SOURCE-LINKED INTELLIGENCE
The first multi-messenger detection of a supermassive black hole binary
rstanding of binary signals and quasar noise. For the first time, we will search for more complex and likely more common non-sinusoidal periodicity. Using the LSST Data Previews, I will develop novel machine learning tools to capitalize on the LSST dataset. With advance preparation, MMMonsters will be poised to reliably detect binaries in the first few LSST data releases, making the project extremely timely and impactful. On the GW side, I will pave the way for the first PTA detection of an individually resolvable binary. I will build novel PTA detection pipelines that directly incorporate EM data, from a catalog of 20 million galaxies (which we will compile) including all the potential binary hosts. My approach will accelerate the first detection of a SMBHB and allow the subsequent identification of the binary host galaxy. I will forge the ultimate boost in binary detectability through the joint analysis of time- domain and PTA data in a multi-messenger data stream. MMMonsters will establish strong EU leadership in time-domain astronomy and GW physics through groundbreaking result
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- recordType
- award
- status
- SIGNED
- region
- EU
- value
- 1711750
- unit
- EUR
Evidence & attribution
European Commission, CORDIS Horizon Europe project dataset. Metadata adapted.
License: CORDIS reuse policy
First collected: 2026-09-20T02:21:08.944Z. This is not the publication date.