Deriving Physical Properties from Broadband Photometry with Prospector: Description of the Model and a Demonstration of its Accuracy Using 129 Galaxies in the Local Universe (1609.09073v2)

Abstract: Broadband photometry of galaxies measures an unresolved mix of complex stellar populations, gas, and dust. Interpreting these data is a challenge for models: many studies have shown that properties derived from modeling galaxy photometry are uncertain by a factor of two or more, and yet answering key questions in the field now requires higher accuracy than this. Here, we present a new model framework specifically designed for these complexities. Our model, Prospector-$\alpha$, includes dust attenuation and re-radiation, a flexible attenuation curve, nebular emission, stellar metallicity, and a 6-component nonparametric star formation history. The flexibility and range of the parameter space, coupled with MCMC sampling within the Prospector inference framework, is designed to provide unbiased parameters and realistic error bars. We assess the accuracy of the model with aperture-matched optical spectroscopy, which was excluded from the fits. We compare spectral features predicted solely from fits to the broadband photometry to the observed spectral features. Our model predicts H$\alpha$ luminosities with a scatter of $\sim$0.18 dex and an offset of $\sim$0.1 dex across a wide range of morphological types and stellar masses. This agreement is remarkable, as the H$\alpha$ luminosity is dependent on accurate star formation rates, dust attenuation, and stellar metallicities. The model also accurately predicts dust-sensitive Balmer decrements, spectroscopic stellar metallicities, PAH mass fractions, and the age- and metallicity-sensitive features D$_{\mathrm{n}}$4000 and H$\delta$. Although the model passes all these tests, we caution that we have not yet assessed its performance at higher redshift or the accuracy of recovered stellar masses.

• The paper introduces the Prospector-$ model to resolve uncertainties in deriving galaxy properties from broadband photometry.
• It employs Monte Carlo Markov Chain sampling with a six-component nonparametric SFH, achieving a 0.1 dex offset and 0.18 dex scatter in H emission predictions.
• The refined recovery of SFR, stellar mass, and dust parameters offers enhanced insights into galaxy assembly and evolution in the local universe.

Deriving Physical Properties from Broadband Photometry with Prospector

This paper presents a comprehensive analysis of the challenges and methodologies involved in deriving physical properties of galaxies from broadband photometry by introducing the Prospector-$$model. It addresses the long-standing issue of model uncertainties that have historically hindered accurate determination of galaxy properties, emphasizing the need for precision in interpreting broadband photometric data amidst complexities involving diverse stellar populations, gas, and dust.</p> <p>The authors propose the Prospector-$$model as a robust solution, incorporating features such as dust attenuation and re-radiation, a flexible attenuation curve, nebular emission, stellar metallicity, and a 6-component nonparametric star formation history (SFH). This framework leverages Monte Carlo Markov Chain (MCMC) sampling to ensure unbiased parameter estimation and realistic error bars. The model’s accuracy is validated against aperture-matched optical spectroscopy, demonstrating a remarkable precision with an offset of ∼0.1 dex and a scatter of ∼0.18 dex in predicted H$$luminosities over diverse galaxy types and masses.</p> <p>The paper highlights critical advancements in modeling galaxy evolution. It affirms that the Prospector-$$model provides a reliable platform for deducing star formation rates (SFRs) and stellar masses, which are pivotal for understanding galaxy assembly history. The researchers caution, however, that while Prospector-$$excels in local universe applications, its performance at higher redshifts and stellar <a href="https://www.emergentmind.com/topics/llm-based-multi-agent-systems-mass" title="" rel="nofollow" data-turbo="false" class="assistant-link" x-data x-tooltip.raw="">mass</a> recovery accuracy requires further investigation.</p> <p>The results underscore the importance of integrating complex star formation histories with other stellar and dust-related parameters. The capability of the Prospector-$$model to predict emission line fluxes such as H$$solely from photometry without spectroscopic data signifies a substantial leap forward for observational cosmology. The paper also emphasizes that the model accurately recovers parameters indicative of stellar metallicities and dust properties, reinforcing its utility in addressing observational discrepancies inherent in prior methodologies.</p> <p>The implications are expansive: refining SFR and stellar mass estimates allow for greater precision in understanding the mass assembly of galaxies. Moreover, the integration of sophisticated nonparametric SFH estimations challenges previous norms of parameterized models, which often failed to encapsulate the complex physical processes at play. The authors suggest that further exploration into high redshift galaxy populations with Prospector-$$ could rectify inconsistencies between observed SFRs and stellar masses, offering a more consistent and unified framework for galaxy evolution studies.

In conclusion, the introduction of Prospector-$$marks an important step towards resolving the systematic uncertainties associated with galaxy property estimations from photometric data. As observational datasets expand and improve, the potential for Prospector-$$ to enhance our understanding of galaxy formation and evolution, especially across varying cosmic epochs, remains promising. The ongoing improvements and future applications of this framework could ultimately redefine our approach to astronomical data analysis, enabling comprehensive insights into the fundamental mechanics of the universe.