TY - JOUR

T1 - The Art of Measuring Physical Parameters in Galaxies

T2 - A Critical Assessment of Spectral Energy Distribution Fitting Techniques

AU - Pacifici, Camilla

AU - Iyer, Kartheik G.

AU - Mobasher, Bahram

AU - da Cunha, Elisabete

AU - Acquaviva, Viviana

AU - Burgarella, Denis

AU - Rivera, Gabriela Calistro

AU - Carnall, Adam C.

AU - Chang, Yu-Yen

AU - Chartab, Nima

AU - Cooke, Kevin C.

AU - Fairhurst, Ciaran

AU - Kartaltepe, Jeyhan

AU - Leja, Joel

AU - Malek, Katarzyna

AU - Salmon, Brett

AU - Torelli, Marianna

AU - Vidal-Garcia, Alba

AU - Boquien, Mederic

AU - Brammer, Gabriel G.

AU - Brown, Michael J. I.

AU - Capak, Peter L.

AU - Chevallard, Jacopo

AU - Circosta, Chiara

AU - Croton, Darren

AU - Davidzon, Iary

AU - Dickinson, Mark

AU - Duncan, Kenneth J.

AU - Faber, Sandra M.

AU - Ferguson, Harry C.

AU - Fontana, Adriano

AU - Guo, Yicheng

AU - Haeussler, Boris

AU - Hemmati, Shoubaneh

AU - Jafariyazani, Marziye

AU - Kassin, Susan A.

AU - Larson, Rebecca L.

AU - Lee, Bomee

AU - Mantha, Kameswara Bharadwaj

AU - Marchi, Francesca

AU - Nayyeri, Hooshang

AU - Newman, Jeffrey A.

AU - Pandya, Viraj

AU - Pforr, Janine

AU - Reddy, Naveen

AU - Sanders, Ryan

AU - Shah, Ekta

AU - Shahidi, Abtin

AU - Stevans, Matthew L.

AU - Triani, Dian Puspita

AU - Tyler, Krystal D.

AU - Vanderhoof, Brittany N.

AU - de la Vega, Alexander

AU - Wang, Weichen

AU - Weston, Madalyn E.

PY - 2023/2/1

Y1 - 2023/2/1

N2 - The study of galaxy evolution hinges on our ability to interpret multiwavelength galaxy observations in terms of their physical properties. To do this, we rely on spectral energy distribution (SED) models, which allow us to infer physical parameters from spectrophotometric data. In recent years, thanks to wide and deep multiwave band galaxy surveys, the volume of high-quality data have significantly increased. Alongside the increased data, algorithms performing SED fitting have improved, including better modeling prescriptions, newer templates, and more extensive sampling in wavelength space. We present a comprehensive analysis of different SED-fitting codes including their methods and output with the aim of measuring the uncertainties caused by the modeling assumptions. We apply 14 of the most commonly used SED-fitting codes on samples from the CANDELS photometric catalogs at z similar to 1 and z similar to 3. We find agreement on the stellar mass, while we observe some discrepancies in the star formation rate (SFR) and dust-attenuation results. To explore the differences and biases among the codes, we explore the impact of the various modeling assumptions as they are set in the codes (e.g., star formation histories, nebular, dust and active galactic nucleus models) on the derived stellar masses, SFRs, and A ( V ) values. We then assess the difference among the codes on the SFR-stellar mass relation and we measure the contribution to the uncertainties by the modeling choices (i.e., the modeling uncertainties) in stellar mass (similar to 0.1 dex), SFR (similar to 0.3 dex), and dust attenuation (similar to 0.3 mag). Finally, we present some resources summarizing best practices in SED fitting.

AB - The study of galaxy evolution hinges on our ability to interpret multiwavelength galaxy observations in terms of their physical properties. To do this, we rely on spectral energy distribution (SED) models, which allow us to infer physical parameters from spectrophotometric data. In recent years, thanks to wide and deep multiwave band galaxy surveys, the volume of high-quality data have significantly increased. Alongside the increased data, algorithms performing SED fitting have improved, including better modeling prescriptions, newer templates, and more extensive sampling in wavelength space. We present a comprehensive analysis of different SED-fitting codes including their methods and output with the aim of measuring the uncertainties caused by the modeling assumptions. We apply 14 of the most commonly used SED-fitting codes on samples from the CANDELS photometric catalogs at z similar to 1 and z similar to 3. We find agreement on the stellar mass, while we observe some discrepancies in the star formation rate (SFR) and dust-attenuation results. To explore the differences and biases among the codes, we explore the impact of the various modeling assumptions as they are set in the codes (e.g., star formation histories, nebular, dust and active galactic nucleus models) on the derived stellar masses, SFRs, and A ( V ) values. We then assess the difference among the codes on the SFR-stellar mass relation and we measure the contribution to the uncertainties by the modeling choices (i.e., the modeling uncertainties) in stellar mass (similar to 0.1 dex), SFR (similar to 0.3 dex), and dust attenuation (similar to 0.3 mag). Finally, we present some resources summarizing best practices in SED fitting.

KW - STAR-FORMING GALAXIES

KW - STELLAR POPULATION SYNTHESIS

KW - ACTIVE GALACTIC NUCLEI

KW - MASS-METALLICITY RELATION

KW - DUST ATTENUATION CURVES

KW - FAR-INFRARED SURVEY

KW - PHOTOMETRIC REDSHIFTS

KW - MAIN-SEQUENCE

KW - FORMATION HISTORIES

KW - LEGACY SURVEY

U2 - 10.3847/1538-4357/acacff

DO - 10.3847/1538-4357/acacff

M3 - Journal article

VL - 944

JO - Astrophysical Journal

JF - Astrophysical Journal

SN - 0004-637X

IS - 2

M1 - 141

ER -