Nearest neighbour regression outperforms model-based prediction of specific star formation rate

TY - GEN

T1 - Nearest neighbour regression outperforms model-based prediction of specific star formation rate

AU - Stensbo-Smidt, Kristoffer

AU - Igel, Christian

AU - Zirm, Andrew Wasmuth

AU - Pedersen, Kim Steenstrup

PY - 2013

Y1 - 2013

N2 - Data in astronomy is rapidly growing with upcoming surveys producing 30 TB of images per night. Highly informative spectra are too expensive to measure for each detected object, hence ways of reliably estimating physical properties from images alone are paramount. The objective of this work is to test whether a 'big data ready' k-nearest neighbour regression can successfully estimate the specific star formation rate (sSFR) from colours of low-redshift galaxies. The nearest neighbour algorithm achieves a root mean square error (RMSE) of 0.30, outperforming the state-of-the-art astronomical model achieving a RMSE of 0.36.

AB - Data in astronomy is rapidly growing with upcoming surveys producing 30 TB of images per night. Highly informative spectra are too expensive to measure for each detected object, hence ways of reliably estimating physical properties from images alone are paramount. The objective of this work is to test whether a 'big data ready' k-nearest neighbour regression can successfully estimate the specific star formation rate (sSFR) from colours of low-redshift galaxies. The nearest neighbour algorithm achieves a root mean square error (RMSE) of 0.30, outperforming the state-of-the-art astronomical model achieving a RMSE of 0.36.

U2 - 10.1109/BigData.2013.6691746

DO - 10.1109/BigData.2013.6691746

M3 - Article in proceedings

SP - 141

EP - 144

BT - 2013 IEEE International Conference on Big Data

PB - IEEE

T2 - IEEE BigData 2013

Y2 - 6 October 2013 through 9 October 2013

ER -