Abstract
We introduce the Automatic Learning for the Rapid Classification of Events (ALeRCE) broker, an astronomical alert broker designed to provide a rapid and self-consistent classification of ...large etendue telescope alert streams, such as that provided by the Zwicky Transient Facility (ZTF) and, in the future, the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). ALeRCE is a Chilean-led broker run by an interdisciplinary team of astronomers and engineers working to become intermediaries between survey and follow-up facilities. ALeRCE uses a pipeline that includes the real-time ingestion, aggregation, cross-matching, machine-learning (ML) classification, and visualization of the ZTF alert stream. We use two classifiers: a stamp-based classifier, designed for rapid classification, and a light curve–based classifier, which uses the multiband flux evolution to achieve a more refined classification. We describe in detail our pipeline, data products, tools, and services, which are made public for the community (see
https://alerce.science
). Since we began operating our real-time ML classification of the ZTF alert stream in early 2019, we have grown a large community of active users around the globe. We describe our results to date, including the real-time processing of 1.5 × 10
8
alerts, the stamp classification of 3.4 × 10
7
objects, the light-curve classification of 1.1 × 10
6
objects, the report of 6162 supernova candidates, and different experiments using LSST-like alert streams. Finally, we discuss the challenges ahead in going from a single stream of alerts such as ZTF to a multistream ecosystem dominated by LSST.
ABSTRACT
In the new era of very large telescopes, where data are crucial to expand scientific knowledge, we have witnessed many deep learning applications for the automatic classification of light ...curves. Recurrent neural networks (RNNs) are one of the models used for these applications, and the Long Short-Term Memory (LSTM) unit stands out for being an excellent choice for the representation of long time series. In general, RNNs assume observations at discrete times, which may not suit the irregular sampling of light curves. A traditional technique to address irregular sequences consists of adding the sampling time to the network’s input, but this is not guaranteed to capture sampling irregularities during training. Alternatively, the Phased LSTM (PLSTM) unit has been created to address this problem by updating its state using the sampling times explicitly. In this work, we study the effectiveness of the LSTM- and PLSTM-based architectures for the classification of astronomical light curves. We use seven catalogues containing periodic and non-periodic astronomical objects. Our findings show that LSTM outperformed PLSTM on six of seven data sets. However, the combination of both units enhances the results in all data sets.
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.
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.
eta Tel is an 18 Myr system composed of a 2.09 M$_ odot $ A-type star with an M7-M8 brown dwarf companion, eta Tel B. The two objects have a projected separation of 4farcs 2 (sim 208 au). This system ...has been targeted by high-contrast imaging campaigns for over 20 years, facilitating its orbital and photometric characterization. The companion, eta Tel B, both bright and on a wide orbit, is an ideal candidate for a detailed examination of its position and the characterization of its atmosphere. To explore the orbital parameters of eta Tel B, measure its contrast, and investigate its close surroundings, we analyzed three new SPHERE/IRDIS coronagraphic observations. Our objectives are to investigate the possibility of a circumplanetary disk or a close companion around eta Tel B, and characterize its orbit by combining this new data set with archival data acquired in the past two decades. The IRDIS data are reduced with state-of-the-art algorithms to achieve a contrast with respect to the star of 1.0$ $ at the location of the companion. Using the NEGative Fake Companion technique (NEGFC), we measure the astrometric positions and flux of eta Tel B for the three IRDIS epochs. Together with the measurements presented in the literature, the baseline of the astrometric follow-up is 19 years. We calculate a contrast for the companion of 6.8 magnitudes in the H band. The separation and position angle measured are 4farcs 218 and 167.3 degrees, respectively. The astrometric positions of the companions are calculated with an uncertainty of 4 milliarcseconds (mas) in separation and 0.2 degrees in position angle. These are the smallest astrometrical uncertainties of eta Tel B obtained so far. The orbital parameters are estimated using the Orvara code, including all available epochs. The orbital analysis is performed taking into account the Gaia-Hipparcos acceleration of the system. Suppressing its point spread function (PSF), we have produced contrast curves centered on the brown dwarf in order to constrain our detection capabilities for a disk or companions around it. After considering only orbits that could not disrupt the outer debris disk around eta Tel A, our orbital analysis reveals a low eccentric orbit (e sim 0.34) with an inclination of 81.9 degrees (nearly edge-on) and a semi-major axis of 218 au. Furthermore, we determine the mass of eta Tel B to be 48 consistent with previous calculations from the literature based on evolutionary models. Finally, we do not detect any significant residual pointing to the presence of a satellite or a disk around the brown dwarf. The retrieved detection limits allow us to discard massive objects around eta Tel B with masses down to 1.6 at a separation of 33 au.
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.
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 of 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 was available. Furthermore, using ASTROMER embeddings decreases 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. The library, main code, and pre-trained weights are available at https://github.com/astromer-science
$\eta$ Tel is an 18 Myr system with a 2.09 M$_{\odot}$ A-type star and an
M7-M8 brown dwarf companion, $\eta$ Tel B, separated by 4.2'' (208 au).
High-contrast imaging campaigns over 20 years have ...enabled orbital and
photometric characterization. $\eta$ Tel B, bright and on a wide orbit, is
ideal for detailed examination.
We analyzed three new SPHERE/IRDIS coronagraphic observations to explore
$\eta$ Tel B's orbital parameters, contrast, and surroundings, aiming to detect
a circumplanetary disk or close companion. Reduced IRDIS data achieved a
contrast of 1.0$\times 10^{-5}$, enabling astrometric measurements with
uncertainties of 4 mas in separation and 0.2 degrees in position angle, the
smallest so far.
With a contrast of 6.8 magnitudes in the H band, $\eta$ Tel B's separation
and position angle were measured as 4.218'' and 167.3 degrees, respectively.
Orbital analysis using Orvara code, considering Gaia-Hipparcos acceleration,
revealed a low eccentric orbit (e $\sim$ 0.34), inclination of 81.9 degrees,
and semi-major axis of 218 au. $\eta$ Tel B's mass was determined to be 48
\MJup, consistent with previous calculations.
No significant residual indicating a satellite or disk around $\eta$ Tel B
was detected. Detection limits ruled out massive objects around $\eta$ Tel B
with masses down to 1.6 \MJup at a separation of 33 au.
In the new era of very large telescopes, where data is crucial to expand scientific knowledge, we have witnessed many deep learning applications for the automatic classification of lightcurves. ...Recurrent neural networks (RNNs) are one of the models used for these applications, and the LSTM unit stands out for being an excellent choice for the representation of long time series. In general, RNNs assume observations at discrete times, which may not suit the irregular sampling of lightcurves. A traditional technique to address irregular sequences consists of adding the sampling time to the network's input, but this is not guaranteed to capture sampling irregularities during training. Alternatively, the Phased LSTM unit has been created to address this problem by updating its state using the sampling times explicitly. In this work, we study the effectiveness of the LSTM and Phased LSTM based architectures for the classification of astronomical lightcurves. We use seven catalogs containing periodic and nonperiodic astronomical objects. Our findings show that LSTM outperformed PLSTM on 6/7 datasets. However, the combination of both units enhances the results in all datasets.
•In this work, we used KT2440 to efficiently coproduce CdS Qdots and mcl-PHAs in batch cultures.•The PHA and biomass yields in KT2440 were almost not affected under coproducing conditions compared to ...the ones showed in cells where PHA was the only target compound.•The fluorescence of cells biosynthesizing CdS QDots depends on the concentration of cadmium used and exposure time.•TEM micrographs show that the PHA and QDots are localized at different sites of the cell, showing no apparent interaction.•To the best of our knowledge this is the first report showing the biological cosynthesis of two important chemicals for nanotechnological applications.
Microbial polymers and nanomaterials production is a promising alternative for sustainable bioeconomics. To this end, we used Pseudomonas putida KT2440 as a cell factory in batch cultures to coproduce two important nanotechnology materials– medium-chain-length (MCL)-polyhydroxyalkanoates (PHAs) and CdS fluorescent nanoparticles (i.e. quantum dots QDots). Due to high cadmium resistance, biomass and PHA yields were almost unaffected by coproduction conditions. Fluorescent nanocrystal biosynthesis was possible only in presence of cysteine. Furthermore, this process took place exclusively in the cell, displaying the classical emission spectra of CdS QDots under UV-light exposure. Cell fluorescence, zeta potential values, and particles size of QDots depended on cadmium concentration and exposure time. Using standard PHA-extraction procedures, the biosynthesized QDots remained associated with the biomass, and the resulting PHAs presented no traces of CdS QDots. Transmission electron microscopy located the synthesized PHAs in the cell cytoplasm, whereas CdS nanocrystals were most likely located within the periplasmic space, exhibiting no apparent interaction. This is the first report presenting the microbial coproduction of MCL-PHAs and CdS QDots in P. putida KT2440, thus constituting a foundation for further bioprocess developments and strain engineering towards the efficient synthesis of these highly relevant bioproducts for nanotechnology.
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