Search

The Deeper, Wider, Faster (DWF) programme is optimized to detect fast transients that show luminosity changes on time-scales of sub-second to days using fast cadence simultaneous observations and rapid response follow up. One of the significant bottlenecks in DWF is the time required to assess candidates for rapid follow up and to manually inspect candidates prior to triggering space-based or large ground-based telescopes. In this paper, we present the Removal Of BOgus Transients (ROBOTs) pipeline that uses a combination of machine learning methods, a Convolutional Neural Network (CNN), and Decision Tree (CART), to analyse source quality and to filter in promising candidates. The ROBOT pipeline is optimized for 'lossy' compressed data required by DWF for fast data transfer to find these candidates within minutes of the light hitting the telescopes. Preliminary testing of the ROBOT pipeline on archival data showed to reduce the number of candidates that require a manual inspection from 69 628 to 3327 (a factor of ∼21 times), whilst simultaneously sorting candidates into categories of priority, with potential for further improvement. Recent real-time operation of the ROBOT pipeline in DWF-O10 showed to further reduce manual inspections from ∼155 000 to ∼5000 (a factor of ∼31 times). [ABSTRACT FROM AUTHOR]

The relativistic outflows that produce long gamma-ray bursts (LGRBs) can be described by a structured jet model where prompt |$\gamma$| -ray emission is restricted to a narrow region in the jet's core. Viewing the jet off-axis from the core, a population of afterglows without an associated GRB detection can be predicted. In this work, we conduct an archival search for these 'orphan' afterglows (OAs) with minute-cadence, deep (⁠|$g\sim 23$|⁠) data from the Dark Energy Camera (DECam) taken as part of the Deeper, Wider, Faster programme (DWF). We introduce a method to select fast-evolving OA candidates within DWF data that comprises a machine learning model, based on a realistic synthetic population of OAs. Using this classifier, we recover 51 OA candidates. Of these candidates, 42 are likely flare events from M-class stars. The remaining nine possess quiescent, coincident sources in archival data with angular profiles consistent with a star and are inconsistent with the expected population of LGRB host galaxies. We therefore conclude that these are likely Galactic events. We calculate an upper limit on the rate of OAs down to |$g\lt 22$| AB mag of 7.46 deg |$^{-2}$| yr |$^{-1}$| using our criteria and constrain possible jet structures. We also place an upper limit of the characteristic angle between the |$\gamma$| -ray-emitting region and the jet's half-opening angle. For a smooth power law and a power law with core jet model, respectively, these values are |$58.3^{\circ }$| and |$56.6^{\circ }$|⁠ , for a power-law index of 0.8 and |$75.3^{\circ }$| and |$76.8^{\circ }$| for a power-law index of 1.2. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]