Archive for March, 2022

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.