<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Manuscript |</title><link>https://lelain.net/publication_types/manuscript/</link><atom:link href="https://lelain.net/publication_types/manuscript/index.xml" rel="self" type="application/rss+xml"/><description>Manuscript</description><generator>HugoBlox Kit (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Mon, 26 Oct 2026 00:00:00 +0000</lastBuildDate><image><url>https://lelain.net/media/icon.svg</url><title>Manuscript</title><link>https://lelain.net/publication_types/manuscript/</link></image><item><title>NORVIS: a portable broadband imaging spectrograph for auroral observations</title><link>https://lelain.net/publications/norvis/</link><pubDate>Mon, 26 Oct 2026 00:00:00 +0000</pubDate><guid>https://lelain.net/publications/norvis/</guid><description>&lt;p&gt;Preprint posted on EGUsphere on October 26, 2026, and under open review for &lt;em&gt;Geoscientific Instrumentation, Methods and Data Systems&lt;/em&gt;.&lt;/p&gt;
&lt;p&gt;Co-authored paper led by the Royal Belgian Institute for Space Aeronomy together with the
team. It describes the instrument whose observations feed the auroral spectra classification work presented at
and in the
.&lt;/p&gt;</description></item><item><title>Self-Supervised Representation Learning: From Spectral Foundation Models to Auroral Emission Spectra</title><link>https://lelain.net/publications/aurora-ssl/</link><pubDate>Fri, 25 Sep 2026 00:00:00 +0000</pubDate><guid>https://lelain.net/publications/aurora-ssl/</guid><description>&lt;p&gt;Preprint posted on arXiv on September 25, 2026, and submitted to IEEE ICASSP 2027. A joint work with the Royal Belgian Institute for Space Aeronomy, based on the ASIS archive of auroral spectra acquired in Skibotn, Norway.&lt;/p&gt;
&lt;p&gt;This work follows the supervised study presented at
, where a 1D Vision Transformer was trained on 719 expert-annotated spectra. Here, the unlabelled part of the archive (223,000 spectra) is used to pretrain the same architecture with a masked autoencoder, which raises the macro-AP from 77.8 to 88.5 on the same benchmark and shows that the learned representation encodes the emission-line ratios used by physicists, without being told about them.&lt;/p&gt;</description></item><item><title>When foundation models meet astronomical data</title><link>https://lelain.net/publications/fm-astro-journal/</link><pubDate>Wed, 10 Jun 2026 00:00:00 +0000</pubDate><guid>https://lelain.net/publications/fm-astro-journal/</guid><description>&lt;p&gt;Journal article currently under review. The preprint is available on SSRN.&lt;/p&gt;
&lt;p&gt;This paper is the extended version of the benchmark first presented at
and
, and of the DINOv2 study published at
. It compares head-only and full fine-tuning of visual foundation models against modern CNNs on Galaxy10 DECaLS, and discusses what this implies for the use of off-the-shelf foundation models in astronomy. Fine-tuned models are released on
.&lt;/p&gt;</description></item><item><title>Orbital parameters of the bright binary alpha Draconis based on amateur spectra from the STAROS database</title><link>https://lelain.net/publications/alpha-draconis/</link><pubDate>Sat, 01 Nov 2025 00:00:00 +0000</pubDate><guid>https://lelain.net/publications/alpha-draconis/</guid><description/></item></channel></rss>