{"keyword":["Space and Planetary Science","Astronomy and Astrophysics"],"main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.1612.04412"}],"publisher":"Oxford University Press","_id":"15242","quality_controlled":"1","oa_version":"Preprint","intvolume":"       466","publication_identifier":{"issn":["0035-8711"],"eissn":["1365-2966"]},"external_id":{"arxiv":["1612.04412"]},"issue":"2","article_processing_charge":"No","page":"2212-2216","scopus_import":"1","month":"12","title":"Using galaxy formation simulations to optimize LIGO follow-up observations","year":"2016","day":"17","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication_status":"published","doi":"10.1093/mnras/stw3292","status":"public","language":[{"iso":"eng"}],"date_updated":"2024-04-08T07:05:23Z","citation":{"ieee":"E. Antolini, I. Caiazzo, R. Davé, and J. S. Heyl, “Using galaxy formation simulations to optimize LIGO follow-up observations,” <i>Monthly Notices of the Royal Astronomical Society</i>, vol. 466, no. 2. Oxford University Press, pp. 2212–2216, 2016.","ista":"Antolini E, Caiazzo I, Davé R, Heyl JS. 2016. Using galaxy formation simulations to optimize LIGO follow-up observations. Monthly Notices of the Royal Astronomical Society. 466(2), 2212–2216.","apa":"Antolini, E., Caiazzo, I., Davé, R., &#38; Heyl, J. S. (2016). Using galaxy formation simulations to optimize LIGO follow-up observations. <i>Monthly Notices of the Royal Astronomical Society</i>. Oxford University Press. <a href=\"https://doi.org/10.1093/mnras/stw3292\">https://doi.org/10.1093/mnras/stw3292</a>","chicago":"Antolini, Elisa, Ilaria Caiazzo, Romeel Davé, and Jeremy S. Heyl. “Using Galaxy Formation Simulations to Optimize LIGO Follow-up Observations.” <i>Monthly Notices of the Royal Astronomical Society</i>. Oxford University Press, 2016. <a href=\"https://doi.org/10.1093/mnras/stw3292\">https://doi.org/10.1093/mnras/stw3292</a>.","ama":"Antolini E, Caiazzo I, Davé R, Heyl JS. Using galaxy formation simulations to optimize LIGO follow-up observations. <i>Monthly Notices of the Royal Astronomical Society</i>. 2016;466(2):2212-2216. doi:<a href=\"https://doi.org/10.1093/mnras/stw3292\">10.1093/mnras/stw3292</a>","mla":"Antolini, Elisa, et al. “Using Galaxy Formation Simulations to Optimize LIGO Follow-up Observations.” <i>Monthly Notices of the Royal Astronomical Society</i>, vol. 466, no. 2, Oxford University Press, 2016, pp. 2212–16, doi:<a href=\"https://doi.org/10.1093/mnras/stw3292\">10.1093/mnras/stw3292</a>.","short":"E. Antolini, I. Caiazzo, R. Davé, J.S. Heyl, Monthly Notices of the Royal Astronomical Society 466 (2016) 2212–2216."},"date_created":"2024-03-26T10:41:04Z","article_type":"original","publication":"Monthly Notices of the Royal Astronomical Society","oa":1,"type":"journal_article","volume":466,"abstract":[{"lang":"eng","text":"The recent discovery of gravitational radiation from merging black holes poses a challenge of how to organize the electromagnetic follow-up of gravitational-wave events as well as observed bursts of neutrinos. We propose a technique to select the galaxies that are most likely to host the event given some assumptions of whether the particular event is associated with recent star formation, low-metallicity stars or simply proportional to the total stellar mass in the galaxy. We combine data from the 2-MASS Photometric Redshift Galaxy Catalogue with results from galaxy formation simulations to develop observing strategies that potentially reduce the area of sky to search by up to a factor of 2 relative to an unweighted search of galaxies, and a factor of 20 to a search over the entire LIGO localization region."}],"extern":"1","date_published":"2016-12-17T00:00:00Z","author":[{"first_name":"Elisa","full_name":"Antolini, Elisa","last_name":"Antolini"},{"full_name":"Caiazzo, Ilaria","id":"8ae5b6e7-2a03-11ee-914d-b58ed7a3b47d","first_name":"Ilaria","last_name":"Caiazzo","orcid":"0000-0002-4770-5388"},{"last_name":"Davé","first_name":"Romeel","full_name":"Davé, Romeel"},{"last_name":"Heyl","full_name":"Heyl, Jeremy S.","first_name":"Jeremy S."}]}