The SZ flux-mass ($Y$-$M$) relation at low halo masses: improvements with symbolic regression and strong constraints on baryonic feedback

• URL: http://arxiv.org/abs/2209.02075v2
• Date: Fri, 28 Apr 2023 14:42:46 GMT
• Title: The SZ flux-mass ($Y$-$M$) relation at low halo masses: improvements with symbolic regression and strong constraints on baryonic feedback
• Authors: Digvijay Wadekar, Leander Thiele, J. Colin Hill, Shivam Pandey, Francisco Villaescusa-Navarro, David N. Spergel, Miles Cranmer, Daisuke Nagai, Daniel Angl\'es-Alc\'azar, Shirley Ho, Lars Hernquist
• Abstract summary: AGN and supernovae feedback can affect measurements of integrated SZ flux of halos from CMB surveys. We search for analogues of the $Y-M$ relation which are more robust to feedback processes for low masses. Our results can be useful for using upcoming SZ surveys to constrain the nature of baryonic feedback.
• Score: 2.436653298863297
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Feedback from active galactic nuclei (AGN) and supernovae can affect measurements of integrated SZ flux of halos ($Y_\mathrm{SZ}$) from CMB surveys, and cause its relation with the halo mass ($Y_\mathrm{SZ}-M$) to deviate from the self-similar power-law prediction of the virial theorem. We perform a comprehensive study of such deviations using CAMELS, a suite of hydrodynamic simulations with extensive variations in feedback prescriptions. We use a combination of two machine learning tools (random forest and symbolic regression) to search for analogues of the $Y-M$ relation which are more robust to feedback processes for low masses ($M\lesssim 10^{14}\, h^{-1} \, M_\odot$); we find that simply replacing $Y\rightarrow Y(1+M_*/M_\mathrm{gas})$ in the relation makes it remarkably self-similar. This could serve as a robust multiwavelength mass proxy for low-mass clusters and galaxy groups. Our methodology can also be generally useful to improve the domain of validity of other astrophysical scaling relations. We also forecast that measurements of the $Y-M$ relation could provide percent-level constraints on certain combinations of feedback parameters and/or rule out a major part of the parameter space of supernova and AGN feedback models used in current state-of-the-art hydrodynamic simulations. Our results can be useful for using upcoming SZ surveys (e.g., SO, CMB-S4) and galaxy surveys (e.g., DESI and Rubin) to constrain the nature of baryonic feedback. Finally, we find that the an alternative relation, $Y-M_*$, provides complementary information on feedback than $Y-M$

Related papers

• Learning Galaxy Intrinsic Alignment Correlations [16.805775045014578]
  The intrinsic alignments (IA) of galaxies represent the correlation of galaxy shapes due to gravitational tidal interactions and galaxy formation processes. We present a deep learning approach to emulate galaxy position-position, position-orientation, and orientation-orientation correlation function measurements and uncertainties.
  arXiv  Detail & Related papers  (2024-04-21T16:16:56Z)
• SimBIG: Field-level Simulation-Based Inference of Galaxy Clustering [2.3988372195566443]
  We present the first simulation-based inference ( SBI) of cosmological parameters from field-level analysis of galaxy clustering. We apply SimBIG to a subset of the BOSS CMASS galaxy sample using a convolutional neural network with weight averaging to perform massive data compression of the galaxy field. This work not only presents competitive cosmological constraints but also introduces novel methods for leveraging additional cosmological information in upcoming galaxy surveys like DESI, PFS, and Euclid.
  arXiv  Detail & Related papers  (2023-10-23T18:05:32Z)
• Simulation-free Schr\"odinger bridges via score and flow matching [89.4231207928885]
  We present simulation-free score and flow matching ([SF]$2$M) Our method generalizes both the score-matching loss used in the training of diffusion models and the recently proposed flow matching loss used in the training of continuous flows. Notably, [SF]$2$M is the first method to accurately model cell dynamics in high dimensions and can recover known gene regulatory networks simulated data.
  arXiv  Detail & Related papers  (2023-07-07T15:42:35Z)
• Theory of free fermions under random projective measurements [43.04146484262759]
  We develop an analytical approach to the study of one-dimensional free fermions subject to random projective measurements of local site occupation numbers. We derive a non-linear sigma model (NLSM) as an effective field theory of the problem.
  arXiv  Detail & Related papers  (2023-04-06T15:19:33Z)
• Neural Inference of Gaussian Processes for Time Series Data of Quasars [72.79083473275742]
  We introduce a new model that enables it to describe quasar spectra completely. We also introduce a new method of inference of Gaussian process parameters, which we call $textitNeural Inference$. The combination of both the CDRW model and Neural Inference significantly outperforms the baseline DRW and MLE.
  arXiv  Detail & Related papers  (2022-11-17T13:01:26Z)
• Augmenting astrophysical scaling relations with machine learning : application to reducing the SZ flux-mass scatter [2.0223261087090303]
  We study the Sunyaev-Zeldovich flux$-$cluster mass relation ($Y_mathrmSZ-M$) We find a new proxy for cluster mass which combines $Y_mathrmSZ$ and concentration of ionized gas. We show that the dependence on $c_mathrmgas$ is linked to cores of clusters exhibiting larger scatter than their outskirts.
  arXiv  Detail & Related papers  (2022-01-04T19:00:01Z)
• Inverting brain grey matter models with likelihood-free inference: a tool for trustable cytoarchitecture measurements [62.997667081978825]
  characterisation of the brain grey matter cytoarchitecture with quantitative sensitivity to soma density and volume remains an unsolved challenge in dMRI. We propose a new forward model, specifically a new system of equations, requiring a few relatively sparse b-shells. We then apply modern tools from Bayesian analysis known as likelihood-free inference (LFI) to invert our proposed model.
  arXiv  Detail & Related papers  (2021-11-15T09:08:27Z)
• Satellite galaxy abundance dependency on cosmology in Magneticum simulations [101.18253437732933]
  We build an emulator of satellite abundance based on cosmological parameters. We find that $A$ and $beta$ depend on cosmological parameters, even if weakly. We also show that satellite abundance cosmology dependency differs between full-physics (FP) simulations, dark-matter only (DMO) and non-radiative simulations.
  arXiv  Detail & Related papers  (2021-10-11T18:00:02Z)
• Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity [67.02490430380415]
  We show that model-based MARL achieves a sample complexity of $tilde O(|S||B|(gamma)-3epsilon-2)$ for finding the Nash equilibrium (NE) value up to some $epsilon$ error. We also show that such a sample bound is minimax-optimal (up to logarithmic factors) if the algorithm is reward-agnostic, where the algorithm queries state transition samples without reward knowledge.
  arXiv  Detail & Related papers  (2020-07-15T03:25:24Z)
• Mass Estimation of Galaxy Clusters with Deep Learning I: Sunyaev-Zel'dovich Effect [0.0]
  We present a new application of deep learning to infer the masses of galaxy clusters directly from images of the microwave sky. We train and test the deep learning model using simulated images of the microwave sky that include signals from the cosmic microwave background (CMB), dusty and radio galaxies, instrumental noise as well as the cluster's own SZ signal. We verify that the model works for realistic SZ profiles even when trained on azimuthally symmetric SZ profiles by using the Magneticum hydrodynamical simulations.
  arXiv  Detail & Related papers  (2020-03-13T07:16:20Z)
• Quantum Algorithms for Simulating the Lattice Schwinger Model [63.18141027763459]
  We give scalable, explicit digital quantum algorithms to simulate the lattice Schwinger model in both NISQ and fault-tolerant settings. In lattice units, we find a Schwinger model on $N/2$ physical sites with coupling constant $x-1/2$ and electric field cutoff $x-1/2Lambda$. We estimate observables which we cost in both the NISQ and fault-tolerant settings by assuming a simple target observable---the mean pair density.
  arXiv  Detail & Related papers  (2020-02-25T19:18:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.