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Galaxies in voids assemble their stars slowly

Nature volume 619, pages 269–271 (2023)Cite this article

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Abstract

Galaxies in the Universe are distributed in a web-like structure characterized by different large-scale environments: dense clusters, elongated filaments, sheetlike walls and under-dense regions, called voids1,2,3,4,5. The low density in voids is expected to affect the properties of their galaxies. Indeed, previous studies6,7,8,9,10,11,12,13,14 have shown that galaxies in voids are, on average, bluer and less massive, and have later morphologies and higher current star formation rates than galaxies in denser large-scale environments. However, it has never been observationally proved that the star formation histories (SFHs) in voids are substantially different from those in filaments, walls and clusters. Here we show that void galaxies have had, on average, slower SFHs than galaxies in denser large-scale environments. We also find two main SFH types present in all the environments: ‘short-timescale’ galaxies are not affected by their large-scale environment at early times but only later in their lives; ‘long-timescale’ galaxies have been continuously affected by their environment and stellar mass. Both types have evolved more slowly in voids than in filaments, walls and clusters.

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Fig. 1: Average cumulative SFH.
Fig. 2: Bimodal distributions of the cumulative SFHs at 12.5 Gyr.
Fig. 3: Two types of SFH.
Fig. 4: Median assembly times versus stellar mass.

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Data availability

The analysis and results in this work are based on public data: SDSS query/CasJobs (http://casjobs.sdss.org/casjobs/) and SDSS spectra (http://data.sdss.org/sas/dr16/sdss/spectro/redux/26/spectra/). Interested researchers can reproduce our analysis following the steps in the Methods section and using the public data and codes. They can also compare with our results using the electronic spreadsheets associated with the figures in the main text. We do not place our results for individual galaxies into public repository at the moment because two PhD students inside the CAVITY project are using these results for their thesis. Additionally, a great effort has been required to carry out this analysis and the CAVITY project plans to base many of their future works on these results. At a later stage, once the PhD projects are finished and convenient exploitation of the work within the collaboration is done, we plan to make an ample dataset available for the community. We need to highlight the ‘legacy’ nature of this project, as agreed in the memorandum of understanding with the Calar Alto observatory, but, first, we reserve our rights for an embargo period for the full exploitation of this project. Source data are provided with this paper.

Code availability

Codes that support the analysis in this study are publicly available: pPXF30,31,32 (https://www-astro.physics.ox.ac.uk/~cappellari/software/) and STECKMAP33,34 (https://urldefense.com/v3/_https://github.com/pocvirk/STECKMAP_;!!D9dNQwwGXtA!VggZnNu4_e840FF17iVF0CW79nTSLkzJ53o14bQwryoS3l_alwG4PzL_OFaVMnHJ8UNWkXs5WYNJtvkFBU-3y7O2nofr$).

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Acknowledgements

We are grateful to the referees who have helped us to improve the readability and clarity of the text. We acknowledge financial support by the research projects AYA2017-84897-P, PID2020-113689GB-I00 and PID2020-114414GB-I00, financed by MCIN/AEI/10.13039/501100011033, the project A-FQM-510-UGR20 financed from FEDER/Junta de Andalucía-Consejería de Transforamción Económica, Industria, Conocimiento y Universidades/Proyecto and by the grants P20_00334 and FQM108, financed by the Junta de Andalucía (Spain). T.R.L. acknowledges support from Juan de la Cierva fellowship (IJC2020-043742-I), financed by MCIN/AEI/10.13039/501100011033. H.C. acknowledges support from the Institut Universitaire de France and the CNES. This work was supported by the Arqus European University and ANR program France 2030. L.S.M. acknowledges support from Juan de la Cierva fellowship (IJC2019-041527-I) financed by MCIN/AEI/10.13039/501100011033. J.F.B. acknowledges support through the RAVET project by the grant PID2019-107427GB-C32 from the Spanish Ministry of Science, Innovation and Universities (MCIU) and through the IAC project TRACES, which is partially supported through the state budget and the regional budget of the Consejería de Economía, Industria, Comercio y Conocimiento of the Canary Islands Autonomous Community. D.E. acknowledges support from a Beatriz Galindo senior fellowship (BG20/00224) from the Spanish Ministry of Science and Innovation. M.A.F. acknowledges support the Emergia program (EMERGIA20_38888) from Consejería de Transformación Económica, Industria, Conocimiento y Universidades and University of Granada. R.G.B. acknowledges financial support from the grants CEX2021-001131-S funded by MCIN/AEI/10.13039/501100011033, SEV-2017-0709 to PID2019-109067-GB100 and grant 202250I003 ‘AYUDAS DE INCORPORACIÓN A CIENTÍFICOS TITULARES’. S.D.P. acknowledges financial support from Juan de la Cierva Formación fellowship (FJC2021-047523-I) financed by MCIN/AEI/10.13039/501100011033 and by the European Union ‘NextGenerationEU’/PRTR, Ministerio de Economía y Competitividad under grant PID2019-107408GB-C44 from Junta de Andalucía Excellence Project P18-FR-2664 and also from the State Agency for Research of the Spanish MCIU through the ‘Center of Excellence Severo Ochoa’ award for the Instituto de Astrofísica de Andalucía (SEV-2017-0709). G.B.-C. acknowledges financial support from grants PID2020-114461GB-I00 and CEX2021-001131-S, funded by MCIN/AEI/10.13039/501100011033, from Junta de Andalucía (Spain) grant P20-00880 (FEDER, EU) and from grant PRE2018-086111 funded by MCIN/AEI/10.13039/501100011033 and by ‘ESF Investing in your future’. K.K. acknowledges funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) in the form of an Emmy Noether Research Group (grant number KR4598/2-1, PI Kreckel). This research made use of Astropy, a community-developed core Python (http://www.python.org) package for astronomy; ipython; matplotlib; SciPy, a collection of open-source software for scientific computing in Python; APLpy, an open-source plotting package for Python; and NumPy, a structure for efficient numerical computation.

Author information

Authors and Affiliations

  1. Departamento de Física Teórica y del Cosmos, Universidad de Granada, Granada, Spain

    Jesús Domínguez-Gómez, Isabel Pérez, Tomás Ruiz-Lara, Ute Lisenfeld, Manuel Alcázar-Laynez, María Argudo-Fernández, Salvador Duarte Puertas, Daniel Espada, Estrella Florido, Andoni Jiménez, Mónica Relaño, Laura Sánchez-Menguiano, Simon Verley & Almudena Zurita

  2. Instituto Carlos I de Física Teórica y Computacional, Universidad de Granada, Granada, Spain

    Isabel Pérez, Ute Lisenfeld, María Argudo-Fernández, Daniel Espada, Estrella Florido, Mónica Relaño, Laura Sánchez-Menguiano, Simon Verley & Almudena Zurita

  3. Kapteyn Astronomical Institute, University of Groningen, Groningen, The Netherlands

    Tomás Ruiz-Lara, Reynier F. Peletier, Thijs van der Hulst & Rien van de Weygaert

  4. Departamento de Física de la Tierra y Astrofísica & IPARCOS, Universidad Complutense de Madrid, Madrid, Spain

    Patricia Sánchez-Blázquez

  5. Instituto de Astrofísica de Canarias, La Laguna, Spain

    Jesús Falcón-Barroso

  6. Departamento de Astrofísica, Universidad de La Laguna, La Laguna, Spain

    Jesús Falcón-Barroso

  7. Instituto de Astrofísica de Andalucía - CSIC, Granada, Spain

    Guillermo Blázquez-Calero, Salvador Duarte Puertas & Rubén García-Benito

  8. Université Claude Bernard Lyon 1, IUF, IP2I Lyon, Villeurbanne, France

    Hélène Courtois

  9. Département de Physique, de Génie Physique et d’Optique, Université Laval, and Centre de Recherche en Astrophysique du Québec (CRAQ), Québec, Québec, Canada

    Salvador Duarte Puertas

  10. Astronomisches Rechen-Institut, Zentrum für Astronomie, Universität Heidelberg, Heidelberg, Germany

    Kathryn Kreckel

Authors
  1. Jesús Domínguez-Gómez
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  2. Isabel Pérez
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  3. Tomás Ruiz-Lara
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  4. Reynier F. Peletier
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  5. Patricia Sánchez-Blázquez
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  6. Ute Lisenfeld
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  8. Manuel Alcázar-Laynez
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  9. María Argudo-Fernández
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  10. Guillermo Blázquez-Calero
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  12. Salvador Duarte Puertas
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  13. Daniel Espada
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  14. Estrella Florido
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  15. Rubén García-Benito
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  19. Laura Sánchez-Menguiano
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  20. Thijs van der Hulst
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Contributions

J.D.G. is the corresponding author and was involved in sample selection, control sample selection, data analysis, results interpretation and writing. I.P. was involved in sample selection, control sample selection, data analysis, results interpretation and writing. T.R.L. was involved in data analysis, results interpretation and writing. R.F.P. was involved in data analysis, results interpretation and writing. P.S.B. was involved in data analysis, results interpretation and writing. U.L. was involved in sample selection, control sample selection, data analysis, results interpretation and writing. J.F.B. was involved in data analysis, results interpretation and writing. M.A.L. was involved in sample selection, results interpretation and writing. M.A.F. was involved in sample selection, results interpretation and writing. G.B.C. was involved in sample selection, results interpretation and writing. H.C. was involved in results interpretation and writing. S.D.P. was involved in sample selection, control sample selection, results interpretation and writing. D.E. was involved in results interpretation and writing. E.F. was involved in sample selection, results interpretation and writing. R.G.B. was involved in results interpretation and writing. A.J. was involved in results interpretation and writing. K.K. was involved in results interpretation and writing. M.R. was involved in sample selection, results interpretation and writing. L.S.M. was involved in results interpretation and writing. T.H. was involved in results interpretation and writing. R.W. was involved in results interpretation and writing. S.V. was involved in sample selection, control sample selection, results interpretation and writing. A.Z. was involved in sample selection, results interpretation and writing.

Corresponding author

Correspondence to Jesús Domínguez-Gómez.

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The authors declare no competing interests.

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Extended data figures and tables

Extended Data Fig. 1 Examples of star formation histories.

a, SFHs and b, cumulative SFHs for galaxies CAVITY59013 (solid magenta line) and CAVITY66461 (dashed cyan line), which have ST-SFH and LT-SFH types, respectively. The shaded regions represent the errors of the stellar mass fraction of the SFH. The dotted lines in b represent the assembly times of the 50% (T50) and 70% (T70) of the stellar mass.

Extended Data Fig. 2 Colour and stellar mass distribution before the quality control.

a–c, Colour versus stellar mass diagram for galaxies in voids (a), filaments (b), and clusters (c). d,e, Normalized distributions of the stellar mass (d) and g − r colour (e) for galaxies in voids (blue dashed line), filaments and walls (green dot-dashed line), and clusters (red solid line).

Extended Data Fig. 3 Colour and stellar mass distribution after the quality control.

a–c, Colour versus stellar mass diagram for galaxies in voids (a), filaments (b), and clusters (c). d,e, Normalized distributions of the stellar mass (d) and g − r colour (e) for galaxies in voids (blue dashed line), filaments and walls (green dot-dashed line), and clusters (red solid line).

Extended Data Fig. 4 Fit residuals versus spectral signal-to-noise, and emission lines.

a, Standard deviation of the spectral fit residual (\({{\sigma }}_{{\rm{res}}}({\rm{H}}\beta )\)) normalized by the level of the continuum (Cont) around Hβ versus the spectral signal-to-noise (S/N) ratio in the continuum. b, Residual-to-noise ratio as (\({{\sigma }}_{{\rm{res}}}({\rm{H}}\beta )\)) normalized by standard deviation of noise in the continuum next to Hβ (σnoise(Hβ)) versus the S/N in the continuum. The Hβ equivalent with (ΔHβeq) is colour-coded in both panels.

Extended Data Fig. 5 Examples of pPXF spectral fit of emission lines.

a, Good fit example of a galaxy with signal-to-noise ratio of S/N = 20.0 and residual-to-noise ratio of \({{\sigma }}_{{\rm{res}}}/{\sigma }_{{\rm{noise}}}=1.1\). b, Bad fit example of a galaxy with S/N = 41.3 and \({{\sigma }}_{{\rm{res}}}/{\sigma }_{{\rm{noise}}}=4.7\). The black and red lines represent the observed and the fitted spectrum of the galaxy, respectively. The grey lines represent the fit residuals.

Extended Data Fig. 6 Distribution of apparent radius.

Normalized number of galaxies as a function of the apparent radius (R90r from SDSS) for galaxies in voids (blue dashed line), filaments and walls (green dot-dashed line), and clusters (red solid line) before the quality control. The apparent radius of the galaxies is represented by the petrosian radius containing the 90% of the total flux of the galaxy in r band (SDSS56).

Extended Data Fig. 7 Correlation between the star formation history type, current morphology and colour of the galaxy.

a–h, Fraction of spiral and elliptical galaxies, or blue and red galaxies with LT-SFH and ST-SFH types is shown for all the environments together (a,e), for voids (b,f), filaments and walls (c,g), and clusters (d,h), with the same stellar mass distribution. The number of galaxies is shown between brackets over each panel. Galaxies are blue if their g − r < 0.7, red if g − r > 0.7, spiral if T-type > 057, and elliptical if T-type < 0. Galaxies with ST-SFH are more likely to be elliptical or red. On the contrary, galaxies with LT-SFH are more likely to be spiral or blue. However, there is a significant fraction galaxies with ST-SFHs that are blue or spiral, and galaxies with LT-SFHs that are red or elliptical.

Extended Data Table 1 Median assembly times
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Domínguez-Gómez, J., Pérez, I., Ruiz-Lara, T. et al. Galaxies in voids assemble their stars slowly. Nature 619, 269–271 (2023). https://doi.org/10.1038/s41586-023-06109-1

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