Summary
Supermassive black hole binaries (SMBHBs) are a natural outcome of galaxy mergers. They can be identified as quasars with periodic variability in time-domain surveys, or from strong gravitational waves (GWs) by Pulsar Timing Arrays (PTAs), making them exceptional multi-messenger sources. Despite their expected ubiquity, they have remained undetected, but upcoming data of unprecedented quantity and quality from the Legacy Survey of Space and Time (LSST) and increasingly more sensitive PTAs will bring these multi-messenger monsters (MMMonsters) within reach.
MMMonsters at the intersection of astronomy and data science is an ambitious program to deliver the first multi-messenger detection of a SMBHB. Leveraging existing time-domain data, we will improve SMBHB searches advancing our understanding 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 results and by training early career scientists in the emerging field of multi-messenger astrophysics.
MMMonsters at the intersection of astronomy and data science is an ambitious program to deliver the first multi-messenger detection of a SMBHB. Leveraging existing time-domain data, we will improve SMBHB searches advancing our understanding 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 results and by training early career scientists in the emerging field of multi-messenger astrophysics.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101117624 |
| Start date: | 01-04-2024 |
| End date: | 31-03-2029 |
| Total budget - Public funding: | 1 711 750,00 Euro - 1 711 750,00 Euro |
Cordis data
Original description
Supermassive black hole binaries (SMBHBs) are a natural outcome of galaxy mergers. They can be identified as quasars with periodic variability in time-domain surveys, or from strong gravitational waves (GWs) by Pulsar Timing Arrays (PTAs), making them exceptional multi-messenger sources. Despite their expected ubiquity, they have remained undetected, but upcoming data of unprecedented quantity and quality from the Legacy Survey of Space and Time (LSST) and increasingly more sensitive PTAs will bring these multi-messenger monsters (MMMonsters) within reach.MMMonsters at the intersection of astronomy and data science is an ambitious program to deliver the first multi-messenger detection of a SMBHB. Leveraging existing time-domain data, we will improve SMBHB searches advancing our understanding 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 results and by training early career scientists in the emerging field of multi-messenger astrophysics.
Status
SIGNEDCall topic
ERC-2023-STGUpdate Date
29-09-2024
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