<html>
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
<style type="text/css" style="display:none;"><!-- P {margin-top:0;margin-bottom:0;} --></style>
</head>
<body dir="ltr">
<div id="divtagdefaultwrapper" dir="ltr" style="font-size: 12pt; color: rgb(0, 0, 0); font-family: Calibri, Helvetica, sans-serif, Helvetica, EmojiFont, "Apple Color Emoji", "Segoe UI Emoji", NotoColorEmoji, "Segoe UI Symbol", "Android Emoji", EmojiSymbols;">
<p><br>
</p>
<p>Dear all,</p>
<p><br>
</p>
<p>The next SU astronomy departmental seminar will take place on Friday October 13 at 10:30am in room FC61 (AlbaNova building, 6th floor) and on zoom (https://stockholmuniversity.zoom.us/s/61002076352).</p>
<p><br>
</p>
<p>———————————</p>
<p>Speaker: Guillaume Guiglion (ZAH/ LSW, MPIA)</p>
<p>Title: Machine-learning for stellar spectroscopy: from RAVE to Gaia-RVS & 4MOST</p>
<p>Event: https://indico.fysik.su.se/e/guillaume-guiglion</p>
<p><br>
</p>
<p>Abstract:</p>
<p>In this seminar, I will present recent developments in the field of machine-learning applied to stellar spectra in the context of large scale spectroscopic surveys, such as Gaia-ESO and RAVE. I will particularly focus on Gaia DR3, which provided the community
with approximately one million RVS spectra covering the CaII triplet region. One third of the spectra have a signal-to-noise ratio from 15 to 25 per pixel. I will demonstrate that precise parametrization can be achieved for such a type of dataset by using
machine-learning and the full power of the Gaia data. I will present a new approach in the form of a hybrid Convolutional Neural-Network (CNN) to derive atmospheric parameters (Teff, log(g), and [M/H]) and chemical abundances ([Fe/H] and [α/M]).</p>
<p>Our CNN is designed to effectively combine the Gaia DR3 RVS spectra, photometry (G, Bp, Rp), parallaxes, and XP coefficients and is able to extract additional information from non-spectral inputs to supplement the limited spectral coverage of the RVS spectra.
We manage to characterize the [α/M] − [M/H] bimodality from the inner regions to the outer part of the Milky Way, which has never been characterized using RVS spectra or similar datasets. I will also discuss the benefits of using CNNs for future large scale
spectroscopic surveys such as 4MOST.</p>
<p><br>
</p>
<p>———————————</p>
<p><br>
</p>
<p>We look forward to seeing many of you there. Thanks!</p>
<p><br>
</p>
<p>Adur and Adélaïde</p>
<p><br>
</p>
<p>Upcoming Events:</p>
<p><br>
</p>
<p>2023-10-20 Adam Rains</p>
<p>2023-10-27 Maryam Saberi</p>
<p>2023-11-03 TBA</p>
<p>2023-11-10 Alberto Saldana Lopez</p>
<p>2023-11-17 TBA</p>
<p>2023-11-24 Aline Chu</p>
<p>2023-12-01 Heitor Ernandes</p>
<p>2023-12-08 Quentin Perceval Xavier Pognan</p>
<p><br>
</p>
<p>You can subscribe to the seminars calendar in: https://ttt.astro.su.se/ical/astro-seminars.ics</p>
<p>(Please note that calendar can be subject to updates)</p>
<p><br>
</p>
<p>———————————</p>
<p>Join Zoom Meeting:</p>
<p>https://stockholmuniversity.zoom.us/s/61002076352</p>
<p>Meeting ID: 610 0207 6352</p>
<div><br>
</div>
</div>
</body>
</html>