The Art of Measuring Physical Parameters in Galaxies: A Critical Assessment of Spectral Energy Distribution Fitting Techniques
The study of galaxy evolution hinges on our ability to interpret multi-wavelength 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, tha...
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creator | Pacifici, Camilla Iyer, Kartheik G Mobasher, Bahram da Cunha, Elisabete Acquaviva, Viviana Burgarella, Denis Rivera, Gabriela Calistro Carnall, Adam C Yu-Yen, Chang Chartab, Nima Cooke, Kevin C Fairhurst, Ciaran Kartaltepe, Jeyhan Leja, Joel Malek, Katarzyna Salmon, Brett Torelli, Marianna Vidal-Garcia, Alba Boquien, Mederic Brammer, Gabriel G Brown, Michael J I Capak, Peter L Chevallard, Jacopo Circosta, Chiara Croton, Darren Davidzon, Iary Dickinson, Mark Duncan, Kenneth J Faber, Sandra M Ferguson, Harry C Fontana, Adriano Guo, Yicheng Haeussler, Boris Hemmati, Shoubaneh Jafariyazani, Marziye Kassin, Susan A Larson, Rebecca L Lee, Bomee Kameswara Bharadwaj Mantha Marchi, Francesca Nayyeri, Hooshang Newman, Jeffrey A Pandya, Viraj Pforr, Janine Reddy, Naveen Sanders, Ryan Shah, Ekta Shahidi, Abtin Stevans, Matthew L Triani, Dian Puspita Tyler, Krystal D Vanderhoof, Brittany N de la Vega, Alexander Wang, Weichen Weston, Madalyn E |
description | The study of galaxy evolution hinges on our ability to interpret multi-wavelength 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 the wide and deep multi-waveband 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 fourteen of the most commonly used SED fitting codes on samples from the CANDELS photometric catalogs at z~1 and z~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 AGN 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 (~0.1dex), SFR (~0.3dex), and dust attenuation (~0.3mag). Finally, we present some resources summarizing best practices in SED fitting. |
doi_str_mv | 10.48550/arxiv.2212.01915 |
format | Article |
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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 the wide and deep multi-waveband 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 fourteen of the most commonly used SED fitting codes on samples from the CANDELS photometric catalogs at z~1 and z~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 AGN 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 (~0.1dex), SFR (~0.3dex), and dust attenuation (~0.3mag). Finally, we present some resources summarizing best practices in SED fitting.</description><identifier>EISSN: 2331-8422</identifier><identifier>DOI: 10.48550/arxiv.2212.01915</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Active galactic nuclei ; Algorithms ; Astronomical models ; Attenuation ; Best practice ; Dust ; Galactic evolution ; Galaxy distribution ; Parameters ; Physical properties ; Physics - Astrophysics of Galaxies ; Spectral energy distribution ; Spectrophotometry ; Star & galaxy formation ; Star formation rate ; Stellar mass ; Uncertainty</subject><ispartof>arXiv.org, 2022-12</ispartof><rights>2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>http://creativecommons.org/licenses/by/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,780,881,27904</link.rule.ids><backlink>$$Uhttps://doi.org/10.48550/arXiv.2212.01915$$DView paper in arXiv$$Hfree_for_read</backlink><backlink>$$Uhttps://doi.org/10.3847/1538-4357/acacff$$DView published paper (Access to full text may be restricted)$$Hfree_for_read</backlink></links><search><creatorcontrib>Pacifici, Camilla</creatorcontrib><creatorcontrib>Iyer, Kartheik G</creatorcontrib><creatorcontrib>Mobasher, Bahram</creatorcontrib><creatorcontrib>da Cunha, Elisabete</creatorcontrib><creatorcontrib>Acquaviva, Viviana</creatorcontrib><creatorcontrib>Burgarella, Denis</creatorcontrib><creatorcontrib>Rivera, Gabriela Calistro</creatorcontrib><creatorcontrib>Carnall, Adam C</creatorcontrib><creatorcontrib>Yu-Yen, Chang</creatorcontrib><creatorcontrib>Chartab, Nima</creatorcontrib><creatorcontrib>Cooke, Kevin C</creatorcontrib><creatorcontrib>Fairhurst, Ciaran</creatorcontrib><creatorcontrib>Kartaltepe, Jeyhan</creatorcontrib><creatorcontrib>Leja, Joel</creatorcontrib><creatorcontrib>Malek, Katarzyna</creatorcontrib><creatorcontrib>Salmon, Brett</creatorcontrib><creatorcontrib>Torelli, Marianna</creatorcontrib><creatorcontrib>Vidal-Garcia, Alba</creatorcontrib><creatorcontrib>Boquien, Mederic</creatorcontrib><creatorcontrib>Brammer, Gabriel G</creatorcontrib><creatorcontrib>Brown, Michael J I</creatorcontrib><creatorcontrib>Capak, Peter L</creatorcontrib><creatorcontrib>Chevallard, Jacopo</creatorcontrib><creatorcontrib>Circosta, Chiara</creatorcontrib><creatorcontrib>Croton, Darren</creatorcontrib><creatorcontrib>Davidzon, Iary</creatorcontrib><creatorcontrib>Dickinson, Mark</creatorcontrib><creatorcontrib>Duncan, Kenneth J</creatorcontrib><creatorcontrib>Faber, Sandra M</creatorcontrib><creatorcontrib>Ferguson, Harry C</creatorcontrib><creatorcontrib>Fontana, Adriano</creatorcontrib><creatorcontrib>Guo, Yicheng</creatorcontrib><creatorcontrib>Haeussler, Boris</creatorcontrib><creatorcontrib>Hemmati, Shoubaneh</creatorcontrib><creatorcontrib>Jafariyazani, Marziye</creatorcontrib><creatorcontrib>Kassin, Susan A</creatorcontrib><creatorcontrib>Larson, Rebecca L</creatorcontrib><creatorcontrib>Lee, Bomee</creatorcontrib><creatorcontrib>Kameswara Bharadwaj Mantha</creatorcontrib><creatorcontrib>Marchi, Francesca</creatorcontrib><creatorcontrib>Nayyeri, Hooshang</creatorcontrib><creatorcontrib>Newman, Jeffrey A</creatorcontrib><creatorcontrib>Pandya, Viraj</creatorcontrib><creatorcontrib>Pforr, Janine</creatorcontrib><creatorcontrib>Reddy, Naveen</creatorcontrib><creatorcontrib>Sanders, Ryan</creatorcontrib><creatorcontrib>Shah, Ekta</creatorcontrib><creatorcontrib>Shahidi, Abtin</creatorcontrib><creatorcontrib>Stevans, Matthew L</creatorcontrib><creatorcontrib>Triani, Dian Puspita</creatorcontrib><creatorcontrib>Tyler, Krystal D</creatorcontrib><creatorcontrib>Vanderhoof, Brittany N</creatorcontrib><creatorcontrib>de la Vega, Alexander</creatorcontrib><creatorcontrib>Wang, Weichen</creatorcontrib><creatorcontrib>Weston, Madalyn E</creatorcontrib><title>The Art of Measuring Physical Parameters in Galaxies: A Critical Assessment of Spectral Energy Distribution Fitting Techniques</title><title>arXiv.org</title><description>The study of galaxy evolution hinges on our ability to interpret multi-wavelength 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 the wide and deep multi-waveband 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 fourteen of the most commonly used SED fitting codes on samples from the CANDELS photometric catalogs at z~1 and z~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 AGN 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 (~0.1dex), SFR (~0.3dex), and dust attenuation (~0.3mag). Finally, we present some resources summarizing best practices in SED fitting.</description><subject>Active galactic nuclei</subject><subject>Algorithms</subject><subject>Astronomical models</subject><subject>Attenuation</subject><subject>Best practice</subject><subject>Dust</subject><subject>Galactic evolution</subject><subject>Galaxy distribution</subject><subject>Parameters</subject><subject>Physical properties</subject><subject>Physics - Astrophysics of Galaxies</subject><subject>Spectral energy distribution</subject><subject>Spectrophotometry</subject><subject>Star & galaxy formation</subject><subject>Star formation rate</subject><subject>Stellar mass</subject><subject>Uncertainty</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GOX</sourceid><recordid>eNotkFFLwzAUhYMgOOZ-gE8GfO5Mbpqm9a3MbQoTB_a9pO3tlrG1M0lle_G323W-3AOXj4_DIeSBs2kYS8metT2ZnykAhynjCZc3ZARC8CAOAe7IxLkdYwwiBVKKEfnNtkhT62lb0w_UrrOm2dD19uxMqfd0ra0-oEfrqGnoUu_1yaB7oSmdWeMHJHUOnTtgMzi-jlh627_nDdrNmb4a560pOm_ahi6M9xd9huW2Md8duntyW-u9w8l_jkm2mGezt2D1uXyfpatAJ1IGokp0hFUCffNSlv2pirqOaoUslKoQgAxBlUKFClihAEXBooizGAVWcSzEmDxetcM4-dGag7bn_DJSPozUE09X4mjbSzGf79rONn2nHHotBxmrSPwBczJq-Q</recordid><startdate>20221204</startdate><enddate>20221204</enddate><creator>Pacifici, Camilla</creator><creator>Iyer, Kartheik G</creator><creator>Mobasher, Bahram</creator><creator>da Cunha, Elisabete</creator><creator>Acquaviva, Viviana</creator><creator>Burgarella, Denis</creator><creator>Rivera, Gabriela Calistro</creator><creator>Carnall, Adam C</creator><creator>Yu-Yen, Chang</creator><creator>Chartab, Nima</creator><creator>Cooke, Kevin C</creator><creator>Fairhurst, Ciaran</creator><creator>Kartaltepe, Jeyhan</creator><creator>Leja, Joel</creator><creator>Malek, Katarzyna</creator><creator>Salmon, Brett</creator><creator>Torelli, Marianna</creator><creator>Vidal-Garcia, Alba</creator><creator>Boquien, Mederic</creator><creator>Brammer, Gabriel G</creator><creator>Brown, Michael J I</creator><creator>Capak, Peter L</creator><creator>Chevallard, Jacopo</creator><creator>Circosta, Chiara</creator><creator>Croton, Darren</creator><creator>Davidzon, Iary</creator><creator>Dickinson, Mark</creator><creator>Duncan, Kenneth J</creator><creator>Faber, Sandra M</creator><creator>Ferguson, Harry C</creator><creator>Fontana, Adriano</creator><creator>Guo, Yicheng</creator><creator>Haeussler, 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arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>GOX</scope></search><sort><creationdate>20221204</creationdate><title>The Art of Measuring Physical Parameters in Galaxies: A Critical Assessment of Spectral Energy Distribution Fitting Techniques</title><author>Pacifici, Camilla ; Iyer, Kartheik G ; Mobasher, Bahram ; da Cunha, Elisabete ; Acquaviva, Viviana ; Burgarella, Denis ; Rivera, Gabriela Calistro ; Carnall, Adam C ; Yu-Yen, Chang ; Chartab, Nima ; Cooke, Kevin C ; Fairhurst, Ciaran ; Kartaltepe, Jeyhan ; Leja, Joel ; Malek, Katarzyna ; Salmon, Brett ; Torelli, Marianna ; Vidal-Garcia, Alba ; Boquien, Mederic ; Brammer, Gabriel G ; Brown, Michael J I ; Capak, Peter L ; Chevallard, Jacopo ; Circosta, Chiara ; Croton, Darren ; Davidzon, Iary ; Dickinson, Mark ; Duncan, Kenneth J ; Faber, Sandra M ; Ferguson, Harry C ; Fontana, Adriano ; Guo, Yicheng ; Haeussler, Boris ; Hemmati, Shoubaneh ; Jafariyazani, Marziye ; Kassin, Susan A ; Larson, Rebecca L ; Lee, Bomee ; Kameswara Bharadwaj Mantha ; Marchi, Francesca ; Nayyeri, Hooshang ; Newman, Jeffrey A ; Pandya, Viraj ; Pforr, Janine ; Reddy, Naveen ; Sanders, Ryan ; Shah, Ekta ; Shahidi, Abtin ; Stevans, Matthew L ; Triani, Dian Puspita ; Tyler, Krystal D ; Vanderhoof, Brittany N ; de la Vega, Alexander ; Wang, Weichen ; Weston, Madalyn E</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a955-3d9a6ed92000c5c00cdbff6f7e0457b32e0e27c374720b72e3b066108e3ed8833</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Active galactic nuclei</topic><topic>Algorithms</topic><topic>Astronomical models</topic><topic>Attenuation</topic><topic>Best practice</topic><topic>Dust</topic><topic>Galactic evolution</topic><topic>Galaxy distribution</topic><topic>Parameters</topic><topic>Physical properties</topic><topic>Physics - Astrophysics of Galaxies</topic><topic>Spectral energy distribution</topic><topic>Spectrophotometry</topic><topic>Star & galaxy formation</topic><topic>Star formation rate</topic><topic>Stellar mass</topic><topic>Uncertainty</topic><toplevel>online_resources</toplevel><creatorcontrib>Pacifici, Camilla</creatorcontrib><creatorcontrib>Iyer, Kartheik G</creatorcontrib><creatorcontrib>Mobasher, Bahram</creatorcontrib><creatorcontrib>da Cunha, Elisabete</creatorcontrib><creatorcontrib>Acquaviva, Viviana</creatorcontrib><creatorcontrib>Burgarella, Denis</creatorcontrib><creatorcontrib>Rivera, Gabriela Calistro</creatorcontrib><creatorcontrib>Carnall, Adam C</creatorcontrib><creatorcontrib>Yu-Yen, 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Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>arXiv.org</collection><jtitle>arXiv.org</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Pacifici, Camilla</au><au>Iyer, Kartheik G</au><au>Mobasher, Bahram</au><au>da Cunha, Elisabete</au><au>Acquaviva, Viviana</au><au>Burgarella, Denis</au><au>Rivera, Gabriela Calistro</au><au>Carnall, Adam C</au><au>Yu-Yen, Chang</au><au>Chartab, Nima</au><au>Cooke, Kevin C</au><au>Fairhurst, Ciaran</au><au>Kartaltepe, Jeyhan</au><au>Leja, Joel</au><au>Malek, Katarzyna</au><au>Salmon, Brett</au><au>Torelli, Marianna</au><au>Vidal-Garcia, Alba</au><au>Boquien, Mederic</au><au>Brammer, Gabriel G</au><au>Brown, Michael J I</au><au>Capak, Peter L</au><au>Chevallard, Jacopo</au><au>Circosta, Chiara</au><au>Croton, Darren</au><au>Davidzon, Iary</au><au>Dickinson, Mark</au><au>Duncan, Kenneth J</au><au>Faber, Sandra M</au><au>Ferguson, Harry C</au><au>Fontana, Adriano</au><au>Guo, Yicheng</au><au>Haeussler, Boris</au><au>Hemmati, Shoubaneh</au><au>Jafariyazani, Marziye</au><au>Kassin, Susan A</au><au>Larson, Rebecca L</au><au>Lee, Bomee</au><au>Kameswara Bharadwaj Mantha</au><au>Marchi, Francesca</au><au>Nayyeri, Hooshang</au><au>Newman, Jeffrey A</au><au>Pandya, Viraj</au><au>Pforr, Janine</au><au>Reddy, Naveen</au><au>Sanders, Ryan</au><au>Shah, Ekta</au><au>Shahidi, Abtin</au><au>Stevans, Matthew L</au><au>Triani, Dian Puspita</au><au>Tyler, Krystal D</au><au>Vanderhoof, Brittany N</au><au>de la Vega, Alexander</au><au>Wang, Weichen</au><au>Weston, Madalyn E</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The Art of Measuring Physical Parameters in Galaxies: A Critical Assessment of Spectral Energy Distribution Fitting Techniques</atitle><jtitle>arXiv.org</jtitle><date>2022-12-04</date><risdate>2022</risdate><eissn>2331-8422</eissn><abstract>The study of galaxy evolution hinges on our ability to interpret multi-wavelength 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 the wide and deep multi-waveband 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 fourteen of the most commonly used SED fitting codes on samples from the CANDELS photometric catalogs at z~1 and z~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 AGN 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 (~0.1dex), SFR (~0.3dex), and dust attenuation (~0.3mag). Finally, we present some resources summarizing best practices in SED fitting.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><doi>10.48550/arxiv.2212.01915</doi><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | EISSN: 2331-8422 |
ispartof | arXiv.org, 2022-12 |
issn | 2331-8422 |
language | eng |
recordid | cdi_arxiv_primary_2212_01915 |
source | arXiv.org; Free E- Journals |
subjects | Active galactic nuclei Algorithms Astronomical models Attenuation Best practice Dust Galactic evolution Galaxy distribution Parameters Physical properties Physics - Astrophysics of Galaxies Spectral energy distribution Spectrophotometry Star & galaxy formation Star formation rate Stellar mass Uncertainty |
title | The Art of Measuring Physical Parameters in Galaxies: A Critical Assessment of Spectral Energy Distribution Fitting Techniques |
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