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

Changing–look NLS1s?

<p>Two major challenges to unification schemes for AGN activity are the existence of NLS1s and the existence of “changing-look” (CL) AGNs. It is therefore important to look at the relationship between these two phenomena. AGNs can drastically change their spectral appearance in the optical (changing Seyfert type) and/or in the X-ray region. We illustrate the CL phenomenon with our multi-wavelength monitoring of the typical CL object NGC 2617 and discuss its properties compared with NLS1s. There are only a few examples of CL NLS1s and they are mostly changing look only in the X-ray region. Only one NLS1, PS16D (Lanchard et al. 2017), is known to have changed to a broad-line Seyfert1 (BLS1). It has been proposed that this could be a case of a tidal-disruption event (TDE). Low-ionization BLRs have a flat geometry (Gaskell 2009). If, NLS1s are flattened BLR geometries seen face-on (e.g., Decarli et al. 2011), then we will see a changing look only if the orientation of the BLR and accretion disc changes because of a major disruption such as a TDE or a close passage of a secondary black hole. If, as we have suggested (Oknyansky et al. 2015), the hot dust is in a bi-conical outflow, several observational tests can be proposed. Firstly, for NLS1s the lags of H beta and the near IR behind optical/UV continuum variability will be similar, rather than the IR lag being a factor of 3 – 10 times larges as seen in normal BLS1s. The IR response functions should not show the double peaks that are found for some BLS1s. We predict that NLS1s showing a changing look in the optical should be very rare; only X-ray or TDE CL cases should be seen, as seems to be the case. An interesting problem is why NLS1 are less variable in UV and optical regions yet strongly variable in X-ray (Klimek et al. 2004). If NLS1s include both high Eddington rate accretion and low-inclination AGNs (e.g., Peterson 2011) then a significant fraction of NLS1s could be obscured and would not be identified as NLS1s. CL cases might happen more often if there is dust sublimation following a strong increase in the optical luminosity that causes some obscured NLS1s to become unobscured.</p

Anomaly Detection in the Open Supernova Catalog

International audienceIn the upcoming decade, large astronomical surveys will discover millions of transients raising unprecedented data challenges in the process. Only the use of the machine learning algorithms can process such large data volumes. Most of the discovered transients will belong to the known classes of astronomical objects. However, it is expected that some transients will be rare or completely new events of unknown physical nature. The task of finding them can be framed as an anomaly detection problem. In this work, we perform for the first time an automated anomaly detection analysis in the photometric data of the Open Supernova Catalog (OSC), which serves as a proof of concept for the applicability of these methods to future large-scale surveys. The analysis consists of the following steps: (1) data selection from the OSC and approximation of the pre-processed data with Gaussian processes, (2) dimensionality reduction, (3) searching for outliers with the use of the isolation forest algorithm, and (4) expert analysis of the identified outliers. The pipeline returned 81 candidate anomalies, 27 (33 per cent) of which were confirmed to be from astrophysically peculiar objects. Found anomalies correspond to a selected sample of 1.4 per cent of the initial automatically identified data sample of approximately 2000 objects. Among the identified outliers we recognized superluminous supernovae, non-classical Type Ia supernovae, unusual Type II supernovae, one active galactic nucleus and one binary microlensing event. We also found that 16 anomalies classified as supernovae in the literature are likely to be quasars or stars. Our proposed pipeline represents an effective strategy to guarantee we shall not overlook exciting new science hidden in the data we fought so hard to acquire. All code and products of this investigation are made publicly available.^

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

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

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