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SUMMARY:A Redshift-Limited Sample Selection and Photometric Redshift Estim
 ation for 4MOST WAVES: Prospects for LSST
DTSTART;VALUE=DATE-TIME:20261020T125000Z
DTEND;VALUE=DATE-TIME:20261020T130500Z
DTSTAMP;VALUE=DATE-TIME:20261112T103443Z
UID:indico-contribution-2265@events.ncbj.gov.pl
DESCRIPTION:Speakers: Gursharanjit Kaur (Center for Theoretical Physics\, 
 PAN)\nIn this talk\, I will present a machine learning-based approach to s
 elect a redshift-limited sample using multiband photometry when spectrosco
 pic redshifts are not available. The technique is a decision tree-based bi
 nary classification\, which has been further extended into a photometric r
 edshift estimation method. This selection methodology has been applied to 
 select targets for the WAVES survey of the Multi-Object Spectroscopy Facil
 ity (4MOST). WAVES has a key science goal of probing the halo mass functio
 n to fainter magnitudes compared to previous surveys like GAMA. The ML sel
 ection method achieves completeness of over 90%\, as desired by the survey
 . We demonstrate that our method achieves photo-z scatter σ_NMAD ~ 0.015 
 when tested against the DESI DR1 sample flux-limited to 21.2 in Z-band mag
 nitude. \n\nThis sample selection and photometric redshift estimation meth
 odology can be successfully applied to a large photometric survey like LSS
 T\, and we see it as a potential contribution to the LSST DESC photo-z com
 munity.\n\nhttps://events.ncbj.gov.pl/event/502/contributions/2265/
LOCATION:
URL:https://events.ncbj.gov.pl/event/502/contributions/2265/
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