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Deep, multi-band photometry of low-mass stars to reveal young clusters: a blind study of the NGC 2264 region
gennaio 23 @ 11:45 - 12:45
The LSST survey will provide multi-epoch, multi-wavelength (u,g,r,i,z,y) mapping of the Southern Hemisphere, with a single-visit depth of r~24.5 and a gain of three magnitudes by the end of the program. This unprecedented spatial coverage will enable detection of young, pre-main sequence stars and stellar clusters down to distances of 5-10 kpc. A crucial and challenging step for spatial analyses of large stellar populations is measuring the extinction Av of individual objects. Multi-color photometry on a (r-i, g-r) or (i-J, r-i) diagram offers a direct solution to this issue for M-type stars: indeed, while the color locus of early-type (< K7) stars on these diagrams is parallel to the reddening vector, the color locus traced by M-type stars is tilted with respect to the reddening vector, which enables a straightforward and empirical measurement of their Av. By investigating the correlation between extinction and spatial properties of M-type stars in a given field, it is therefore possible to reconstruct the structure of the region and probe the nature of its population. In this study, we test the method on the NGC 2264 field. We selected a 2°x2° area centered on the NGC 2264 cluster, and collected the available r,i,J photometry from existing large-scale surveys (notably Pan-STARRS and UKIDSS). Then, assuming no prior knowledge on the nature of stars in the field, we used the (i-J, r-i) diagram to identify and deredden M-type stars in the sample, and the (r-i, r) + (RA, Dec) diagrams to investigate the nature and spatial distribution of stars as a function of their Av. We derived a non-uniform distribution of Av across the region, and could distinguish between a diffuse field population and a clustered stellar population toward the center of the field. An a posteriori comparison between the inferred spatial density map of the clustered population and the literature census of the NGC 2264 cluster enabled us to assess the performance of the method and its predictive capability.