ORCID Profile
0000-0002-5788-9280
Current Organisations
Massachusetts Institute of Technology
,
University of Cambridge
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Publisher: Oxford University Press (OUP)
Date: 23-06-2020
Abstract: The large sky localization regions offered by the gravitational-wave interferometers require efficient follow-up of the many counterpart candidates identified by the wide field-of-view telescopes. Given the restricted telescope time, the creation of prioritized lists of the many identified candidates becomes mandatory. Towards this end, we use astrorapid, a multiband photometric light-curve classifier, to differentiate between kilonovae, supernovae, and other possible transients. We demonstrate our method on the photometric observations of real events. In addition, the classification performance is tested on simulated light curves, both ideally and realistically s led. We show that after only a few days of observations of an astronomical object, it is possible to rule out candidates as supernovae and other known transients.
Publisher: American Astronomical Society
Date: 21-07-2023
Abstract: Next-generation surveys like the Legacy Survey of Space and Time (LSST) on the Vera C. Rubin Observatory (Rubin) will generate orders of magnitude more discoveries of transients and variable stars than previous surveys. To prepare for this data deluge, we developed the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC), a competition that aimed to catalyze the development of robust classifiers under LSST-like conditions of a nonrepresentative training set for a large photometric test set of imbalanced classes. Over 1000 teams participated in PLAsTiCC, which was hosted in the Kaggle data science competition platform between 2018 September 28 and 2018 December 17, ultimately identifying three winners in 2019 February. Participants produced classifiers employing a erse set of machine-learning techniques including hybrid combinations and ensemble averages of a range of approaches, among them boosted decision trees, neural networks, and multilayer perceptrons. The strong performance of the top three classifiers on Type Ia supernovae and kilonovae represent a major improvement over the current state of the art within astronomy. This paper summarizes the most promising methods and evaluates their results in detail, highlighting future directions both for classifier development and simulation needs for a next-generation PLAsTiCC data set.
Publisher: American Astronomical Society
Date: 02-04-2019
Publisher: American Astronomical Society
Date: 10-10-2019
Publisher: American Astronomical Society
Date: 02-2021
Abstract: Time-domain science has undergone a revolution over the past decade, with tens of thousands of new supernovae (SNe) discovered each year. However, several observational domains, including SNe within days or hours of explosion and faint, red transients, are just beginning to be explored. Here we present the Young Supernova Experiment (YSE), a novel optical time-domain survey on the Pan-STARRS telescopes. Our survey is designed to obtain well-s led griz light curves for thousands of transient events up to z ≈ 0.2. This large s le of transients with four-band light curves will lay the foundation for the Vera C. Rubin Observatory and the Nancy Grace Roman Space Telescope, providing a critical training set in similar filters and a well-calibrated low-redshift anchor of cosmologically useful SNe Ia to benefit dark energy science. As the name suggests, YSE complements and extends other ongoing time-domain surveys by discovering fast-rising SNe within a few hours to days of explosion. YSE is the only current four-band time-domain survey and is able to discover transients as faint as ∼21.5 mag in gri and ∼20.5 mag in z , depths that allow us to probe the earliest epochs of stellar explosions. YSE is currently observing approximately 750 deg 2 of sky every 3 days, and we plan to increase the area to 1500 deg 2 in the near future. When operating at full capacity, survey simulations show that YSE will find ∼5000 new SNe per year and at least two SNe within 3 days of explosion per month. To date, YSE has discovered or observed 8.3% of the transient candidates reported to the International Astronomical Union in 2020. We present an overview of YSE, including science goals, survey characteristics, and a summary of our transient discoveries to date.
Publisher: Oxford University Press (OUP)
Date: 19-09-2022
Abstract: New time-domain surveys, such as the Vera C. Rubin Observatory Legacy Survey of Space and Time, will observe millions of transient alerts each night, making standard approaches of visually identifying new and interesting transients infeasible. We present two novel methods of automatically detecting anomalous transient light curves in real-time. Both methods are based on the simple idea that if the light curves from a known population of transients can be accurately modelled, any deviations from model predictions are likely anomalies. The first modelling approach is a probabilistic neural network built using Temporal Convolutional Networks (TCNs) and the second is an interpretable Bayesian parametric model of a transient. We demonstrate our methods’ ability to provide anomaly scores as a function of time on light curves from the Zwicky Transient Facility. We show that the flexibility of neural networks, the attribute that makes them such a powerful tool for many regression tasks, is what makes them less suitable for anomaly detection when compared with our parametric model. The parametric model is able to identify anomalies with respect to common supernova classes with high precision and recall scores, achieving area under the precision-recall curves above 0.79 for most rare classes such as kilonovae, tidal disruption events, intermediate luminosity transients, and pair-instability supernovae. Our ability to identify anomalies improves over the lifetime of the light curves. Our framework, used in conjunction with transient classifiers, will enable fast and prioritized followup of unusual transients from new large-scale surveys.
Publisher: IOP Publishing
Date: 23-11-2016
Publisher: Oxford University Press (OUP)
Date: 09-09-2020
Abstract: Identification of anomalous light curves within time-domain surveys is often challenging. In addition, with the growing number of wide-field surveys and the volume of data produced exceeding astronomers’ ability for manual evaluation, outlier and anomaly detection is becoming vital for transient science. We present an unsupervised method for transient discovery using a clustering technique and the astronomaly package. As proof of concept, we evaluate 85 553 min-cadenced light curves collected over two ∼1.5 h periods as part of the Deeper, Wider, Faster program, using two different telescope dithering strategies. By combining the clustering technique HDBSCAN with the isolation forest anomaly detection algorithm via the visual interface of astronomaly, we are able to rapidly isolate anomalous sources for further analysis. We successfully recover the known variable sources, across a range of catalogues from within the fields, and find a further seven uncatalogued variables and two stellar flare events, including a rarely observed ultrafast flare (∼5 min) from a likely M-dwarf.
Publisher: American Astronomical Society
Date: 11-2019
Publisher: American Astronomical Society
Date: 10-2023
Publisher: Apollo - University of Cambridge Repository
Date: 2019
DOI: 10.17863/CAM.41375
Publisher: arXiv
Date: 2020
Publisher: Apollo - University of Cambridge Repository
Date: 2019
DOI: 10.17863/CAM.45849
Publisher: American Astronomical Society
Date: 09-02-2023
Abstract: The TESS mission produces a large amount of time series data, only a small fraction of which contain detectable exoplanetary transit signals. Deep-learning techniques such as neural networks have proved effective at differentiating promising astrophysical eclipsing candidates from other phenomena such as stellar variability and systematic instrumental effects in an efficient, unbiased, and sustainable manner. This paper presents a high-quality data set containing light curves from the Primary Mission and 1st Extended Mission full-frame images and periodic signals detected via box least-squares. The data set was curated using a thorough manual review process then used to train a neural network called Astronet-Triage-v2 . On our test set, for transiting/eclipsing events, we achieve a 99.6% recall (true positives over all data with positive labels) at a precision of 75.7% (true positives over all predicted positives). Since 90% of our training data is from the Primary Mission, we also test our ability to generalize on held-out 1st Extended Mission data. Here, we find an area under the precision-recall curve of 0.965, a 4% improvement over Astronet-Triage . On the TESS object of interest (TOI) Catalog through 2022 April, a shortlist of planets and planet candidates, Astronet-Triage-v2 is able to recover 3577 out of 4140 TOIs, while Astronet-Triage only recovers 3349 targets at an equal level of precision. In other words, upgrading to Astronet-Triage-v2 helps save at least 200 planet candidates from being lost. The new model is currently used for planet candidate triage in the Quick-Look Pipeline.
Publisher: IOP Publishing
Date: 30-09-2019
Publisher: EDP Sciences
Date: 09-2023
Location: United States of America
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Daniel Muthukrishna.