Searcharxiv⌕ Search

arXiv · 2610.22246

Bridging the gap between stars and planets

Abstract

The formation mechanisms of brown dwarfs (BDs) and free-floating planets (FFPs) are poorly constrained. The two main competing scenarios include the star-like scenario, where these objects correspond to the least massive gravitationally unstable fragments of molecular clouds, and the planet-like scenario, where these objects form in a circumstellar disk and are subsequently ejected after a dynamical interaction. A new generation millimeter interferometer, such as ALMA2040, is required to disentangle the relevance of these scenarios. For this, ALMA2040 must provide the appropriate performance to explore BD/FFP formation from the cloud scale (~10 pc) down to the disk scale (~10 au). At cloud scales, ALMA2040 must detect a ~1 Mjup embedded FFP in the outer Galaxy (~10 kpc) in 1 h, or the same object in nearby clouds (<~ 400 pc) in about tens of seconds so that large-scale surveys of entire clouds can be performed. This would provide key censuses to compare the results with the predictions from each scenario. At disk scales, ALMA2040 must detect CO outflows from first hydrostatic cores in the substellar regime and potential molecular outflows from ~1 Mjup FFPs in nearby clouds, both with specific predictions in the star-like scenario. In addition, the planet-like scenario predicts a disk mass-to-star mass ratio for BDs larger than the star-like case, and testing this requires detecting disks of ~2 Earth masses around young BDs. Furthermore, the predicted emission for the star-like case of complex organic molecules in proto-BDs at 400 pc must be detected with ALMA2040, requiring a factor of 10 increase in line sensitivity at a spatial resolution of 5-10 au. Finally, ALMA2040 must detect Earth-like protoplanets around young BDs at 400 pc, in order to provide key observational constraints on the radial drift behaviour and planet formation mechanisms in the substellar regime.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Aina Palau, Dimitris Stamatellos, Matthew Bate, Manuel Zamora-Avilés, Gisela Ortiz-León, O. Sergio Rojas-García, Mariana Gómez-Cuartas, Nuria Huélamo, Álvaro Ribas, Philippe André, Carlos Carrasco-González, Eduardo Martín, Stella Tsilia, Myriam Benisty, Pamela Freeman, Emmanuel Caux, Audrey Coutens, Ryohei Kawabe, Pavel Kroupa, Aleks Scholz, Richard Parker, Amelia Bayo, Alejandro Santamaría-Miranda, Swastik Chowbay, Patricia Luppe, Kengo Tomida, José Juan González-Avilés, Álvaro Sánchez-Monge, Luis Felipe Rodríguez, Itziar de Gregorio-Monsalvo, Stefano Facchini, Ian Czekala, Fumitaka Nakamura, Lukasz Tychoniec, Genaro Suárez, Ngoc Phan-Bao, Adnan-Ali Ahmad, Benoit Commerçon, Ricardo Hernández-Garnica, Aurora Sicilia-Aguilar, Claudia Toci, Paola Pinilla, Quentin Kral, Yinuo Han, Matthew De Furio, William DeRocco, Catherine Walsh, Luca Matrà, Hervé Bouy, Adam Burgasser, Catherine Espaillat, Daniel Harsono, Masahiro N. Machida, Koraljka Muzic, Rakesh Pandey, Ignasi Ribas, Carlos Román-Zúñiga, Jeremy Smallwood, Anthony Whitworth, Luis Zapata, David Blank. 2026-10-15. Bridging the gap between stars and planets. https://arxiv.org/abs/2610.22246

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

KEEP EXPLORING

Related papers

Using 23 Years of ACS/SBC Data to Understand Backgrounds: Significant Reductions in Expected Background Levels

We have used 23 years of Hubble Space Telescope ACS/SBC data to study what background levels are encountered in practice and how much they vary. The backgrounds vary considerably, with F115LP, F122M, F125LP, PR110L, and PR130L all showing over an order of magnitude of variation in background between observations, apparently due to changes in airglow. The F150LP and F165LP filters, which are dominated by dark rate, not airglow, exhibit a far smaller variation in backgrounds. For the filters where the background is generally dominated by airglow, the backgrounds measured from the data are significantly lower than what the ETC predicts (as of ETC v33.2). The ETC predictions for `average' airglow are greater than the median of our measured background values by factors of 2.51, 2.64, 105, and 3.64, for F115LP, F122M, F125LP, and F140LP, respectively. A preliminary analysis suggests this could be due to certain OI airglow lines usually being fainter than expected by the ETC. With reduced reduced background levels, the shorter-wavelength SBC filters can conduct background-limited observations much more rapidly than had previously been expected. As of ETC v34.1, a new option will be included for SBC calculations, allowing users to employ empirical background percentiles to estimate required exposure times.

astro-ph.IM↗

Using 23 Years of ACS/SBC Data to Understand Backgrounds: Explaining & Predicting Background Variations

Recent analysis of 23 years of Hubble Space Telescope ACS/SBC data has shown that background levels can vary considerably between observations, with most filters showing over an order of magnitude variation. For the shorter-wavelength filters, the background is understood to be dominated by airglow; however, what precisely drives background variations is not well constrained for any filter. Here, we explore the causes of the background variation. Using over 8,000 archival SBC observations, we developed a machine learning model that can accurately predict the background for an observation, based upon a set of 23 observational parameters. This model indicates that, depending on filter, the SBC background is generally dominated by Solar elevation, Solar separation angle, Earth limb angle of observation, SBC temperature, and target Galactic latitude.

astro-ph.IM↗

Beyond the BLUE I: the advantage ceiling - how much can any estimator beat the matched filter in mm/submm survey data?

Papers claiming that convolutional neural networks outperform the matched filter are now common in astronomy. Since that filter is the best linear unbiased estimator (BLUE) for any noise of given covariance, and the minimum-variance unbiased estimator under Gaussian noise, such a claim requires that the noise covariance varies from image to image or the noise is non-Gaussian. We introduce the advantage ceiling eta >= 1, which quantifies both cases: the Fisher information for the amplitude in units of the matched filter's, computable from noise-only simulations before any network is trained, and a bound on the variance of any estimator unbiased at each true amplitude. We compute eta for a suite of millimeter/submillimeter survey noise models and for compact and extended source templates, using a variational score-matching ladder whose rungs are estimator classes of increasing statistical order, from linear to convolutional. With an instrumental white-noise floor, Gaussian noise returns eta = 0.94-1.06 (+-0.04); covariance mixtures give exact ceilings of 10.2 (spectral tilt, extended source), 2.1 (leaked components) and 1.1-1.8 otherwise; residual artifacts give eta <= 1.09. Source confusion gives the largest advantage, eta >= 3.8 (extended) and >= 2.4 (compact). Three unmodelled channels - a zeroed DC mode, a missing white floor and floating-point rounding - can manufacture spurious advantages of up to two orders of magnitude. A ResNet regressor is 6-15% less efficient than the matched filter on Gaussian noise once prior shrinkage is divided out, and realizes a factor 1.8 of the 10.2 available on the spectral-tilt mixture; a companion paper tests such regressors against these ceilings.

astro-ph.IM↗