CN Features Research Articles

The impact of incorrect dissociation energies on inferred photospheric abundances

ABSTRACT Spectral synthesis codes are essential for inferring stellar parameters and detailed chemical abundances. These codes require many physical inputs to predict an emergent spectrum. Developers adopt the best measurements of those inputs at the time they release their code, but those measurements usually improve over time faster than the software is updated. In general, the impact of using incorrect or uncertain dissociation energies is largely unknown. Here, we evaluate how incorrect dissociation energies impact abundances measured from C2, CN, CH, TiO, and MgO features. For each molecule, we synthesized optical spectra of FGKM-type main-sequence and giant stars using the literature dissociation energy and an incorrect (perturbed) dissociation energy. We find that the uncertainties in the dissociation energies adopted by spectral synthesis codes for CN, CH, TiO, and MgO lead to negligible differences in flux or abundance. C2 is the only diatomic molecule where the uncertainty of the inputted dissociation energy translates to a significant difference in flux and carbon abundance differences of up to 0.2 dex. For solar-like stars, the impact on carbon abundance is up to 0.09 dex. These large abundance differences demonstrate the importance of updating the inputs adopted by spectral synthesis codes, as well as a consensus on appropriate values between different codes.

Statistical analysis on Alzheimer's disease

Alzheimer's disease is a progressive neurodegenerative disease that occurs mostly in the elderly and has memory impairment as the main clinical symptom. There is no ideal treatment for Alzheimer's disease, so early prevention is important. In this paper, we use brain structural information to diagnose Alzheimer's disease features and cognitive-behavioral characteristics, which is important for early and accurate diagnosis of mild cognitive impairment. To investigate the factors influencing Alzheimer's disease, a correlation analysis model was developed after preprocessing the missing values of the data. First, the data features were viewed, the missing values of the data were analyzed, and the useless features were removed and the missing values of the remaining features were filled with the average value. To verify the accuracy of the subsequent intelligent diagnosis model and clustering model, this paper divides the training set and test set according to PTID. Finally, the top ten important features are selected and the Spearman coefficients are chosen according to the distribution of the features for correlation analysis. Machine learning methods were utilized to build an Alzheimer's classification model to solve the problem of intelligent diagnosis of Alzheimer's disease. The pre-processed dataset in the above paper was trained with the model, and five methods of logistic regression, support vector machine, KNN classification, decision tree classification and XGB were utilized to build the classification model, and the accuracy, recall and F1 value of each model were visualized and compared, among which the accuracy of XGB model reached 83%, which is reasonable for the intelligent diagnosis of the disease. A K-Means-based clustering model for disease types was established using the K-Means clustering algorithm, clustering CN, MCI and AD into three major classes, and then refining MCI into three subclasses. The optimal K-values and random seeds were firstly found using the elbow principle, then the cluster analysis was performed using the feature values and data sets selected after preprocessing, and finally the MCI in MCI was extracted and sub-clustered into three subclasses SMC, EMCI and LMCI. In order to investigate the evolution pattern of different categories of diseases over time, patients with 3 categories of diseases are screened separately for analysis in this paper. Firstly, by combining the results above and reviewing the data, the features irrelevant to this task and columns containing a large number of missing values were removed, the remaining features were selected and probability density plots were drawn, and all discrete features and all features that were essentially zero were continued to be screened out. After that, the 15 features of CN, MCI and AD diseases were plotted separately over time to reveal their evolution patterns over time. We reviewed the relevant literature, sorted out and summarized the existing studies at home and abroad, and summarized the criteria for determining the five stages of Alzheimer's disease and the early intervention of the disease.

CN and CO features: key indicators of red giant evolutionary phase in moderate-resolution X-shooter spectra

ABSTRACT Data-driven analysis methods can help to infer physical properties of red giant stars where ‘gold-standard’ asteroseismic data are not available. The study of optical and infrared spectra of red giant stars with data-driven analyses has revealed that differences in oscillation frequencies and their separations are imprinted in said spectra. This makes it possible to confidently differentiate core helium burning red clump (RC) stars from those that are still on their first ascent of the red giant branch (RGB). We extend these studies to a tenfold larger wavelength range of 0.33–2.5 µm with the moderate-resolution VLT/X-shooter spectrograph. Our analysis of 49 stars with asteroseismic data from the K2 mission confirms that CN, CO, and CH features are indeed the primary carriers of spectroscopic information on the evolutionary stages of red giant stars. We report 215 informative features for differentiating the RC from the RGB within the range of 0.33–2.5 µm. This makes it possible for existing and future spectroscopic surveys to optimize their wavelength regions to deliver both a large variety of elemental abundances and reliable age estimates of luminous red giant stars.

Adaptive Spatial Regularization Target Tracking Algorithm Based on Multifeature Fusion

The accuracy and robustness of object-tracking algorithms are challenging tasks in the field of artificial intelligence. The discriminative correlation filter has fast tracking speed and target discrimination ability in visual target tracking, but it will be affected by unnecessary boundary effects. Although spatially regularized discriminative correlation filters (SRDCF) effectively solve this problem, it faces a slow tracking speed and cannot meet the real-time requirement. Moreover, the constraints added by this algorithm are fixed during the tracking process, which needs to reflect better the characteristics and appearance changes of a particular object. Therefore, we propose an adaptive spatial regularization target tracking model based on multifeature fusion and learn the spatial regularization weights by adaptive spatial regularization terms, so that the filter can adapt to the changes of the target, which can highlight the target area and suppress the background area more accurately. At the same time, in order to cope with the problem of target loss when the target is obscured by a large area or rotated to a large extent, texture features are introduced to improve the deficiencies of HOG features and CN features, so as to achieve the complementary advantages among different features. In addition, the benchmark algorithm SRDCF uses Gaussian–Seidel iterative method to solve the filter, which makes the tracking speed very slow and cannot be tracked in real time. Therefore, this paper proposes an ADMM (alternating direction method of multiplier) algorithm to optimize the solution of the filter and achieve the tracking real-time requirement. The experimental results on the OTB-2015 dataset show that the accuracy and success rate of the improved algorithm reach 86% and 81.9%, respectively. At the same time, in the OTB-2013 dataset, it has increased by 3.4% and 6.3%, respectively, on the basis of the benchmark algorithm. It is verified that the improved model not only improves the robustness of the algorithm but also can better adapt to the changes of the target, while effectively reducing the computational overhead and meeting the tracking real-time requirements.

Gaia-ESO Survey: Detailed elemental abundances in red giants of the peculiar globular cluster NGC 1851

Context. NGC 1851 is one of several globular clusters for which multiple stellar populations of the subgiant branch have been clearly identified and a difference in metallicity detected. A crucial piece of information on the formation history of this cluster can be provided by the sum of A(C+N+O) abundances. However, these values have lacked a general consensus thus far. The separation of the subgiant branch can be based on age and/or A(C+N+O) abundance differences. Aims. Our main aim was to determine carbon, nitrogen, and oxygen abundances for evolved giants in the globular cluster NGC 1851 in order to check whether or not the double populations of stars are coeval. Methods. High-resolution spectra, observed with the FLAMES-UVES spectrograph on the ESO VLT telescope, were analysed using a differential model atmosphere method. Abundances of carbon were derived using spectral synthesis of the C2 band heads at 5135 and 5635.5 Å. The wavelength interval 6470−6490 Å, with CN features, was analysed to determine nitrogen abundances. Oxygen abundances were determined from the [O I] line at 6300 Å. Abundances of other chemical elements were determined from equivalent widths or spectral syntheses of unblended spectral lines. Results. We provide abundances of up to 29 chemical elements for a sample of 45 giants in NGC 1851. The investigated stars can be separated into two populations with a difference of 0.07 dex in the mean metallicity, 0.3 dex in the mean C/N, and 0.35 dex in the mean s-process dominated element-to-iron abundance ratios [s/Fe]. No significant difference was determined in the mean values of A(C+N+O) as well as in abundance to iron ratios of carbon, α- and iron-peak-elements, and of europium. Conclusions. As the averaged A(C+N+O) values between the two populations do not differ, additional evidence is given that NGC 1851 is composed of two clusters, the metal-rich cluster being by about 0.6 Gyr older than the metal-poor one. A global overview of NGC 1851 properties and the detailed abundances of chemical elements favour its formation in a dwarf spheroidal galaxy that was accreted by the Milky Way.

Light Element Abundances and Multiple Populations in M53

Abstract We present results from a study of 94 red giant stars in the globular cluster M53. We use low-resolution spectra to measure the strength of CN and CH features at ∼3800 and 4300 Å, respectively. The strengths of these features are used to classify stars into a CN-enhanced and CN-normal population and to measure C and N abundances in all 94 stars. We find the red giant branch stars to be evenly split between the two populations identified, and observe the presence of CN-enhanced stars on the asymptotic giant branch. In addition, we identify five CH star candidates based on the strength of their CN and CH band features, and the presence of a P-branch in their CH band. We compare our identification of multiple populations to those based on the Na–O anticorrelation and pseudo-color indices in Hubble Space Telescope UV photometry, and find general agreement between all three methods. Our large sample size also allows us to study the radial distribution of each population, and we find that the CN-enhanced population is more centrally concentrated. We use our C and N measurements to compare the evolutionary changes in these elements as a function of magnitude between the two populations, and show that both populations experience similar evolutionary changes to the surface abundances of C and N. Finally, we calculate C+N+O abundances for each population and compare them to similar measurements made in M10; we find that in both clusters, CN-enhanced stars have a slightly enhanced C+N+O (Δ(C+N+O) ∼ 0.2 dex).

Learning adaptive spatial regularization and aberrance repression correlation filters for visual tracking

This paper proposes a correlation filter tracking algorithm based on adaptive spatial regularization and aberrance repression aiming at the problem that the spatial regularization weight of the background-aware correlation filter is fixed and does not adapt to the change of the target, and the problem that enlarging search area may introduce background noise, decreasing the discrimination ability of filters. First, FHOG features, CN features, and gray features are extracted to enhance the algorithm's ability to express the target. Second, aberrance repression terms are added to the target function to constrain the response map of the current frame, and to enhance the filter's discrimination ability to alleviate the filter model degradation. Finally, adaptive spatial regularization terms are added to the objective function to make the spatial regularization weights being updated as the objective changes, so that the filter can make full use of the target's diversity information. This paper involves experiments on the public data sets OTB-2013, OTB-2015 and VOT2016 to evaluate the proposed algorithm. The experimental results show that the speed of the algorithm used in this paper is 20 frames/s, evaluation indicators such as distance accuracy and success rate are superior to comparison algorithms, and it has good robustness in a variety of complex scenarios such as occlusion, background interference, and rotation changes.

Multi-Feature Fusion Target Re-Location Tracking Based on Correlation Filters

Target tracking has been a research hotspot in computer vision, and the correlation filtered target tracking algorithm has the benefits of low computational complexity and fast speed. Still, the tracking effect is not good when dealing with complicated circumstances. This paper proposes a multi-feature fusion target repositioning tracking algorithm for the target tracking problem in complex environments. First, a multi-feature weighted fusion algorithm is presented. Since each feature has different advantages in different environments, we combine HOG, CN, ULBP, and image edge features and use the weighted coefficient method to adaptively fuse each feature component. Second, to address the target occlusion problem, an occlusion judgment mechanism is introduced, and the target is re-located by fusion weighted filtering. Third, the scale pool is established, and the scale filter is trained by the classification search method. Finally, an adaptive model update strategy is proposed. We conduct comparison experiments with current mainstream algorithms on the publicly available datasets OTB-2015, VOT2018, UAV123, and TColor-128, respectively, and the experimental results show that our proposed algorithm is more robust in complex scenarios.

The Genomic Complexity of Multiple Myeloma Precursor Disease Can be Predicted Using Copy Number Signatures on Targeted Sequencing and SNP Array Data

The Genomic Complexity of Multiple Myeloma Precursor Disease Can be Predicted Using Copy Number Signatures on Targeted Sequencing and SNP Array Data

Copy Number Signatures Predict Chromothripsis and Poor Clinical Outcome in Newly Diagnosed Multiple Myeloma Patients

Copy Number Signatures Predict Chromothripsis and Poor Clinical Outcome in Newly Diagnosed Multiple Myeloma Patients

A Massive Molecular Torus inside a Gas-poor Circumnuclear Disk in the Radio Galaxy NGC 1052 Discovered with ALMA

Abstract We report ALMA observations of NGC 1052 to search for mass accretion in a gas-poor active galactic nucleus. We detected CO emission representing a rotating ring-like circumnuclear disk (CND) seen edge-on with a gas mass of 5.3 × 105 M ⊙. The CND has smaller gas mass than that in typical Seyfert galaxies with circumnuclear star formation and is too gas-poor to drive mass accretion onto the central engine. The continuum emission casts molecular absorption features of CO, HCN, HCO+, SO, SO2, CS, CN, and H2O, with H13CN and HC15N and vibrationally excited (v 2 = 1) HCN and HCO+. Broader absorption line widths than CND emission-line widths imply the presence of a geometrically thick molecular torus with a radius of 2.4 ± 1.3 pc and a thickness ratio of 0.7 ± 0.3. We obtain an H2 column density of (3.3 ± 0.7) × 1025 cm−2 using H12CN, H13CN, and HCO+ absorption features and adopting abundance ratios of 12C to 13C and HCO+ to H2, and we derived a torus gas mass of (1.3 ± 0.3) × 107 M ⊙, which is ∼9% of the central black hole mass. The molecular gas in the torus is clumpy, with an estimated covering factor of . The gas density of the clumps inside the torus is inferred to be (6.4 ± 1.3) × 107 cm−3, which meets the excitation conditions for an H2O maser. The specific angular momentum in the torus exceeds the flat rotation curve extrapolated from that of the CND, indicating a Keplerian rotation inside a 14.4 pc sphere of influence.

Identifying Multiple Populations in M71 Using CN

Abstract We have observed the CN features at ∼3800 and 4120 Å as well as the CH band at ∼4300 Å for 145 evolved stars in the Galactic globular cluster M71 using the multi-object spectrograph, Hydra, on the Wisconsin-Indiana-Yale-NOAO-3.5 m telescope. We use these measurements to create two δCN indices finding that both distributions are best fit by two populations: a CN-enhanced and CN-normal. We find that 42% ± 4% of the red giant branch stars in our sample are CN-enhanced. The percentage of CN-enhanced is 40% ± 13% for the asymptotic giant branch and 33% ± 9% for the horizontal-branch stars (HB stars), which suggests there are no missing second generation stars at these stages of stellar evolution. The two generations also separate in magnitude and color on the HB, which allows us to find the difference in He abundance between the two populations by fitting appropriate zero-age horizontal branches. The broad range of distances from the cluster’s center covered by our sample allows us to study the dependence of the ratio of the number of first to second population stars on the distance from the cluster’s center, and we find that this ratio does not vary radially and that the two populations are spatially mixed. Finally, we compare our identification of multiple populations with the classification based on the Na–O anti-correlation and the Hubble Space Telescope UV photometry, and we find good agreement with both methods.

Correlation Filter With Motion Detection for Robust Tracking of Shape-Deformed Targets

Target tracking is an important area of research in computer vision where stable target's tracking has been well solved. But in real world, it is difficult to ensure that the camera or lens could be fixed and the target could maintain its shape in whole video sequence. And as a result, in these unstable cases, robust tracking algorithms have to deal with the problem of target shape-deforming. Once the scenes video sequence contains shape-deformed target, tracking become a real challenging problem. Most previous tracking algorithms based on craft features only used HOG or/and CN features. This paper proposed an algorithm named as Correlation Filtering with Motion Detection (CFMD). This algorithm takes into account the camera shake and target motion information of the video sequence. After removing the effects of lens shake and camera movement, this algorithm can predict the motion information of the target, thereby effectively improving the tracking accuracy and robustness. In CFMD, the target position is determined by the weighted outputs of motion detection and correlation filter tracker. We evaluated our CMFD algorithm on the OTB-100 and VOT-2018 dataset compared with other target tracking algorithms, including Kernel Correlation Filter (KCF), Scale Adaptive with Multiple Features tracker (SAMF), Discriminative Scale Space Tracker (DSST), and Sum of Template and Pixel-wise LEarners (Staple), Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking(STRCF), Multi-Cue Correlation Filters for Robust Visual Tracking(MCCT). The experimental results showed that our algorithm owns the property of robust tracking of shape-deformed targets in video sequences containing lens shaking or camera moving and it achieves the state-of-the-art precision and tracking effects.

Chemical abundances of open clusters from high-resolution infrared spectra – II. NGC 752

ABSTRACT We present a detailed near-infrared chemical abundance analysis of 10 red giant members of the Galactic open cluster NGC 752. High-resolution (R ≃ 45000) near-infrared spectral data were gathered with the Immersion Grating Infrared Spectrograph, providing simultaneous coverage of the complete H and K bands. We derived the abundances of H-burning (C, N, O), α (Mg, Si, S, Ca), light odd-Z (Na, Al, P, K), Fe-group (Sc, Ti, Cr, Fe, Co, Ni), and neutron-capture (Ce, Nd, Yb) elements. We report the abundances of S, P, K, Ce, and Yb in NGC 752 for the first time. Our analysis yields solar-metallicity and solar abundance ratios for almost all of the elements heavier than the CNO group in NGC 752. O and N abundances were measured from a number of OH and CN features in the H band, and C abundances were determined mainly from CO molecular lines in the K band. High-excitation $\rm{C\,\small {I}}$ lines present in both near-infrared and optical spectra were also included in the C abundance determinations. Carbon isotopic ratios were derived from the R-branch band heads of first overtone (2−0) and (3−1) 12CO and (2−0) 13CO lines near 23 440 Å and (3−1) 13CO lines at about 23 730 Å. The CNO abundances and 12C/13C ratios are all consistent with our giants having completed ‘first dredge-up’ envelope mixing of CN-cyle products. We independently assessed NGC 752 stellar membership from Gaia astrometry, leading to a new colour–magnitude diagram for this cluster. Applications of Victoria isochrones and MESA models to these data yield an updated NGC 752 cluster age (1.52 Gyr) and evolutionary stage indications for the programme stars. The photometric evidence and spectroscopic light element abundances all suggest that the most, perhaps all of the programme stars are members of the helium-burning red clump in this cluster.

Carbon Isotope Ratios in M10 Giants

Abstract We measured carbon abundances and the 12C/13C ratio in 31 giant branch stars with previous CN and CH band measurements that span −2.33 < M V < 0.18 in the globular cluster M10 (NGC 6254). Abundances were determined by comparing CO features at ∼2.3 μm and specifically the 13CO bandhead at 2.37 μm, to synthetic spectra generated with MOOG. The observed spectra were obtained with GNIRS on Gemini North with a resolution of R ≈ 3500. The carbon abundances derived from the IR spectra agree with previous [C/Fe] measurements found using CN and CH features at the near-UV/blue wavelength range. We found an average carbon isotope ratio of 12C/13C = for first generation stars (CN-normal; 13 stars total) and 12C/13C = for second generation stars (CN-enhanced; 15 stars). We therefore find no statistically significant difference in 12C/13C ratio between stars in either population for the observed magnitude range. Finally, we created models of the expected carbon, nitrogen, and 12C/13C surface abundance evolution on the red giant branch due to thermohaline mixing using the MESA stellar evolution code. The efficiency of the thermohaline mixing must be increased to a factor of ≈60 to match [C/Fe] abundances, and by a factor of ≈666 to match 12C/13C ratios. We could not simultaneously fit the evolution of both carbon and the 12C/13C ratio with models using the same thermohaline efficiency parameter.

Chemical abundances of open clusters from high-resolution infrared spectra – I. NGC 6940

We present near-infrared spectroscopic analysis of 12 red giant members of the Galactic open cluster NGC 6940. High-resolution (R$\simeq$45000) and high signal-to-noise ratio (S/N > 100) near-infrared H and K band spectra were gathered with the Immersion Grating Infrared Spectrograph (IGRINS) on the 2.7m Smith Telescope at McDonald Observatory. We obtained abundances of H-burning (C, N, O), ${\alpha}$ (Mg, Si, S, Ca), light odd-Z (Na, Al, P, K), Fe-group (Sc, Ti, Cr, Fe, Co, Ni) and neutron-capture (Ce, Nd, Yb) elements. We report the abundances of S, P, K, Ce, and Yb in NGC 6940 for the first time. Many OH and CN features in the H band were used to obtain O and N abundances. C abundances were measured from four different features: CO molecular lines in the K band, high excitation C I lines present in both near-infrared and optical, CH and $C_2$ bands in the optical region. We have also determined $^{12}C/^{13}C$ ratios from the R-branch band heads of first overtone (2-0) and (3-1) $^{12}CO$ (2-0) $^{13}CO$ lines near 23440 $\overset{\lower.5em\circ}{\mathrm{A}}$ and (3-1) $^{13}CO$ lines at about 23730 $\overset{\lower.5em\circ}{\mathrm{A}}$. We have also investigated the HF feature at 23358.3 $\overset{\lower.5em\circ}{\mathrm{A}}$, finding solar fluorine abundances without ruling out a slight enhancement. For some elements (such as the ${\alpha}$ group), IGRINS data yield more internally self-consistent abundances. We also revisited the CMD of NGC 6940 by determining the most probable cluster members using Gaia DR2. Finally, we applied Victoria isochrones and MESA models in order to refine our estimates of the evolutionary stages of our targets.

Coma Morphology, Numerical Modeling, and Production Rates for Comet C/Lulin (2007 N3)

Abstract We report on narrowband photometry and extensive imaging observations of comet C/Lulin (2007 N3) obtained at Lowell Observatory during 2008 and 2009. Enhanced CN images revealed a double-corkscrew morphology with two near-polar jets oriented approximately east–west, and both CN and dust images showed nightly rotational variability and seasonal changes in bulk morphology. We determined a rotational pole direction of R.A./decl. = 81°/+29° with an obliquity of 97° and a sidereal rotation period of 41.45 ± 0.05 hr. Monte Carlo numerical modeling best replicated the observed CN features with an eastern source area at lat/long −80°/125° and an ∼10° radius and a western source area at lat/long +77°/245° and an ∼20° radius, ∼4× larger than the eastern source. An additional small, near-equatorial source was necessary to reproduce some dust features. Water morphology based on OH was quite different from that of the carbon-bearing species, implying a different driver for the polar jets such as CO or CO2. Ion tails were detected in decontaminated images from both the dust and NH filters, likely being H2O+ and OH+, respectively. We measured water production both before and after perihelion and extrapolated peak water production at perihelion to be about 1.0 × 1029 molecules s−1. We estimated an active fraction of only 4%–5% and a nucleus radius of up to ∼8 km. Our data suggest that Lulin, defined as dynamically new in a statistical sense, behaves more like a long-period comet due to its nearly asteroidal early appearance, isolated source regions, and dust properties.

Aluminium abundances in five discrete stellar populations of the globular cluster NGC 2808

We observed a sample of 90 red giant branch (RGB) stars in NGC 2808 using FLAMES/GIRAFFE and the high resolution grating with the set-up HR21. These stars have previous accurate atmospheric parameters and abundances of light elements. We derived aluminium abundances for them from the strong doublet Al i 8772–8773 Å as in previous works of our group. In addition, we were able to estimate the relative CN abundances for 89 of the stars from the strength of a large number of CN features. When adding self-consistent abundances from previous UVES spectra analysed by our team, we gathered [Al/Fe] ratios for a total of 108 RGB stars in NGC 2808. The full dataset of proton-capture elements is used to explore in detail the five spectroscopically detected discrete components in this globular cluster. We found that various classes of polluters are required to reproduce (anti)-correlations among all proton-capture elements in the populations P2, I1, and I2 with intermediate composition. This is in agreement with the detection of lithium in lower RGB second generation stars, requiring at least two kind of polluters. For chemically homogeneous populations, the best subdivision of our sample is into six components as derived from statistical cluster analysis. By comparing different diagrams [element/Fe] versus [element/Fe], we show for the first time that a simple dilution model is not able to reproduce all the subpopulations in this cluster. Polluters of different masses are required. NGC 2808 is confirmed to be a tough challenge to any scenario for globular cluster formation.

Measuring Oxygen Abundances from Stellar Spectra without Oxygen Lines

Abstract Oxygen is the most abundant “metal” element in stars and in the cosmos. But determining oxygen abundances in stars has proven challenging, because of the shortage of detectable atomic oxygen lines in their optical spectra as well as observational and theoretical complications with these lines (e.g., blends, three-dimensional, non-LTE). Nonetheless, Ting et al. were recently able to demonstrate that oxygen abundances can be determined from low-resolution (R ≃ 2000) optical spectra. Here, we investigate the physical processes that enable such a measurement for cool stars, such as K-giants. We show that the strongest spectral diagnostics of oxygen come from the CNO atomic-molecular network but are manifested in spectral features that do not involve oxygen. In the outer atmosphere layers, most of the carbon is locked up in CO, and changes to the oxygen abundance directly affect the abundances of all other carbon-bearing molecules, thereby changing the strength of CH, CN, and C2 features across the optical spectrum. In deeper atmosphere layers, most of the carbon is in atomic form, and any change in the oxygen abundance has little effect on the other carbon-bearing molecules. The key physical effect enabling such oxygen abundance measurements is that spectral features in the optical arise from both the CO-dominant and the atomic carbon-dominant regions, providing non-degenerate constraints on both C and O. Beyond the case at hand, the results show that physically sound abundances measurements need not be limited to those elements that have observable lines themselves.

CNO abundances and carbon isotope ratios in evolved stars of the open clusters NGC 2324, NGC 2477, and NGC 3960

Our main aim is to determine carbon-to-nitrogen and carbon isotope ratios for evolved giants in the open clusters NGC 2324, NGC 2477, and NGC 3960, which have turn-off masses of about 2 Msun, and to compare them with predictions of theoretical models. High-resolution spectra were analysed using a differential synthetic spectrum method. Abundances of carbon were derived using the C2 Swan (0,1) band heads at 5135 and 5635.5 A. The wavelength interval 7940-8130 A with strong CN features was analysed to determine nitrogen abundances and carbon isotope ratios. The oxygen abundances were determined from the [Oi] line at 6300 A. The mean values of the CNO abundances are [C/Fe]=-0.35+-0.06 (s.d.), [N/Fe]=0.28+-0.05, and [O/Fe]=-0.02+-0.10 in seven stars of NGC 2324; [C/Fe]=-0.26+-0.02, [N/Fe]=0.39+-0.04, and [O/Fe]=-0.11+-0.06 in six stars of NGC 2477; and [C/Fe]=-0.39+-0.04, [N/Fe]=0.32+-0.05, and [O/Fe]=-0.19+-0.06 in six stars of NGC 3960. The mean C/N ratio is equal to 0.92+-0.12, 0.91+-0.09, and 0.80+-0.13, respectively. The mean 12C/13C ratio is equal to 21+-1, 20+-1, and 16+-4, respectively. The 12C/13C and C/N ratios of stars in the investigated open clusters were compared with the ratios predicted by stellar evolution models. The mean values of the 12C/13C and C/N ratios in NGC 2324 and NGC 2477 agree well with the first dredge-up and thermohaline-induced extra-mixing models, which are similar for intermediate turn-off mass stars. The 12C/13C ratios in the investigated clump stars of NGC 3960 span from 10 to 20. The mean carbon isotope and C/N ratios in NGC 3960 are close to predictions of the model in which the thermohaline- and rotation-induced (if rotation velocity at the zero-age main sequence was 30% of the critical velocity) extra-mixing act together.