Subjects: Astronomy >> Astrophysical processes submitted time 2023-02-19
Daniel de Andres
Weiguang Cui
Florian Ruppin
Marco De Petris
Gustavo Yepes
Giulia Gianfagna
Ichraf Lahouli
Gianmarco Aversano
Romain Dupuis
Mahmoud Jarraya
Jesús Vega-Ferrero
Abstract： Galaxy clusters are useful laboratories to investigate the evolution of the Universe, and accurately measuring their total masses allows us to constrain important cosmological parameters. However, estimating mass from observations that use different methods and spectral bands introduces various systematic errors. This paper evaluates the use of a Convolutional Neural Network (CNN) to reliably and accurately infer the masses of galaxy clusters from the Compton-y parameter maps provided by the Planck satellite. The CNN is trained with mock images generated from hydrodynamic simulations of galaxy clusters, with Planck's observational limitations taken into account. We observe that the CNN approach is not subject to the usual observational assumptions, and so is not affected by the same biases. By applying the trained CNNs to the real Planck maps, we find cluster masses compatible with Planck measurements within a 15% bias. Finally, we show that this mass bias can be explained by the well known hydrostatic equilibrium assumption in Planck masses, and the different parameters in the Y500-M500 scaling laws. This work highlights that CNNs, supported by hydrodynamic simulations, are a promising and independent tool for estimating cluster masses with high accuracy, which can be extended to other surveys as well as to observations in other bands.
Peer Review Status:
Awaiting Review
Subjects: Astronomy >> Astrophysical processes submitted time 2023-02-19
Yuchen Guo
Shardha Jogee
Steven L. Finkelstein
Zilei Chen
Eden Wise
Micaela B. Bagley
Guillermo Barro
Stijn Wuyts
Dale D. Kocevski
Jeyhan S. Kartaltepe
Elizabeth J. McGrath
Henry C. Ferguson
Bahram Mobasher
Mauro Giavalisco
Ray A. Lucas
Jorge A. Zavala
Jennifer M. Lotz
Norman A. Grogin
Marc Huertas-Company
Jesús Vega-Ferrero
Abstract： Stellar bars are key drivers of secular evolution in galaxies and can be effectively studied using rest-frame near-infrared (NIR) images, which trace the underlying stellar mass and are less impacted by dust and star formation than rest-frame UV or optical images. We leverage the power of {\it{JWST}} CEERS NIRCam images to present the first quantitative identification and characterization of stellar bars at $z>1$ based on rest-frame NIR F444W images of high resolution (~1.3 kpc at z ~ 1-3). We identify stellar bars in these images using quantitative criteria based on ellipse fits. For this pilot study, we present six examples of robustly identified bars at $z>1$ with spectroscopic redshifts, including the two highest redshift bars at ~2.136 and 2.312 quantitatively identified and characterized to date. The stellar bars at $z$ ~ 1.1-2.3 presented in our study have projected semi-major axes of ~2.9-4.3 kpc and projected ellipticities of ~0.41-0.53 in the rest-frame NIR. The barred host galaxies have stellar masses ~ $ 1 \times 10^{10}$ to $2 \times 10^{11}$ $M_{\odot}$, star formation rates of ~ 21-295 $M_{\odot}$ yr$^{-1}$, and several have potential nearby companions. Our finding of bars at $z$ ~1.1-2.3 demonstrates the early onset of such instabilities and supports simulations where bars form early in massive dynamically cold disks. It also suggests that if these bars at lookback times of 8-10 Gyr survive out to present epochs, bar-driven secular processes may operate over a long time and have a significant impact on some galaxies by z ~ 0.
Peer Review Status:Awaiting Review
Subjects: Astronomy >> Astrophysical processes submitted time 2023-02-19
Jorge A. Zavala
Veronique Buat
Caitlin M. Casey
Denis Burgarella
Steven L. Finkelstein
Micaela B. Bagley
Laure Ciesla
Emanuele Daddi
Mark Dickinson
Henry C. Ferguson
Maximilien Franco
E. F. Jim'enez-Andrade
Jeyhan S. Kartaltepe
Anton M. Koekemoer
Aurélien Le Bail
E. J. Murphy
Casey Papovich
Sandro Tacchella
Stephen M. Wilkins
Itziar Aretxaga
Abstract： Lyman Break Galaxy (LBG) candidates at z>10 are rapidly being identified in JWST/NIRCam observations. Due to the (redshifted) break produced by neutral hydrogen absorption of rest-frame UV photons, these sources are expected to drop out in the bluer filters while being well detected in redder filters. However, here we show that dust-enshrouded star-forming galaxies at lower redshifts (z<7) may also mimic the near-infrared (near-IR) colors of z>10 LBGs, representing potential contaminants in LBG candidate samples. First, we analyze CEERS-DSFG-1, a NIRCam dropout undetected in the F115W and F150W filters but detected at longer wavelengths. Combining the JWST data with (sub)millimeter constraints, including deep NOEMA interferometric observations, we show that this source is a dusty star-forming galaxy (DSFG) at z~5.1. We also present a tentative 2.6sigma SCUBA-2 detection at 850um around a recently identified z~16 LBG candidate in the same field and show that, if the emission is real and associated with this candidate, the available photometry is consistent with a z~5 dusty galaxy with strong nebular emission lines despite its blue near-IR colors. Further observations on this candidate are imperative to mitigate the low confidence of this tentative submillimeter emission and its positional uncertainty. Our analysis shows that robust (sub)millimeter detections of NIRCam dropout galaxies likely imply z=4-6 redshift solutions, where the observed near-IR break would be the result of a strong rest-frame optical Balmer break combined with high dust attenuation and strong nebular line emission, rather than the rest-frame UV Lyman break. This provides evidence that DSFGs may contaminate searches for ultra high-redshift LBG candidates from JWST observations.
Peer Review Status:Awaiting Review
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