First Impressions: Early-Time Classification of Supernovae using Host Galaxy Information and Shallow Learning

Substantial effort has been devoted to the characterization of transient phenomena from photometric information. Automated approaches to this problem have taken advantage of complete phase-coverage of an event, limiting their use for triggering rapid follow-up of ongoing phenomena. In this work, we introduce a neural network with a single recurrent layer designed explicitly for early photometric classification of supernovae. Our algorithm leverages transfer learning to account for model misspecification, host galaxy photometry to solve the data scarcity problem soon after discovery, and a custom weighted loss to prioritize accurate early classification. We first train our algorithm using state-of-the-art transient and host galaxy simulations, then adapt its weights and validate it on the spectroscopically-confirmed SNe Ia, SNe II, and SNe Ib/c from the Zwicky Transient Facility Bright Transient Survey. On observed data, our method achieves an overall accuracy of $82 \pm 2$% within 3 days of an event's discovery, and an accuracy of $87 \pm 5$% within 30 days of discovery. At both early and late phases, our method achieves comparable or superior results to the leading classification algorithms with a simpler network architecture. These results help pave the way for rapid photometric and spectroscopic follow-up of scientifically-valuable transients discovered in massive synoptic surveys.Comment: 24 pages, 8 figures. Accepted to Ap

Supernova search with active learning in ZTF DR3

We provide the first results from the complete SNAD adaptive learning pipeline in the context of a broad scope of data from large-scale astronomical surveys. The main goal of this work is to explore the potential of adaptive learning techniques in application to big data sets. Our SNAD team used Active Anomaly Discovery (AAD) as a tool to search for new supernova (SN) candidates in the photometric data from the first 9.4 months of the Zwicky Transient Facility (ZTF) survey, namely, between March 17 and December 31 2018 (58194 < MJD < 58483). We analysed 70 ZTF fields at a high galactic latitude and visually inspected 2100 outliers. This resulted in 104 SN-like objects being found, 57 of which were reported to the Transient Name Server for the first time and with 47 having previously been mentioned in other catalogues, either as SNe with known types or as SN candidates. We visually inspected the multi-colour light curves of the non-catalogued transients and performed fittings with different supernova models to assign it to a probable photometric class: Ia, Ib/c, IIP, IIL, or IIn. Moreover, we also identified unreported slow-evolving transients that are good superluminous SN candidates, along with a few other non-catalogued objects, such as red dwarf flares and active galactic nuclei. Beyond confirming the effectiveness of human-machine integration underlying the AAD strategy, our results shed light on potential leaks in currently available pipelines. These findings can help avoid similar losses in future large-scale astronomical surveys. Furthermore, the algorithm enables direct searches of any type of data and based on any definition of an anomaly set by the expert.Comment: 22 pages with appendix, 12 figures, 2 tables, accepted for publication in Astronomy and Astrophysic

The ANTARES Astronomical Time-Domain Event Broker

We describe the Arizona-NOIRLab Temporal Analysis and Response to Events System (ANTARES), a software instrument designed to process large-scale streams of astronomical time-domain alerts. With the advent of large-format CCDs on wide-field imaging telescopes, time-domain surveys now routinely discover tens of thousands of new events each night, more than can be evaluated by astronomers alone. The ANTARES event broker will process alerts, annotating them with catalog associations and filtering them to distinguish customizable subsets of events. We describe the data model of the system, the overall architecture, annotation, implementation of filters, system outputs, provenance tracking, system performance, and the user interface.Comment: 24 Pages, 8 figures, Accepted by A

The Most Interesting Anomalies Discovered in ZTF DR3 from the SNAD-III Workshop

International audienceThe search for objects with unusual astronomical properties, or anomalies, is one of the most anticipated results to be delivered by the next generation of large scale astronomical surveys. Moreover, given the volume and complexity of current data sets, machine learning algorithms will undoubtedly play an important role in this endeavor. The SNAD team is specialized in the development, adaptation and improvement of such techniques with the goal of constructing optimal anomaly detection strategies for astronomy. We present here the preliminary results from the third annual SNAD workshop (https://snad.space/2020/) that was held on-line in 2020 July

Could SNAD160 be a Pair-instability Supernova?

International audienceThe SNAD team reports the discovery of SNAD160 (AT2018lzi) within the Zwicky Transient Facility third data release. The transient has been found using the active anomaly detection algorithm, an adaptive learning strategy aimed at incorporating expert knowledge into machine learning models. Our preliminary analysis shows that SNAD160 could be a superluminous supernova powered by a pair-instability mechanism—its light curve behavior is consistent with the observed slow rise and slow decay expected from these events

The Most Interesting Anomalies Discovered in ZTF DR17 from the SNAD-VI Workshop

International audienceThe SNAD team has developed an adaptive learning algorithm, named Pine Forest (PF), to enhance anomaly detection in astronomical data. Recognizing the essential role of human engagement in the discovery process, PF presents outliers to a human expert for review, and filters out trees which disagree with the feedback provided. During the sixth annual SNAD workshop (https://snad.space/2023/), held in 2023 July, we applied PF to the Zwicky Transient Facility’s DR17 data. Interesting discoveries include long-duration objects such as supernovae, along with fast transients like red dwarf flares and one microlensing event. As a result, new variable stars were identified and labeled in the SNAD knowledge database

The SNAD Viewer: Everything You Want to Know about Your Favorite ZTF Object

International audienceWe describe the SNAD Viewer, a web portal for astronomers which presents a centralized view of individual objects from the Zwicky Transient Facility’s (ZTF) data releases, including data gathered from multiple publicly available astronomical archives and data sources. Initially built to enable efficient expert feedback in the context of adaptive machine learning applications, it has evolved into a full-fledged community asset that centralizes public information and provides a multi-dimensional view of ZTF sources. For users, we provide detailed descriptions of the data sources and choices underlying the information displayed in the portal. For developers, we describe our architectural choices and their consequences such that our experience can help others engaged in similar endeavors or in adapting our publicly released code to their requirements. The infrastructure we describe here is scalable and flexible and can be personalized and used by other surveys and for other science goals. The Viewer has been instrumental in highlighting the crucial roles domain experts retain in the era of big data in astronomy. Given the arrival of the upcoming generation of large-scale surveys, we believe similar systems will be paramount in enabling an optimal exploitation of the scientific potential enclosed in current terabyte and future petabyte-scale data sets. The Viewer is publicly available online at https://ztf.snad.space

Supernova search with active learning in ZTF DR3

In order to explore the potential of adaptive learning techniques to big data sets, the SNAD team used Active Anomaly Discovery (AAD) as a tool to search for new supernova (SN) candidates in the photometric data from the first 9.4 months of the Zwicky Transient Facility survey - between 2018 March 17 and December 31 (58194 < MJD < 58483). We analysed 70 ZTF fields with high galactic latitude and visually inspected 2100 outliers. This resulted in 104 supernova-like objects found, 57 of them were reported to the Transient Name Server for the first time and 47 were previously mentioned in other catalogues either as supernovae with known types or as supernova candidates. We visually inspected the multi-colour light curves of the non-catalogued transients and performed their fit with different supernova models to assign it to a proper class: Ia, Ib/c, IIP, IIL, IIn. Moreover, we also identified unreported slow-evolving transients which are good superluminous SN candidates, and a few others non-catalogued objects, such as red dwarf flares and active galactic nuclei. Beyond confirming the effectiveness of human-machine integration underlying the AAD strategy, our results shed light on potential leaks in currently available pipelines and can help avoid similar losses in future large scale astronomical surveys. The algorithm enables directed search of any type of data and definition of anomaly chosen by the expert