Beyond the GW Horizon: Hunting for Kilonovae with KNTraP

Beyond the GW Horizon: Hunting for Kilonovae with KNTraP

Kilonovae (KNe) are bright, rapidly fading astronomical events believed to be caused by the radioactive decay of heavy elements produced during the merger of neutron stars or a neutron star and a black hole.

While there are several candidate KNe events observed in association with short gamma-ray bursts (GRBs), there is only one confirmed KN associated with a gravitational wave (GW) event, AT2017gfo, detected in 2017.

The Kilonova and Transients Program (KNTraP) is a new survey project using the Dark Energy Camera (DECam) to search for KNe independent of GW and GRB triggers.

KNTraP is designed to be a deep, wide-field survey with a nightly cadence in the g and i filters, allowing it to probe a large volume of the sky and identify rapidly evolving transients. The first KNTraP observing run, conducted over 11 nights in 2022, did not detect any convincing KNe candidates but did identify several other interesting fast-evolving transients, including AT2022kak, a rapidly fading transient, and SN2022dmf, an early Type Ia supernova.

The KNTraP project offers several advantages over traditional GW and GRB follow-up searches:

  • It can probe volumes beyond the current detection range of GW detectors.
  • It can search for KNe regardless of the merger orientation, unlike GW and GRB detectors that are sensitive to orientation.
  • It allows for early detection of KNe, capturing their rise, peak, and fade, providing valuable data for theoretical models.
  • Future KNTraP observing runs are expected to be more sensitive due to improved observing conditions and the availability of template images from this first run.
  • Upcoming instruments like the Keck Wide-Field Imager (KWFI) could further enhance KNTraP's efficiency in detecting KNe.


Reference Article: Van Bemmel, N., Zhang, J. et al. "An Optically Led Search for Kilonovae to z∼0.3 with the Kilonova and Transients Program (KNTraP)". MNRAS 000, 1–16 (2024).


Acknowledements: Podcast prepared with Google/NotebookLM. Illustration credits: Dark Energy Camera

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