Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

An investigation of spectral line stacking techniques and application to the detection of HC11N

Nature Astronomy volume 5, pages 188–196 (2021)Cite this article

Subjects

Abstract

As the inventory of interstellar molecules continues to grow, the gulf between small species, whose individual rotational lines can be observed with radio telescopes, and large ones, such as polycyclic aromatic hydrocarbons best studied in bulk via infrared and optical observations, is slowly being bridged. Understanding the connection between these two molecular reservoirs is critical to understanding the interstellar carbon cycle, but will require pushing the boundaries of how far we can probe molecular complexity while still retaining observational specificity. Towards this end, we present a method for detecting and characterizing new molecular species in single-dish observations towards sources with sparse line spectra. We have applied this method to data from the ongoing GOTHAM (GBT Observations of TMC-1: Hunting Aromatic Molecules) Green Bank Telescope large programme, discovering six new interstellar species. Here we highlight the detection of HC11N, the largest cyanopolyyne in the interstellar medium.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Schematic diagram of our method for molecular detection and characterization.
Fig. 2: Schematic showing two spatial distribution regimes into which the emission from TMC-1 may fall and the approximations we use in our analysis.
Fig. 3: Individual line detections of HC9N in the GOTHAM data.
Fig. 4: HC9N spectra in TMC-1.
Fig. 5: Fractional modification to rotational constants plotted versus normalized matched filter response.
Fig. 6: Individual line observations of HC11N in the GOTHAM data.
Fig. 7: HC11N spectra in TMC-1.
Fig. 8: A comparison of cyanopolyyne column densities in TMC-1 from observations and a chemical model.

Similar content being viewed by others

Data availability

The datasets analysed during the current study are available in the Green Bank Telescope archive (https://archive.nrao.edu/archive/advquery.jsp; PI: B.A.M.). A user manual for their reduction and analysis is also available (https://greenbankobservatory.org/science/gbt-observers/visitor-facilities-policies/data-reduction-gbt-using-idl/). The complete, reduced survey data in the X band are available as supplementary information in ref. 8. The individual portions of the reduced spectra used in the analysis of the individual species presented here are available in the Harvard Dataverse Archive42.

Code availability

All the codes used in the MCMC fitting and stacking analysis presented in this paper are open source and publicly available at https://github.com/ryanaloomis/TMC1_mcmc_fitting. The open source code for our spectral simulator can be found at https://github.com/ryanaloomis/spectral_simulator.

References

  1. McGuire, B. A. 2018 census of interstellar, circumstellar, extragalactic, protoplanetary disk, and exoplanetary molecules. Astrophys. J. Suppl. Ser. 239, 17 (2018).

    Article  ADS  Google Scholar 

  2. Czekala, I. DiskJockey: protoplanetary disk modeling for dynamical mass derivationAstrophys. Source Code Libr. (2016). ascl:1603.011.

  3. Gratier, P. et al. A new reference chemical composition for TMC-1. Astrophys. J. Suppl. Ser. 225, 25 (2016).

    Article  ADS  Google Scholar 

  4. Loomis, R. A. et al. Non-detection of HC11N towards TMC-1: constraining the chemistry of large carbon-chain molecules. Mon. Not. R. Astron. Soc. 463, 4175–4183 (2016).

    Article  ADS  Google Scholar 

  5. Walsh, C. et al. First detection of gas-phase methanol in a protoplanetary disk. Astrophys. J. Lett. 823, L10 (2016).

    Article  ADS  Google Scholar 

  6. Loomis, R. A. et al. Detecting weak spectral lines in interferometric data through matched filtering. Astron. J. 155, 182 (2018).

    Article  ADS  Google Scholar 

  7. Loomis, R. A. et al. An unbiased ALMA spectral survey of the LkCa 15 and MWC 480 protoplanetary disks. Astrophys. J. 893, 101 (2020).

    Article  ADS  Google Scholar 

  8. McGuire, B. A. et al. Early science from GOTHAM: Project overview, methods, and the detection of interstellar propargyl cyanide (HCCCH2CN) in TMC-1. Astrophys. J. Lett. 900, L10 (2020).

    Article  ADS  Google Scholar 

  9. Liu, S.-Y., Mehringer, D. M. & Snyder, L. E. Observations of formic acid in hot molecular cores. Astrophys. J. 552, 654–663 (2001).

    Article  ADS  Google Scholar 

  10. Remijan, A. J., Hollis, J. M., Lovas, F. J., Plusquellic, D. F. & Jewell, P. R. Interstellar isomers: the importance of bonding energy differences. Astrophys. J. 632, 333–339 (2005).

    Article  ADS  Google Scholar 

  11. Mangum, J. G. & Shirley, Y. L. How to calculate molecular column density. Publ. Astron. Soc. Pac. 127, 266 (2015).

    Article  ADS  Google Scholar 

  12. Pickett, H. M. The fitting and prediction of vibration-rotation spectra with spin interactions. J. Mol. Spectrosc. 148, 371–377 (1991).

    Article  ADS  Google Scholar 

  13. Drouin, B. J. Practical uses of SPFIT. J. Mol. Spectrosc. 340, 1–15 (2017).

    Article  ADS  Google Scholar 

  14. Dobashi, K. et al. Spectral tomography for the line-of-sight structures of the Taurus Molecular Cloud 1. Astrophys. J. 864, 82 (2018).

    Article  ADS  Google Scholar 

  15. Dobashi, K. et al. Discovery of CCS velocity-coherent substructures in the Taurus Molecular Cloud 1. Astrophys. J. 879, 88 (2019).

    Article  ADS  Google Scholar 

  16. GBT Support Staff. Proposer’s Guide for the Green Bank Telescope (Green Bank Observatory, 2020); https://www.gb.nrao.edu/scienceDocs/GBTpg.pdf

  17. Remijan, A. J., Hollis, J. M., Snyder, L. E., Jewell, P. R. & Lovas, F. J. Methyltriacetylene (CH3C6H) toward TMC-1: the largest detected symmetric top. Astrophys. J. Lett. 643, L37–L40 (2006).

    Article  ADS  Google Scholar 

  18. Foreman-Mackey, D., Hogg, D. W., Lang, D. & Goodman, J. emcee: the MCMC hammer. Publ. Astron. Soc. Pac. 125, 306–312 (2013).

    Article  ADS  Google Scholar 

  19. Gelman, A. & Rubin, D. B. Inference from iterative simulation using multiple sequences. Stat. Sci. 7, 457–472 (1992).

    Article  Google Scholar 

  20. Langston, G. & Turner, B. Detection of 13C isotopomers of the molecule HC7N. Astrophys. J. 658, 455–461 (2007).

    Article  ADS  Google Scholar 

  21. Woodward, P. Probability and Information Theory: With Applications to Radar Vol. 3 (Elsevier Science & Technology, 1953).

  22. North, D. O. An analysis of the factors which determine signal/noise discrimination in pulsed-carrier systems. Proc. IEEE 51, 1016–1027 (1963).

    Article  Google Scholar 

  23. Portillo, S. K. N., Lee, B. C. G., Daylan, T. & Finkbeiner, D. P. Improved point-source detection in crowded fields using probabilistic cataloging. Astron. J. 154, 132 (2017).

    Article  ADS  Google Scholar 

  24. Siemiginowska, A. et al. The next decade of astroinformatics and astrostatistics. Bull. Am. Astron. Soc. 51, 355 (2019).

    Google Scholar 

  25. McCarthy, M. C. et al. Detection of the highly polar five-membered ring cyanocyclopentadiene. Nat. Astron. https://doi.org/10.1038/s41550-020-01213-y (2020).

  26. Crabtree, K. N. et al. Microwave spectral taxonomy: a semi-automated combination of chirped-pulse and cavity Fourier-transform microwave spectroscopy. J. Chem. Phys. 144, 124201 (2016).

    Article  ADS  Google Scholar 

  27. Bell, M. B., Feldman, P. A., Kwok, S. & Matthews, H. E. Detection of HC11N in IRC + 10°216. Nature 295, 389–391 (1982).

    Article  ADS  Google Scholar 

  28. Oka, T. The prediction of the rotational constants of polyacetylene compounds H-(H ≡ H)n-H ≡ N. J. Mol. Spectrosc. 72, 172–174 (1978).

  29. Bell, M. B. & Matthews, H. E. Detection of HC11N in the cold dust cloud TMC-1. Astrophys. J. Lett. 291, L63–L65 (1985).

    Article  ADS  Google Scholar 

  30. Travers, M. J., McCarthy, M. C., Kalmus, P., Gottlieb, C. A. & Thaddeus, P. Laboratory detection of the linear cyanopolyyne HC11N. Astrophys. J. Lett. 469, L65–L68 (1996).

    Article  ADS  Google Scholar 

  31. Bell, M. B. et al. Detection of HC11N in the cold dust cloud TMC-1. Astrophys. J. Lett. 483, L61–L64 (1997).

    Article  ADS  Google Scholar 

  32. Cordiner, M. A., Charnley, S. B., Kisiel, Z., McGuire, B. A. & Kuan, Y.-J. Deep K-band observations of TMC-1 with the Green Bank Telescope: detection of HC7O, nondetection of HC11N, and a search for new organic molecules. Astrophys. J. 850, 187 (2017).

    Article  ADS  Google Scholar 

  33. Bujarrabal, V., Guelin, M., Morris, M. & Thaddeus, P. The abundance and excitation of the carbon chains in interstellar molecular clouds. Astron. Astrophys. 99, 239–247 (1981).

    ADS  Google Scholar 

  34. Ohishi, M. & Kaifu, N. Chemical and physical evolution of dark clouds: molecular spectral line survey toward TMC-1. Faraday Discuss. 109, 205–216 (1998).

    Article  ADS  Google Scholar 

  35. McGuire, B. A. et al. Detection of the aromatic molecule benzonitrile (c-C6H5CN) in the interstellar medium. Science 359, 202–205 (2018).

    Article  ADS  Google Scholar 

  36. Toelle, F., Ungerechts, H., Walmsley, C. M., Winnewisser, G. & Churchwell, E. A molecular line study of the elongated daark dust cloud TMC 1. Astron. Astrophys. 95, 143–155 (1981).

    ADS  Google Scholar 

  37. Churchwell, E., Winnewisser, G. & Walmsley, C. M. Molecular observations of a possible proto-solar nebula in a dark cloud in Taurus. Astron. Astrophys. 67, 139–147 (1978).

    ADS  Google Scholar 

  38. Olano, C. A., Walmsley, C. M. & Wilson, T. L. The relative distribution of NH3, HC7N and C4H in the Taurus Molecular Cloud 1 (TMC 1). Astron. Astrophys. 196, 194–200 (1988).

    ADS  Google Scholar 

  39. Bell, M. B., Watson, J. K. G., Feldman, P. A. & Travers, M. J. The excitation temperatures of HC9N and other long cyanopolyynes in TMC-1. Astrophys. J. 508, 286–290 (1998).

    Article  ADS  Google Scholar 

  40. Burkhardt, A. M. et al. Ubiquitous aromatic carbon chemistry at the earliest stages of star formation. Nat. Astron. https://doi.org/10.1038/s41550-020-01253-4 (2020).

  41. Xue, C. et al. Detection of interstellar HC4NC and an investigation of isocyanopolyyne chemistry in TMC-1 conditions. Astrophys. J. Lett. 900, L9 (2020).

    Article  ADS  Google Scholar 

  42. GOTHAM Collaboration. Spectral stacking data for Phase 1 science release of GOTHAM. Harvard Dataverse https://doi.org/10.7910/DVN/PG7BHO (2020).

  43. Fuente, A. et al. Gas phase elemental abundances in molecular cloudS (GEMS). I. The prototypical dark cloud TMC 1. Astron. Astrophys. 624, A105 (2019).

    Article  Google Scholar 

Download references

Acknowledgements

A.M.B. acknowledges support from the Smithsonian Institution as a Submillimeter Array (SMA) Fellow. M.C.M. and K.L.K.L. acknowledge support from NSF grant number AST-1615847 and NASA grant number 80NSSC18K0396. Support for B.A.M. during the initial portions of this work was provided by NASA through Hubble Fellowship grant number HST-HF2-51396 awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., for NASA, under contract number NAS5-26555. C.N.S. thanks the Alexander von Humboldt Stiftung/Foundation for their support, as well as V. Wakelam for use of the NAUTILUS v1.1 code. C.X. is a Grote Reber Fellow, and support for this work was provided by the NSF through the Grote Reber Fellowship Program administered by Associated Universities, Inc./National Radio Astronomy Observatory and the Virginia Space Grant Consortium. E.H. thanks the National Science Foundation for support through grant number AST 1906489. S.B.C. and M.A.C. were supported by the NASA Astrobiology Institute through the Goddard Center for Astrobiology. The National Radio Astronomy Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc. The Green Bank Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc.

Author information

Authors and Affiliations

  1. National Radio Astronomy Observatory, Charlottesville, VA, USA

    Ryan A. Loomis & Brett A. McGuire

  2. Center for Astrophysics, Harvard & Smithsonian, Cambridge, MA, USA

    Andrew M. Burkhardt, Kin Long Kelvin Lee, Anthony J. Remijan, Michael C. McCarthy & Brett A. McGuire

  3. Department of Physics and Astronomy, Benedictine College, Atchison, KS, USA

    Christopher N. Shingledecker

  4. Center for Astrochemical Studies, Max Planck Institute for Extraterrestrial Physics, Garching, Germany

    Christopher N. Shingledecker

  5. Institute for Theoretical Chemistry, University of Stuttgart, Stuttgart, Germany

    Christopher N. Shingledecker

  6. Astrochemistry Laboratory and the Goddard Center for Astrobiology, NASA Goddard Space Flight Center, Greenbelt, MD, USA

    Steven B. Charnley & Martin A. Cordiner

  7. Institute for Astrophysics and Computational Sciences, The Catholic University of America, Washington, DC, USA

    Martin A. Cordiner

  8. Department of Chemistry, University of Virginia, Charlottesville, VA, USA

    Eric Herbst, Eric R. Willis & Ci Xue

  9. Department of Astronomy, University of Virginia, Charlottesville, VA, USA

    Eric Herbst

  10. Astro Space Center, Lebedev Physical Institute, Russian Academy of Sciences, Moscow, Russia

    Sergei Kalenskii

  11. Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA

    Brett A. McGuire

Authors
  1. Ryan A. Loomis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrew M. Burkhardt
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Christopher N. Shingledecker
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Steven B. Charnley
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Martin A. Cordiner
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Eric Herbst
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Sergei Kalenskii
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Kin Long Kelvin Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Eric R. Willis
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Ci Xue
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Anthony J. Remijan
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Michael C. McCarthy
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Brett A. McGuire
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

R.A.L. wrote the manuscript and developed the MCMC and spectral stacking analysis code described here. M.C.M. and K.L.K.L. performed the laboratory experiments and theoretical calculations for several of the catalogues used in this analysis, and helped revise the manuscript. A.M.B and B.A.M. performed the astronomical observations and subsequent data reduction. E.H. determined and/or estimated rate coefficients and designed many of the original chemical simulations. A.M.B. and C.N.S. contributed or undertook the astronomical modelling and simulations. E.R.W., M.A.C., S.B.C., S.K. and B.A.M. contributed to the design of the GOTHAM survey, and helped revise the manuscript. C.X. modified and contributed the chemical networks of the related species and helped revise the manuscript. C.X. and A.J.R. performed the astronomical observations.

Corresponding authors

Correspondence to Ryan A. Loomis or Brett A. McGuire.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Astronomy thanks the anonymous reviewers for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Parameter covariances and marginalized posterior distributions for the HC9N MCMC fit.

16th, 50th, and 84th confidence intervals (corresponding to ± 1 sigma for a Gaussian posterior distribution) are shown as vertical lines.

Extended Data Fig. 2 HC9N best-fit parameters from MCMC analysis.

The quoted uncertainties represent the 16th and 84th percentile (1σ for a Gaussian distribution) uncertainties. †Column density values are highly covariant with the derived source sizes. The marginalized uncertainties on the column densities are therefore dominated by the largely unconstrained nature of the source sizes, and not by the signal-to-noise of the observations. ‡Uncertainties derived by adding the uncertainties of the individual components in quadrature.

Extended Data Fig. 3 Parameter covariances and marginalized posterior distributions for the HC13N MCMC fit.

The 97.8th confidence interval (corresponding to 2σ for a Gaussian posterior distribution) is shown as a vertical line.

Extended Data Fig. 4 Parameter covariances and marginalized posterior distributions for the HC11N MCMC fit.

16th, 50th, and 84th confidence intervals (corresponding to ± 1σ for a Gaussian posterior distribution) are shown as vertical lines.

Extended Data Fig. 5 HC11N best-fit parameters from MCMC analysis.

The quoted uncertainties represent the 16th and 84th percentile (1σ for a Gaussian distribution) uncertainties. Values in the table are also available in the files provided at ref. 42. †Column density values are highly covariant with the derived source sizes. The marginalized uncertainties on the column densities are therefore dominated by the largely unconstrained nature of the source sizes, and not by the signal-to-noise of the observations. ‡Uncertainties derived by adding the uncertainties of the individual components in quadrature.

Supplementary information

Supplementary Information

Supplementary Figs. 1–25, Tables 1–7 and discussion.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Loomis, R.A., Burkhardt, A.M., Shingledecker, C.N. et al. An investigation of spectral line stacking techniques and application to the detection of HC11N. Nat Astron 5, 188–196 (2021). https://doi.org/10.1038/s41550-020-01261-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41550-020-01261-4

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing