New paper on evaluating outreach activities

Posted November 11, 2019 By grigoris

A short paper has been presented as a poster in the EPSC-DPS Joint Meeting 2019, held 15-20 September 2019 in Geneva, Switzerland. The data originate from a series of outreach activities performed by the Hellenic Amateur Astronomy Association, entitled “Introduction to Observational Astronomy“, which aimed to introduce interested individuals to the aspects of the observational techniques for scientifically useful observations, i.e. how amateur observations can help professionals and contribute to Astronomy in general (pro-am networking).

Evaluating introductory seminars on observational astronomy, using the Europlanet Evaluation Toolkit

Moutsouroufi, Konstantina; Maravelias, Grigoris; Marios Strikis, Iakovos; Kardasis, Emmanuel; Voutyras, Orfefs; Kountouris, Giorgos; Evangelopoulos, Athanasios; Aggelis, Konstantinos; Papadeas, Pierros; Schmidt, Tamara; Christou, Apostolos

During December 2018 – February 2019, the Hellenic Amateur Astronomy Association coordinated a series of seminars entitled “Introduction to Observational Astronomy”. The goal of this series was to introduce interested individuals to the aspects of the observational techniques for scientifically useful observations. Using the Europlanet Evaluation Toolkit we implemented a number of evaluation methods to receive feedback. The results show the participation of a mainly young audience ( 60% between 18-39), where females are represented more than equally ( 52%). Using the “pebbles in a jar” method a 94% of satisfied attendees was measured, while by using post-event surveys (questionnaires) the lectures were perceived as “(very) explicit” and “(very) interesting” (94%), fulfilling the attendees’ expectations (92%). It is important to note that 88% considers that their interest in Astronomy increased and is willing to get involved in observations.

NASA/ADS bibcode: 2019EPSC…13.1749M , poster: EPSC2019-poster

Conference contributions – summer 2019 edition

Posted November 8, 2019 By grigoris

Although the summer has finished long ago, only now I got some time to update on summer activities, i.e. a number of conferences I attended and contributed to.

Since 2018, I have been working in an automated classifier for massive stars in nearby galaxies, using photometric datasets. These have been produced by my colleagues within the ASSESS team, an ERC project led by Alceste Bonanos at the National Observatory of Athens, and I have been responsible to develop a machine learning method to achieve this. We have made a lot of progress and we have reached to the point that the results are almost final (working now on the Maravelias et al. paper). So, this work has been presented in:

  1. A poster presentation at the Supernova Remnants II, Chania, Greece, 3-8 June 2019,
    as “Identifying massive stars in nearby galaxies, in a smart way”
  2. A talk, done by Frank Tramper due to my unavailability to attend the 14th Hellenic Astronomical Conference, Volos, Greece, 8-11 July 2019,
    as “Automated classification of massive stars in nearby galaxies”
  3. A talk at the Computational Intelligence in Remote Sensing and Astrophysics, FORTH workshop, Heraklion, Greece, 17-19 July 2019,
    as “An automated classifier of massive stars in nearby galaxies”
  4. A remote talk for the ASTROSTAT 2nd Consortium meeting, Boston, USA, 18-19 July 2019,
    as “Towards an automated classifier of massive stars in nearby galaxies”

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

Abstract:
Current photometric surveys can provide us with multiwavelength measurements for a vast numbers of stars in many nearby galaxies. Although the majority of these stars are evolved luminous stars (e.g. Wolf-Rayet, Blue/Yellow/Red Supergiants), we lack an accurate spectral classification, due to the demands that spectroscopy faces at these distances and for this number of stars. What we can do instead is to take advantage of machine learning algorithms (such as Support Vector Machines, Random Forests, Convolutional Neural Networks) to build an automated classifier based on a large multi-wavelength photometric catalog. We have compiled such a catalog based on optical (e.g. Pan-STARRS, OGLE) and IR (e.g. 2MASS, Spitzer) surveys, combined with astrometric information from the GAIA mission. We have also gathered spectroscopic samples of massive stars for a number of nearby galaxies (e.g. the Magellanic Clouds, M31, M33) and by using our algorithm we have achieved a success ratio of more than 80% for the training and test samples. By applying the fully trained algorithm to the available photometric datasets, we can uncover previously unclassified sources, which will become our prime candidates for spectroscopic follow-up aiming to confirm their nature and our approach.


Also Ming has presented his work in a couple of conferences:

1. As a poster presentation at the Supernova Remnants II, Chania, Greece, 3-8 June 2019,

“Evolved Massive Stars at Low-metallicity: A Source Catalog for the Small Magellanic Cloud”

Ming Yang, Alceste Z. Bonanos, Bi-Wei Jiang, Jian Gao, Panagiotis Gavras, Grigoris Maravelias, Yi Ren, Shu Wang, Meng-Yao Xue, Frank Tramper, Zoi T. Spetsieri, Ektoras Pouliasis, Stephan A. S. de Wit

We present a clean, magnitude-limited (IRAC1 or WISE1 ≤ 15.0 mag) multiwavelength source catalog for the SMC with 45,466 targets in total, with the purpose of building an anchor for future studies, especially for the massive star populations at low-metallicity. The catalog contains data in 50 different bands including 21 optical and 29 infrared bands, ranging from the ultraviolet to the far-infrared. Additionally, radial velocities and spectral classifications were collected from the literature, as well as infrared and optical variability statistics were retrieved from different datasets. The catalog was essentially built upon a 1′′ crossmatching and a 3′′ deblending between the SEIP source list and Gaia DR2 photometric data. Further constraints on the proper motions and parallaxes from Gaia DR2 allowed us to remove the foreground contamination. We estimated that about 99.5% of the targets in our catalog were most likely genuine members of the SMC. By using the evolutionary tracks and synthetic photometry from MIST and the theoretical J−Ks color cuts, we identified 1,405 RSG, 217 YSG and 1,369 BSG candidates in the SMC in five different CMDs. We ranked the candidates based on the intersection of different CMDs. A comparison between the models and observational data shows that the lower limit of initial masses for the RSGs population may be as low as 7 or even 6 M⊙, making RSGs a unique population connecting the evolved massive and intermediate stars, since stars with initial mass around 6 to 8 M⊙ are thought to go through a second dredge-up to become AGBs. We encourage the interested reader to further exploit the potential of our catalog.

2. As a talk at the ESO workshop “A synoptic view of the Magellanic Clouds: VMC, Gaia and beyond”, Garching near Munich, Germany, September 9-13, 2019

“Evolved Massive Stars and Red Supergiant Stars in the Magellanic Clouds”

Ming Yang, Alceste Z. Bonanos, Bi-Wei Jiang, Jian Gao, Panagiotis Gavras, Grigoris Maravelias, Yi Ren, Shu Wang, Meng-Yao Xue, Frank Tramper, Zoi T. Spetsieri, Ektoras Pouliasis, and Stephan de Wit

We present an ongoing investigation of infrared properties, variabilities, and mass loss rate (MLR) of evolved massive stars in the Magellanic Clouds, especially the red supergiant stars (RSGs). For the LMC, 744 RSGs compiled from the literature are identified and analysed by using the color-magnitude diagram (CMD), spectral energy distribution (SED) and mid-infrared (MIR) variability, based on 12 bands of near-infrared (NIR) to MIR co-added data from 2MASS, Spitzer and WISE, and ∼6.6 yr of MIR time-series data collected by the ALLWISE and NEOWISE-R projects. The results show that there is a relatively tight and positive correlation between the brightness, MIR variability, MLR, and the warm dust or continuum, where both the variability and the luminosity may be important for the MLR. The identified RSG sample has been compared with the theoretical evolutionary models and shown that the discrepancy between observation and evolutionary models can be mitigated by considering both variability and extinction. For the SMC, we present a relatively clean, magnitude-limited (IRAC1 or WISE1 ≤ 15.0 mag) multiwavelength source catalog with 45,466 targets in total, intending to build an anchor for the future studies, especially the massive stars at low-metallicity. It contains data in 50 different bands including 21 optical and 29 infrared bands, retrieved from SEIP, VMC, IRSF, AKARI, Heritage, Gaia, SkyMapper, NSC, Massey et al. (2002), and GALEX, ranging from the ultraviolet to the far-infrared. Additionally, radial velocities and spectral classifications are collected from the literature, as well as the infrared and optical variability information derived from WISE, SAGE-Var, VMC, IRSF, Gaia, NSC, and OGLE. The catalog is essentially built upon a 1” crossmatching and a 3” deblending between the Spitzer Enhanced Imaging Products (SEIP) source list and Gaia Data Release 2 (DR2) photometric data. Further constraints on the proper motions and parallaxes from Gaia DR2 allow us to remove the foreground contamination. We estimate that about 99.5% of the targets in our catalog are likely to be the genuine members of the SMC. By using the evolutionary tracks and synthetic photometry from MESA Isochrones & Stellar Tracks and the theoretical J−Ks color cuts, we identify 1,405 red supergiant, 217 yellow supergiant and 1,369 blue supergiant candidates in the SMC in five different CMDs. We rank the candidates based on the intersection of the different CMDs. A comparison between the models and observational data shows that, the lower limit of the RSGs population may reach to 7 or even 6M⊙, making RSGs an unique population connecting the evolved massive and intermediate stars, since stars with initial mass around 6 to 8M⊙ are thought to go through a second dredge-up to become asymptotic giant branch stars. We encourage the interested reader to further exploit the potential of our catalog, including, but not limited to, massive stars, supernova progenitors, star formation history and stellar population. Detailed analysis and comparison of RSGs in the LMC and SMC may be also presented depending on the progress of the investigation.

Master presentation by Elias Kyritsis

Posted September 30, 2019 By grigoris

Elias Kyritsis has been a student at the Physics Department of the University of Crete that I have co-supervised with Prof. Andreas Zezas since 2017.

Initially as an undergraduate student he worked on the visual classification of High-Mass X-ray Binary sources in the Large Magellanic Cloud, but later on he decided to work on a more automated approach. Over the last year he has developed an automated spectral classifier (focusing on the early OB type stars) with Random Forests.Last Friday (Sep 27, 2019) he defended his work and obtained his MSc. diploma. Congratulations!

 

Elias Kyritsis on the day of his MSc, defense.Elias Kyritsis on the day of his MSc, defense. (Credit: Elias Kyritsis)

Evolved Massive Stars at Low-metallicity I. A Source Catalog for the Small Magellanic Cloud

Ming Yang, Alceste Z. Bonanos, Bi-Wei Jiang, Jian Gao, Panagiotis Gavras, Grigoris Maravelias, Yi Ren, Shu Wang, Meng-Yao Xue, Frank Tramper, Zoi T. Spetsieri, Ektoras Pouliasis

We present a clean, magnitude-limited (IRAC1 or WISE1 ≤ 15.0 mag) multiwavelength source catalog for the SMC with 45,466 targets in total, with the purpose of building an anchor for future studies, especially for the massive star populations at low-metallicity. The catalog contains data in 50 different bands including 21 optical and 29 infrared bands, ranging from the ultraviolet to the far-infrared. Additionally, radial velocities and spectral classifications were collected from the literature, as well as infrared and optical variability statistics were retrieved from different projects. The catalog was essentially built upon a 1′′ crossmatching and a 3′′ deblending between the SEIP source list and Gaia DR2 photometric data. Further constraints on the proper motions and parallaxes from Gaia DR2 allowed us to remove the foreground contamination. We estimated that about 99.5\% of the targets in our catalog were most likely genuine members of the SMC. By using the evolutionary tracks and synthetic photometry from MIST and the theoretical J−KS color cuts, we identified 1,405 RSG, 217 YSG and 1,369 BSG candidates in the SMC in five different CMDs, where attention should also be paid to the incompleteness of our sample. We ranked the candidates based on the intersection of different CMDs. A comparison between the models and observational data shows that the lower limit of initial mass for the RSGs population may be as low as 7 or even 6 M⊙ and the RSG is well separated from the AGB population even at faint magnitude, making RSGs a unique population connecting the evolved massive and intermediate stars, since stars with initial mass around 6 to 8 M⊙ are thought to go through a second dredge-up to become AGBs. We encourage the interested reader to further exploit the potential of our catalog

arXiv.org: 1907.06717

The ASSESS group roster 2019

Posted June 27, 2019 By grigoris
The ASSESS group during the Supernova Remnants II meeting in Chania, Freece (photo by Dimitra Abartzi)

The ASSESS group during the Supernova Remnants II meeting in Chania, Freece (photo by Dimitra Abartzi)

During the last four days I was busy with the actual materialization of the Astrostatistics Summer School Crete 2019, which took place at the University of Crete, in Heraklion (18-21 June 2019). My duties were mainly those of the Teaching Assistant and I contributed with a short presentation of the Random Forests method for classification. Unfortunately I didn’t have the time to post more on this school, so I ended up doing something only today, at the very last day!

In summary this is/was a school for graduate and early-stage post-docs to get a grasp of the modern field of Astrostatistics, which practically means the application of statistics in Astronomy which incorporates also machine-learning techniques. Topics include: Intro to Python and Jupyter notebook, linear regression, classical statistical distribution tests and hypothesis testing, Bayesian statistics, Markov-Chain Monte Carlo, machine learning classification/regression/clustering, time series analysis.

The school is split in “teaching”/explanatory parts (through Jupyter notebooks though where you could also interact and run the examples) and practical workshops, where important hands-on experience with all the tools presented was provided (and hopefully gained!). However, I think the importance lies in the fact that all the material is publicly available through a github repository: https://github.com/astrostatistics-in-crete (including the notebooks with the introduction notes, the exercises in the workshops, and the data). So this is a valuable source both for students of the school, as well as others interested to try and experiment with these tools.

During a session – from a galaxy far far away – at the Supernova Remnants II conference in Chania, Greece 2019 (photo/editing by A. Manousakis).

At the Supernova Remnants II conference

Posted June 5, 2019 By grigoris

Working and supporting the Supernova Remnants II conference in Chania, Greece 2019 (photo by A. Manousakis).

Fixing GPG error “NO_PUBKEY”

Posted March 28, 2019 By grigoris

In Debian, Ubuntu and similar distros that use the APT (Advanced Package Tool – which is a set of tools for managing Debian packages / applications), to update the system you need to run:

sudo apt update

This will read all repositories (as they are listed in /etc/apt/sources.list and under /etc/apt/sources.list.d/) and checks if everything is correct (e.g. if the links are working and these sites / repositories are trusted sources to install from). So by doing this in my system I got the following:

Hit:1 https://repo.skype.com/deb stable InRelease
Hit:2 http://security.debian.org/debian-security buster/updates InRelease
Hit:3 http://deb.debian.org/debian buster InRelease
Hit:4 http://deb.debian.org/debian buster-updates InRelease
Err:1 https://repo.skype.com/deb stable InRelease
The following signatures couldn't be verified because the public key is not available: NO_PUBKEY 1F3045A5DF7587C3
Reading package lists... Done
Building dependency tree
Reading state information... Done
All packages are up to date.
W: An error occurred during the signature verification. The repository is not updated and the previous index files will be used. GPG error: https://repo.skype.com/deb stable InRelease: The following signatures couldn't be verified because the public key is not available: NO_PUBKEY 1F3045A5DF7587C3
W: Failed to fetch https://repo.skype.com/deb/dists/stable/InRelease The following signatures couldn't be verified because the public key is not available: NO_PUBKEY 1F3045A5DF7587C3
W: Some index files failed to download. They have been ignored, or old ones used instead.

In this case there is an error with respect to Skype. The systems does not have the public key for this package, so it complains and prevents the system from downloading something which is not secure (and something that we want!).
At the same time this means that I didn’t do something correctly when installing Skype (and to be honest … I do not remember what I did!). Anyway, the proper procedure is a two-step process:

1. Add the repository under the /etc/apt/sources.list.d/ as a separate file,e.g. by:

echo "deb [arch=amd64] https://repo.skype.com/deb stable main" | sudo tee /etc/apt/sources.list.d/skype-stable.list

(adding the repository to /etc/apt/sources.list is actually equivalent – the only difference being that you need to edit that file while it is more convenient, especially for automated scripts, to create a new file under sources.list.d/)

2. Then, the second step is to download the GPG public key that verifies the repository. To do that we can simply:

sudo apt-key adv --fetch-keys https://repo.skype.com/data/SKYPE-GPG-KEY

and we will get:

Executing: /tmp/apt-key-gpghome.fD2Z003jib/gpg.1.sh --fetch-keys https://repo.skype.com/data/SKYPE-GPG-KEY
gpg: requesting key from 'https://repo.skype.com/data/SKYPE-GPG-KEY'
gpg: key 1F3045A5DF7587C3: public key "Skype Linux Client Repository " imported
gpg: Total number processed: 1
gpg: imported: 1

(This is similar or better of doing:

wget URL -O - | apt-key add -

or

curl URL | apt-key add ).

This adds the GPG key in the /etc/apt/trusted.gpg file, and now if we try again to update the system we will see no error or warning.

Hint: to see all the contents of the trusted.gpg file just type: apt-key list !