Herschel investigation of cores and filamentary structures in L1251 located in the Cepheus flare

Molecular clouds are the prime locations of star formation. These clouds contain filamentary structures and cores which are crucial in the formation of young stars. In this work, we aim to quantify the physical properties of structural characteristics within the molecular cloud L1251 to better understand the initial conditions for star formation. We applied the getsf algorithm to identify cores and filaments within the molecular cloud L1251 using the Herschel multi-band dust continuum image, enabling us to measure their respective physical properties. Additionally, we utilized an enhanced differential term algorithm to produce high-resolution temperature maps and column density maps with a resolution of \({13.5}''\). We identified 122 cores in the region. Of those, 23 are protostellar cores, 13 are robust prestellar cores, 32 are candidate prestellar cores (including 13 robust prestellar cores and 19 strictly candidate prestellar cores), and 67 are unbound starless cores. getsf also found 147 filament structures in the region. Statistical analysis of the physical properties (mass (M), temperature (T), size and core brightness (hereafter, we are using the word luminosity (L)) for the core brightness) of obtained cores shows a negative correlation between core mass and temperature and a positive correlation between (M/L) and (M/T). Analysis of the filaments gives a median width of 0.14 pc and no correlation between width and length. Out of those 122 cores, 92 are present in filaments (\(\sim \) 75.4%) and the remaining were outside them. Out of the cores present in filaments, 57 (\(\sim \) 62%) cores are present in supercritical filaments (\(M_\textrm{line}>16 \ M_{\odot }/\textrm{pc}\)).

1. http://archives.esac.esa.int/hsa/whsa/.

We would like to thank Alexander Men’shchikov for his repeated assistance in downloading and using getsf, which made most of this work possible. The Herschel data was obtained from the ESA’s Herschel Science Archive (HSA). DD and AS thank the IIA VSP program for providing internship opportunities and supporting this work. The Basic Science Research Program supports C.W.L. through the National Research Foundation of Korea (NRF), funded by the Ministry of Education, Science and Technology (NRF-2019R1A2C1010851) and by the Korea Astronomy and Space Science Institute grant funded by the Korea government (MSIT; Project No. 2023-1-84000).

Authors and Affiliations

1. Indian Institute of Science Education and Research, Campus Road, Mohanpur, Kolkata, 741246, India

   Divyansh Dewan

2. Indian Institute of Astrophysics, II Block, Koramangala, Bengaluru, 560034, India

   Divyansh Dewan, Archana Soam, Akhil Lasrado & Saikhom Pravash Singh

3. National Astronomical Observatories, Chinese Academy of Sciences, Beijing, 100101, People’s Republic of China

   Guo-Yin Zhang

4. Korea Astronomy and Space Science Institute, 776 Daedeokdae-ro, Yuseong-gu, Daejeon, 34055, Republic of Korea

   Chang Won LEE

5. University of Science and Technology, Korea (UST), 217 Gajeong-ro, Yuseong-gu, Daejeon, 34113, Republic of Korea

   Chang Won LEE

Corresponding author

Correspondence to Divyansh Dewan.

Appendix A. Brief description of the working of \(g\!\!~e\!\!~t\!\!~s\!\!\!~f\)

We used the inbuilt script hires of getsf to obtain our column density and temperature maps.To make column density and temperature maps, getsf uses pixel-by-pixel SED fitting to the Herschel data with a modified blackbody function. It produces a base map of the region by convolving all the provided Herschel maps at 160, 250, 350 and 500 \(\upmu \)m to the lowest resolution (in our case, 36.3\({}^{\prime \prime }\)) and using it to create column density and temperature maps via fitting to the blackbody function. Then, the 160–350 \(\upmu \)m maps are convolved and fitted to create a less accurate map at 24.9\({}^{\prime \prime }\) resolution. This map is then convolved to the lower dimension (36.3\({}^{\prime \prime }\)), and the difference between this map and the base map is found. This process is repeated for all images at all resolutions. The difference terms are then added to the base density map. To identify and select cores and filaments, getsf performs the following steps:

1. 1.

   Multi-wavelength images resampled to the same pixel size and the same grid of pixels are taken as input.

2. 2.

   The images are spatially decomposed into single-scale images. Separation of the structural components of sources and filaments from each other and from their backgrounds happens from these spatially decomposed images.

3. 3.

   The residual noise and background fluctuations in the images of the separated components of sources and filaments are removed via image flattening.

4. 4.

   These cleaned single-scale images are then combined over all the wavelengths.

5. 5.

   Sources (positions) and filaments (skeletons) are detected in the combined images of the components in their spatially decomposed images.

The properties of cores and filaments are measured, and multi-wavelength output catalogs are formed using inbuilt scripts smeasure and fmeasure.

getsf identifies several skeletal structures of the filamentary network and considers the remaining network as its branches. fmeasure takes in input as the background-subtracted column density map and uses two methods to get the linear densities of filaments; one is by directly integrating the area between the skeleton and the largest extent on each side and then dividing by length to get density. The other method takes various sampling points along a crest and considers its density. The median value of all sampling points is taken as the density of this filamentary structure. The method is described in full in Men’shchikov (2021).

Appendix B. Selection criteria for source selection after \(g\!\!~e\!\!~t\!\!~s\!\!\!~f\) extraction

The catalog obtained after a getsf extraction contains information on the detected courses. This information is used to select reliable cores from all the detections. The information includes their detection significance and goodness, which are parameters relating to signal-to-noise ratio and source reliability, the background subtracted peak intensities of the detected cores, and the major size of half-maximum and the minor size of half-maximum of the detections.

The selection criteria used to select reliable cores are based on benchmark tests from Men’shchikov (2021):

• \(|\textrm{GOODM}| > 1\), where GOODM is monochromatic goodness.

• \(|\textrm{SIGNM}| > 1\), where SIGNM is the detection significance from monochromatic single scales.

• \(\mathrm{FXP_{BST}/FXP_{ERR}} > 2\), where \(\mathrm{FXP_{BST}}\) is peak intensity and \(\mathrm{FXP_{ERR}}\) is peak intensity error.

• \(\mathrm{FXT_{BST}/FXT_{ERR}} > 2\), where \(\mathrm{FXT_{BST}}\) is total flux and \(\mathrm{FXT_{ERR}}\) is total flux error.

• \(\mathrm{AFWHM/BFWHM} {<} 2\), where AFWHM and BFWHM are the major and minor axis full widths at half-maximum of the elliptical approximations.

• \(\mathrm{FOOA/AFWHM} > 1.15\), where FOOA is a full major axis of an elliptical footprint.

The first four conditions ensure that detected sources are well distinguished from their background. The other 2 are conditions on their size and shape, with the 5th condition ensuring the core is circular or elliptical in shape and the last condition removing cores with unrealistically small ratios of their footprint and half-maximum sizes.

Appendix C. Matches found by cross-referencing protostellar cores with SIMBAD

We cross-referenced the positions of the protostellar cores detected via getsf with the SIMBAD database or young stellar objects or cores. Out of 23 protostellar detections, 14 were found to have corresponding SIMBAD entries lying within 10\({}^{\prime \prime }\). Their positions and SIMBAD identifiers are tabulated in Table 2.

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