Pablo Villanueva Domingo

PhD in Physics | Cosmologist | Deep Learning Scientist at @CVC_UAB and @carlasimulator

[Pablo Villanueva Domingo @CosmoPabloVD]

Here is my recent talk at IFIC on deep learning applications in cosmology!
https://t.co/7bAYNp21yn

[CARLA Simulator @carlasimulator]

CARLA version 0.10.0 has arrived with a huge upgrade to Unreal Engine 5.5. The upgrade allows CARLA to render detailed high-poly environments with incredible lighting through Unreal Engine's Lumen and Nanite technologies. Check out the release notes: https://t.co/qXUcSx4R4Q

[Pablo Villanueva Domingo @CosmoPabloVD]

@TingAstro @wfastronomer @zexpe I started around 2016 using Fortran and Mathematica, I moved a few years later to full Python.

[Pablo Villanueva Domingo @CosmoPabloVD]

@DarkCosmologist Funny that the "spirit of European unity" gauge field is not coupled to any field... 😂

[CARLA Simulator @carlasimulator]

CARLA 0.9.15 is now released!!

CARLA now supports SimReady assets through the #NVIDIAOmniverse Unreal Engine plugin.

CARLA 0.9.15 brings two great new maps, a procedural map generation tool, an HGV vehicle and more!

Read all about it here: https://t.co/XfDlt9HE7U

[Pablo Villanueva Domingo @CosmoPabloVD]

@annamporredon @CSIC Muchas gracias! ☺️

[Pablo Villanueva Domingo @CosmoPabloVD]

I have received a "2021 relevant PhD thesis" prize from @CSIC ! Thanks to all that helped me to make it possible!

He recibido un premio del CSIC a tesis doctoral relevante 2021! Gracias a todos los que me ayudaron a hacerlo posible!

[Pablo Villanueva Domingo @CosmoPabloVD]

@AvelinoQuantum @CSIC Gracias @AvelinoQuantum ! ☺️

[CSIC @CSIC]

El CSIC ha entregado hoy los Premios Margarita Salas a Mejor trayectoria en supervisión de personal investigador y Premio Tesis doctoral más relevante de 2021 🥇
¡Enhorabuena a todos!
Puedes ver el acto aquí ▶️
https://t.co/AhSoCCwhiU

[natalidesanti @natalidesanti]

For a video summarizing the results, see https://t.co/ARHvsAkakB 😊

[natalidesanti @natalidesanti]

Paper day: https://t.co/4Zzdguexl5. We train GNNs to perform field-level likelihood-free inference using galaxy catalogs from @camels_project. Our models have no scale cutoff, achieved a precision of ~12% when inferring Ωm, and it is robust for 5 != subgrid models! Check it out!

[Pablo Villanueva Domingo @CosmoPabloVD]

@BeyondStdJorge Este molt recent en nature de biologia potser et serveix:
https://t.co/5tGgvFJWWT

[Nature Portfolio @NaturePortfolio]

A @NatureComms paper suggests that mammals that live in groups may generally live longer than members of solitary species. https://t.co/G4E4ShYbyB

[Chaitanya K. Joshi @chaitjo]

🚀Excited+nervous to share our latest work on understanding geometric GNNs for biomolecules, materials, etc.

"On the Expressive Power of Geometric GNNs" with @crisbodnar @SimMat20 @TacoCohen @pl219_Cambridge

PDF: https://t.co/ywQCmMY0nG
Code: https://t.co/h4soieuW1O
Findings👇

[Pablo Villanueva Domingo @CosmoPabloVD]

@a51776901 @rishabh16_ @PetarV_93 When dealing with sparse and irregular data, point clouds... Examples are publications in a citation network, atoms in molecules or galaxies in the universe.

[higgsinocat @higgsinocat]

Current and future neutrino limits on the abundance of primordial black holes. (arXiv:2203.14979v2 [hep-ph] UPDATED) relevance:100% https://t.co/K6vxWwYMQ9 #darkmatter @carambolos @CosmoPabloVD

[Pablo Villanueva Domingo @CosmoPabloVD]

Happy to announce our new paper on robust cosmological inference in halo catalogs with deep learning. Project led by Helen Shao, undergraduate student at @PU_Astro, and @paco_astro.

[Francisco Villaescusa-Navarro @paco_astro]

Really proud of our latest paper: "Robust field-level inference with dark matter halos". We study the robustness of field-level inference to differences in N-body codes, hydrodynamics, and astrophysics using thousands of N-body and hydrodynamic simulations. We have developed a

[higgsinocat @higgsinocat]

Robust field-level inference with dark matter halos. (arXiv:2209.06843v1 [https://t.co/brMgLCOctK]) relevance:100% https://t.co/l2Ti5M2WW3 #darkmatter @paco_astro @CosmoPabloVD

[higgsinocat @higgsinocat]

Boosting the 21 cm forest signals by the clumpy substructures. (arXiv:2209.01305v1 [https://t.co/brMgLCNEEc]) relevance:31% https://t.co/XzCCF8G9V3 #darkmatter @CosmoPabloVD

[Pablo Villanueva Domingo @CosmoPabloVD]

Graph neural networks become an ideal tool for these tasks, since they are specially suited to deal with sparse and irregular data, like galaxy catalogues, and they are not limited by a minimum scale.

See more info in the paper https://t.co/Vkmdg8ljal ! (4/4)

[Pablo Villanueva Domingo @CosmoPabloVD]

Happy to announce a new paper with @paco_astro !

We apply deep learning methods to infer clustering and cosmological parameters on galaxy catalogues from CAMELS simulations @camels_project. (1/4)

https://t.co/Vkmdg8ljal

[Pablo Villanueva Domingo @CosmoPabloVD]

Our models can also be trained to predict the power spectrum of a galaxy catalogue with a 3% accuracy. This illustrates how graph neural networks can learn clustering information. (3/4)