This is the first paper that results from my work with the ASSESS team over the last years. It focuses on the development of a machine-learning photometric classifier to characterize massive stars originating from IR (Spitzer) catalogs, which will help us understand the episodic mass loss. The first paper presents the method and the multiple test we performed to understand its capabilities and limitations. Now we proceed with the derivation of the catalogs and their analysis.

A machine-learning photometric classifier for massive stars in nearby galaxies I. The method

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

Context. 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. Moreover, episodic mass loss in evolved massive stars is not included in the models while the importance of its role in the evolution of massive stars is currently undetermined.
Aims. 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.
Methods. 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 which helps with foreground source detection. We grouped them in 7 classes (Blue, Red, Yellow, B[e] supergiants, Luminous Blue Variables, Wolf-Rayet, and outliers, e.g. QSOs and background galaxies). As this training set is highly imbalanced, we implemented synthetic data generation to populate the underrepresented classes and improve separation by undersampling the majority class. We built an ensemble classifier utilizing color indices as features. The probabilities from three machine-learning algorithms (Support Vector Classification, Random Forests, Multi-layer Perceptron) were combined to obtain the final classification.
Results. The overall weighted balanced accuracy of the classifier is ∼ 83%. Red supergiants are always recovered at ∼ 94%. Blue and Yellow supergiants, B[e] supergiants, and background galaxies achieve ∼ 50 − 80%. Wolf-Rayet sources are detected at ∼ 45% while Luminous Blue Variables are recovered at ∼ 30% from one method mainly. This is primarily due to the small sample sizes of these classes. In addition, the mixing of spectral types, as there are no strict boundaries in the features space (color indices) 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 ∼ 70%. This discrepancy is attributed to the different metallicity and extinction effects of their host galaxies. Motivated by the presence of missing values we investigated the impact of missing data imputation using simple replacement with mean values and an iterative imputor, which proved to be more capable. We also investigated the feature importance to find that r − i and y − [3.6] were the most important, although different classes are sensitive to different features (with potential improvement with additional features).
Conclusions. The prediction capability of the classifier is limited by the available number of sources per class (which corresponds to the sampling of their feature space), 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, as well as at lower metallicities (with some accuracy loss), making it an excellent tool for accentuating interesting objects and prioritizing targets for observations.

The confusion matrix for 54 sources without missing values in the three galaxies (IC 1613, WLM, and Sextans A). We achieve an overall accuracy of ~70%, and we notice that the largest confusion occurs between BSG and YSG. The overall difference in the accuracy compared to that obtained with the M31 and M33 sample is attributed to the photometric errors, and the effect of metallicity and extinction in these galaxies.

arXiv: 2203.08125

The following paper is the result of a tedious task that my good friend Manos Kardasis undertook over the last two+ years. He noticed the presence of this (relatively newly discovered) feature in Venus and collected images from amateur observers worldwide to study in detail the discontinuity and constrain some of its properties by comparison with data from JAXA’s Akatsuki.

The importance of this work is twofold: a. it shows the high potential of observations with small telescopes to perform scientific studies of quality, and b. it promotes and encourage encourage amateur observers to perform and increase the observations of Venus.

I am really happy with this paper as it is a very well-deserved outcome of the work and effort that Manos put into this (fighting and joggling with many other things at the same time) and it showcases how a professional-amateur collaboration can succeed. Well done Manos!

Amateur Observers Witness the Return of Venus’ Cloud Discontinuity

Kardasis E., Peralta J., Maravelias G., Imai M., Wesley A., Olivetti T., Naryzhniy Y., Morrone L., Gallardo A., Calapai G., Camarena J., Casquinha P., Kananovich D., MacNeill N., Viladrich C., Takoudi A.

Firstly identified in images from JAXA’s orbiter Akatsuki, the cloud discontinuity of Venus is a planetary-scale phenomenon known to be recurrent since, at least, the 1980s. Interpreted as a new type of Kelvin wave, this disruption is associated to dramatic changes in the clouds’ opacity and distribution of aerosols, and it may constitute a critical piece for our understanding of the thermal balance and atmospheric circulation of Venus. Here, we report its reappearance on the dayside middle clouds four years after its last detection with Akatsuki/IR1, and for the first time, we characterize its main properties using exclusively near-infrared images from amateur observations. In agreement with previous reports, the discontinuity exhibited temporal variations in its zonal speed, orientation, length, and its effect over the clouds’ albedo during the 2019/2020 eastern elongation. Finally, a comparison with simultaneous observations by Akatsuki UVI and LIR confirmed that the discontinuity is not visible on the upper clouds’ albedo or thermal emission, while zonal speeds are slower than winds at the clouds’ top and faster than at the middle clouds, evidencing that this Kelvin wave might be transporting momentum up to upper clouds.

Figure 1: Observations of cloud discontinuities, observed in the 2019/2020 eastern elongation of Venus, showing different morphologies.

arXiv: 2202.12601
Journal: Atmosphere 2022, 13(2), 348

GTC/OSIRIS instrument footprint

Posted March 2, 2022 By grigoris

When creating finding charts for observations with the OSIRIS instrument on GTC you need to check the field-of-view (fov). With the Aladin Sky Atlas it is easy to download an image and overplot an instrument footprint (Edit > Load instrument footprint). Although there is a selection of instruments, by default the OSIRIS is not included. It is possible however to “create your footprint” which opens a link to an online editor.

Using this and the information collected from the manual and the web for OSIRIS I created a footprint that displays the fov for the imaging (larger box) along with the fov for the Multi-Object Spectroscopy (inner boxes) which display the two CCDs with the gap in between. Then, it is easy to place the footprint to the exact coordinates you wish in order to include (or exclude) sources of interest, check which parts are outside the fields or in the gap.

You can find the file here: gtc-osiris-v2.vot (better right click on that and save as…)

An example image for NGC 2403 with three footprints overplotted.

Revisiting the evolved hypergiants in the Magellanic Clouds

Kourniotis, M.; Kraus, M.; Maryeva, O.; Borges Fernandes, M.; Maravelias, G.

The massive stars that survive the phase of red supergiants (RSGs) spend the rest of their life in extremity. Their unstable atmospheres facilitate the formation and episodic ejection of shells that alter the stellar appearance and surroundings. In the present study, we revise the evolutionary state of eight hypergiants in the Magellanic Clouds, four of early-A type and four of FG type, and complement the short list of the eruptive post-RSGs termed as yellow hypergiants (YHGs). We refine the outdated temperatures and luminosities of the stars by means of high-resolution spectroscopy with FEROS. The A-type stars are suggested to be in their early, post-main sequence phase, showing spectrophotometric characteristics of redward evolving supergiants. On the other hand, the FG-type stars manifest themselves through the enhanced atmospheric activity that is traced by emission filling in Hα and the dynamical modulation of the low-excitation Ba II line. Of these stars, the dusty HD269723 is suggested to have recently departed from a cool phase. We identify double-peaked emission in the FEROS data of HD269953 that emerges from an orbiting disk-hosting companion. The highlight of the study is an episode of enhanced mass loss of HD271182 that manifests as a dimming event in the lightcurve and renders the star “modest” analogue to ρ Cas. The luminosity log (L/L) = 5.6 of HD271182 can serve as an updated threshold for the luminosity of stars exhibiting a post-RSG evolution in the Large Magellanic Cloud.

arXiv: 2202.04667

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.


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”