Star Formation across the Galaxy

Alvaro Sanchez-Monge; Henrik Beuther; Maryvonne Gerin; Frederique Motte; Gary Fuller; Maite Beltran; Elias Brinks; Gemma Busquet; Laura Colzi; Audrey Coutens; Timea Csengeri; Claudia Cyganowski; Maria Drozdovskaya; Manuel Fernandez-Lopez; Francesco Fontani; Roberto Galvan-Madrid; Adam Ginsburg; Josep Miquel Girart; Alvaro Hacar; Katharine Johnston; Jes K. Jorgensen; Kee-Tae Kim; Pamela Klaassen; Michael Kuffmeier; Qiuyi Luo; Sebastien Maret; Sergio Martin; Chiara Mininni; Kaho Morii; Alice Nucara; Thomas Nony; Fernando Olguin; Mayra Osorio; Aina Palau; Jaime E. Pineda; Victor M. Rivilla; Patricio Sanhueza; Miriam G. Santa-Maria; Amelia Stutz; Alessio Traficante; Fengwei Xu; Maxime Valeille-Manet; Qizhou Zhang; Birka Zimmermann

arXiv · 2610.21944

Star Formation across the Galaxy

View original source ↗Read source document ↗Export metadata ↓

Abstract

Star formation is both a multi-scale and highly dynamic process. It is regulated by the combination of gravity, turbulence, feedback and magnetic fields, which connect more than six orders of magnitude in scale: from large molecular clouds (several 10s to 100s pc in size) down to protoplanetary disks (a few 10s to 100s of astronomical units in size, or 10^{-5} to 10^{-4} pc). Resolving the processes of fragmentation, accretion, and feedback that result in the formation of stellar systems throughout all relevant scales, from clouds to disks, and across the entire range of different environments in the Milky Way is key to constructing a comprehensive understanding and a predictive model of the formation process of stars of all masses that populate not only our Galaxy but the wider Universe across cosmic times. With current astronomical facilities, we are only able to grasp the details of star formation in <5% of Galactic star-forming regions, being heavily biased towards those close to us. Thus, characterizing this multi-scale phenomenon at the necessary sensitivity and resolution across all Galactic environments requires the next generation (sub)millimeter-wavelength facility: ALMA2040.

Explore related subjects

astro-ph.IM astro-ph.GA

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Alvaro Sanchez-Monge, Henrik Beuther, Maryvonne Gerin, Frederique Motte, Gary Fuller, Maite Beltran, Elias Brinks, Gemma Busquet, Laura Colzi, Audrey Coutens, Timea Csengeri, Claudia Cyganowski, Maria Drozdovskaya, Manuel Fernandez-Lopez, Francesco Fontani, Roberto Galvan-Madrid, Adam Ginsburg, Josep Miquel Girart, Alvaro Hacar, Katharine Johnston, Jes K. Jorgensen, Kee-Tae Kim, Pamela Klaassen, Michael Kuffmeier, Qiuyi Luo, Sebastien Maret, Sergio Martin, Chiara Mininni, Kaho Morii, Alice Nucara, Thomas Nony, Fernando Olguin, Mayra Osorio, Aina Palau, Jaime E. Pineda, Victor M. Rivilla, Patricio Sanhueza, Miriam G. Santa-Maria, Amelia Stutz, Alessio Traficante, Fengwei Xu, Maxime Valeille-Manet, Qizhou Zhang, Birka Zimmermann. 2026-10-15. Star Formation across the Galaxy. https://arxiv.org/abs/2610.21944

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Validation of Satellite Lifetime Predictions at Leonid Space

This paper validates Leonid Space's satellite lifetime prediction pipeline through a backtesting campaign of 1283 deorbited satellites spanning six decades of spaceflight, showing that it accurately recreates their last year of flight. The pipeline combines a custom high-speed orbit propagator, ballistic coefficient estimation from on-orbit TLE data, atmospheric density corrections from data assimilation, custom space weather forecasts, and probabilistic Monte Carlo analysis. Validation proceeds in four steps that progressively remove the benefit of hindsight: starting from a "perfect knowledge" scenario using actual space weather data and ephemerides, we gradually replace historical data with contemporary predictions to arrive at a fully predictive scenario analogous to a deorbit prediction performed for a spacecraft flying today. These forecasts are scored using the continuously ranked probability score and root mean square calibration error, yielding a median one-year prediction accuracy of 2.4 days (0.7%) with perfect knowledge and 35.3 days (9.7%) in the fully predictive scenario. Solar flux forecasts contribute 75% of the variance in our predictions, dominating all other sources of error. To our knowledge, this is the first comprehensive backtesting of long-term satellite lifetime predictions that reports quantitative results for the fully predictive case, and it establishes a new framework for scoring solar cycle forecasts by grounding forecast accuracy in operationally-relevant satellite lifetime predictions.

astro-ph.IM↗

arXiv2026

A Solar Optical Clock

Precision spectroscopy underpins quantum sensing, atomic timekeeping, and the search for terrestrial-mass exoplanets. Finding an Earth-Sun analog requires fractional splitting of spectral features that rivals the precision of the best laboratory spectroscopy. This corresponds to resolution of a few parts in $10^5$ of a typical stellar spectral line, or a Doppler velocity precision of less than 10 cm s$^{-1}$. Only a few astrophysical spectrometers reach this regime, and only by dispersing starlight with large gratings and averaging thousands of lines. Here, we show that comparable precision can be achieved on a single solar line by leveraging techniques used in quantum sensors and optical clocks. A laser is frequency-locked to the 1565 nm solar Fe I line by coupling to a single mode of the solar thermal field and counted against an optical frequency comb referenced to the SI second, thus creating a solar optical clock. Operating at the fundamental photon-noise limit of thermal heterodyne spectroscopy, we achieve centroid precision of one part in $10^9$ of the optical frequency, or 30 cm s$^{-1}$ in radial velocity. We recover the Sun's five-minute acoustic oscillations and the expected Rossiter-McLaughlin signal during a solar eclipse. Combining these techniques from quantum sensing with a thirty-meter-class telescope could enable our demonstrated precision on the nearest Sun-like stars in a few hours. Multiplexing such absolute frequency measurements across lines with different magnetic sensitivity and formation depth would reduce averaging time and help disentangle stellar granulation and activity from the Doppler signature of terrestrial exoplanets, providing new approaches to address some of the most demanding measurements in astrophysical spectroscopy.

astro-ph.IM↗

arXiv2026

Data-driven characterisation of variable source light curves with multimodal representation learning

Surveys such as the Zwicky Transient Facility (ZTF) and the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) produce light curves for hundreds of millions of variables. Their characterisation traditionally relies on supervised classifiers with hand-crafted features and curated labels, limiting them to predefined taxonomies and individual surveys. We present a general, survey-agnostic framework that characterises variable sources without labelled training data, producing low-dimensional representations of light curve properties such as amplitude, periodicity, and asymmetry. A multimodal architecture combines two complementary views, a Transformer encoder on raw observations and a convolutional neural network on a generalised Lomb-Scargle periodogram. Each light curve is randomly split into two temporal halves as independent views, and the branches are trained jointly with VICReg to produce embeddings invariant across halves while keeping each dimension informative. We evaluate on 37 006 ZTF sources spanning 14 variable star classes in four broad physical categories. A linear classifier on the frozen 64-dimensional embeddings achieves 77.1% balanced accuracy for classes and 90.5% for broad categories. Combining both branches improves balanced accuracy by up to 14.9 percentage points over the best single modality, confirming their complementarity. Interpretable light curve properties are encoded on distinct dimensions, and within-class analysis reveals physical substructure unresolved by reference labels. Retraining on ZTF g and i bands without modification reproduces the main results, and concatenating multi-band embeddings improves balanced accuracy to 86.1%. The framework needs no labels, phase folding, or survey-specific preprocessing and applies directly to other surveys with different cadences and wavelength coverage.

astro-ph.IM↗