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Abstract / Description of output
We demonstrate that the inference of galaxy stellar masses via spectral energy distribution (SED) fitting techniques for galaxies formed in the first billion years after the Big Bang carries fundamental uncertainties owing to the loss of star formation history (SFH) information from the very first episodes of star formation in the integrated spectra of galaxies. While this early star formation can contribute substantially to the total stellar mass of high-redshift systems, ongoing star formation at the time of detection outshines the residual light from earlier bursts, hampering the determination of accurate stellar masses. As a result, order-of-magnitude uncertainties in stellar masses can be expected. We demonstrate this potential problem via direct numerical simulation of galaxy formation in a cosmological context. In detail, we carry out two cosmological simulations with significantly different stellar feedback models, which span a significant range in SFH burstiness. We compute the mock SEDs for these model galaxies at z = 7 via calculations of 3D dust radiative transfer, and then backward fit these SEDs with prospector SED fitting software. The uncertainties in derived stellar masses that we find for z > 7 galaxies motivate the development of new techniques and/or priors for SFH to model star formation in the early Universe.
Original language | English |
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Article number | 73 |
Pages (from-to) | 1-30 |
Number of pages | 30 |
Journal | Astrophysical Journal |
Volume | 961 |
Issue number | 1 |
DOIs | |
Publication status | Published - 16 Jan 2024 |
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Dive into the research topics of 'Outshining by Recent Star Formation Prevents the Accurate Measurement of High-z Galaxy Stellar Masses'. Together they form a unique fingerprint.Projects
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Astronomy and Astrophysics at Edinburgh
Best, P., Biller, B., Dave, R., Dunlop, J., Heymans, C., Khochfar, S., McLure, R., Palmer, P., Peacock, J., Rice, K., Smith, B. & Taylor, A.
Science and Technology Facilities Council
1/04/21 → 31/03/25
Project: Research