no code implementations • 4 May 2023 • Luisa Lucie-Smith, Hiranya V. Peiris, Andrew Pontzen
We use explainable neural networks to connect the evolutionary history of dark matter halos with their density profiles.
1 code implementation • 31 Oct 2022 • Davide Piras, Hiranya V. Peiris, Andrew Pontzen, Luisa Lucie-Smith, Ningyuan Guo, Brian Nord
We develop the use of mutual information (MI), a well-established metric in information theory, to interpret the inner workings of deep learning models.
no code implementations • 9 May 2022 • Luisa Lucie-Smith, Susmita Adhikari, Risa H. Wechsler
We find two primary scales in the initial conditions (ICs) that impact the final mass profile: the density at approximately the scale of the haloes' Lagrangian patch $R_L$ ($R\sim 0. 7\, R_L$) and that in the large-scale environment ($R\sim 1. 7~R_L$).
no code implementations • 16 Mar 2022 • Luisa Lucie-Smith, Hiranya V. Peiris, Andrew Pontzen, Brian Nord, Jeyan Thiyagalingam, Davide Piras
The additional dimension in the representation contains information about the infalling material in the outer profiles of dark matter halos, thus discovering the splashback boundary of halos without prior knowledge of the halos' dynamical history.
2 code implementations • 20 Nov 2020 • Luisa Lucie-Smith, Hiranya V. Peiris, Andrew Pontzen, Brian Nord, Jeyan Thiyagalingam
We train a three-dimensional convolutional neural network (CNN) to predict the mass of dark matter halos from the initial conditions, and quantify in full generality the amounts of information in the isotropic and anisotropic aspects of the initial density field about final halo masses.
no code implementations • 5 Nov 2019 • Brian Nord, Andrew J. Connolly, Jamie Kinney, Jeremy Kubica, Gautaum Narayan, Joshua E. G. Peek, Chad Schafer, Erik J. Tollerud, Camille Avestruz, G. Jogesh Babu, Simon Birrer, Douglas Burke, João Caldeira, Douglas A. Caldwell, Joleen K. Carlberg, Yen-Chi Chen, Chuanfei Dong, Eric D. Feigelson, V. Zach Golkhou, Vinay Kashyap, T. S. Li, Thomas Loredo, Luisa Lucie-Smith, Kaisey S. Mandel, J. R. Martínez-Galarza, Adam A. Miller, Priyamvada Natarajan, Michelle Ntampaka, Andy Ptak, David Rapetti, Lior Shamir, Aneta Siemiginowska, Brigitta M. Sipőcz, Arfon M. Smith, Nhan Tran, Ricardo Vilalta, Lucianne M. Walkowicz, John ZuHone
The field of astronomy has arrived at a turning point in terms of size and complexity of both datasets and scientific collaboration.
1 code implementation • 14 Jun 2019 • Luisa Lucie-Smith, Hiranya V. Peiris, Andrew Pontzen
The addition of tidal shear information does not yield an improved halo collapse model over one based on density information alone; the difference in their predictive performance is consistent with the statistical uncertainty of the density-only based model.
Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics
1 code implementation • 12 Feb 2018 • Luisa Lucie-Smith, Hiranya V. Peiris, Andrew Pontzen, Michelle Lochner
We train a machine learning algorithm to learn cosmological structure formation from N-body simulations.
Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics