 12%. We find
ulisse
to be most effective in retrieving AGN in early-type host galaxies, as opposed to prototypes with spiral- or late-type properties.
Conclusions.
Based on the results described in this work,
ulisse
could be a promising tool for selecting different types of astro-physical objects in current and future wide-field surveys (e.g.,
Euclid
, LSST etc.) that target millions of sources every single night.
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