The properties of SCUBA cores in the Perseus molecular cloud: the bias of clump-finding algorithms

TL;DR

This study analyzes star-forming cores in the Perseus molecular cloud using SCUBA data, comparing clump-finding algorithms to assess how extraction methods influence core property measurements and their implications for star formation.

Contribution

It provides a comparative analysis of two clump-finding algorithms, revealing how their differences affect core property distributions and interpretations in star formation studies.

Findings

Clumps become more elongated over time.

Clump mass distribution resembles the stellar initial mass function.

Star-forming efficiency estimated between 10-20%.

Abstract

We present a new analysis of the properties of star-forming cores in the Perseus molecular cloud, identified in SCUBA 850 micron data. Our goal is to determine which core properties can be robustly identified and which depend on the extraction technique. Four regions in the cloud are examined: NGC1333, IC348/HH211, L1448 and L1455. We identify clumps of dust emission using two popular automated algorithms, CLFIND and GAUSSCLUMPS, finding 85 and 122 clumps in total respectively. Some trends are true for both populations: clumps become increasingly elongated over time and are consistent with constant surface brightness objects, with an average brightness ~4 to 10 times larger than the surrounding molecular cloud; the clump mass distribution (CMD) resembles the stellar intial mass function, with a slope alpha = -2.0+/-0.1 for CLFIND and alpha = -3.15+/-0.08 for GAUSSCLUMPS, which straddle the Salpeter value. The mass at which the slope shallows (similar for both algorithms at M~6 Msun) implies a star-forming efficiency of between 10 and 20 per cent. Other trends reported elsewhere depend on the clump-finding technique: we find protostellar clumps are both smaller (for GAUSSCLUMPS) and larger (for CLFIND) than their starless counterparts; the functional form, best-fitting to the CMD, is different for the two algorithms. The GAUSSCLUMPS CMD is best-fitted with a log-normal distribution, whereas a broken power law is best for CLFIND; the reported lack of massive starless cores in previous studies can be seen in the CLFIND but not the GAUSSCLUMPS data. Our approach highlights similarities and differences between the clump populations, illustrating the caution that must be exercised when comparing results from different studies and interpreting the properties of continuum cores.