Tibor Dome, Simon May, Alex Laguë, David J. E. Marsh, Sarah Johnston, Sownak Bose, Alex Tocher, Anastasia Fialkov
{"title":"超轻轴子混合暗物质模型的改进型光环模型校准","authors":"Tibor Dome, Simon May, Alex Laguë, David J. E. Marsh, Sarah Johnston, Sownak Bose, Alex Tocher, Anastasia Fialkov","doi":"arxiv-2409.11469","DOIUrl":null,"url":null,"abstract":"We study the implications of relaxing the requirement for ultralight axions\nto account for all dark matter in the Universe by examining mixed dark matter\n(MDM) cosmologies with axion fractions $f \\leq 0.3$ within the fuzzy dark\nmatter (FDM) window $10^{-25}$ eV $\\lesssim m \\lesssim 10^{-23}$ eV. Our\nsimulations, using a new MDM gravity solver implemented in AxiREPO, capture\nwave dynamics across various scales with high accuracy down to redshifts\n$z\\approx 1$. We identify halos with Rockstar using the CDM component and find\ngood agreement of inferred halo mass functions (HMFs) and concentration-mass\nrelations with theoretical models across redshifts $z=1-10$. This justifies our\nhalo finder approach a posteriori as well as the assumptions underlying the MDM\nhalo model AxionHMcode. Using the inferred axion halo mass - cold halo mass\nrelation $M_{\\text{a}}(M_{\\text{c}})$ and calibrating a generalised smoothing\nparameter $\\alpha$ to our MDM simulations, we present a new version of\nAxionHMcode. The code exhibits excellent agreement with simulations on scales\n$k< 20 \\ h$ cMpc$^{-1}$ at redshifts $z=1-3.5$ for $f\\leq 0.1$ around the\nfiducial axion mass $m = 10^{-24.5}$ eV $ = 3.16\\times 10^{-25}$ eV, with\nmaximum deviations remaining below 10%. For axion fractions $f\\leq 0.3$, the\nmodel maintains accuracy with deviations under 20% at redshifts $z\\approx 1$\nand scales $k< 10 \\ h$ cMpc$^{-1}$, though deviations can reach up to 30% for\nhigher redshifts when $f=0.3$. Reducing the run-time for a single evaluation of\nAxionHMcode to below $1$ minute, these results highlight the potential of\nAxionHMcode to provide a robust framework for parameter sampling across MDM\ncosmologies in Bayesian constraint and forecast analyses.","PeriodicalId":501207,"journal":{"name":"arXiv - PHYS - Cosmology and Nongalactic Astrophysics","volume":"20 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Improved Halo Model Calibrations for Mixed Dark Matter Models of Ultralight Axions\",\"authors\":\"Tibor Dome, Simon May, Alex Laguë, David J. E. Marsh, Sarah Johnston, Sownak Bose, Alex Tocher, Anastasia Fialkov\",\"doi\":\"arxiv-2409.11469\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We study the implications of relaxing the requirement for ultralight axions\\nto account for all dark matter in the Universe by examining mixed dark matter\\n(MDM) cosmologies with axion fractions $f \\\\leq 0.3$ within the fuzzy dark\\nmatter (FDM) window $10^{-25}$ eV $\\\\lesssim m \\\\lesssim 10^{-23}$ eV. Our\\nsimulations, using a new MDM gravity solver implemented in AxiREPO, capture\\nwave dynamics across various scales with high accuracy down to redshifts\\n$z\\\\approx 1$. We identify halos with Rockstar using the CDM component and find\\ngood agreement of inferred halo mass functions (HMFs) and concentration-mass\\nrelations with theoretical models across redshifts $z=1-10$. This justifies our\\nhalo finder approach a posteriori as well as the assumptions underlying the MDM\\nhalo model AxionHMcode. Using the inferred axion halo mass - cold halo mass\\nrelation $M_{\\\\text{a}}(M_{\\\\text{c}})$ and calibrating a generalised smoothing\\nparameter $\\\\alpha$ to our MDM simulations, we present a new version of\\nAxionHMcode. The code exhibits excellent agreement with simulations on scales\\n$k< 20 \\\\ h$ cMpc$^{-1}$ at redshifts $z=1-3.5$ for $f\\\\leq 0.1$ around the\\nfiducial axion mass $m = 10^{-24.5}$ eV $ = 3.16\\\\times 10^{-25}$ eV, with\\nmaximum deviations remaining below 10%. For axion fractions $f\\\\leq 0.3$, the\\nmodel maintains accuracy with deviations under 20% at redshifts $z\\\\approx 1$\\nand scales $k< 10 \\\\ h$ cMpc$^{-1}$, though deviations can reach up to 30% for\\nhigher redshifts when $f=0.3$. Reducing the run-time for a single evaluation of\\nAxionHMcode to below $1$ minute, these results highlight the potential of\\nAxionHMcode to provide a robust framework for parameter sampling across MDM\\ncosmologies in Bayesian constraint and forecast analyses.\",\"PeriodicalId\":501207,\"journal\":{\"name\":\"arXiv - PHYS - Cosmology and Nongalactic Astrophysics\",\"volume\":\"20 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - PHYS - Cosmology and Nongalactic Astrophysics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2409.11469\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - PHYS - Cosmology and Nongalactic Astrophysics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.11469","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
我们通过研究在模糊暗物质(FDM)窗口$10^{-25}$ eV $\lesssim m \lesssim 10^{-23}$ eV范围内轴子分数为$f \leq 0.3$的混合暗物质(MDM)宇宙学,研究了放宽对超轻轴子的要求以解释宇宙中所有暗物质的影响。我们的模拟使用了在AxiREPO中实现的一种新的MDM引力求解器,能够高精度地捕捉到各种尺度的波动力学,直至红移$z(约1$)。我们用 "摇滚之星"(Rockstar)利用CDM部分识别光环,发现推断出的光环质量函数(HMF)和浓度-质量关系与理论模型在红移$z=1-10$之间非常吻合。这证明了我们的后验光晕发现方法以及MDM光晕模型AxionHMcode的假设是正确的。利用推断出的轴晕质量-冷晕质量相关性$M_{\text{a}}(M_{\text{c}})$,并校准一个通用的平滑参数$\alpha$到我们的MDM模拟,我们提出了一个新版本的AxionHMcode。在红移$z=1-3.5$、轴子质量$m = 10^{-24.5}$ eV $ = 3.16乘以10^{-25}$eV的条件下,该代码在尺度$k< 20\ h$ cMpc$^{-1}$ 和$f\leq 0.1$周围的模拟结果显示出极好的一致性,最大偏差保持在10%以下。对于轴子分数$f\leq 0.3$,该模型在红移$z\approx 1$和尺度$k< 10 \ h$ cMpc$^{-1}$时保持了低于20%的精确度,尽管当$f=0.3$时,在更高的红移下偏差可能达到30%。这些结果使AxionHMcode的单次评估运行时间缩短到1分钟以下,突出了AxionHMcode在贝叶斯约束和预测分析中为跨MDM宇宙学的参数采样提供稳健框架的潜力。
Improved Halo Model Calibrations for Mixed Dark Matter Models of Ultralight Axions
We study the implications of relaxing the requirement for ultralight axions
to account for all dark matter in the Universe by examining mixed dark matter
(MDM) cosmologies with axion fractions $f \leq 0.3$ within the fuzzy dark
matter (FDM) window $10^{-25}$ eV $\lesssim m \lesssim 10^{-23}$ eV. Our
simulations, using a new MDM gravity solver implemented in AxiREPO, capture
wave dynamics across various scales with high accuracy down to redshifts
$z\approx 1$. We identify halos with Rockstar using the CDM component and find
good agreement of inferred halo mass functions (HMFs) and concentration-mass
relations with theoretical models across redshifts $z=1-10$. This justifies our
halo finder approach a posteriori as well as the assumptions underlying the MDM
halo model AxionHMcode. Using the inferred axion halo mass - cold halo mass
relation $M_{\text{a}}(M_{\text{c}})$ and calibrating a generalised smoothing
parameter $\alpha$ to our MDM simulations, we present a new version of
AxionHMcode. The code exhibits excellent agreement with simulations on scales
$k< 20 \ h$ cMpc$^{-1}$ at redshifts $z=1-3.5$ for $f\leq 0.1$ around the
fiducial axion mass $m = 10^{-24.5}$ eV $ = 3.16\times 10^{-25}$ eV, with
maximum deviations remaining below 10%. For axion fractions $f\leq 0.3$, the
model maintains accuracy with deviations under 20% at redshifts $z\approx 1$
and scales $k< 10 \ h$ cMpc$^{-1}$, though deviations can reach up to 30% for
higher redshifts when $f=0.3$. Reducing the run-time for a single evaluation of
AxionHMcode to below $1$ minute, these results highlight the potential of
AxionHMcode to provide a robust framework for parameter sampling across MDM
cosmologies in Bayesian constraint and forecast analyses.