ABSTRACT We present a catalog of visual-like H-band morphologies of ∼50.000 galaxies (Hf160w < 24.5) in the 5 CANDELS fields (GOODS-N, GOODS-S, UDS, EGS, and COSMOS). Morphologies are estimated using ...Convolutional Neural Networks (ConvNets). The median redshift of the sample is The algorithm is trained on GOODS-S, for which visual classifications are publicly available, and then applied to the other 4 fields. Following the CANDELS main morphology classification scheme, our model retrieves for each galaxy the probabilities of having a spheroid or a disk, presenting an irregularity, being compact or a point source, and being unclassifiable. ConvNets are able to predict the fractions of votes given to a galaxy image with zero bias and ∼10% scatter. The fraction of mis-classifications is less than 1%. Our classification scheme represents a major improvement with respect to Concentration-Asymmetry-Smoothness-based methods, which hit a 20%-30% contamination limit at high z. The catalog is released with the present paper via the Rainbow database (http://rainbowx.fis.ucm.es/Rainbow_navigator_public/).
ABSTRACT We quantify the morphological evolution of massive galaxies ( ) from in the 5 CANDELS fields. The progenitors are selected using abundance matching techniques to account for the mass growth. ...At , the population matches the massive end of the Hubble sequence, with 30% of pure spheroids, 50% of galaxies with equally dominant disk and bulge components, and 20% of disks. At however, there is a majority of irregular systems ( ) with still 30% of pure spheroids. We then analyze the stellar populations, star formation rates (SFRs), gas fractions, and structural properties for the different morphologies independently. Our results suggest two distinct channels for the growth of bulges in massive galaxies. Around were already bulges at , with low average SFRs and gas fractions ( ), high Sérsic indices ( ), and small effective radii ( kpc), pointing toward an even earlier formation through gas-rich mergers or violent disk instabilities. Between and , they rapidly increase their size by a factor of , are quenched, and slightly increase their Sérsic indices ( ) but their global morphology remains unaltered. The structural evolution is independent of the gas fractions, suggesting that it is driven by ex situ events. The remaining 60% experience a gradual morphological transformation, from clumpy disks to more regular bulge+disk systems, essentially happening at . This results in the growth of a significant bulge component ( ) for 2/3 of the systems, possibly through the migration of clumps, while the remaining 1/3 retain a rather small bulge ( ). The transition phase between disturbed and relaxed systems and the emergence of the bulge is correlated with a decrease in the star formation activity and the gas fractions, suggesting a morphological quenching process as a plausible mechanism for the formation of these bulges.
Abstract
We present a real-time stamp classifier of astronomical events for the Automatic Learning for the Rapid Classification of Events broker, ALeRCE. The classifier is based on a convolutional ...neural network, trained on alerts ingested from the Zwicky Transient Facility (ZTF). Using only the
science, reference,
and
difference
images of the first detection as inputs, along with the metadata of the alert as features, the classifier is able to correctly classify alerts from active galactic nuclei, supernovae (SNe), variable stars, asteroids, and bogus classes, with high accuracy (∼94%) in a balanced test set. In order to find and analyze SN candidates selected by our classifier from the ZTF alert stream, we designed and deployed a visualization tool called SN Hunter, where relevant information about each possible SN is displayed for the experts to choose among candidates to report to the Transient Name Server database. From 2019 June 26 to 2021 February 28, we have reported 6846 SN candidates to date (11.8 candidates per day on average), of which 971 have been confirmed spectroscopically. Our ability to report objects using only a single detection means that 70% of the reported SNe occurred within one day after the first detection. ALeRCE has only reported candidates not otherwise detected or selected by other groups, therefore adding new early transients to the bulk of objects available for early follow-up. Our work represents an important milestone toward rapid alert classifications with the next generation of large etendue telescopes, such as the Vera C. Rubin Observatory.
Abstract
The classic classification scheme for active galactic nuclei (AGNs) was recently challenged by the discovery of the so-called changing-state (changing-look) AGNs. The physical mechanism ...behind this phenomenon is still a matter of open debate and the samples are too small and of serendipitous nature to provide robust answers. In order to tackle this problem, we need to design methods that are able to detect AGNs right in the act of changing state. Here we present an anomaly-detection technique designed to identify AGN light curves with anomalous behaviors in massive data sets. The main aim of this technique is to identify CSAGN at different stages of the transition, but it can also be used for more general purposes, such as cleaning massive data sets for AGN variability analyses. We used light curves from the Zwicky Transient Facility data release 5 (ZTF DR5), containing a sample of 230,451 AGNs of different classes. The ZTF DR5 light curves were modeled with a Variational Recurrent Autoencoder (VRAE) architecture, that allowed us to obtain a set of attributes from the VRAE latent space that describes the general behavior of our sample. These attributes were then used as features for an Isolation Forest (IF) algorithm that is an anomaly detector for a “one class” kind of problem. We used the VRAE reconstruction errors and the IF anomaly score to select a sample of 8809 anomalies. These anomalies are dominated by bogus candidates, but we were able to identify 75 promising CSAGN candidates.
Abstract
We report the observations of solar system objects during the 2015 campaign of the High cadence Transient Survey (HiTS). We found 5740 bodies (mostly Main Belt asteroids), 1203 of which were ...detected in different nights and in
g
′ and
r
′. Objects were linked in the barycenter system and their orbital parameters were computed assuming Keplerian motion. We identified 6 near Earth objects, 1738 Main Belt asteroids and 4 Trans-Neptunian objects. We did not find a
g
′−
r
′ color–size correlation for 14 <
H
g
′
< 18 (1 <
D
< 10 km) asteroids. We show asteroids’ colors are disturbed by HiTS’ 1.6 hr cadence and estimate that observations should be separated by at most 14 minutes to avoid confusion in future wide-field surveys like LSST. The size distribution for the Main Belt objects can be characterized as a simple power law with slope ∼0.9, steeper than in any other survey, while data from the 2014 HiTS campaign has a distribution consistent with previous ones (slopes ∼0.68 at the bright end and ∼0.34 at the faint end). This difference is likely due to the ecliptic distribution of the Main Belt since the 2015 campaign surveyed farther from the ecliptic than did 2014's and most previous surveys.
We present visual-like morphologies over 16 photometric bands, from ultraviolet to near-infrared, for 8412 galaxies in the Cluster Lensing And Supernova survey with Hubble (CLASH) obtained using a ...convolutional neural network (ConvNet) model. Our model follows the Cosmic Assembly Near-IR Deep Extragalactic Legacy Survey (CANDELS) main morphological classification scheme, obtaining the probability for each galaxy at each CLASH band of being spheroid, disk, irregular, point source, or unclassifiable. Our catalog contains morphologies for each galaxy with Hmag < 24.5 in every filter where the galaxy is observed. We trained an initial ConvNet model using approximately 7500 expert eyeball labels from CANDELS. We created eyeball labels for 100 randomly selected galaxies per each of the 16-filter set of CLASH (1600 galaxy images in total), where each image was classified by at least five of us. We use these labels to fine-tune the network to accurately predict labels for the CLASH data and to evaluate the performance of our model. We achieve a root-mean-square error of 0.0991 on the test set. We show that our proposed fine-tuning technique reduces the number of labeled images needed for training, as compared to directly training over the CLASH data, and achieves a better performance. This approach is very useful to minimize eyeball labeling efforts when classifying unlabeled data from new surveys. This will become particularly useful for massive data sets such as those coming from near-future surveys such as EUCLID or the LSST. Our catalog consists of prediction of probabilities for each galaxy by morphology in their different bands and is made publicly available at http://www.inf.udec.cl/~guille/data/Deep-CLASH.csv.
ABSTRACT
We propose a phylogenetic approach (PA) as a novel and robust tool to detect galaxy populations (GPs) based on their chemical composition. The branches of the tree are interpreted as ...different GPs and the length between nodes as the internal chemical variation along a branch. We apply the PA using 30 abundance indices from the Sloan Digital Sky Survey to 475 galaxies in the Coma Cluster and 438 galaxies in the field. We find that a dense environment, such as Coma, shows several GPs, which indicates that the environment is promoting galaxy evolution. Each population shares common properties that can be identified in colour–magnitude space, in addition to minor structures inside the red sequence. The field is more homogeneous, presenting one main GP. We also apply a principal component analysis (PCA) to both samples, and find that the PCA does not have the same power in identifying GPs.
Abstract
We present the first version of the Automatic Learning for the Rapid Classification of Events (ALeRCE) broker light curve classifier. ALeRCE is currently processing the Zwicky Transient ...Facility (ZTF) alert stream, in preparation for the Vera C. Rubin Observatory. The ALeRCE light curve classifier uses variability features computed from the ZTF alert stream and colors obtained from AllWISE and ZTF photometry. We apply a balanced random forest algorithm with a two-level scheme where the top level classifies each source as periodic, stochastic, or transient, and the bottom level further resolves each of these hierarchical classes among 15 total classes. This classifier corresponds to the first attempt to classify multiple classes of stochastic variables (including core- and host-dominated active galactic nuclei, blazars, young stellar objects, and cataclysmic variables) in addition to different classes of periodic and transient sources, using real data. We created a labeled set using various public catalogs (such as the Catalina Surveys and Gaia DR2 variable stars catalogs, and the Million Quasars catalog), and we classify all objects with ≥6
g
-band or ≥6
r
-band detections in ZTF (868,371 sources as of 2020 June 9), providing updated classifications for sources with new alerts every day. For the top level we obtain macro-averaged precision and recall scores of 0.96 and 0.99, respectively, and for the bottom level we obtain macro-averaged precision and recall scores of 0.57 and 0.76, respectively. Updated classifications from the light curve classifier can be found at the ALeRCE Explorer website (
http://alerce.online
).
We report on the serendipitous observations of solar system objects imaged during the High cadence Transient Survey 2014 observation campaign. Data from this high-cadence wide-field survey was ...originally analyzed for finding variable static sources using machine learning to select the most-likely candidates. In this work, we search for moving transients consistent with solar system objects and derive their orbital parameters. We use a simple, custom motion detection algorithm to link trajectories and assume Keplerian motion to derive the asteroid's orbital parameters. We use known asteroids from the Minor Planet Center database to assess the detection efficiency of the survey and our search algorithm. Trajectories have an average of nine detections spread over two days, and our fit yields typical errors of , e ∼ 0.07 and i ∼ 0 5 in semimajor axis, eccentricity, and inclination, respectively, for known asteroids in our sample. We extract 7700 orbits from our trajectories, identifying 19 near-Earth objects, 6687 asteroids, 14 Centaurs, and 15 trans-Neptunian objects. This highlights the complementarity of supernova wide-field surveys for solar system research and the significance of machine learning to clean data of false detections. It is a good example of the data-driven science that Large Synoptic Survey Telescope will deliver.
ASTROMER Donoso-Oliva, C.; Becker, I.; Protopapas, P. ...
Astronomy and astrophysics (Berlin),
02/2023, Letnik:
670
Journal Article
Recenzirano
Odprti dostop
Taking inspiration from natural language embeddings, we present ASTROMER, a transformer-based model to create representations of light curves. ASTROMER was pre-trained in a self-supervised manner, ...requiring no human-labeled data. We used millions of R-band light sequences to adjust the ASTROMER weights. The learned representation can be easily adapted to other surveys by re-training ASTROMER on new sources. The power of ASTROMER consists in using the representation to extract light curve embeddings that can enhance the training of other models, such as classifiers or regressors. As an example, we used ASTROMER embeddings to train two neural-based classifiers that use labeled variable stars from MACHO, OGLE-III, and ATLAS. In all experiments, ASTROMER-based classifiers outperformed a baseline recurrent neural network trained on light curves directly when limited labeled data were available. Furthermore, using ASTROMER embeddings decreases the computational resources needed while achieving state-of-the-art results. Finally, we provide a Python library that includes all the functionalities employed in this work.
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