Archive for 2021

This is a paper that I finally managed to complete. Starting back in 2016 we looked into the light curves for ρ Cas to identify potential correlations with its latest outburst in 2013, but not all data made it through the final paper (Kraus et al. 2019). Given this first analysis and the fact that visual observations cover almost a century of star’s behavior, we continued the study and we looked into the four distinct outbursts. The result is even more interesting as there is a clear trend of shorter and more frequent outbursts, as if ρ Cas is bouncing against the Yellow Void.


Bouncing against the Yellow Void — exploring the outbursts of ρ Cas from visual observations

Grigoris Maravelias and Michaela Kraus

Massive stars are rare but of paramount importance for their immediate environment and their host galaxies. They lose mass from their birth through strong stellar winds up to their spectacular end of their lives as supernovae. The mass loss changes as they evolve and in some phases it becomes episodic or displays outburst activity. One such phase is the Yellow Hypergiants, in which they experience outbursts due to their pulsations and atmosphere instabilities. This is depicted in photometry as a decrease in their apparent magnitude. The object ρ Cassiopeia (Cas) is a bright and well known variable star that has experienced four major outbursts over the last century, with the most recent one detected in 2013. We derived the light curves from both visual and digital observations and we show that with some processing and a small correction (∼0.2 mag) for the visual the two curves match. This highlights the importance of visual observations both because of the accuracy we can obtain and because they fully cover the historic activity (only the last two of the four outbursts are well covered by digital observations) with a homogeneous approach. By fitting the outburst profiles from visual observations we derive the duration of each outburst. We notice a decreasing trend in the duration, as well as shorter intervals between the outbursts. This activity indicates that ρ Cas may be preparing to pass to the next evolutionary phase.

Figure 3.The duration of each outburst (dots) with time(using the minimum dates as identified from the fitting process). There is a trend of shorter outbursts with time (linear model indicated with the violet dashed line). They also seem to occur more frequently, as it is indicated by the time difference between the outbursts (violet arrows).

arXiv: 2112.13158

Digitizing (some) older observations of rho Cassiopeia

Posted December 22, 2021 By grigoris

In the old days observations were not coming in such convenient formats like machine readable tables or though Vizier catalogs. There were written in text within the papers. So, for today’s standards it is a bit frustrating to find data in this format when you need them. The way to digitize them can be automated today but still some manual treatment may be needed.

Anyways…this whole introduction was made to justify somehow this post. I found myself trying to include some data to build a light curve for rho Cassiopeia, and in particular V measurements around its outburst in 1986. Zsoldos & Percy (1991) and Leiker & Hoff (1987) are two papers with about 70-80 observations each. The V magnitudes were given either directly or as a difference with a standard star. I have done all the necessary … eye processing to “copy” all the observations form the two papers to two separate simple ascii files each one containing the Julian Data, the magnitude, and its error (if available).

So, if you ever (..!) find yourself trying to do the same thing, just use the following files ! Enjoy!

Zsoldos & Percy (1991) data | Leiker & Hoff (1987) data

JWST is about to launch!

Posted December 20, 2021 By grigoris

(by Peblo)

The week 1-5 of November 2021, I (virtually) participated to the IAU Symposium 366 on the origin of outflows in evolved stars. I had the opportunity to present our recently submitted work on a photometric machine-learning classifier.

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.

arXiv:2110.10669

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.

During the 15th Hellenic Astronomical Conference I had the opportunity to present advances in two major topics:

1. Applying machine-learning methods to build a photometric classifier for massive stars in nearby galaxies”  (talk)
2. “Bouncing against the Yellow Void – the case of rho Cas” (poster)

I have also contributed to the following works:

by E. Kyritsis on “A new automated tool for the spectral classification of OB stars”
by Stephan de Wit on “Spectral analysis of evolved massive stars in the SMC and LMC”

EAS 2021 poster contributions

Posted June 28, 2021 By grigoris

Three poster contributions during EAS 2021 with the following … statistics: all of them on massive stars,  two within the framework of the ASSESS project, and two on machine-learning applications.

1. Applying machine-learning methods to build a photometric classifier for massive stars in nearby galaxies

Grigoris Maravelias, Alceste Bonanos, Frank Tramper, Stephan de Wit, Ming Yang, Paolo Bonfini

Mass loss is a key parameter in the evolution of massive stars. Despite the recent progress in the theoretical understanding of how stars lose mass, discrepancies between theory and observations still hold. Even worse, episodic mass loss in evolved massive stars is not included in the models while the importance of its role in the evolution os massive stars is currently undetermined. A major hindrance to determining the role of episodic mass loss is the lack of large samples of classified stars. Given the recent availability of extensive photometric catalogs from various surveys spanning a range of metallicity environments, we aim to remedy the situation by applying machine learning techniques to these catalogs.We compiled a large catalog of known massive stars in M31 and M33, using IR (Spitzer) and optical (Pan-STARRS) photometry, as well as Gaia astrometric information. We grouped them in 7 classes (Blue, Red, Yellow, B[e] supergiants, Luminous Blue Variables, Wolf-Rayet, and outliers, e.g. QSO’s and background galaxies). Using this catalog as a training set, we built an ensemble classifier utilizing color indices as features. The probabilities from three machine-learning algorithms (Support Vector Classification, Random Forests, Neural Networks) are combined to obtain the final classifications. The overall performance of the classifier is ~87%. Highly populated (Red/Blue/Yellow Supergiants) and well-defined classes (B[e] Supergiants) have a high recovery rate between ~98-74%. On the contrary, Wolf-Rayet sources are detected at ~20% while Luminous Blue Variables are almost non-existent. The is mainly due to the small sample sizes of these classes, although M31 and M33 have spectral classifications for several massive stars (about 2500). In addition, the mixing of spectral types, as there are no strict boundaries in the features space (color indexes) between those classes, complicates the classification. In an independent application of the classifier to other galaxies (IC 1613, WLM, Sextans A) we obtained an overall accuracy of ~71% despite the missing values on their features (which we replace with averaged values from the training sample). This approach results only in a few percent difference, with the remaining discrepancy attributed to the different metallicity environments of their host galaxies. The classifier’s prediction capability is only limited by the available number of sources per class, reflecting the rarity of these objects and the possible physical links between these massive star phases. Our methodology is also efficient in correctly classifying sources with missing data and at lower metallicities, making it an excellent tool for spotting interesting objects and prioritizing targets for observations. Future spectroscopic observations will offer a test-bed of its actual performance along with opportunities for improvement.

For more see this k-poster (submitted for SS32: Machine Learning and Visualisation in Data Intensive Era ).

2. A new automated tool for the spectral classification of OB stars

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

As more and more large spectroscopic surveys become available, an automated approach in spectral classification becomes necessary. Due to the importance of the massive stars it is of paramount importance to identify the phenomenological parameters of these stars (e.g., the spectral type ) which can be used as proxies to their physical parameters (e.g mass, temperature).
In this work, we use the Random Forest (RF) algorithm to develop a tool for automated spectral classification of the OB-type stars into their sub-types. We use the regular RF algorithm, the Probabilistic RF (PRF) which is an extension of RF that incorporates uncertainties, 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 measured in the spectra from large Galactic (LAMOST, GOSSS) and extragalactic surveys (2dF, VFTS) with available spectral-type classification. By following an adaptive binning approach we group the labels of these data on 11 sub-types within the range O3-B9. We examined which of the characteristic spectral lines (features) are more important to use based on a number of feature selection methods and we searched for the optimal hyper-parameters of the classifiers, to achieve the best performance.
From the feature screening process, we find 13 spectral lines as the optimal number of features. We find that the overall accuracy score is ~ 76 % with similar results across all approaches, with our KDE – RF being slightly lower at ~ 73 %. In addition, we show that our optimized RF model can reach an overall accuracy score of ~ 85 % in the ideal case of robust measurement of the weakest characteristic spectral lines. 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, especially for data with S/N in the range 50-300. Furthermore, we present an alternative model for lower quality data S/N < 25 based on a reduced feature-set classification scheme, including only the strongest spectral lines.
The similarity in the performances of our models indicates the robustness and the reliability of the RF algorithm when used for spectral classification of early-type stars. This is strengthened also by the fact that we are working with real-world data and not with simulations. In addition, the approach presented in this work is very fast and applicable to products from different surveys in terms of quality (e.g different resolutions) and of different formats (e.g., absolute or normalized flux).

For more see this k-poster (submitted for S16: Massive stars: birth, rotation, and chemical evolution).

3. Evolved massive stars in the Magellanic Clouds

Ming Yang, Alceste Bonanos, Biwei Jiang, Jian Gao, Panagiotis Gavras, Grigoris Maravelias, Man I Lam, Shu Wang, Xiaodian Chen, Yi Ren, Frank Tramper, Zoi Spetsieri

We present two clean, magnitude-limited (IRAC1 or WISE1≤15.0 mag) multiwavelength source catalogs for the Large and Small Magellanic Cloud (LMC and SMC). The catalogs were built by crossmatching (1”) and deblending (3”) between the source list of Spitzer Enhanced Imaging Products (SEIP) and Gaia Data Release 2 (DR2), with strict constraints on the Gaia astrometric solution in order to remove the foreground contamination. It is estimated that about 99.5% of the targets in our catalog are most likely genuine members of the LMC and SMC. The LMC catalog contains 197,004 targets in 52 different bands, while SMC catalog including contains 45,466 targets in 50 different bands, ranging from the ultraviolet to the far-infrared. Additional information about radial velocities and spectral and photometric classifications were collected from the literature. For the LMC, we compare our sample with the sample from Gaia Collaboration et al. (2018), indicating that the bright end of our sample is mostly comprised of blue helium-burning stars (BHeBs) and red HeBs with inevitable contamination of main sequence stars at the blue end. For the SMC, by using the evolutionary tracks and synthetic photometry from MESA Isochrones & Stellar Tracks and the theoretical J-Ks color cuts, we identified and ranked 1,405 red supergiant (RSG), 217 yellow supergiant (YSG), and 1,369 blue supergiant (BSG) candidates in the SMC in five different color-magnitude diagrams (CMDs), where attention should also be paid to the incompleteness of our sample. For the LMC, due to the problems with models, we applied modified magnitude and color cuts based on previous studies, and identified and ranked 2,974 RSG, 508 YSG, and 4,786 BSG candidates in the LMC in six CMDs. The comparison between the CMDs from the two catalogs of the LMC SMC indicates that the most distinct difference appears at the bright red end of the optical and near-infrared CMDs, where the cool evolved stars (e.g., RSGs, asymptotic giant branch stars, and red giant stars) are located, which is likely due to the effect of metallicity and star formation history. A further quantitative comparison of colors of massive star candidates in equal absolute magnitude bins suggests that there is essentially no difference for the BSG candidates, but a large discrepancy for the RSG candidates since LMC targets are redder than the SMC ones, which may be due to the combined effect of metallicity on both spectral type and mass-loss rate as well as the age effect. The effective temperatures (Teff) of massive star populations are also derived from reddening-free color of (J-Ks). The Teff ranges are 3500≤Teff≤5000 K for an RSG population, 5000≤Teff≤8000 K for a YSG population, and Teff≥8000 K for a BSG population, with larger uncertainties toward the hotter stars.

For more see this k-poster (submitted for S16: Massive stars: birth, rotation, and chemical evolution).

Evolved Massive Stars at Low-metallicity IV.Using 1.6μm “H-bump” to identify red supergiant stars:a case study of NGC 6822

Ming Yang, Alceste Z. Bonanos, Biwei Jiang, Man I Lam, Jian Gao, Panagiotis Gavras, Grigoris Maravelias, Shu Wang, Xiao-Dian Chen, Frank Tramper, Yi Ren, Zoi T. Spetsieri

Abstract

We present a case study of using a novel method to identify red supergiant (RSG) candidates in NGC 6822, based on their 1.6 μm “H-bump”. We collected 32 bands of photometric data for NGC 6822 ranging from optical to MIR. By using the theoretical spectra from MARCS, we demonstrate that there is a prominent difference around 1.6 μm (“H-bump”) between low-surface-gravity (LSG) and high-surface-gravity (HSG) targets. Taking advantage of this feature, we identify efficient color-color diagrams (CCDs) of rzH and rzK to separate HSG and LSG targets from crossmatching of optical and NIR data. Moreover, synthetic photometry from ATLAS9 also give similar results. Further separating RSG candidates from the rest of the LSG candidates is done by using semi-empirical criteria on NIR CMDs and resulted in 323 RSG candidates. Meanwhile, the simulation of foreground stars from Besançon models also indicates that our selection criteria is largely free from the contamination of Galactic giants. In addition to the “H-bump” method, we also use the traditional BVR method as a comparison and/or supplement, by applying a slightly aggressive cut to select as much as possible RSG candidates (358 targets). Furthermore, the Gaia astrometric solution is used to constrain the sample, where 181 and 193 targets were selected from the “H-bump” and BVR method, respectively. The percentages of selected targets in both methods are similar as 60\%, indicating the comparable accuracy of the two methods. In total, there are 234 RSG candidates after combining targets from both methods with 140 (60\%) of them in common. The final RSG candidates are in the expected locations on the MIR CMDs, while the spatial distribution is also coincident with the FUV-selected star formation regions, suggesting the selection is reasonable and reliable.
arXiv.org: 2101.08689