Papers
Topics
Authors
Recent
Search
2000 character limit reached

Machine Learning LSST 3x2pt analyses -- forecasting the impact of systematics on cosmological constraints using neural networks

Published 18 Mar 2024 in astro-ph.CO | (2403.11797v1)

Abstract: Validating modeling choices through simulated analyses and quantifying the impact of different systematic effects will form a major computational bottleneck in the preparation for 3$\times$2 analysis with Stage-IV surveys such as Vera Rubin Observatory's Legacy Survey of Space and Time (LSST). We can significantly reduce the computational requirements by using machine learning based emulators, which allow us to run fast inference while maintaining the full realism of the data analysis pipeline. In this paper, we use such an emulator to run simulated 3$\times$2 (cosmic shear, galaxy-galaxy lensing, and galaxy clustering) analyses for mock LSST-Y1/Y3/Y6/Y10 surveys and study the impact of various systematic effects (galaxy bias, intrinsic alignment, baryonic physics, shear calibration and photo-$z$ uncertainties). Closely following the DESC Science Requirement Document (with several updates) our main findings are: {\it a)} The largest contribution to the `systematic error budget' of LSST 3$\times$2 analysis comes from galaxy bias uncertainties, while the contribution of baryonic and shear calibration uncertainties are significantly less important. {\it b)} Tighter constraints on intrinsic alignment and photo-$z$ parameters can improve cosmological constraints noticeably, which illustrates synergies of LSST and spectroscopic surveys. {\it c)} The scale cuts adopted in the DESC SRD may be too conservative and pushing to smaller scales can increase cosmological information significantly. {\it d)} We investigate the impact of photo-$z$ outliers on 3$\times$2 pt analysis and find that we need to determine the outlier fraction to within $5-10\%$ accuracy to ensure robust cosmological analysis. We caution that these findings depend on analysis choices (parameterizations, priors, scale cuts) and can change for different settings.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (42)
  1. Ž. Ivezić et al., Astrophys. J.  873, 111 (2019), arXiv:0805.2366 [astro-ph] .
  2. R. Laureijs et al., Euclid definition study report (2011), arXiv:1110.3193 [astro-ph.CO] .
  3. D. Spergel et al., arXiv e-prints , arXiv:1503.03757 (2015), arXiv:1503.03757 [astro-ph.IM] .
  4. DESI Collaboration, arXiv e-prints , arXiv:1611.00036 (2016), arXiv:1611.00036 [astro-ph.IM] .
  5. The LSST Dark Energy Science Collaboration, arXiv e-prints , arXiv:1809.01669 (2018), arXiv:1809.01669 [astro-ph.CO] .
  6. R. Mandelbaum, ARA&A 56, 393 (2018), arXiv:1710.03235 [astro-ph.CO] .
  7. C. Hikage et al., PASJ 71, 43 (2019), arXiv:1809.09148 [astro-ph.CO] .
  8. C. Heymans et al., A&A 646, A140 (2021), arXiv:2007.15632 [astro-ph.CO] .
  9. E. Krause et al., arXiv e-prints , arXiv:2105.13548 (2021), arXiv:2105.13548 [astro-ph.CO] .
  10. Euclid Collaboration, A&A 642, A191 (2020), arXiv:1910.09273 [astro-ph.CO] .
  11. A. Paranjape, arXiv e-prints , arXiv:2205.07906 (2022), arXiv:2205.07906 [astro-ph.CO] .
  12. E. Schaan, S. Ferraro, and U. Seljak, JCAP 2020, 001 (2020), arXiv:2007.12795 [astro-ph.CO] .
  13. D. N. Limber, Astrophys. J.  117, 134 (1953).
  14. M. LoVerde and N. Afshordi, Phys. Rev. D 78, 123506 (2008), arXiv:0809.5112 [astro-ph] .
  15. A. Stebbins, arXiv e-prints , astro-ph/9609149 (1996), arXiv:astro-ph/9609149 [astro-ph] .
  16. V. Miranda, P. Rogozenski, and E. Krause, Mon. Not. Roy. Astron. Soc. 509, 5218 (2021), arXiv:2009.14241 [astro-ph.CO] .
  17. E. Krause and T. Eifler, MNRAS 470, 2100 (2017), arXiv:1601.05779 [astro-ph.CO] .
  18. J. Torrado and A. Lewis, JCAP 2021, 057 (2021), arXiv:2005.05290 [astro-ph.IM] .
  19. X. Fang, T. Eifler, and E. Krause, MNRAS 497, 2699 (2020a), arXiv:2004.04833 [astro-ph.CO] .
  20. Dark Energy Survey Collaboration, Phys. Rev. D 98, 043526 (2018), arXiv:1708.01530 [astro-ph.CO] .
  21. DES Collaboration, Phys. Rev. D 105, 023520 (2022), arXiv:2105.13549 [astro-ph.CO] .
  22. M. Chevallier and D. Polarski, International Journal of Modern Physics D 10, 213 (2001), arXiv:gr-qc/0009008 [gr-qc] .
  23. E. V. Linder, Phys. Rev. Lett.  90, 091301 (2003), arXiv:astro-ph/0208512 [astro-ph] .
  24. A. Lewis, Phys. Rev. D 87, 103529 (2013), arXiv:1304.4473 [astro-ph.CO] .
  25. Y. Dubois et al., MNRAS 444, 1453 (2014), arXiv:1402.1165 [astro-ph.CO] .
  26. S. Bridle and L. King, New Journal of Physics 9, 444 (2007), arXiv:0705.0166 [astro-ph] .
  27. E. Krause, T. Eifler, and J. Blazek, MNRAS 456, 207 (2016), arXiv:1506.08730 [astro-ph.CO] .
  28. Z. Vlah, N. E. Chisari, and F. Schmidt, JCAP 2020, 025 (2020), arXiv:1910.08085 [astro-ph.CO] .
  29. A. Nicola et al., JCAP 2020, 044 (2020), arXiv:1912.08209 [astro-ph.CO] .
  30. R. Mandelbaum et al., BAAS 51, 363 (2019), arXiv:1903.09323 [astro-ph.CO] .
  31. J. Newman et al., BAAS 51, 358 (2019), arXiv:1903.09325 [astro-ph.CO] .
  32. J. A. Blazek et al., arXiv e-prints , arXiv:2204.01992 (2022), arXiv:2204.01992 [astro-ph.CO] .
  33. D. J. Schlegel et al., arXiv e-prints , arXiv:2209.04322 (2022a), arXiv:2209.04322 [astro-ph.IM] .
  34. J. A. Newman and D. Gruen, ARA&A 60, 363 (2022), arXiv:2206.13633 [astro-ph.CO] .
  35. J. McCullough et al., arXiv e-prints , arXiv:2309.13109 (2023), arXiv:2309.13109 [astro-ph.CO] .
  36. S. Singh, R. Mandelbaum, and S. More, MNRAS 450, 2195 (2015), arXiv:1411.1755 [astro-ph.CO] .
  37. H. Johnston et al., A&A 624, A30 (2019), arXiv:1811.09598 [astro-ph.CO] .
  38. S. Samuroff et al., arXiv e-prints , arXiv:2212.11319 (2022), arXiv:2212.11319 [astro-ph.CO] .
  39. C. Sánchez and G. M. Bernstein, MNRAS 483, 2801 (2019), arXiv:1807.11873 [astro-ph.CO] .
  40. S. Pandey and DES Collaboration, Phys. Rev. D 102, 123522 (2020), arXiv:2008.05991 [astro-ph.CO] .
  41. G. Bernstein and D. Huterer, MNRAS 401, 1399 (2010), arXiv:0902.2782 [astro-ph.CO] .
  42. D. J. Schlegel et al., arXiv e-prints , arXiv:2209.03585 (2022b), arXiv:2209.03585 [astro-ph.CO] .

Summary

No one has generated a summary of this paper yet.

Sign Up to Summarize

Paper to Video (Beta)

No one has generated a video about this paper yet.

Sign Up to Generate All Videos Subscribe on YouTube

Whiteboard

No one has generated a whiteboard explanation for this paper yet.

Sign Up to Generate

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Generate Now

Continue Learning

We haven't generated follow-up questions for this paper yet.

Generate Now

Collections

Sign up for free to add this paper to one or more collections.

Sign Up

Tweets

Sign up for free to view the 2 tweets with 1 like about this paper.

Sign Up for Free