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New paper: X-Shooting ULLYSES: massive stars at low metallicity I. Project Description

It was back in 2020 during the pandemic that the first call for the XShootU collaboration was sent. I had really little idea of what would follow, but I fount it definitely motivating to participate. Therefore I volunteer to help with the data reduction. After 2.5 years we are getting close to the first public release of this data. The current first (in a probable very long series of papers to follow) paper is on the project description. The second paper on data reduction will soon follow…

X-Shooting ULLYSES: massive stars at low metallicityI. Project Description

Jorick S. Vink, A. Mehner, P. A. Crowther, A. Fullerton, M. Garcia, F. Martins, N. Morrell, L.M.
Oskinova, N. St-Louis, A. ud-Doula, A.A.C. Sander, H. Sana, J.-C. Bouret, B. Kubátová, P. Marchant, L.P.
Martins, A. Wofford, J. Th. van Loon, O. Grace Telford, Y. Götberg, D.M. Bowman, C. Erba,
V.M. Kalari, M. Abdul-Masih, T. Alkousa, F. Backs, C.L. Barbosa, S.R. Berlanas, M. Bernini-Peron,
J.M. Bestenlehner, R. Blomme, J. Bodensteiner, S.A. Brands, C.J. Evans, A. David-Uraz, F.A.
Driessen, K. Dsilva, S. Geen, V.M.A.Gómez-González, L. Grassitelli, W.-R. Hamann, C. Hawcroft, A.
Herrero, E.R. Higgins, D. John Hillier, R. Ignace, A.G. Istrate, L. Kaper, N.D. Kee, C. Kehrig, Z.
Keszthelyi, J. Klencki, A. de Koter, R. Kuiper, E. Laplace, C.J.K. Larkin, R. R. Lefever, C.
Leitherer, D.J. Lennon, L. Mahy, J. Maíz Apellániz, G. Maravelias, W. Marcolino, A. F. McLeod,
S.E. de Mink, F. Najarro, M. S. Oey, T.N. Parsons, D. Pauli, M.G. Pedersen, R.K. Prinja, V.
Ramachandran, M.C. Ramírez-Tannus, G.N. Sabhahit, A. Schootemeijer, S. Reyero Serantes, T. Shenar,
G.S. Stringfellow, N. Sudnik, F. Tramper, and L. Wang

Observations of individual massive stars, super-luminous supernovae, gamma-ray bursts, and gravitational-wave events involving spectacular black-hole mergers, indicate that the low-metallicity Universe is fundamentally different from our own Galaxy. Many transient phenomena will remain enigmatic until we achieve a firm understanding of the physics and evolution of massive stars at low metallicity (Z). The Hubble Space Telescope has devoted 500 orbits to observe ∼250 massive stars at low Z in the ultraviolet (UV) with the COS and STIS spectrographs under the ULLYSES program. The complementary “X-Shooting ULLYSES” (XShootU) project provides enhanced legacy value with high-quality optical and near-infrared spectra obtained with the wide-wavelength coverage X-shooter spectrograph at ESO’s Very Large Telescope. We present an overview of the XShootU project, showing that combining ULLYSES UV and XShootU optical spectra is critical for the uniform determination of stellar parameters such as effective temperature, surface gravity, luminosity, and abundances, as well as wind properties such as mass-loss rates in function of Z. As uncertainties in stellar and wind parameters percolate into many adjacent areas of Astrophysics, the data and modelling of the XShootU project is expected to be a game-changer for our physical understanding of massive stars at low Z. To be able to confidently interpret James Webb Space Telescope (JWST) spectra of the first stellar generations, the individual spectra of low Z stars need to be understood, which is exactly where XShootU can deliver.

Fig. 6. Reduced X-shooter spectra for a range of spectral types of single-star supergiants (top) and dwarfs (bottom – not shown). For illustration purposes the flux of each spectrum was divided by its mean value and an arbitrary offset was added. The grey regions correspond to the UVB-VIS common wavelength coverage (∼ 5500 Å), a gap due to bad pixel masking (∼ 6360 Å), and telluric absorption. Minor manual treatment to remove strong cosmic rays was performed.

arXiv: 2305.06376

New Paper: Evolved Massive Stars at Low-metallicity V. Mass-Loss Rate of Red Supergiant Stars in the Small Magellanic Cloud

Evolved Massive Stars at Low-metallicity V. Mass-Loss Rate of Red Supergiant Stars in the Small Magellanic Cloud

Ming Yang (杨明), Alceste Z. Bonanos, Biwei Jiang (姜碧沩), Emmanouil Zapartas, Jian Gao (高健), Yi Ren(任逸), Man I Lam (林敏仪), Tianding Wang (王天丁), Grigoris Maravelias, Panagiotis Gavras, Shu Wang (王舒), Xiaodian Chen (陈孝钿), Frank Tramper, Stephan de Wit, Bingqiu Chen (陈丙秋), Jing Wen (文静), Jiaming Liu (刘佳明), Hao Tian (田浩), Konstantinos Antoniadis, and Changqing Luo (罗常青)

The mass-loss rate (MLR) is one of the most important parameters in astrophysics, since it impacts many areas of astronomy, such as, the ionizing radiation, wind feedback, star-formation rates, initial mass functions, stellar remnants, supernovae, and so on. However, the most important modes of mass-loss are also the most uncertain, as we are still far from clear about the dominant physical mechanisms of the mass-loss. Here we assemble the most complete and clean red supergiant (RSG) sample (2,121 targets) so far in the Small Magellanic Cloud (SMC) with 53 different bands of data to study the MLR of RSGs. In order to match the observed spectral energy distributions (SEDs), a theoretical grid of 17,820 Oxygen-rich models (“normal” and “dusty” grids are half-and-half) is created by the radiatively-driven wind model of the DUSTY code, covering a wide range of dust parameters. We select the best model for each target by calculating the minimal modified chi-square and visual inspection. The resulting MLRs from DUSTY are converted to real MLRs based on the scaling relation, for which a total MLR of 6.16 × 10−3 M yr-1 is measured (corresponding to a dust-production rate of ∼ 6 × 10−6 M yr-1), with a typical MLR of ∼ 10−6 M yr-1 for the general population of the RSGs. The complexity of mass-loss estimation based on the SED is fully discussed for the first time, indicating large uncertainties based on the photometric data (potentially up to one order of magnitude or more). The Hertzsprung-Russell and luminosity versus median absolute deviation diagrams of the sample indicate the positive relation between luminosity and MLR. Meanwhile, the luminosity versus MLR diagrams show a “knee-like” shape with enhanced mass-loss occurring above log10(L/L) ≈ 4.6, which may be due to the degeneracy of luminosity, pulsation, low surface gravity, convection, and other factors. We derive our MLR relation by using a third-order polynomial to fit the sample and compare our result with previous empirical MLR prescriptions. Given that our MLR prescription is based on a much larger sample than previous determinations, it provides a more accurate relation at the cool and luminous region of the H-R diagram at low-metallicity compared to previous studies. Finally, 9 targets in our sample were detected in the UV, which could be an indicator of OB-type companions of binary RSGs.

Fig. 15. Derived MLR-L relation from this work (left) and comparison of the same relation between this and previous works (right). In the left panel, the very dusty targets (τ > 1.0) are marked with red colors. In the right panel, lines of the same color are variations of the same relation.

arXiv.org: 2304.01835

New paper: A new automated tool for the spectral classification of OB stars

This paper is a result of an attempt that started way back during my PhD thesis actually. back then in early 2010’s we started investigating a way to automate the spectral classification of Be X-ray binaries. The problem with these sources is that due to the strong emission in the Balmer lines they cannot be used as characteristic features for their corresponding classes. Thus, a different automated approach is needed (based on a classification scheme that we have developed in Maravelias et al. 2014). We started with a rather small sample of well-classified OB stars in the Galaxy and the Small Magellanic Cloud and implemented a Naive Bayesian Classifier, that actually proved to work very well. However, more tests and a larger sample was in need to proceed to a publication. And as time was limited I was postponing the project.

Finally Elias Kyritsis showed up as graduate student willing to deal with this. After a successful undergraduate thesis on spectral classification of BeXBs in the Large Magellanic Cloud Elias moved from the visual inspection to the automated approach. He was successful in many fields: increasing drastically the sample, trying/optimizing/developing a different machine-learning approach, improving the line measurements, and submitting the paper to A&A. His tremendous effort has paid out finally!

I am really excited about this journey and his accomplishment. Without his help this project will at least delayed a loooooot! Thanks Elia!

A new automated tool for the spectral classification of OB stars

E. Kyritsis, G. Maravelias, A. Zezas, P. Bonfini, K. Kovlakas, P. Reig

(abridged) We develop a tool for the automated spectral classification of OB stars according to their sub-types. We use the regular Random Forest (RF) algorithm, the Probabilistic RF (PRF), and we introduce the KDE-RF method which is a combination of the Kernel-Density Estimation and the RF algorithm. We train the algorithms on the Equivalent Width (EW) of characteristic absorption lines (features) measured in high-quality spectra from large Galactic (LAMOST,GOSSS) and extragalactic surveys (2dF,VFTS) with available spectral-types and luminosity classes. We find that the overall accuracy score is ∼70% with similar results across all approaches. We show that the full set of 17 spectral lines is needed to reach the maximum performance per spectral class. We apply our model in other observational data sets providing examples of potential application of our classifier on real science cases. We find that it performs well for both single massive stars and for the companion massive stars in Be X-ray binaries. In addition, we propose a reduced 10-features scheme that can be applied to large data sets with lower S/N. The similarity in the performances of our models indicates the robustness and the reliability of the RF algorithm when it is used for the spectral classification of early-type stars. The score of ∼70% is high if we consider (a) the complexity of such multi-class classification problems, (b) the intrinsic scatter of the EW distributions within the examined spectral classes, and (c) the diversity of the training set since we use data obtained from different surveys with different observing strategies. In addition, the approach presented in this work, is applicable to data of different quality and of different format (e.g.,absolute or normalized flux) while our classifier is agnostic to the Luminosity Class of a star and, as much as possible, metallicity independent.


Fig. 8.Top left panel shows the confusion matrix of the best RF model applied to the test sample. The right panel shows the confusion matrix of the PRF best model applied to the same data set. The bottom panel shows the confusion matrix of the KDE-RF method. The overall accuracy is the same for all algorithms, 70 %, with the majority of misclassified objects belonging to neighboring classes, indicating the reliability of the algorithms.