13 research outputs found
Maximum likelihood estimation for disk image parameters
We present a novel technique for estimating disk parameters (the centre and
the radius) from its 2D image. It is based on the maximal likelihood approach
utilising both edge pixels coordinates and the image intensity gradients. We
emphasise the following advantages of our likelihood model. It has closed-form
formulae for parameter estimating, requiring less computational resources than
iterative algorithms therefore. The likelihood model naturally distinguishes
the outer and inner annulus edges. The proposed technique was evaluated on both
synthetic and real data.Comment: 13 pages, 4 figures. in IEEE Signal Processing Letter
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
Reduction of supernova light curves by vector Gaussian processes
International audienceBolometric light curves play an important role in understanding the underlying physics of various astrophysical phenomena, as they allow for a comprehensive modeling of the event and enable comparison between different objects. However, constructing these curves often requires the approximation and extrapolation from multicolor photometric observations. In this study, we introduce vector Gaussian processes as a new method for reduction of supernova light curves. This method enables us to approximate vector functions, even with inhomogeneous time-series data, while considering the correlation between light curves in different passbands. We applied this methodology to a sample of 29 superluminous supernovae (SLSNe) assembled using the Open Supernova Catalog. Their multicolor light curves were approximated using vector Gaussian processes. Subsequently, under the black-body assumption for the SLSN spectra at each moment of time, we reconstructed the bolometric light curves. The vector Gaussian processes developed in this work are accessible via the Python library gp-multistate-kernel on GitHub. Our approach provides an efficient tool for analyzing light curve data, opening new possibilities for astrophysical research
Reduction of supernova light curves by vector Gaussian processes
International audienceBolometric light curves play an important role in understanding the underlying physics of various astrophysical phenomena, as they allow for a comprehensive modeling of the event and enable comparison between different objects. However, constructing these curves often requires the approximation and extrapolation from multicolor photometric observations. In this study, we introduce vector Gaussian processes as a new method for reduction of supernova light curves. This method enables us to approximate vector functions, even with inhomogeneous time-series data, while considering the correlation between light curves in different passbands. We applied this methodology to a sample of 29 superluminous supernovae (SLSNe) assembled using the Open Supernova Catalog. Their multicolor light curves were approximated using vector Gaussian processes. Subsequently, under the black-body assumption for the SLSN spectra at each moment of time, we reconstructed the bolometric light curves. The vector Gaussian processes developed in this work are accessible via the Python library gp-multistate-kernel on GitHub. Our approach provides an efficient tool for analyzing light curve data, opening new possibilities for astrophysical research
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