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Unsupervised Searches for Cosmological Parity-Violation: An Investigation with Convolutional Neural Networks

Published 14 Dec 2023 in astro-ph.CO | (2312.09287v1)

Abstract: Recent measurements of the $4$-point correlation functions (4PCF) from spectroscopic surveys provide evidence for parity-violations in the large-scale structure of the Universe. If physical in origin, this could point to exotic physics during the epoch of inflation. However, searching for parity-violations in the 4PCF signal relies on a large suite of simulations to perform a rank test, or an accurate model of the 4PCF covariance to claim a detection, and this approach is incapable of extracting parity information from the higher-order $N$-point functions. In this work we present an unsupervised method which overcomes these issues, before demonstrating the approach is capable of detecting parity-violations in a few toy models using convolutional neural networks. This technique is complementary to the 4-point method and could be used to discover parity-violations in several upcoming surveys including DESI, Euclid and Roman.

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References (38)
  1. D. Grasso and H. R. Rubinstein, Phys. Rept. 348, 163 (2001), arXiv:astro-ph/0009061 .
  2. P. A. R. Ade et al. (Planck), Astron. Astrophys. 594, A19 (2016), arXiv:1502.01594 [astro-ph.CO] .
  3. M. Shiraishi, JCAP 06, 015 (2012), arXiv:1202.2847 [astro-ph.CO] .
  4. J. L. Cook and L. Sorbo, Phys. Rev. D 85, 023534 (2012), [Erratum: Phys.Rev.D 86, 069901 (2012)], arXiv:1109.0022 [astro-ph.CO] .
  5. N. Bartolo and G. Orlando, JCAP 07, 034 (2017), arXiv:1706.04627 [astro-ph.CO] .
  6. S. Alexander and N. Yunes, Phys. Rept. 480, 1 (2009), arXiv:0907.2562 [hep-th] .
  7. L. Bordin and G. Cabass, JCAP 07, 014 (2020), arXiv:2004.00619 [astro-ph.CO] .
  8. L. Pogosian and M. Wyman, Phys. Rev. D 77, 083509 (2008), arXiv:0711.0747 [astro-ph] .
  9. I. Y. Rybak and L. Sousa, Phys. Rev. D 104, 023507 (2021), arXiv:2104.08375 [astro-ph.CO] .
  10. O. Özsoy, Phys. Rev. D 104, 123523 (2021), arXiv:2106.14895 [astro-ph.CO] .
  11. M. Kamionkowski and T. Souradeep, Phys. Rev. D 83, 027301 (2011), arXiv:1010.4304 [astro-ph.CO] .
  12. Y. Minami and E. Komatsu, Phys. Rev. Lett. 125, 221301 (2020), arXiv:2011.11254 [astro-ph.CO] .
  13. O. H. E. Philcox, Phys. Rev. Lett. 131, 181001 (2023), arXiv:2303.12106 [astro-ph.CO] .
  14. O. H. E. Philcox and M. Shiraishi,   (2023), arXiv:2308.03831 [astro-ph.CO] .
  15. A. Nishizawa and T. Kobayashi, Phys. Rev. D 98, 124018 (2018), arXiv:1809.00815 [gr-qc] .
  16. O. H. E. Philcox, Phys. Rev. D 106, 063501 (2022), arXiv:2206.04227 [astro-ph.CO] .
  17. J. R. Eskilt and E. Komatsu, Phys. Rev. D 106, 063503 (2022), arXiv:2205.13962 [astro-ph.CO] .
  18. J. R. Eskilt, Astron. Astrophys. 662, A10 (2022), arXiv:2201.13347 [astro-ph.CO] .
  19. P. Diego-Palazuelos et al., Phys. Rev. Lett. 128, 091302 (2022), arXiv:2201.07682 [astro-ph.CO] .
  20. Z. Slepian and D. J. Eisenstein, Mon. Not. Roy. Astron. Soc. 454, 4142 (2015), arXiv:1506.02040 [astro-ph.CO] .
  21. Z. Slepian and D. J. Eisenstein, Mon. Not. Roy. Astron. Soc. 455, L31 (2016), arXiv:1506.04746 [astro-ph.CO] .
  22. F.-S. Kitaura et al., Mon. Not. Roy. Astron. Soc. 456, 4156 (2016), arXiv:1509.06400 [astro-ph.CO] .
  23. S. A. Rodríguez-Torres et al., Mon. Not. Roy. Astron. Soc. 460, 1173 (2016), arXiv:1509.06404 [astro-ph.CO] .
  24. R. N. Cahn and Z. Slepian,   (2020), arXiv:2010.14418 [astro-ph.CO] .
  25. A. Blanchard et al. (Euclid), Astron. Astrophys. 642, A191 (2020), arXiv:1910.09273 [astro-ph.CO] .
  26. R. Laureijs et al. (EUCLID),   (2011), arXiv:1110.3193 [astro-ph.CO] .
  27. A. Aghamousa et al. (DESI),   (2016), arXiv:1611.00036 [astro-ph.IM] .
  28. C. G. Lester and R. Tombs,   (2021), arXiv:2111.00616 [hep-ph] .
  29. R. Tombs and C. G. Lester, JINST 17, P08024 (2022), arXiv:2111.05442 [hep-ph] .
  30. M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu,  and X. Zheng, “TensorFlow: Large-scale machine learning on heterogeneous systems,”  (2015), software available from tensorflow.org.
  31. F. Chollet et al., “Keras,” https://keras.io (2015).
  32. P. Villanueva-Domingo and F. Villaescusa-Navarro, Astrophys. J. 937, 115 (2022), arXiv:2204.13713 [astro-ph.CO] .
  33. Z. Slepian,   (2023), arXiv:2306.05383 [astro-ph.CO] .
  34. P. Virtanen et al., Nature Methods 17, 261 (2020).
  35. C. R. Harris et al., Nature 585, 357–362 (2020).
  36. J. D. Hunter, Computing in Science Engineering 9, 90 (2007).
  37. A. F. Agarap, arXiv preprint arXiv:1803.08375  (2018).
  38. D. P. Kingma and J. Ba, arXiv preprint arXiv:1412.6980  (2014).
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