Tootfinder

Predicting the resolved CO emission of $z=1-3$ star-forming galaxies
R. Anirudh, Melanie Kaasinen, Gerg\"o Popping, Desika Narayanan, Karolina Garcia, Dariannette Valentin-Martinez
https://arxiv.org/abs/2506.13899

Predicting the resolved CO emission of $z=1-3$ star-forming galaxies
(Abridged) Resolved observations of the CO emission from $z=1-3$ star-forming galaxies are becoming increasingly common, with new high-resolution surveys on the horizon. We aim to inform the interpretation of this resolved CO emission by creating synthetic observations and testing to what extent routinely observed CO transitions can be used to trace H$_2$ across galaxy disks. To this end, we extract $z=1-3$ massive star-forming galaxies (on and above the main sequence) from the SIMBA cosmologic…

Identifying Merger-Driven Long Gamma-Ray Bursts based on Machine Learning
Si-Yuan Zhu, Hui-Ying Deng, Fu-Wen Zhang, Qian-Zi Mo, Pak-Hin Thomas Tam
https://arxiv.org/abs/2506.08675

Identifying Merger-Driven Long Gamma-Ray Bursts based on Machine Learning
Gamma-ray bursts (GRBs) are classified as Type I GRBs originated from compact binary mergers and Type II GRBs originated from massive collapsars. While Type I GRBs are typically shorter than 2 seconds, recent observations suggest that some extend to tens of seconds, forming a potential subclass, Type IL GRBs. However, apart from their association with kilonovae, so far no rapid identification is possible. Given the uncertainties and limitations of optical and infrared afterglow observations, an…

SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics
Mustafa Shukor, Dana Aubakirova, Francesco Capuano, Pepijn Kooijmans, Steven Palma, Adil Zouitine, Michel Aractingi, Caroline Pascal, Martino Russi, Andres Marafioti, Simon Alibert, Matthieu Cord, Thomas Wolf, Remi Cadene
https://arxiv.org/abs/2506.018…

SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics
Vision-language models (VLMs) pretrained on large-scale multimodal datasets encode rich visual and linguistic knowledge, making them a strong foundation for robotics. Rather than training robotic policies from scratch, recent approaches adapt VLMs into vision-language-action (VLA) models that enable natural language-driven perception and control. However, existing VLAs are typically massive--often with billions of parameters--leading to high training costs and limited real-world deployability. …

This https://arxiv.org/abs/2505.22727 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

The galaxy-halo connection of disc galaxies over six orders of magnitude in stellar mass
(Abridged) The relations between stellar ($M_\ast$), gas ($M_{\rm gas}$), baryonic ($M_{\rm bar} = M_\ast + M_{\rm gas}$), and dark matter halo mass ($M_{200}$) provide unique constraints on galaxy formation and cosmology. The shape of the relations constrains how galaxies regulate their growth through gas accretion, star formation, and feedback; their scatter probes the stochasticity of galaxy assembly. Here, we assemble a sample of 49 nearby gas-rich dwarf and massive disc galaxies with unm…

The galaxy-halo connection of disc galaxies over six orders of magnitude in stellar mass
Pavel E. Mancera Pi\~na, Justin I. Read, Stacy Kim, Antonino Marasco, Jos\'e A. Benavides, Marcin Glowacki, Gabriele Pezzulli, Claudia del P. Lagos
https://arxiv.org/abs/2505.22727

The galaxy-halo connection of disc galaxies over six orders of magnitude in stellar mass
(Abridged) The relations between stellar ($M_\ast$), gas ($M_{\rm gas}$), baryonic ($M_{\rm bar} = M_\ast + M_{\rm gas}$), and dark matter halo mass ($M_{200}$) provide unique constraints on galaxy formation and cosmology. The shape of the relations constrains how galaxies regulate their growth through gas accretion, star formation, and feedback; their scatter probes the stochasticity of galaxy assembly. Here, we assemble a sample of 49 nearby gas-rich dwarf and massive disc galaxies with unm…