Assessing the accuracy of star formation rate measurements by direct star count in molecular clouds

Star formation estimates based on the counting of young stellar objects (YSOs) are commonly carried out for nearby star-forming regions in the Galaxy, and in principle could be extended to any star-forming region where direct star counts are possible. With this method, the SFRs are measured using the counts of YSOs in a particular class, a typical mass, and the lifetime associated with this class. Another variant of this method is to use the total number of YSOs found in a star-forming region along with a characteristic YSO timescale. However, the assumptions underlying the validity of this method, such as that of a constant star formation history (SFH), have never been fully tested, and it remains unclear as to whether or not the method is valid for all protostellar classes. In this work, we use Monte Carlo models to test the validity and robustness of the method. We build synthetic clusters in which stars form at times that are randomly drawn from a specified SFH distribution function. The latter is either constant or time dependent, with a burst like behavior. The masses of the YSOs are randomly drawn from a stellar initial mass function (IMF), which can be either similar to that of the Milky Way field or be variable within the limits of the variations observed among young stellar clusters in the Galaxy. For each star in every cluster, the lifetimes associated with the different protostellar classes are also randomly drawn from Gaussian distribution functions centered around their most likely value as suggested by the observations. We find that only the SFR derived using the Class 0 population can reproduce the true SFR at all epochs, and this is true irrespective of the shape of the SFH. For a constant SFH, the SFR derived using the more evolved populations of YSOs (Class I, Class F, Class II, and Class III) reproduce the real SFR only at later epochs, which correspond to epochs at which their numbers have reached a steady state. For a time-dependent burst-like SFH, all SFR estimates based on the number counts of the evolved populations fail to reproduce the true SFR. We show that these conclusions are independent of the IMF. We argue that the SFR based on the Class 0 alone can yield reliable estimates of the SFR. We also show how the offsets between Class I- and Class II-based SFRs and the true SFR plotted as a function of the number ratios of Class I and Class II versus Class III YSOs can be used in order to provide information on the SFH of observed molecular clouds.

Authors

Publication Details

Journal
Astronomy and Astrophysics
Published
2024-11-28
DOI
https://doi.org/10.1051/0004-6361/202450434
Citations
4
Primary Topic
Astrophysics and Star Formation Studies
Type
article
Field-Weighted Citation Impact
1.33

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Assessing the accuracy of star formation rate measurements by direct star count in molecular clouds

Tie Liu, В. В. Кравцов, S. Comerón, Guang-Xing Li et al.
4 citations
Astronomy and Astrophysics
Astrophysics and Star Formation Studies
1.33
article

Assessing the accuracy of star formation rate measurements by direct star count in molecular clouds

Tie Liu, В. В. Кравцов, S. Comerón, Guang-Xing Li, M. López, Sami Dib, L. E. Garduño, Mohsen Shadmehri, Paul C. Clark, James R. Doughty, Jianwen Zhou
article en
4 citations

Abstract

Star formation estimates based on the counting of young stellar objects (YSOs) are commonly carried out for nearby star-forming regions in the Galaxy, and in principle could be extended to any star-forming region where direct star counts are possible. With this method, the SFRs are measured using the counts of YSOs in a particular class, a typical mass, and the lifetime associated with this class. Another variant of this method is to use the total number of YSOs found in a star-forming region along with a characteristic YSO timescale. However, the assumptions underlying the validity of this method, such as that of a constant star formation history (SFH), have never been fully tested, and it remains unclear as to whether or not the method is valid for all protostellar classes. In this work, we use Monte Carlo models to test the validity and robustness of the method. We build synthetic clusters in which stars form at times that are randomly drawn from a specified SFH distribution function. The latter is either constant or time dependent, with a burst like behavior. The masses of the YSOs are randomly drawn from a stellar initial mass function (IMF), which can be either similar to that of the Milky Way field or be variable within the limits of the variations observed among young stellar clusters in the Galaxy. For each star in every cluster, the lifetimes associated with the different protostellar classes are also randomly drawn from Gaussian distribution functions centered around their most likely value as suggested by the observations. We find that only the SFR derived using the Class 0 population can reproduce the true SFR at all epochs, and this is true irrespective of the shape of the SFH. For a constant SFH, the SFR derived using the more evolved populations of YSOs (Class I, Class F, Class II, and Class III) reproduce the real SFR only at later epochs, which correspond to epochs at which their numbers have reached a steady state. For a time-dependent burst-like SFH, all SFR estimates based on the number counts of the evolved populations fail to reproduce the true SFR. We show that these conclusions are independent of the IMF. We argue that the SFR based on the Class 0 alone can yield reliable estimates of the SFR. We also show how the offsets between Class I- and Class II-based SFRs and the true SFR plotted as a function of the number ratios of Class I and Class II versus Class III YSOs can be used in order to provide information on the SFH of observed molecular clouds.

Astronomy and AstrophysicsVol. 693
Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación
Openalex Percentile: Top 19%
Astrophysics and Star Formation Studies
1.33
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.