92 citations · 800 across the 37 of their papers we have counts for
20 papers · 1 filter
Estimating the triaxiality of massive clusters from 2D observables in MillenniumTNG with machine learning
Ana Maria Delgado, Michelle Ntampaka, Sownak Bose +7
Properties of massive galaxy clusters, such as mass abundance and concentration, are sensitive to cosmology, making cluster statistics a powerful tool for cosmological studies. How…
A Multi-Wavelength Technique for Estimating Galaxy Cluster Mass Accretion Rates
John Soltis, Michelle Ntampaka, Benedikt Diemer +7
The mass accretion rate of galaxy clusters is a key factor in determining their structure, but a reliable observational tracer has yet to be established. We present a state-of-the-…
Reconstructing Galaxy Cluster Mass Maps using Score-based Generative Modeling
Alan Hsu, Matthew Ho, Joyce Lin +4
We present a novel approach to reconstruct gas and dark matter projected density maps of galaxy clusters using score-based generative modeling. Our diffusion model takes in mock SZ…
LoVoCCS. II. Weak Lensing Mass Distributions, Red-Sequence Galaxy Distributions, and Their Alignment with the Brightest Cluster Galaxy in 58 Nearby X-ray-Luminous Galaxy Clusters
Shenming Fu, Ian Dell'Antonio, Zacharias Escalante +31
The Local Volume Complete Cluster Survey (LoVoCCS) is an on-going program to observe nearly a hundred low-redshift X-ray-luminous galaxy clusters (redshifts and X-ray…
Painting baryons onto N-body simulations of galaxy clusters with image-to-image deep learning
Urmila Chadayammuri, Michelle Ntampaka, John ZuHone +2
Galaxy cluster mass functions are a function of cosmology, but mass is not a direct observable, and systematic errors abound in all its observable proxies. Mass-free inference can…
Benchmarks and Explanations for Deep Learning Estimates of X-ray Galaxy Cluster Masses
Matthew Ho, John Soltis, Arya Farahi +3
We evaluate the effectiveness of deep learning (DL) models for reconstructing the masses of galaxy clusters using X-ray photometry data from next-generation surveys. We establish t…
A Machine Learning Approach to Enhancing eROSITA Observations
John Soltis, Michelle Ntampaka, John Wu +5
The eROSITA X-ray telescope, launched in 2019, is predicted to observe roughly 100,000 galaxy clusters. Follow-up observations of these clusters from Chandra, for example, will be…
The Dynamical Mass of the Coma Cluster from Deep Learning
Matthew Ho, Michelle Ntampaka, Markus Michael Rau +4
In 1933, Fritz Zwicky's famous investigations of the mass of the Coma cluster led him to infer the existence of dark matter \cite{1933AcHPh...6..110Z}. His fundamental discoveries…
Emulating Sunyaev-Zeldovich Images of Galaxy Clusters using Auto-Encoders
Tibor Rothschild, Daisuke Nagai, Han Aung +3
We develop a machine learning algorithm that generates high-resolution thermal Sunyaev-Zeldovich (SZ) maps of novel galaxy clusters given only halo mass and mass accretion rate. Th…
A deep learning view of the census of galaxy clusters in IllustrisTNG
Y. Su, Y. Zhang, G. Liang +8
The origin of the diverse population of galaxy clusters remains an unexplained aspect of large-scale structure formation and cluster evolution. We present a novel method of using X…
Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning
Sheridan B. Green, Michelle Ntampaka, Daisuke Nagai +4
We present a machine learning approach for estimating galaxy cluster masses, trained using both Chandra and eROSITA mock X-ray observations of 2,041 clusters from the Magneticum si…
A Hybrid Deep Learning Approach to Cosmological Constraints From Galaxy Redshift Surveys
Michelle Ntampaka, Daniel J. Eisenstein, Sihan Yuan +1
We present a deep machine learning (ML)-based technique for accurately determining and from mock 3D galaxy surveys. The mock surveys are built from the AbacusCosmos sui…
Cluster Cosmology with the Velocity Distribution Function of the HeCS-SZ Sample
Michelle Ntampaka, Ken Rines, Hy Trac
We apply the Velocity Distribution Function (VDF) to a sample of Sunyaev-Zel'dovich (SZ)-selected clusters, and we report preliminary cosmological constraints in the - co…
A Robust and Efficient Deep Learning Method for Dynamical Mass Measurements of Galaxy Clusters
Matthew Ho, Markus Michael Rau, Michelle Ntampaka +3
We demonstrate the ability of convolutional neural networks (CNNs) to mitigate systematics in the virial scaling relation and produce dynamical mass estimates of galaxy clusters wi…
Machine Learning Applied to the Reionization History of the Universe in the 21 cm Signal
Paul La Plante, Michelle Ntampaka
The Epoch of Reionization (EoR) features a rich interplay between the first luminous sources and the low-density gas of the intergalactic medium (IGM), where photons from these sou…
A Deep Learning Approach to Galaxy Cluster X-ray Masses
M. Ntampaka, J. ZuHone, D. Eisenstein +9
We present a machine-learning approach for estimating galaxy cluster masses from Chandra mock images. We utilize a Convolutional Neural Network (CNN), a deep machine learning tool…
The Velocity Distribution Function of Galaxy Clusters as a Cosmological Probe
M. Ntampaka, H. Trac, J. Cisewski +1
We present a new approach for quantifying the abundance of galaxy clusters and constraining cosmological parameters using dynamical measurements. In the standard method, galaxy lin…
Dynamical Mass Measurements of Contaminated Galaxy Clusters Using Machine Learning
M. Ntampaka, H. Trac, D. J. Sutherland +3
We study dynamical mass measurements of galaxy clusters contaminated by interlopers and show that a modern machine learning (ML) algorithm can predict masses by better than a facto…
A Machine Learning Approach for Dynamical Mass Measurements of Galaxy Clusters
Michelle Ntampaka, Hy Trac, Danica J. Sutherland +3
We present a modern machine learning approach for cluster dynamical mass measurements that is a factor of two improvement over using a conventional scaling relation. Different meth…
A First Look at creating mock catalogs with machine learning techniques
Xiaoying Xu, Shirley Ho, Hy Trac +3
We investigate machine learning (ML) techniques for predicting the number of galaxies (N_gal) that occupy a halo, given the halo's properties. These types of mappings are crucial f…