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.
Human pluripotent stem cells are a promising source of differentiated cells for developmental studies, cell transplantation, disease modeling, and drug testing. However, their widespread use even for ...intensely studied cell types like spinal motor neurons is hindered by the long duration and low yields of existing protocols for in vitro differentiation and by the molecular heterogeneity of the populations generated. We report a combination of small molecules that within 3 weeks induce motor neurons at up to 50% abundance and with defined subtype identities of relevance to neurodegenerative disease. Despite their accelerated differentiation, motor neurons expressed combinations of HB9, ISL1, and column-specific markers that mirror those observed in vivo in human embryonic spinal cord. They also exhibited spontaneous and induced activity, and projected axons toward muscles when grafted into developing chick spinal cord. Strikingly, this novel protocol preferentially generates motor neurons expressing markers of limb-innervating lateral motor column motor neurons (FOXP1(+)/LHX3(-)). Access to high-yield cultures of human limb-innervating motor neuron subtypes will facilitate in-depth study of motor neuron subtype-specific properties, disease modeling, and development of large-scale cell-based screening assays.
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
).
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.
Autonomous navigation of artificial agents is a challenging task for changing and complex environments. Reinforcement learning (RL) algorithms are widely used for autonomous navigation, where the ...agent, through the interaction with the environment, learns the behaviors needed to maximize the reward. Recent architectures extract information from the environment using convolutional neural networks, where the visual features needed to maximize the reward are unknown and uncertain, and then, increasing the number of parameters learned by the entire system. Moreover, the presence of sparse rewards complicates, even more, the task generating unstable results in the learning problem. The work here presented is twofold. First, we show the advantages of using retina physiology knowledge to design a visual sensor feeding the RL network. Secondly, based on intrinsic motivation, we propose the use of auxiliary tasks to deal with sparse rewards, generating a continuous learning process. We define two auxiliary tasks, state, and action predictions, forcing the network to learn characteristics of environment; and also, to detect which of them are valuable for the task. These two contributions were implemented in the DeepMind Lab environment simulating an agent moving inside two different maze scenarios. The results obtained reveal a promising extension of the inclusion of biological-plausible mechanisms inside artificial intelligence applications. Moreover, to include auxiliary tasks improves the performance adding robustness to the system.
We present the results of a search for rapidly evolving transients in the Dark Energy Survey Supernova Programme. These events are characterized by fast light-curve evolution (rise to peak in ≲10 d ...and exponential decline in ≲30 d after peak). We discovered 72 events, including 37 transients with a spectroscopic redshift from host galaxy spectral features. The 37 events increase the total number of rapid optical transients by more than a factor of two. They are found at a wide range of redshifts (0.05 < z < 1.56) and peak brightnesses (-15.75 > Mg > -22.25). The multiband photometry is well fit by a blackbody up to few weeks after peak. The events appear to be hot (T ≈ 10 000–30 000 K) and large (R ≈ 1014 - 2 × 1015 cm) at peak, and generally expand and cool in time, though some events show evidence for a receding photosphere with roughly constant temperature. Spectra taken around peak are dominated by a blue featureless continuum consistent with hot, optically thick ejecta. We compare our events with a previously suggested physical scenario involving shock breakout in an optically thick wind surrounding a core-collapse supernova, we conclude that current models for such a scenario might need an additional power source to describe the exponential decline. We find that these transients tend to favour star-forming host galaxies, which could be consistent with a core-collapse origin. However, more detailed modelling of the light curves is necessary to determine their physical origin.
ABSTRACT
We present an improved measurement of the Hubble constant (H0) using the ‘inverse distance ladder’ method, which adds the information from 207 Type Ia supernovae (SNe Ia) from the Dark ...Energy Survey (DES) at redshift 0.018 < z < 0.85 to existing distance measurements of 122 low-redshift (z < 0.07) SNe Ia (Low-z) and measurements of Baryon Acoustic Oscillations (BAOs). Whereas traditional measurements of H0 with SNe Ia use a distance ladder of parallax and Cepheid variable stars, the inverse distance ladder relies on absolute distance measurements from the BAOs to calibrate the intrinsic magnitude of the SNe Ia. We find H0 = 67.8 ± 1.3 km s−1 Mpc−1 (statistical and systematic uncertainties, 68 per cent confidence). Our measurement makes minimal assumptions about the underlying cosmological model, and our analysis was blinded to reduce confirmation bias. We examine possible systematic uncertainties and all are below the statistical uncertainties. Our H0 value is consistent with estimates derived from the Cosmic Microwave Background assuming a ΛCDM universe.
We present griz light curves of 251 SNe Ia from the first 3 years of the Dark Energy Survey Supernova Program's (DES-SN) spectroscopically classified sample. The photometric pipeline described in ...this paper produces the calibrated fluxes and associated uncertainties used in the cosmological parameter analysis by employing a scene modeling approach that simultaneously models a variable transient flux and temporally constant host galaxy. We inject artificial point sources onto DECam images to test the accuracy of our photometric method. Upon comparison of input and measured artificial supernova fluxes, we find that flux biases peak at 3 mmag. We require corrections to our photometric uncertainties as a function of host galaxy surface brightness at the transient location, similar to that seen by the DES Difference Imaging Pipeline used to discover transients. The public release of the light curves can be found at https://des.ncsa.illinois.edu/releases/sn.
We describe the creation, content, and validation of the Dark Energy Survey (DES) internal year-one cosmology data set, Y1A1 GOLD, in support of upcoming cosmological analyses. The Y1A1 GOLD data set ...is assembled from multiple epochs of DES imaging and consists of calibrated photometric zero-points, object catalogs, and ancillary data products-e.g., maps of survey depth and observing conditions, star-galaxy classification, and photometric redshift estimates-that are necessary for accurate cosmological analyses. The Y1A1 GOLD wide-area object catalog consists of million objects detected in co-added images covering in the DES grizY filters. The 10 limiting magnitude for galaxies is , , , , and . Photometric calibration of Y1A1 GOLD was performed by combining nightly zero-point solutions with stellar locus regression, and the absolute calibration accuracy is better than 2% over the survey area. DES Y1A1 GOLD is the largest photometric data set at the achieved depth to date, enabling precise measurements of cosmic acceleration at z 1.
We describe catalog-level simulations of Type Ia Supernova (SN Ia) light curves in the Dark Energy Survey Supernova Program (DES-SN), and in low-redshift samples from the Center for Astrophysics ...(CfA) and the Carnegie Supernova Project (CSP). These simulations are used to model biases from selection effects and light curve analysis, and to determine bias corrections for SN Ia distance moduli that are used to measure cosmological parameters. To generate realistic light curves the simulation uses a detailed SN Ia model, incorporates information from observations (PSF, sky noise, zero point), and uses summary information (e.g., detection efficiency vs. signal to noise ratio) based on 10,000 fake SN light curves whose fluxes were overlaid on images and processed with our analysis pipelines. The quality of the simulation is illustrated by predicting distributions observed in the data. Averaging within redshift bins, we find distance modulus biases up to 0.05 mag over the redshift ranges of the low-z and DES-SN samples. For individual events, particularly those with extreme red or blue color, distance biases can reach 0.4 mag. Therefore, accurately determining bias corrections is critical for precision measurements of cosmological parameters. Files used to make these corrections are available at https://des.ncsa.illinois.edu/releases/sn.
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