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Published May 18, 2022 | Accepted Version
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The Effect of Molecular Cloud Properties on the Kinematics of Stars Formed in the Trifid Region


The dynamical states of molecular clouds may affect the properties of the stars they form. In the vicinity of the Trifid Nebula (d = 1180 ± 25 pc), the main star cluster (Trifid Main) lies within an expanding section of the molecular cloud; however, ~0.3 deg to the north (Trifid North), the cloud's velocity structure is more tranquil. We acquired a Chandra X-ray observation to identify pre-main-sequence stars in Trifid North, complementing a previous observation of Trifid Main. In Trifid North, we identified 51 candidate pre-main-sequence stars, of which 13 are high-confidence Trifid members based on Gaia EDR3 parallaxes and proper motions. We also re-analyzed membership of Trifid Main and separated out multiple background stellar associations. Trifid North represents a stellar population ~10% as rich as Trifid Main that formed in a separate part of the cloud. The 1D stellar velocity dispersion in Trifid North (0.6 ± 0.2 km/s) is three times lower than in Trifid Main (1.9 ± 0.2 km/s). Furthermore, in Trifid Main, proper motions indicate that the portion of the star cluster superimposed on the optical nebula is expanding. Expansion of the HII region around the O-star HD 164492A, and the resulting gas expulsion, can explain both the motions of the stars and gas in Trifid Main. Contrary to previous studies, we find no evidence that a cloud-cloud collision triggered star formation in the region.

Additional Information

This research was supported by Chandra grant GO9-20002X. This work is based on data from ESA's Gaia mission (Gaia Collaboration et al. 2016), processed by the Data Processing and Analysis Consortium, funded by national institutions, particularly those participating in the Gaia Multilateral Agreement. We thank David James and Sean Points for assistance with ARCoIRIS, Katelyn Allers for the modified Spextool software, Paul Crowther and Philip Massey for suggestions about spectral classification, and the anonymous referee for useful comments. I.E.F. was supported by Caltech's Freshman Summer Research Institute (FSRI). M.G. is supported by the EU Horizon 2020 research and innovation programme under grant agreement No. 101004719. Facility: 2MASS, APEX, Blanco (ARCoIRIS), CXO (ACIS), Gaia, Herschel, IRSA, Spitzer (IRAC, MIPS), UKIRT Software: ACIS Extract & TARA (Broos et al. 2010, 2012), AstroLib (Landsman 1993), astropy (Astropy Collaboration et al. 2013; Price-Whelan et al. 2018), CIAO (Fruscione et al. 2006), HEASOFT (HEASARC 2014), MARX (Wise et al. 2013), mclust (Scrucca et al. 2016), numpy (van derWalt et al. 2011), PIMMS (Mukai 1993), pvextractor (Ginsburg et al. 2015), R (R Core Team 2018), SAOImage DS9 (Joye & Mandel 2003), scipy (Virtanen et al. 2020), Spextool (Cushing et al. 2014), TOPCAT & STILTS (Taylor 2005), wavdetect (Freeman et al. 2002), weights (Pasek 2016), XPHOT (Getman et al. 2010), XSPEC (Arnaud 1996)

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August 22, 2023
October 24, 2023