(r19) InterOpNov2023Apps < IVOA

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---+ Applications Working Group : November 2023, Online

* [[InterOpNov2023][back to main programme page]]]*
---++ Schedule
| *Applications Session 1: Saturday Nov 11 2023 @ 14:00-15:30 (Session #1)* ||||
| *Speaker* | *Title* | *Materials* | *Time* |
| Jonathan Normand | <p> *Solar system Open Database Network (!SsODNet)* </p> <p>The sample of Solar system objects (asteroids, comets) has dramatically increased over the last decade. The number of measured properties (e.g. diameter, mass, etc.) has expanded even more quickly. We aim at offering a practical solution to the identification of Solar system objects from any of their multiple designation. We also aim at compiling and rationalizing the properties to provide an easy access to them. During Victoria 2018 Interop I presented the identification of Solar system objects and now I would like to focus on how to access to all these properties.</p> | [[%ATTACHURL%/IVOA_2023_Tucson-SsODNet.pdf][pdf]] | 10+3 |
| Chiara Marmo | <p> *Planetary World Coordinate System in Astropy* </p> <p>Different research communities are involved in planetary coordinate standardization: astronomers, geologists, plasma physics researchers,. In the framework of the !EuroplanetH2024 European grant [1] some work has been done to define planetary standards for FITS World Coordinate System [2], in connection with Geographical Information System standards. To improve interoperability between those worlds we implemented the planetary WCS description in Astropy.<br />This presentation describes the related new features available in Astropy 6.0:</p> <ul> <li>the possibility to define a Geodetic Coordinate Representation on a custom spheroid using its equatorial radius and flattening</li> <li>the definition of a new Bodycentric coordinate representation for custom spheroids.</li> <li>the possibility to read and write planetary WCS keywords to define a body-fixed planetary reference frame.</li> </ul> | [[%ATTACHURL%/Marmo-interop2023.pdf][pdf]] | 8+3 mins  |
| Pierre Fernique | <p> *Position recognition using the current VOTable standard - Aladin Desktop implementation* </p> <p>We will briefly describe how Aladin Desktop calculates the positions of objects described in a VOTable using COOSYS and XML references.</p> | [[%ATTACHURL%/Position_recognition_in_VOTable_-_Interop_nov_2023_-_Fernique.pdf][pdf]]  | 5+3 |
| Laurent Michel | <p> *Mivot approach for the Epoch propagation* </p> <p>We will show how the [MIVOT + model] pattern can resolve the Epoch propagation case in a convenient way.<br />We will first give a brief reminder on both MIVOT standard and MANGO draft.<br />After that, we will present our !AstroPy /PyVO implementation.</p> | | 16+16 |
| Tom Donaldson | <p> *VOTable 1.5 Status* </p> <p>Status of VOTable 1.5 before moving to RFC.</p> | | 12+14 |

---++ Schedule
| *Applications Session 2: Sunday Nov 12 2023 @ 9:00-10:30 (Session #2)* ||||
| *Speaker* | *Title* | *Materials* | *Time* |
| Markus Demleitner | <p> *Global image discovery in !PyVO* </ps></p> <p>Global dataset discovery is one of the promises of the VO that is still very hard to do for normal users. I am currently writing code to provide a pyVO-based API for the particularly hairy case of image discovery (https://github.com/astropy/pyvo/pull/470). Problems to be solved include: (a) dealing with the three available protocols (SIA1, SIA2, Obscore); (b) keeping the number of services to be queried low while accomodating services still not declaring !STC coverage; (c) providing a sensible API to keep track of unresponsive or broken services without confusing casual usage; (d) keeping the number of dupicates low. This talk will discuss the state of affairs – and of course invite participation.</p> | [[%ATTACHURL%/twoup.pdf][Notes]] [[%ATTACHURL%/pop.pdf][slides]] | 12+3 |
| Pierre Fernique | <p> *The IVOA !HiPS network: status and evolutions* </p> <p>We will present the state of the art of the IVOA !HiPS network at the end of 2023: the evolution of content, servers and tools. We will also address the weak points identified and we will suggest some solutions.</p> | | 12+3 |
| Mark Taylor | <p> *Authentication in TOPCAT* </p> <p>The most recent release of TOPCAT/STILTS can work with services that implement the "SSO_next" draft authentication proposal. That means TOPCAT users can log in within the application to access TAP and other (e.g. !DataLink) services that declare their authentication arrangements as described by SSO_next. Currently known services that this covers include !DaCHS, CADC, and soon ESDC. The library that enables this is suitable for use by third-party java applications.</p> | | 12+3 |
| Adrian Damian | <p> *Using s_region in Obscore* </p> <p>I'll provide a brief practitioner's experience on using `s_region` in ObsCore with ALMA pointing out missing/outdated standards.</p> | | 12+3 |
| Pierre Le Sidaner | <p> *Geospatial VESPA portal, alternative search tool in EPN-TAP services using HIPS and MOC* </p> <p>an approach based on harvesting metadata in a !NoSQL database, allowing natural language interogation and simplified selections. We have added a spatial selection function</p> | | 12+3 |
| Kai Polsterer | <p> *HiPSter: Using !HiPS tiling to allow for explorative access to simulations* </p> <p>Simulations are the best, and often the only, approximation to experimental laboratories in Astrophysics. However, the complexity and richness of their outputs severely limits the interpretability of their predictions. We describe a new conceptual approach to obtaining useful scientific insights from a broad range of astrophysical simulations. These methods can be applied to state-of-the-art simulations and will be essential to automate the data exploration and analysis of the next-generation exascale simulations and the extreme data challenges they will present. Our concept is based on applying the latest advances in unsupervised deep learning algorithms to efficiently represent the multidimensional datasets produced by Astrophysics simulations and to learn compact but accurate representations of the data in a low-dimensional manifold that naturally describes the data in an optimal feature space. The data can seemingly be projected onto this latent space for interactive inspection, visual interpretation, and quantitative analysis, including the option of deriving symbolic expressions to build interpretable models. We present a working prototype of the pipeline using an autoencoder trained on galaxy images from SDSS (or equivalently simulated galaxies) as well as the Illustris simulations, to produce a natural ‘Hubble tuning fork’ similarity space that can be visualized interactively on the surface of a sphere by exploiting the power of !HiPS tilings in !AladinLite.</p> | | 12+3 |
Position recognition using the current VOTable standard - Aladin Desktop implementation

   * [[%ATTACHURL%/IVOA_2023_Tucson-SsODNet.pdf][IVOA_2023_Tucson-SsODNet.pdf]]: ssodnet presentation

Attachments

Topic attachments
I	Attachment	History	Action	Size	Date	Who	Comment
pdf	Marmo-interop2023.pdf	r1	manage	383.6 K	2023-11-11 - 17:13	PierreLeSidaner
pdf	Position_recognition_in_VOTable_-_Interop_nov_2023_-_Fernique.pdf	r1	manage	1035.6 K	2023-11-11 - 16:11	PierreFernique	Position recognition using the current VOTable standard Aladin Desktop implementation
pdf	pop.pdf	r1	manage	127.1 K	2023-11-10 - 17:55	MarkusDemleitner
pdf	twoup.pdf	r1	manage	122.7 K	2023-11-10 - 17:54	MarkusDemleitner

Topic revision: r19 - 2023-11-11 - PierreLeSidaner

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