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Augmenting astrophysical scaling relations with machine learning:
  application to reducing the Sunyaev-Zeldovich flux-mass scatter
v1v2v3 (latest)

Augmenting astrophysical scaling relations with machine learning: application to reducing the Sunyaev-Zeldovich flux-mass scatter

Proceedings of the National Academy of Sciences of the United States of America (PNAS), 2022
4 January 2022
D. Wadekar
L. Thiele
F. Villaescusa-Navarro
J. Hill
M. Cranmer
D. Spergel
N. Battaglia
D. Anglés-Alcázar
L. Hernquist
S. Ho
ArXiv (abs)PDFHTML

Papers citing "Augmenting astrophysical scaling relations with machine learning: application to reducing the Sunyaev-Zeldovich flux-mass scatter"

4 / 4 papers shown
SymbolNet: Neural Symbolic Regression with Adaptive Dynamic Pruning for Compression
SymbolNet: Neural Symbolic Regression with Adaptive Dynamic Pruning for Compression
Ho Fung Tsoi
Vladimir Loncar
S. Dasu
Philip C. Harris
513
13
0
18 Jan 2024
Deep symbolic regression for physics guided by units constraints: toward
  the automated discovery of physical laws
Deep symbolic regression for physics guided by units constraints: toward the automated discovery of physical lawsAstrophysical Journal (ApJ), 2023
Wassim Tenachi
Rodrigo Ibata
F. Diakogiannis
AI4CE
331
123
0
06 Mar 2023
The SZ flux-mass ($Y$-$M$) relation at low halo masses: improvements
  with symbolic regression and strong constraints on baryonic feedback
The SZ flux-mass (YYY-MMM) relation at low halo masses: improvements with symbolic regression and strong constraints on baryonic feedbackMonthly notices of the Royal Astronomical Society (MNRAS), 2022
D. Wadekar
L. Thiele
J. Hill
S. Pandey
F. Villaescusa-Navarro
...
M. Cranmer
D. Nagai
D. Anglés-Alcázar
S. Ho
L. Hernquist
AI4CE
179
22
0
05 Sep 2022
SymFormer: End-to-end symbolic regression using transformer-based
  architecture
SymFormer: End-to-end symbolic regression using transformer-based architectureIEEE Access (IEEE Access), 2022
Martin Vastl
Jonáš Kulhánek
Jiří Kubalík
Erik Derner
Robert Babuška
497
85
0
31 May 2022
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