Context. Fast radio bursts (FRBs) are extremely energetic pulses of millisecond duration and unknown origin. To understand the phenomenon that emits these pulses, targeted and un-targeted searches have been performed for multiwavelength counterparts, including the optical. Aims. The objective of this work is to search for optical transients at the positions of eight well-localized (< 1″) FRBs after the arrival of the burst on different timescales (typically at one day, several months, and one year after FRB detection). We then compare this with known optical light curves to constrain progenitor models. Methods. We used the Las Cumbres Observatory Global Telescope (LCOGT) network to promptly take images with its network of 23 telescopes working around the world. We used a template subtraction technique to analyze all the images collected at differing epochs. We have divided the difference images into two groups: In one group we use the image of the last epoch as a template, and in the other group we use the image of the first epoch as a template. We then searched for optical transients at the localizations of the FRBs in the template subtracted images. Results. We have found no optical transients and have therefore set limiting magnitudesmore »
Cosmic-CoNN: A Cosmic-Ray Detection Deep-learning Framework, Data Set, and Toolkit
Rejecting cosmic rays (CRs) is essential for the scientific interpretation of CCD-captured data, but detecting CRs in single-exposure images has remained challenging. Conventional CR detectors require experimental parameter tuning for different instruments, and recent deep-learning methods only produce instrument-specific models that suffer from performance loss on telescopes not included in the training data. We present Cosmic-CoNN, a generic CR detector deployed for 24 telescopes at the Las Cumbres Observatory, which has been made possible by the three contributions in this work: (1) We build a large and diverse ground-based CR data set leveraging thousands of images from a global telescope network. (2) We propose a novel loss function and a neural network optimized for telescope imaging data to train generic CR-detection models. At 95% recall, our model achieves a precision of 93.70% on Las Cumbres imaging data and maintains a consistent performance on new ground-based instruments never used for training. Specifically, the Cosmic-CoNN model trained on the Las Cumbres CR data set maintains high precisions of 92.03% and 96.69% on Gemini GMOS-N/S 1 × 1 and 2 × 2 binning images, respectively. (3) We build a suite of tools including an interactive CR mask visualization and editing interface, console more »
- Publication Date:
- NSF-PAR ID:
- 10391098
- Journal Name:
- The Astrophysical Journal
- Volume:
- 942
- Issue:
- 2
- Page Range or eLocation-ID:
- Article No. 73
- ISSN:
- 0004-637X
- Publisher:
- DOI PREFIX: 10.3847
- Sponsoring Org:
- National Science Foundation
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