IRAS 03382+3145 , the SIMBAD biblio

IRAS 03382+3145 , the SIMBAD biblio (12 results) C.D.S. - SIMBAD4 rel 1.8 - 2026.10.15CEST12:35:41


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Title First 3 Authors
1988IRASP.C......0J viz 14       D               1 245223 ~ Infrared Astronomical Satellite Catalogs, 1988. The Point Source Catalog, version 2.0, NASA RP-1190 JOINT IRAS SCIENCE WORKING GROUP
1993A&AS...98..589W viz 1214 138 IRAS sources beyond the solar circle. III. Observations of H2O, OH, CH3OH and CO. WOUTERLOOT J.G.A., BRAND J. and FIEGLE K.
1993ApJ...410..168L 77 58 An infrared survey for embedded young stars in the Perseus molecular cloud complex. LADD E.F., LADA E.A. and MYERS P.C.
1997ApJ...489..719M 95 293 A search for infall motions toward nearby young stellar objects. MARDONES D., MYERS P.C., TAFALLA M., et al.
2003AJ....126.1423K viz 1344 26 Observations of star-forming regions with the Midcourse Space Experiment. KRAEMER K.E., SHIPMAN R.F., PRICE S.D., et al.
2004AJ....127.1747H 92 51 A near-infrared multiplicity survey of class I/Flat-spectrum systems in six nearby molecular clouds. HAISCH K.E.Jr, GREENE T.P., BARSONY M., et al.
2006AJ....132.2675H 75 17 Mid-infrared observations of class I/Flat-spectrum systems in six nearby molecular clouds. HAISCH K.E.Jr, BARSONY M., RESSLER M.E., et al.
2007ApJS..171..447R 180 107 The Spitzer c2d survey of large, nearby, interstellar clouds. VI. Perseus observed with MIPS. REBULL L.M., STAPELFELDT K.R., EVANS II N.J., et al.
2007PASJ...59.1185S viz 15       D               1597 41 Water maser and ammonia survey toward IRAS sources in the Galaxy. I. H2O maser data. SUNADA K., NAKAZATO T., IKEDA N., et al.
2009ApJ...690..496C 44           X         1 10 145 The mid-infrared extinction law in the Ophiuchus, Perseus, and Serpens molecular clouds. CHAPMAN N.L., MUNDY L.G., LAI S.-P., et al.
2011ApJ...742..105A 280       D     X C       7 46 88 A bubbling nearby molecular cloud: complete shells in Perseus. ARCE H.G., BORKIN M.A., GOODMAN A.A., et al.
2017ApJ...851..149X 16       D               1 15 6 Assessing the performance of a machine learning algorithm in identifying bubbles in dust emission. XU D. and OFFNER S.S.R.

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