new run happened

index.html

@@ -1,16 +1,5 @@
-search_query=cat:astro-ph.*+AND+lastUpdatedDate:[202406222000+TO+202406282000]&start=0&max_results=5000
-<h1>New astro-ph.* submissions cross listed on stat.*, cs.AI, cs.LG, physics.data-an staritng 202406222000 and ending 202406282000</h1>Feed last updated: 2024-06-28T00:00:00-04:00<a href="http://arxiv.org/pdf/2406.17057v1"><h2>At First Sight: Zero-Shot Classification of Astronomical Images with
-  Large Multimodal Models</h2></a>Authors:  Dimitrios Tanoglidis, Bhuvnesh Jain</br>Comments: 5 pages, 3 images. Prepared for submission to RNAAS</br>Primary Category: astro-ph.IM</br>All Categories: astro-ph.IM, astro-ph.GA, cs.AI</br><p>Vision-Language multimodal Models (VLMs) offer the possibility for zero-shot
-classification in astronomy: i.e. classification via natural language prompts,
-with no training. We investigate two models, GPT-4o and LLaVA-NeXT, for
-zero-shot classification of low-surface brightness galaxies and artifacts, as
-well as morphological classification of galaxies. We show that with natural
-language prompts these models achieved significant accuracy (above 80 percent
-typically) without additional training/fine tuning. We discuss areas that
-require improvement, especially for LLaVA-NeXT, which is an open source model.
-Our findings aim to motivate the astronomical community to consider VLMs as a
-powerful tool for both research and pedagogy, with the prospect that future
-custom-built or fine-tuned models could perform better.</p></br><a href="http://arxiv.org/pdf/2406.18175v1"><h2>Galaxy spectroscopy without spectra: Galaxy properties from photometric
+search_query=cat:astro-ph.*+AND+lastUpdatedDate:[202406252000+TO+202407012000]&start=0&max_results=5000
+<h1>New astro-ph.* submissions cross listed on physics.data-an, cs.AI, stat.*, cs.LG staritng 202406252000 and ending 202407012000</h1>Feed last updated: 2024-07-01T00:00:00-04:00<a href="http://arxiv.org/pdf/2406.18175v1"><h2>Galaxy spectroscopy without spectra: Galaxy properties from photometric
   images with conditional diffusion models</h2></a>Authors:  Lars Doorenbos, Eva Sextl, Kevin Heng, Stefano Cavuoti, Massimo Brescia, Olena Torbaniuk, Giuseppe Longo, Raphael Sznitman, Pablo Márquez-Neila</br>Comments: Code is available here:
   https://github.com/LarsDoorenbos/generate-spectra</br>Primary Category: astro-ph.GA</br>All Categories: astro-ph.GA, astro-ph.IM, cs.AI</br><p>Modern spectroscopic surveys can only target a small fraction of the vast
 amount of photometrically cataloged sources in wide-field surveys. Here, we
@@ -33,57 +22,4 @@ <h1>New astro-ph.* submissions cross listed on stat.*, cs.AI, cs.LG, physics.dat
 up to an accuracy of 82%. With our method, scientifically interesting galaxy
 properties, normally requiring spectroscopic inputs, can be obtained in future
 data sets from large-scale photometric surveys alone. The spectra prediction
-via AI can further assist in creating realistic mock catalogs.</p></br><a href="http://arxiv.org/pdf/2406.17316v1"><h2>A review of unsupervised learning in astronomy</h2></a>Authors:  Sotiria Fotopoulou</br>Comments: 30 pages, 6 figures. Invited contribution to special issue in
-  Astronomy & Computing</br>Primary Category: astro-ph.IM</br>All Categories: astro-ph.IM, cs.LG</br><p>This review summarizes popular unsupervised learning methods, and gives an
-overview of their past, current, and future uses in astronomy. Unsupervised
-learning aims to organise the information content of a dataset, in such a way
-that knowledge can be extracted. Traditionally this has been achieved through
-dimensionality reduction techniques that aid the ranking of a dataset, for
-example through principal component analysis or by using auto-encoders, or
-simpler visualisation of a high dimensional space, for example through the use
-of a self organising map. Other desirable properties of unsupervised learning
-include the identification of clusters, i.e. groups of similar objects, which
-has traditionally been achieved by the k-means algorithm and more recently
-through density-based clustering such as HDBSCAN. More recently, complex
-frameworks have emerged, that chain together dimensionality reduction and
-clustering methods. However, no dataset is fully unknown. Thus, nowadays a lot
-of research has been directed towards self-supervised and semi-supervised
-methods that stand to gain from both supervised and unsupervised learning.</p></br><a href="http://arxiv.org/pdf/2406.17323v1"><h2>XAMI -- A Benchmark Dataset for Artefact Detection in XMM-Newton Optical
-  Images</h2></a>Authors:  Elisabeta-Iulia Dima, Pablo Gómez, Sandor Kruk, Peter Kretschmar, Simon Rosen, Călin-Adrian Popa</br>Comments: submitted to SPAICE 2024</br>Primary Category: cs.CV</br>All Categories: cs.CV, astro-ph.IM, cs.LG</br><p>Reflected or scattered light produce artefacts in astronomical observations
-that can negatively impact the scientific study. Hence, automated detection of
-these artefacts is highly beneficial, especially with the increasing amounts of
-data gathered. Machine learning methods are well-suited to this problem, but
-currently there is a lack of annotated data to train such approaches to detect
-artefacts in astronomical observations. In this work, we present a dataset of
-images from the XMM-Newton space telescope Optical Monitoring camera showing
-different types of artefacts. We hand-annotated a sample of 1000 images with
-artefacts which we use to train automated ML methods. We further demonstrate
-techniques tailored for accurate detection and masking of artefacts using
-instance segmentation. We adopt a hybrid approach, combining knowledge from
-both convolutional neural networks (CNNs) and transformer-based models and use
-their advantages in segmentation. The presented method and dataset will advance
-artefact detection in astronomical observations by providing a reproducible
-baseline. All code and data are made available
-(https://github.com/ESA-Datalabs/XAMI-model and
-https://github.com/ESA-Datalabs/XAMI-dataset).</p></br><a href="http://arxiv.org/pdf/2406.16730v1"><h2>Convolutional neural network for Lyman break galaxies classification and
-  redshift regression in DESI (Dark Energy Spectroscopic Instrument)</h2></a>Authors:  Julien Taran</br>Comments: No comment found</br>Primary Category: astro-ph.CO</br>All Categories: astro-ph.CO, cs.AI</br><p>DESI is a groundbreaking international project to observe more than 40
-million quasars and galaxies over a 5-year period to create a 3D map of the
-sky. This map will enable us to probe multiple aspects of cosmology, from dark
-energy to neutrino mass. We are focusing here on one type of object observed by
-DESI, the Lyman Break Galaxies (LBGs). The aim is to use their spectra to
-determine whether they are indeed LBGs, and if so, to determine their distance
-from the Earth using a phenomenon called redshift. This will enable us to place
-these galaxies on the DESI 3D map.
-  The aim is therefore to develop a convolutional neural network (CNN) inspired
-by QuasarNET (See arXiv:1808.09955), performing simultaneously a classification
-(LBG type or not) and a regression task (determine the redshift of the LBGs).
-Initially, data augmentation techniques such as shifting the spectra in
-wavelengths, adding noise to the spectra, or adding synthetic spectra were used
-to increase the model training dataset from 3,019 data to over 66,000. In a
-second phase, modifications to the QuasarNET architecture, notably through
-transfer learning and hyperparameter tuning with Bayesian optimization, boosted
-model performance.
-  Gains of up to 26% were achieved on the Purity/Efficiency curve, which is
-used to evaluate model performance, particularly in areas with interesting
-redshifts, at low (around 2) and high (around 4) redshifts. The best model
-obtained an average score of 94%, compared with 75% for the initial model.</p></br>
+via AI can further assist in creating realistic mock catalogs.</p></br>