SNAD transient miner: Finding missed transient events in ZTF DR4 using k-D trees

Aleo, Patrick D.; Malanchev, Konstantin; Pruzhinskaya, M.; Ishida, Emille E. O.; Russeil, Etienne; Kornilov, M.; Korolev, Vladimir; Sreejith, Sreevarsha; Volnova, A.; Narayan, Gautham

doi:10.1016/j.newast.2022.101846

Cited by 6 publications

(12 citation statements)

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“…Nevertheless, the results presented here confirm the effectiveness of adaptive learning approaches in filtering large astronomical data sets for expert analysis. They reveal important characteristics of ZTF data releases that ought to be further scrutinised to avoid similar losses in the future (Aleo et al 2022b).…”

Section: Discussionmentioning

confidence: 99%

“…It is possible that SNAD187 is not associated with the host AGN activity and could be a SLSN. Recently, the ANTARES broker AD filter reported the discovery of a SLSN -SN 2022mnj at the central region of an AGN (Aleo et al 2022a;Ashall 2022; see also Moriya et al 2017).…”

Section: Superluminous Supernovae Candidatesmentioning

confidence: 99%

“…Sarkar et al (2022) used the Earth as an anomaly example in order to estimate the habitability of exoplanets using a multi-stage memetic algorithm. Kovačević et al (2022) used self-organising maps to analyse temporal-only parameters computed from ⪆ 10 5 sources from the Exploring the X-ray Transient and variable Sky catalogue and Aleo et al (2022b) used simulated light curves to search for counterparts in ZTF DR4, identifying 11 non-catalogued transients.…”

Section: Introductionmentioning

confidence: 99%

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Supernova search with active learning in ZTF DR3

Pruzhinskaya

Ishida

Novinskaya

et al. 2023

A&A

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Context. 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. Aims. The main goal of this work is to explore the potential of adaptive learning techniques in application to big data sets. Methods. 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. Results. 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. Conclusions. 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.

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Section: Discussionmentioning

confidence: 99%

Section: Superluminous Supernovae Candidatesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Supernova search with active learning in ZTF DR3

Pruzhinskaya

Ishida

Novinskaya

et al. 2023

A&A

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“…For example, most of the ZTF Bright Transient Survey supernovae (Fremling et al 2020;Perley et al 2020) are missing from the DRs. However, some robust SN candidates not presented in the alert stream were found in ZTF DRs by the SNAD team in Aleo et al (2022), Pruzhinskaya et al (2022). Moreover, the DR light curves have better limiting magnitudes and may also include observations that do not pass the difference imaging pipeline detection threshold (see Section 3.4.2 and Figure 4).…”

Section: The Zwicky Transient Facility Data Releasesmentioning

confidence: 99%

“…So far, the Viewer enabled all the ZTF-based results reported by the SNAD team (Malanchev et al 2021;Aleo et al 2022;Pruzhinskaya et al 2022), whose summary can be found in the SNAD catalog, 19 currently hosting 144 candidate transients found by the SNAD team on ZTF DRs, while being absent from systematic searches from other groups.…”

Section: Introductionmentioning

confidence: 99%

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

Malanchev

Kornilov

Pruzhinskaya

et al. 2023

PASP

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We 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.

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Persistent and occasional: Searching for the variable population of the ZTF/4MOST sky using ZTF Data Release 11

Sánchez-Sáez¹,

Arredondo²,

Bayo³

et al. 2023

A&A

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Aims. We present a variability-, color-, and morphology-based classifier designed to identify multiple classes of transients and persistently variable and non-variable sources from the Zwicky Transient Facility (ZTF) Data Release 11 (DR11) light curves of extended and point sources. The main motivation to develop this model was to identify active galactic nuclei (AGN) at different redshift ranges to be observed by the 4MOST Chilean AGN/Galaxy Evolution Survey (ChANGES). That being said, it also serves as a more general time-domain astronomy study. Methods. The model uses nine colors computed from CatWISE and Pan-STARRS1 (PS1), a morphology score from PS1, and 61 single-band variability features computed from the ZTF DR11 g and r light curves. We trained two versions of the model, one for each ZTF band, since ZTF DR11 treats the light curves observed in a particular combination of field, filter, and charge-coupled device (CCD) quadrant independently. We used a hierarchical local classifier per parent node approach-where each node is composed of a balanced random forest model. We adopted a taxonomy with 17 classes: non-variable stars, non-variable galaxies, three transients (SNIa, SN-other, and CV/Nova), five classes of stochastic variables (lowz-AGN, midz-AGN, highz-AGN, Blazar, and YSO), and seven classes of periodic variables (LPV, EA, EB/EW, DSCT, RRL, CEP, and Periodic-other). Results. The macro-averaged precision, recall, and F1-score are 0.61, 0.75, and 0.62 for the g-band model, and 0.60, 0.74, and 0.61, for the r-band model. When grouping the four AGN classes (lowz-AGN, midz-AGN, highz-AGN, and Blazar) into one single class, its precision-recall, and F1-score are 1.00, 0.95, and 0.97, respectively, for both the g and r bands. This demonstrates the good performance of the model in classifying AGN candidates. We applied the model to all the sources in the ZTF/4MOST overlapping sky (−28 ≤ Dec ≤ 8.5), avoiding ZTF fields that cover the Galactic bulge (|gal_b| ≤ 9 and gal_l ≤ 50). This area includes 86 576 577 light curves in the g band and 140 409 824 in the r band with 20 or more observations and with an average magnitude in the corresponding band lower than 20.5. Only 0.73% of the g-band light curves and 2.62% of the r-band light curves were classified as stochastic, periodic, or transient with high probability (Pinit ≥ 0.9). Even though the metrics obtained for the two models are similar, we find that, in general, more reliable results are obtained when using the g-band model. With it, we identified 384 242 AGN candidates (including low-, mid-, and high-redshift AGN and Blazars), 287 156 of which have Pinit ≥ 0.9.

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SNAD transient miner: Finding missed transient events in ZTF DR4 using k-D trees

Cited by 6 publications

References 50 publications

Supernova search with active learning in ZTF DR3

Supernova search with active learning in ZTF DR3

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

Persistent and occasional: Searching for the variable population of the ZTF/4MOST sky using ZTF Data Release 11

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