Bohdan Bidenko, Léon V. E. Koopmans, P. Daniel Meerburg
The 21-cm brightness-temperature field of neutral hydrogen during the Epoch of Reionization and Cosmic Dawn is a rich source of cosmological and astrophysical information, primarily due to its significant non-Gaussian features. However, the complex, nonlinear nature of the underlying physical processes makes analytical modelling of this signal challenging. Consequently, studies often resort to semi-numerical simulations. Traditional analysis methods, which rely on a limited set of summary statistics, may not adequately capture the non-Gaussian content of the data, as the most informative statistics are not predetermined. This paper explores the application of machine learning (ML) to surpass the limitations of summary statistics by leveraging the inherent non-Gaussian characteristics of the 21-cm signal. We demonstrate that a well-trained neural network can independently reconstruct the hydrogen density, spin-temperature, and neutral-fraction fields with cross-coherence values exceeding 0.95 for $k$-modes below $0.5$ Mpc h$^{-1}$, based on a representative simulation at a redshift of $z approx 15$. To achieve this, the neural network utilises the non-Gaussian information in brightness temperature images over many scales. We discuss how these reconstructed fields, which vary in their sensitivity to model parameters, can be employed for parameter inference, offering more direct insights into underlying cosmological and astrophysical processes only using limited summary statistics of the brightness temperature field, such as its power spectrum.
{"title":"Inferring the density, spin-temperature and neutral-fraction fields of HI from its 21-cm brightness temperature field using machine learning","authors":"Bohdan Bidenko, Léon V. E. Koopmans, P. Daniel Meerburg","doi":"arxiv-2409.06769","DOIUrl":"https://doi.org/arxiv-2409.06769","url":null,"abstract":"The 21-cm brightness-temperature field of neutral hydrogen during the Epoch\u0000of Reionization and Cosmic Dawn is a rich source of cosmological and\u0000astrophysical information, primarily due to its significant non-Gaussian\u0000features. However, the complex, nonlinear nature of the underlying physical\u0000processes makes analytical modelling of this signal challenging. Consequently,\u0000studies often resort to semi-numerical simulations. Traditional analysis\u0000methods, which rely on a limited set of summary statistics, may not adequately\u0000capture the non-Gaussian content of the data, as the most informative\u0000statistics are not predetermined. This paper explores the application of\u0000machine learning (ML) to surpass the limitations of summary statistics by\u0000leveraging the inherent non-Gaussian characteristics of the 21-cm signal. We\u0000demonstrate that a well-trained neural network can independently reconstruct\u0000the hydrogen density, spin-temperature, and neutral-fraction fields with\u0000cross-coherence values exceeding 0.95 for $k$-modes below $0.5$ Mpc h$^{-1}$,\u0000based on a representative simulation at a redshift of $z approx 15$. To\u0000achieve this, the neural network utilises the non-Gaussian information in\u0000brightness temperature images over many scales. We discuss how these\u0000reconstructed fields, which vary in their sensitivity to model parameters, can\u0000be employed for parameter inference, offering more direct insights into\u0000underlying cosmological and astrophysical processes only using limited summary\u0000statistics of the brightness temperature field, such as its power spectrum.","PeriodicalId":501207,"journal":{"name":"arXiv - PHYS - Cosmology and Nongalactic Astrophysics","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The 408 MHz Haslam map is widely used as a low-frequency anchor for the intensity and morphology of Galactic synchrotron emission. Multi-frequency, multi-experiment fits show evidence of spatial variation and curvature in the synchrotron frequency spectrum, but there are also poorly-understood gain factors between experiments. We perform a Bayesian model comparison across a range of scenarios, using fits that include recent spectroscopic observations at $sim 1$~GHz by MeerKAT. A large uncorrected gain factor of about 60% in the Haslam data is strongly preferred, partly undermining its use as a reference template.
{"title":"Bayesian evidence for uncorrected gain factors in Galactic synchrotron template maps","authors":"Michael J. Wilensky, Melis O. Irfan, Philip Bull","doi":"arxiv-2409.06770","DOIUrl":"https://doi.org/arxiv-2409.06770","url":null,"abstract":"The 408 MHz Haslam map is widely used as a low-frequency anchor for the\u0000intensity and morphology of Galactic synchrotron emission. Multi-frequency,\u0000multi-experiment fits show evidence of spatial variation and curvature in the\u0000synchrotron frequency spectrum, but there are also poorly-understood gain\u0000factors between experiments. We perform a Bayesian model comparison across a\u0000range of scenarios, using fits that include recent spectroscopic observations\u0000at $sim 1$~GHz by MeerKAT. A large uncorrected gain factor of about 60% in\u0000the Haslam data is strongly preferred, partly undermining its use as a\u0000reference template.","PeriodicalId":501207,"journal":{"name":"arXiv - PHYS - Cosmology and Nongalactic Astrophysics","volume":"75 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Victor C. Chan, Renée Hložek, Joel Meyers, Alexander van Engelen
The Small-Correlated-Against-Large Estimator (SCALE) for small-scale lensing of the cosmic microwave background (CMB) provides a novel method for measuring the amplitude of CMB lensing power without the need for reconstruction of the lensing field. In our previous study, we showed that the SCALE method can outperform existing reconstruction methods to detect the presence of lensing at small scales ($ell gg 3000$). Here we develop a procedure to include information from SCALE in cosmological parameter inference. We construct a precise neural network emulator to quickly map cosmological parameters to desired CMB observables such as temperature and lensing power spectra and SCALE cross spectra. We also outline a method to apply SCALE to full-sky maps of the CMB temperature field, and construct a likelihood for the application of SCALE in parameter estimation. SCALE supplements conventional observables such as the CMB power spectra and baryon acoustic oscillations in constraining parameters that are sensitive to the small-scale lensing amplitude such as the neutrino mass $m_nu$. We show that including estimates of the small-scale lensing amplitude from SCALE in such an analysis provides enough constraining information to measure the minimum neutrino mass at $4sigma$ significance in the scenario of minimal mass, and higher significance for higher mass. Finally, we show that SCALE will play a powerful role in constraining models of clustering that generate scale-dependent modulation to the distribution of matter and the lensing power spectrum, as predicted by models of warm or fuzzy dark matter.
{"title":"SCALE at Scale: Cosmological applications of small-scale CMB lensing","authors":"Victor C. Chan, Renée Hložek, Joel Meyers, Alexander van Engelen","doi":"arxiv-2409.05326","DOIUrl":"https://doi.org/arxiv-2409.05326","url":null,"abstract":"The Small-Correlated-Against-Large Estimator (SCALE) for small-scale lensing\u0000of the cosmic microwave background (CMB) provides a novel method for measuring\u0000the amplitude of CMB lensing power without the need for reconstruction of the\u0000lensing field. In our previous study, we showed that the SCALE method can\u0000outperform existing reconstruction methods to detect the presence of lensing at\u0000small scales ($ell gg 3000$). Here we develop a procedure to include\u0000information from SCALE in cosmological parameter inference. We construct a\u0000precise neural network emulator to quickly map cosmological parameters to\u0000desired CMB observables such as temperature and lensing power spectra and SCALE\u0000cross spectra. We also outline a method to apply SCALE to full-sky maps of the\u0000CMB temperature field, and construct a likelihood for the application of SCALE\u0000in parameter estimation. SCALE supplements conventional observables such as the\u0000CMB power spectra and baryon acoustic oscillations in constraining parameters\u0000that are sensitive to the small-scale lensing amplitude such as the neutrino\u0000mass $m_nu$. We show that including estimates of the small-scale lensing\u0000amplitude from SCALE in such an analysis provides enough constraining\u0000information to measure the minimum neutrino mass at $4sigma$ significance in\u0000the scenario of minimal mass, and higher significance for higher mass. Finally,\u0000we show that SCALE will play a powerful role in constraining models of\u0000clustering that generate scale-dependent modulation to the distribution of\u0000matter and the lensing power spectrum, as predicted by models of warm or fuzzy\u0000dark matter.","PeriodicalId":501207,"journal":{"name":"arXiv - PHYS - Cosmology and Nongalactic Astrophysics","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Chaves-Montero, L. Cabayol-Garcia, M. Lokken, A. Font-Ribera, J. Aguilar, S. Ahlen, D. Bianchi, D. Brooks, T. Claybaugh, S. Cole, A. de la Macorra, S. Ferraro, J. E. Forero-Romero, E. Gaztañaga, S. Gontcho A Gontcho, G. Gutierrez, K. Honscheid, R. Kehoe, D. Kirkby, A. Kremin, A. Lambert, M. Landriau, M. Manera, P. Martini, R. Miquel, A. Muñoz-Gutiérrez, G. Niz, I. Pérez-Ràfols, G. Rossi, E. Sanchez, M. Schubnell, D. Sprayberry, G. Tarlé, B. A. Weaver
On large scales, measurements of the Lyman-$alpha$ forest offer insights into the expansion history of the Universe, while on small scales, these impose strict constraints on the growth history, the nature of dark matter, and the sum of neutrino masses. This work introduces ForestFlow, a cosmological emulator designed to bridge the gap between large- and small-scale Lyman-$alpha$ forest analyses. Using conditional normalizing flows, ForestFlow emulates the 2 Lyman-$alpha$ linear biases ($b_delta$ and $b_eta$) and 6 parameters describing small-scale deviations of the 3D flux power spectrum ($P_mathrm{3D}$) from linear theory. These 8 parameters are modeled as a function of cosmology $unicode{x2013}$ the small-scale amplitude and slope of the linear power spectrum $unicode{x2013}$ and the physics of the intergalactic medium. Thus, in combination with a Boltzmann solver, ForestFlow can predict $P_mathrm{3D}$ on arbitrarily large (linear) scales and the 1D flux power spectrum ($P_mathrm{1D}$) $unicode{x2013}$ the primary observable for small-scale analyses $unicode{x2013}$ without the need for interpolation or extrapolation. Consequently, ForestFlow enables for the first time multiscale analyses. Trained on a suite of 30 fixed-and-paired cosmological hydrodynamical simulations spanning redshifts from $z=2$ to $4.5$, ForestFlow achieves $3$ and $1.5%$ precision in describing $P_mathrm{3D}$ and $P_mathrm{1D}$ from linear scales to $k=5,mathrm{Mpc}^{-1}$ and $k_parallel=4,mathrm{Mpc}^{-1}$, respectively. Thanks to its parameterization, the precision of the emulator is also similar for both ionization histories and two extensions to the $Lambda$CDM model $unicode{x2013}$ massive neutrinos and curvature $unicode{x2013}$ not included in the training set. ForestFlow will be crucial for the cosmological analysis of Lyman-$alpha$ forest measurements from the DESI survey.
{"title":"ForestFlow: cosmological emulation of Lyman-$α$ forest clustering from linear to nonlinear scales","authors":"J. Chaves-Montero, L. Cabayol-Garcia, M. Lokken, A. Font-Ribera, J. Aguilar, S. Ahlen, D. Bianchi, D. Brooks, T. Claybaugh, S. Cole, A. de la Macorra, S. Ferraro, J. E. Forero-Romero, E. Gaztañaga, S. Gontcho A Gontcho, G. Gutierrez, K. Honscheid, R. Kehoe, D. Kirkby, A. Kremin, A. Lambert, M. Landriau, M. Manera, P. Martini, R. Miquel, A. Muñoz-Gutiérrez, G. Niz, I. Pérez-Ràfols, G. Rossi, E. Sanchez, M. Schubnell, D. Sprayberry, G. Tarlé, B. A. Weaver","doi":"arxiv-2409.05682","DOIUrl":"https://doi.org/arxiv-2409.05682","url":null,"abstract":"On large scales, measurements of the Lyman-$alpha$ forest offer insights\u0000into the expansion history of the Universe, while on small scales, these impose\u0000strict constraints on the growth history, the nature of dark matter, and the\u0000sum of neutrino masses. This work introduces ForestFlow, a cosmological\u0000emulator designed to bridge the gap between large- and small-scale\u0000Lyman-$alpha$ forest analyses. Using conditional normalizing flows, ForestFlow\u0000emulates the 2 Lyman-$alpha$ linear biases ($b_delta$ and $b_eta$) and 6\u0000parameters describing small-scale deviations of the 3D flux power spectrum\u0000($P_mathrm{3D}$) from linear theory. These 8 parameters are modeled as a\u0000function of cosmology $unicode{x2013}$ the small-scale amplitude and slope of\u0000the linear power spectrum $unicode{x2013}$ and the physics of the\u0000intergalactic medium. Thus, in combination with a Boltzmann solver, ForestFlow\u0000can predict $P_mathrm{3D}$ on arbitrarily large (linear) scales and the 1D\u0000flux power spectrum ($P_mathrm{1D}$) $unicode{x2013}$ the primary observable\u0000for small-scale analyses $unicode{x2013}$ without the need for interpolation\u0000or extrapolation. Consequently, ForestFlow enables for the first time\u0000multiscale analyses. Trained on a suite of 30 fixed-and-paired cosmological\u0000hydrodynamical simulations spanning redshifts from $z=2$ to $4.5$, ForestFlow\u0000achieves $3$ and $1.5%$ precision in describing $P_mathrm{3D}$ and\u0000$P_mathrm{1D}$ from linear scales to $k=5,mathrm{Mpc}^{-1}$ and\u0000$k_parallel=4,mathrm{Mpc}^{-1}$, respectively. Thanks to its\u0000parameterization, the precision of the emulator is also similar for both\u0000ionization histories and two extensions to the $Lambda$CDM model\u0000$unicode{x2013}$ massive neutrinos and curvature $unicode{x2013}$ not\u0000included in the training set. ForestFlow will be crucial for the cosmological\u0000analysis of Lyman-$alpha$ forest measurements from the DESI survey.","PeriodicalId":501207,"journal":{"name":"arXiv - PHYS - Cosmology and Nongalactic Astrophysics","volume":"72 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Daniele Sorini, Sownak Bose, Romeel Davé, Daniel Anglés Alcázar
The radial distribution of gas within galactic haloes is connected to the star formation rate and the nature of baryon-driven feedback processes. Using six variants of the hydrodynamic simulation Simba, we study the impact of different stellar/AGN feedback prescriptions on the gas density profiles of haloes in the total mass range $10^{11} , mathrm{M}_{odot} <