Abstract: Carbon dioxide (CO2) is a key chemical species that is found in a wide range of planetary atmospheres. In the context of exoplanets, CO2 is an indicator of the metal enrichment (that is, elements heavier than helium, also called 'metallicity')(1-3), and thus the formation processes of the primary atmospheres of hot gas giants(4-6). It is also one of the most promising species to detect in the secondary atmospheres of terrestrial exoplanets(7-9). Previous photometric measurements of transiting planets with the Spitzer Space Telescope have given hints of the presence of CO2, but have not yielded definitive detections owing to the lack of unambiguous spectroscopic identification(10-12). Here we present the detection of CO2 in the atmosphere of the gas giant exoplanet WASP-39b from transmission spectroscopy observations obtained with JWST as part of the Early Release Science programme(13,14). The data used in this study span 3.0-5.5micrometres in wavelength and show a prominent CO2 absorption feature at 4.3micrometres (26-sigma significance). The overall spectrum is well matched by one-dimensional, ten-times solar metallicity models that assume radiative-convective-thermochemical equilibrium and have moderate cloud opacity. These models predict that the atmosphere should have water, carbon monoxide and hydrogen sulfide in addition to CO2, but little methane. Furthermore, we also tentatively detect a small absorption feature near 4.0micrometres that is not reproduced by these models.

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 (similar to 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.