Archive for 2022

Setting up virtual environments for Python

Posted October 6, 2022 By grigoris

The biggest issue when dealing with multiple projects is how to keep track of the various Python and other packages’ versions you are using in each project. It is not uncommon to update something and get something else broken…

The solution is virtual environments. And for these there are few options such as virtualenv and conda (among others). So far I have postponed (and not with good results) the use of environments. But finally, I took the necessary time to investigate pros and cons and make a decision. I found the following guides from WhiteBox exceptional well written and useful:

along with this great meme …

Darth Vader about conda envs (as presented in WhiteBox)

Javier Gorosabel award on pro-am collaborations

Posted October 4, 2022 By grigoris

In the latest meeting of the Spanish Astronomical Society (5-9 September, Tenerife, Spain) it was announced that our work with Emmanuel (Manos) Kardasis on Venus’ Cloud Discontinuity (published at the beginning of 2022, in the journal Atmosphere) was the winner for the 2nd iteration of the Javier Gorosabel Award on pro-am collaborations.

I am actually excited about this. But not because I am part of the award… I am excited because this award recognizes the effort put by some amateurs in order to produce not only scientifically useful images/data but document their effort, share it with both the amateur and professional communities and contribute to the advance of our knowledge by analyzing and publishing their results. I am honored that I could help Manos to achieve that. I strongly believe that it is a worthy recognition of his more than two decades of contribution in Astronomy and the community in general.

Of course, kudos to Javier – without his expertise and guidance this work wouldn’t have materialized.

The two main driving forces behind the work: Javier Peralta (left) and Manos Kardasis (right) holding the Javier Gorosabel award.

Properties of luminous red supergiant stars in the Magellanic Clouds

S. de Wit, A.Z. Bonanos, F. Tramper, M. Yang, G. Maravelias, K. Boutsia, N. Britavskiy, and E. Zapartas

There is evidence that some red supergiants (RSGs) experience short lived phases of extreme mass loss, producing copious amounts of dust. These episodic outburst phases help to strip the hydrogen envelope of evolved massive stars, drastically affecting their evolution. However, to date, the observational data of episodic mass loss is limited. This paper aims to derive surface properties of a spectroscopic sample of fourteen dusty sources in the Magellanic Clouds using the Baade telescope. These properties may be used for future spectral energy distribution fitting studies to measure the mass loss rates from present circumstellar dust expelled from the star through outbursts. We apply MARCS models to obtain the effective temperature (Teff) and extinction (AV) from the optical TiO bands. We use a χ2 routine to determine the best fit model to the obtained spectra. We compute the Teff using empirical photometric relations and compare this to our modelled Teff. We have identified a new yellow supergiant and spectroscopically confirmed eight new RSGs and one bright giant in the Magellanic Clouds. Additionally, we observed a supergiant B[e] star and found that the spectral type has changed compared to previous classifications, confirming that the spectral type is variable over decades. For the RSGs, we obtained the surface and global properties, as well as the extinction AV. Our method has picked up eight new, luminous RSGs. Despite selecting dusty RSGs, we find values for AV that are not as high as expected given the circumstellar extinction of these evolved stars. The most remarkable object from the sample, LMC3, is an extremely massive and luminous evolved massive star and may be grouped amongst the largest and most luminous RSGs known in the Large Magellanic Cloud (log(L∗/L⊙)∼5.5 and R=1400 R⊙).

Fig. 9: Top: HRD indicating the locations of our LMC targets with inverted red triangles. The Teff for all data points was derived through the TiO method. Smaller light grey squares and stars are objects from Levesque et al. (2006) and Davies et al. (2013), respectively. For the two outliers we have extended the uncertainty assuming a shift of 0.3 mag in the K−band (dotted vertical error bar) instead of only the propagated uncertainty, to visualize the effect of intrinsic variability. The colour map represents the central 12C mass fraction, while the nodes on the track again indicate a step of 104 years

arXiv: 2209.11239

This is actually a preview of what will follow after the first paper of the machine-learning classifier. We put it into action to get predictions for a number of galaxies and we start exploring the results. Of more interest is the fractions of the populations with metallicity, although a more detailed study is needed to take care of all caveats.

Using machine learning to investigate the populations of dusty evolved stars in various metallicities

Grigoris Maravelias, Alceste Z. Bonanos, Frank Tramper, Stephan de Wit, Ming Yang, Paolo Bonfini, Emmanuel Zapartas, Konstantinos Antoniadis, Evangelia Christodoulou, Gonzalo Muñoz-Sanchez

Mass loss is a key property to understand stellar evolution and in particular for low-metallicity environments. Our knowledge has improved dramatically over the last decades both for single and binary evolutionary models. However, episodic mass loss although definitely present observationally, is not included in the models, while its role is currently undetermined. A major hindrance is the lack of large enough samples of classified stars. We attempted to address this by applying an ensemble machine-learning approach using color indices (from IR/Spitzer and optical/Pan-STARRS photometry) as features and combining the probabilities from three different algorithms. We trained on M31 and M33 sources with known spectral classification, which we grouped into Blue/Yellow/Red/B[e] Supergiants, Luminous Blue Variables, classical Wolf-Rayet and background galaxies/AGNs. We then applied the classifier to about one million Spitzer point sources from 25 nearby galaxies, spanning a range of metallicites (1/15 to ∼3 Z⊙). Equipped with spectral classifications we investigated the occurrence of these populations with metallicity.

The fractions, of the predicted class members over the total sample size for each galaxy, with metallicity.

arXiv: 2209.06303

JWST is alive and … so do we!

Posted July 12, 2022 By grigoris

JWST delivered its first images (publicly) today! That was the best news to get as an astronomer for two reasons. The one is of course the scientific reasons, as it is one of the most important telescopes sent to space and perhaps the most challenging mission to launch and deploy at that distance (L1). There should have been already tens (or hundreds ??) of articles with respect to this part.

However, there is another important reason. Everything went finally smooth and we have a working telescope. That means that Astronomy will not dismay! Imagine if all this effort and funding of 10 billion dollars would fail… I cannot and I do not want to! Thankfully this did not happen and hopefully this will motivate more funding towards Astronomy that should be reflected to jobs also….

So let’s enjoy and cheer about this new era in Astronomy!

Webb’s First Deep Field, the image of galaxy cluster SMACS 0723 (Credit: NASA)

In March I got invited to give a talk for the Thüringer Landessternwarte Tautenburg group. Back then June 21 looked like a far date in a relative relaxed time period. I was so wrong… they are so hectic days!

Nevertheless, I managed to prepare a talk entitled: “The ASSESS classifier: a machine-learning tool to uncover populations of evolved massive stars in nearby galaxies”. I tried to prepare a more general introduction to the massive stars and their evolution, along with the issues we are facing as far as mass loss. I highlighted the importance of the ASSESS project and the provide a short description of what is ASSESS all about. Then I described the machine-learning classifier we developed and our first results from this.

Although pressing, at the end I really enjoyed this talk!

Lenovo X-1 not charging … frustration solved!

Posted June 20, 2022 By grigoris

Last night I was working on the laptop when naturally it asked for some power. Typically, when you plug in the power is should charge the battery, right? Well… no, it didn’t do that at all!

And then dread start developing! What is wrong? Is it the plug, the connector or even worse the port? As it was late I called it a day and went to bed. Today, I started by trying another power cable and finally another charger … in vain! Then, Tassos K. told me the obvious “Why you don’t look it up, somebody should have written about it“.

And I did, to find out this post by Steven Allen “Fix For Lenovo X1 Carbon Not Charging“. The solution is to reset the batter, and as described:

  1. Unplug from any power sources (this won’t work if you don’t do this).
  2. Reboot into the BIOS setup (F1 on boot).
  3. Navigate to the Power menu.
  4. Select the “Disable built-in battery” option.
  5. Wait for the laptop to power off and then wait 30 seconds.
  6. Connect the power and start the laptop.

This will temporarily disable the battery which seems to reset any “bad charger” bits. And this works in Gen 6 also!

Hopefully, this will save others some time and frustration.

Steven Allen

You bet! Only the idea that I would have to return the laptop for inspection and the time lost is simply put … terrifying !

And there is light !

In-person participation at IAUS 361

Posted June 1, 2022 By grigoris

Finally after two years of pandemic and two postponements of the IAU Symposium 361: “Massive Stars Near and Far” it was held in Ballyconnell, Ireland in the week of 8-13 May 2022.

It was really a great opportunity. Not only I managed to meet in-person people and collaborators that I met through zoom meetings but actually it was the first time (since 2020) to meet also my colleagues working in the ASSESS project (while some of them for the first time!).

The event was of course a great place to present our latest results. I got the privilege to have a talk about our latest results from the machine-learning classifier we have developed (see this post).

  • Talk: “Introducing the ASSESS project: Episodic Mass Loss in Evolved Massive Stars – Key to Understanding the Explosive Early Universe” – Alceste Bonanos
  • Talk: “Using machine-learning to investigate the populations of dusty evolved stars in various metallicities” – Grigoris Maravelias
  • Poster: “Progenitors and companions of stripped-envelope supernovae” – Manos Zapartas
  • Poster: “Properties of dusty Red Supergiant stars in the Magellanic Clouds” – Stephan de Wit
  • Poster: “ASSESSing evolved massive stars in NGC 6822 and IC 10” – Gonzalo Munoz Sanchez
From left: Grigoris Maravelias, Stephan de Wit, Konstantinos Antoniadis, Frank Tramper, Alceste Bonanos, Manos Zapartas, Gonzalo Munoz Sanchez, Evangelia Christodoulou

Tuesday 12 April , 1900 CEST ! See you there !

Following the success of the first Astrostatistics Summer School in Crete in 2019 (18-21 June), we now organize its second iteration (11-15 July). Initially scheduled for 2020, but we all now what happened and it was delayed. Now things to have matured so that we can actually repeat it and of course with an in person meeting as this school presents the theory but it is heavily based on actual hands-on experience with real astronomical data.

For all interested individuals please check the website. There is not much time before we meet in July !