Calibration of evolutionary diagnostics in high-mass star formation

The evolutionary classification of massive clumps that are candidate progenitors of high-mass young stars and clusters relies on a variety of independent diagnostics based on observables from the near-infrared to the radio. A promising evolutionary indicator for massive and dense cluster-progenitor clumps is the L/M ratio between the bolometric luminosity and the mass of the clumps. With the aim of providing a quantitative calibration for this indicator we used SEPIA/APEX to obtain CH3C2H(12-11) observations, that is an excellent thermometer molecule probing densities > 10^5 cm^-3 , toward 51 dense clumps with M>1000 solar masses, and uniformly spanning -2 < Log(L/M) < 2.3. We identify three distinct ranges of L/M that can be associated to three distinct phases of star formation in massive clumps. For L/M <1 no clump is detected in CH3C2H , suggesting an inner envelope temperature below 30K. For 1< L/M < 10 we detect 58% of the clumps, with a temperature between 30 and 35 K independently from the exact value of L/M; such clumps are building up luminosity due to the formation of stars, but no star is yet able to significantly heat the inner clump regions. For L/M> 10 we detect all the clumps, with a gas temperature rising with Log(L/M), marking the appearance of a qualitatively different heating source within the clumps; such values are found towards clumps with UCHII counterparts, suggesting that the quantitative difference in T - L/M behaviour above L/M >10 is due to the first appearance of ZAMS stars in the clumps.Comment: Astrophysical Journal Letters, Accepte

Search for massive protostar candidates in the southern hemisphere: II. Dust continuum emission

In an ongoing effort to identify and study high-mass protostellar candidates we have observed in various tracers a sample of 235 sources selected from the IRAS Point Source Catalog, mostly with dec < -30 deg, with the SEST antenna at millimeter wavelengths. The sample contains 142 Low sources and 93 High, which are believed to be in different evolutionary stages. Both sub-samples have been studied in detail by comparing their physical properties and morphologies. Massive dust clumps have been detected in all but 8 regions, with usually more than one clump per region. The dust emission shows a variety of complex morphologies, sometimes with multiple clumps forming filaments or clusters. The mean clump has a linear size of ~0.5 pc, a mass of ~320 Msolar for a dust temperature Td=30 K, an H_2 density of 9.5E5 cm-3, and a surface density of 0.4 g cm-2. The median values are 0.4 pc, 102 Msolar, 4E4 cm-3, and 0.14 g cm-2, respectively. The mean value of the luminosity-to-mass ratio, L/M ~99 Lsolar/Msolar, suggests that the sources are in a young, pre-ultracompact HII phase. We have compared the millimeter continuum maps with images of the mid-IR MSX emission, and have discovered 95 massive millimeter clumps non-MSX emitters, either diffuse or point-like, that are potential prestellar or precluster cores. The physical properties of these clumps are similar to those of the others, apart from the mass that is ~3 times lower than for clumps with MSX counterpart. Such a difference could be due to the potential prestellar clumps having a lower dust temperature. The mass spectrum of the clumps with masses above M ~100 Msolar is best fitted with a power-law dN/dM proportional to M-alpha with alpha=2.1, consistent with the Salpeter (1955) stellar IMF, with alpha=2.35.Comment: 83 pages, 10 figures, 3 tables. Accepted for publication by A&A. The full paper, including Fig.2 with the maps of all the individual regions, complete Tables 1 and 2 can be found at http://www.arcetri.astro.it/~starform/publ2005.ht

Chemical Diversity in Protoplanetary Disks and Its Impact on the Formation History of Giant Planets

Giant planets can interact with multiple and chemically diverse environments in protoplanetary discs while they form and migrate to their final orbits. The way this interaction affects the accretion of gas and solids shapes the chemical composition of the planets and of their atmospheres. Here we investigate the effects of different chemical structures of the host protoplanetary disc on the planetary composition. We consider both scenarios of molecular (inheritance from the pre-stellar cloud) and atomic (complete chemical reset) initial abundances in the disc. We focus on four elemental tracers of different volatility: C, O, N, and S. We explore the entire extension of possible formation regions suggested by observations by coupling the disc chemical scenarios with N-body simulations of forming and migrating giant planets. The planet formation process produces giant planets with chemical compositions significantly deviating from that of the host disc. We find that the C/N, N/O, and S/N ratios follow monotonic trends with the extent of migration. The C/O ratio shows a more complex behaviour, dependent on the planet accretion history and on the chemical structure of the formation environment. The comparison between S/N* and C/N* (where * indicates normalisation to the stellar value), constrains the relative contribution of gas and solids to the total metallicity. Giant planets whose metallicity is dominated by the contribution of the gas are characterised by N/O* > C/O* > C/N* and allow for constraining the disc chemical scenario. When the planetary metallicity is instead dominated by the contribution of the solids we find that C/N* > C/O* > N/O*.Comment: 27 pages, 10 figures, 1 table. Published in The Astrophysical Journa

Spectral classification of young stars using conditional invertible neural networks I. Introducing and validating the method

Aims. We introduce a new deep learning tool that estimates stellar parameters (such as effective temperature, surface gravity, and extinction) of young low-mass stars by coupling the Phoenix stellar atmosphere model with a conditional invertible neural network (cINN). Our networks allow us to infer the posterior distribution of each stellar parameter from the optical spectrum. Methods. We discuss cINNs trained on three different Phoenix grids: Settl, NextGen, and Dusty. We evaluate the performance of these cINNs on unlearned Phoenix synthetic spectra and on the spectra of 36 Class III template stars with well-characterised stellar parameters. Results. We confirm that the cINNs estimate the considered stellar parameters almost perfectly when tested on unlearned Phoenix synthetic spectra. Applying our networks to Class III stars, we find good agreement with deviations of at most 5--10 per cent. The cINNs perform slightly better for earlier-type stars than for later-type stars like late M-type stars, but we conclude that estimations of effective temperature and surface gravity are reliable for all spectral types within the network's training range. Conclusions. Our networks are time-efficient tools applicable to large amounts of observations. Among the three networks, we recommend using the cINN trained on the Settl library (Settl-Net), as it provides the best performance across the largest range of temperature and gravity.Comment: 29 pages, 19 figures, Accepted for publication by Astronomy & Astrophysics on 10. Apri

The population of young low-mass stars in Trumpler 14

Massive star-forming regions are thought to be the most common birth environments in the Galaxy and the only birth places of very massive stars. Their presence in the stellar cluster alters the conditions within the cluster impacting at the same time the evolution of other cluster members. In principle, copious amounts of ultraviolet radiation produced by massive stars can remove material from outer parts of the protoplanetary disks around low- and intermediate-mass stars in the process of external photoevaporation, effectively reducing the planet-formation capabilities of those disks. Here, we present deep VLT/MUSE observations of low-mass stars in Trumpler 14, one of the most massive, young, and compact clusters in the Carina Nebula Complex. We provide spectral and stellar properties of 717 sources and based on the distribution of stellar ages derive the cluster age of $\sim$1~Myr. The majority of the stars in our sample have masses $\leqslant$1~$M_\odot$, what makes our spectroscopic catalogue the most deep to date in term of masses, and proves that detailed investigations of low-mass stars are possible in the massive but distant regions. Spectroscopic studies of low-mass members of the whole Carina Nebula Complex are missing. Our work provides an important step forward towards filling this gap and set the stage for follow-up investigation of accretion properties in Trumpler 14.Comment: Accepted for publication in A&A, 27 pages, 28 figure

The population of young low-mass stars in Trumpler 14

Massive star-forming regions are thought to be the most common birth environments in the Galaxy and the only birth places of very massive stars. Their presence in the stellar cluster alters the conditions within the cluster, impacting at the same time the evolution of other cluster members. In principle, copious amounts of ultraviolet radiation produced by massive stars can remove material from outer parts of the protoplanetary discs around low- and intermediate-mass stars in the process of external photoevaporation, effectively reducing the planet formation capabilities of those discs. Here, we present deep VLT/MUSE observations of low-mass stars in Trumpler 14, one of the most massive, young, and compact clusters in the Carina Nebula Complex. We provide spectral and stellar properties of 717 sources and based on the distribution of stellar ages, derive the cluster age of ∼1 Myr. The majority of the stars in our sample have masses ≤1 M⊙, which makes our spectroscopic catalogue the deepest to date in term of mass and proves that detailed investigations of low-mass stars are possible in the massive but distant regions. Spectroscopic studies of low-mass members of the whole Carina Nebula Complex are missing. Our work marks an important step forward towards filling this gap and sets the stage for follow-up investigations of accretion properties in Trumpler 14

High diagnostic accuracy of RT-QuIC assay in a prospective study of patients with suspected sCJD

The early and accurate in vivo diagnosis of sporadic Creutzfeldt-Jakob disease (sCJD) is essential in order to differentiate CJD from treatable rapidly progressive dementias. Diagnostic investigations supportive of clinical CJD diagnosis include magnetic resonance imaging (MRI), electroencephalogram (EEG), 14-3-3 protein detection, and/or real-time quaking-induced conversion (RT-QuIC) assay positivity in the cerebrospinal fluid (CSF) or in other tissues. The total CSF tau protein concentration has also been used in a clinical setting for improving the CJD diagnostic sensitivity and specificity. We analyzed 182 CSF samples and 42 olfactory mucosa (OM) brushings from patients suspected of having sCJD with rapidly progressive dementia (RPD), in order to determine the diagnostic accuracy of 14-3-3, the total tau protein, and the RT-QuIC assay. A probable and definite sCJD diagnosis was assessed in 102 patients. The RT-QuIC assay on the CSF samples showed a 100% specificity and a 96% sensitivity, significantly higher compared with 14-3-3 (84% sensitivity and 46% specificity) and tau (85% sensitivity and 70% specificity); however, the combination of RT-QuIC testing of the CSF and OM samples resulted in 100% sensitivity and specificity, proving a significantly higher accuracy of RT-QuIC compared with the surrogate biomarkers in the diagnostic setting of patients with RPD. Moreover, we showed that CSF blood contamination or high protein levels might interfere with RT-QuIC seeding. In conclusion, we provided further evidence that the inclusion of an RT-QuIC assay of the CSF and OM in the diagnostic criteria for sCJD has radically changed the clinical approach towards the diagnosis

Physical properties of Galactic Planck cold cores revealed by the Hi-GAL survey

Context. Previous studies of the initial conditions of massive star and star cluster formation have mainly targeted infrared-dark clouds (or IRDCs) toward the inner Galaxy. This is because IRDCs were first detected in absorption against the bright mid-infrared (IR) background of the inner Galaxy, requiring a favorable location to be observed. By selection, IRDCs therefore represent only a fraction of the Galactic clouds capable of forming massive stars and star clusters. Owing their low dust temperatures, however, IRDCs are bright in the far-IR and millimeter and, thus, observations at these wavelengths have the potential to provide a complete sample of star-forming massive clouds across the Galaxy. Aims: Our aim is to identify the clouds at the initial conditions of massive star and star cluster formation across the Galaxy and compare their physical properties as a function of Galactic longitude and Galactocentric distance. Methods: We have examined the physical properties of a homogeneous Galactic cold core sample obtained with the Planck satellite across the Galactic plane. With the use of Herschel Hi-GAL observations, we characterized the internal structure of the most reliable Galactic cold clumps within the Early Cold Core (ECC) Planck catalog. By using background-subtracted Herschel images, we derived the H2 column density and dust temperature maps for 48 Planck clumps covered by the Herschel Hi-GAL survey. We calculated and analyzed the basic physical parameters (size, mass, and average dust temperature) of these clumps as a function of location within the Galaxy. We also compared these properties with the empirical relation for massive star formation previously derived. Results: Most of the Planck clumps contain signs of star formation. About 25% of the clumps are massive enough to form high-mass stars and star clusters since they exceed the empirical threshold for massive star formation. Planck clumps toward the Galactic center region show higher peak column densities and higher average dust temperatures than those of the clumps in the outer Galaxy. Although we only have seven clumps without associated YSOs, the Hi-GAL data show no apparent differences in the properties of Planck cold clumps with and without star formation. Herschel is an ESA space observatory with science instruments provided by European-led Principal Investigator consortia and with important participation from NASA

A deep learning approach for the 3D reconstruction of dust density and temperature in star-forming regions

Funding: The team in Heidelberg acknowledges funding from the European Research Council via the ERC Synergy Grant “ECOGAL” (project ID 855130), from the German Excellence Strategy via the Heidelberg Cluster of Excellence (EXC 2181 - 390900948) “STRUCTURES”, and from the German Ministry for Economic Affairs and Climate Action in project “MAINN” (funding ID 50OO2206). They also thank for computing resources provided by The Länd and DFG through grant INST 35/1134-1 FUGG and for data storage at SDS@hd through grant INST 35/1314-1 FUGG.Aims. We introduce a new deep learning approach for the reconstruction of 3D dust density and temperature distributions from multi-wavelength dust emission observations on the scale of individual star-forming cloud cores (< 0.2 pc). Methods. We construct a training data set by processing cloud cores from the Cloud Factory simulations with the POLARIS radiative transfer code to produce synthetic dust emission observations at 23 wavelengths between 12 and 1300 µm. We simplify the task by reconstructing the cloud structure along individual lines of sight and train a conditional invertible neural network (cINN) for this purpose. The cINN belongs to the group of normalising flow methods and is able to predict full posterior distributions for the target dust properties. We test different cINN setups, ranging from a scenario that includes all 23 wavelengths down to a more realistically limited case with observations at only seven wavelengths. We evaluate the predictive performance of these models on synthetic test data. Results. We report an excellent reconstruction performance for the 23-wavelengths cINN model, achieving median absolute relative errors of about 1.8% in log(ndust/m−3) and 1% in log(Tdust/K), respectively. We identify trends towards overestimation at the low end of the density range and towards underestimation at the high end of both density and temperature, which may be related to a bias in the training data. Limiting coverage to a combination of only seven wavelengths, we still find a satisfactory performance with average absolute relative errors of about 3.3% and 2.5% in log(ndust/m−3) and log(Tdust/K). Conclusions. This proof of concept study shows that the cINN-based approach for 3D reconstruction of dust density and temperature is very promising and even feasible under realistic observational constraints.Peer reviewe