The SED Machine: a robotic spectrograph for fast transient classification
Nadejda Blagorodnova, James D. Neill, Richard Walters, Shrinivas R., Kulkarni, Christoffer Fremling, Sagi Ben-Ami, Richard G. Dekany, Jason R., Fucik, Nick Konidaris, Reston Nash, Chow-Choong Ngeow, Eran O. Ofek, Donal O', Sullivan, Robert Quimby, Andreas Ritter

TL;DR
The SED Machine is a robotic spectrograph designed for rapid classification of astronomical transients, addressing the current bottleneck in follow-up observations and enabling faster scientific discoveries.
Contribution
This paper introduces the design, operation, and initial results of the SEDM, a spectrograph optimized for spectral classification on a 60-inch telescope.
Findings
Proved effective in classifying transients during iPTF
Demonstrated high observing efficiency with the SEDM
Shows potential to alleviate follow-up bottlenecks in transient astronomy
Abstract
Current time domain facilities are finding several hundreds of transient astronomical events a year. The discovery rate is expected to increase in the future as soon as new surveys such as the Zwicky Transient Facility (ZTF) and the Large Synoptic Sky Survey (LSST) come on line. At the present time, the rate at which transients are classified is approximately one order or magnitude lower than the discovery rate, leading to an increasing "follow-up drought". Existing telescopes with moderate aperture can help address this deficit when equipped with spectrographs optimized for spectral classification. Here, we provide an overview of the design, operations and first results of the Spectral Energy Distribution Machine (SEDM), operating on the Palomar 60-inch telescope (P60). The instrument is optimized for classification and high observing efficiency. It combines a low-resolution…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
