Search Results for author:
Found 56 papers, 36 papers with code
Surrogate Modeling for Computationally Expensive Simulations of Supernovae in High-Resolution Galaxy Simulations
SNe release a substantial amount of matter and energy to the interstellar medium, resulting in significant feedback to star formation and gas dynamics in a galaxy.
SimBIG: Field-level Simulation-Based Inference of Galaxy Clustering
We demonstrate the robustness of our analysis by showcasing our ability to infer unbiased cosmological constraints from a series of test simulations that are constructed using different forward models than the one used in our training dataset.
xVal: A Continuous Number Encoding for Large Language Models
Large Language Models have not yet been broadly adapted for the analysis of scientific datasets due in part to the unique difficulties of tokenizing numbers.
Multiple Physics Pretraining for Physical Surrogate Models
We introduce multiple physics pretraining (MPP), an autoregressive task-agnostic pretraining approach for physical surrogate modeling.
AstroCLIP: Cross-Modal Pre-Training for Astronomical Foundation Models
We present AstroCLIP, a strategy to facilitate the construction of astronomical foundation models that bridge the gap between diverse observational modalities.
Reusability report: Prostate cancer stratification with diverse biologically-informed neural architectures
This suggests that different neural architectures are sensitive to different aspects of the data, an important yet under-explored challenge for clinical prediction tasks.
Learnable wavelet neural networks for cosmological inference
Convolutional neural networks (CNNs) have been shown to both extract more information than the traditional two-point statistics from cosmological fields, and marginalise over astrophysical effects extremely well.
Predicting the Initial Conditions of the Universe using a Deterministic Neural Network
Finding the initial conditions that led to the current state of the universe is challenging because it involves searching over an intractable input space of initial conditions, along with modeling their evolution via tools such as N-body simulations which are computationally expensive.
Learning Integrable Dynamics with Action-Angle Networks
Here, we propose an alternative construction for learned physical simulators that are inspired by the concept of action-angle coordinates from classical mechanics for describing integrable systems.
A Neural Network Subgrid Model of the Early Stages of Planet Formation
Planet formation is a multi-scale process in which the coagulation of $\mathrm{\mu m}$-sized dust grains in protoplanetary disks is strongly influenced by the hydrodynamic processes on scales of astronomical units ($\approx 1. 5\times 10^8 \,\mathrm{km}$).
$\texttt{Mangrove}$: Learning Galaxy Properties from Merger Trees
Efficiently mapping baryonic properties onto dark matter is a major challenge in astrophysics.
Particle clustering in turbulence: Prediction of spatial and statistical properties with deep learning
The simulation data are used to train a U-Net deep learning model to predict gridded three-dimensional representations of the particle density and velocity fields, given as input the corresponding fluid fields.
The SZ flux-mass ($Y$-$M$) relation at low halo masses: improvements with symbolic regression and strong constraints on baryonic feedback
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.
Robust Simulation-Based Inference in Cosmology with Bayesian Neural Networks
Simulation-based inference (SBI) is rapidly establishing itself as a standard machine learning technique for analyzing data in cosmological surveys.
Field Level Neural Network Emulator for Cosmological N-body Simulations
We build a field level emulator for cosmic structure formation that is accurate in the nonlinear regime.
Simple lessons from complex learning: what a neural network model learns about cosmic structure formation
We find our model generalizes well to these well understood scenarios, demonstrating that the networks have inferred general physical principles and learned the nonlinear mode couplings from the complex, random Gaussian training data.
Predicting the Thermal Sunyaev-Zel'dovich Field using Modular and Equivariant Set-Based Neural Networks
We train neural networks on the IllustrisTNG-300 cosmological simulation to predict the continuous electron pressure field in galaxy clusters from gravity-only simulations.
Rediscovering orbital mechanics with machine learning
We then use symbolic regression to discover an analytical expression for the force law implicitly learned by the neural network, which our results showed is equivalent to Newton's law of gravitation.
Augmenting astrophysical scaling relations with machine learning: application to reducing the Sunyaev-Zeldovich flux-mass scatter
Using SR on the data from the IllustrisTNG hydrodynamical simulation, we find a new proxy for cluster mass which combines $Y_\mathrm{SZ}$ and concentration of ionized gas ($c_\mathrm{gas}$): $M \propto Y_\mathrm{conc}^{3/5} \equiv Y_\mathrm{SZ}^{3/5} (1-A\, c_\mathrm{gas})$.
Learned Coarse Models for Efficient Turbulence Simulation
We show that our proposed model can simulate turbulent dynamics more accurately than classical numerical solvers at the comparably low resolutions across various scientifically relevant metrics.
Super-resolving Dark Matter Halos using Generative Deep Learning
Generative deep learning methods built upon Convolutional Neural Networks (CNNs) provide a great tool for predicting non-linear structure in cosmology.
Learned Simulators for Turbulence
We show that our proposed model can simulate turbulent dynamics more accurately than classical numerical solvers at the same low resolutions across various scientifically relevant metrics.
Inferring Black Hole Properties from Astronomical Multivariate Time Series with Bayesian Attentive Neural Processes
Among the most extreme objects in the Universe, active galactic nuclei (AGN) are luminous centers of galaxies where a black hole feeds on surrounding matter.
A Deep Learning Approach for Active Anomaly Detection of Extragalactic Transients
There is a shortage of multi-wavelength and spectroscopic followup capabilities given the number of transient and variable astrophysical events discovered through wide-field, optical surveys such as the upcoming Vera C. Rubin Observatory.
A Bayesian neural network predicts the dissolution of compact planetary systems
Our model, trained directly from short N-body time series of raw orbital elements, is more than two orders of magnitude more accurate at predicting instability times than analytical estimators, while also reducing the bias of existing machine learning algorithms by nearly a factor of three.
Learning the Evolution of the Universe in N-body Simulations
Understanding the physics of large cosmological surveys down to small (nonlinear) scales will significantly improve our knowledge of the Universe.
Neural networks as optimal estimators to marginalize over baryonic effects
For this data, we show that neural networks can 1) extract the maximum available cosmological information, 2) marginalize over baryonic effects, and 3) extract cosmological information that is buried in the regime dominated by baryonic physics.
Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies Instrumentation and Methods for Astrophysics
deep21: a Deep Learning Method for 21cm Foreground Removal
We seek to remove foreground contaminants from 21cm intensity mapping observations.
Anomaly Detection for Multivariate Time Series of Exotic Supernovae
Supernovae mark the explosive deaths of stars and enrich the cosmos with heavy elements.
The CAMELS project: Cosmology and Astrophysics with MachinE Learning Simulations
We present the Cosmology and Astrophysics with MachinE Learning Simulations --CAMELS-- project.
Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies Instrumentation and Methods for Astrophysics
HInet: Generating neutral hydrogen from dark matter with neural networks
Upcoming 21cm surveys will map the spatial distribution of cosmic neutral hydrogen (HI) over very large cosmological volumes.
Cosmology and Nongalactic Astrophysics
Predicting the long-term stability of compact multiplanet systems
Our Stability of Planetary Orbital Configurations Klassifier (SPOCK) predicts stability using physically motivated summary statistics measured in integrations of the first $10^4$ orbits, thus achieving speed-ups of up to $10^5$ over full simulations.
Earth and Planetary Astrophysics
Meta-Learning for One-Class Classification with Few Examples using Order-Equivariant Network
This paper presents a meta-learning framework for few-shots One-Class Classification (OCC) at test-time, a setting where labeled examples are only available for the positive class, and no supervision is given for the negative example.
Discovering Symbolic Models from Deep Learning with Inductive Biases
The technique works as follows: we first encourage sparse latent representations when we train a GNN in a supervised setting, then we apply symbolic regression to components of the learned model to extract explicit physical relations.
Lagrangian Neural Networks
Accurate models of the world are built upon notions of its underlying symmetries.
From Dark Matter to Galaxies with Convolutional Neural Networks
Cosmological simulations play an important role in the interpretation of astronomical data, in particular in comparing observed data to our theoretical expectations.
Learning neutrino effects in Cosmology with Convolutional Neural Networks
In this work, we propose a new method, based on a deep learning network, to quickly generate simulations with massive neutrinos from standard $\Lambda$CDM simulations without neutrinos.
Learning Symbolic Physics with Graph Networks
We introduce an approach for imposing physically motivated inductive biases on graph networks to learn interpretable representations and improved zero-shot generalization.
The Quijote simulations
The Quijote simulations are a set of 44, 100 full N-body simulations spanning more than 7, 000 cosmological models in the $\{\Omega_{\rm m}, \Omega_{\rm b}, h, n_s, \sigma_8, M_\nu, w \}$ hyperplane.
Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics
Modeling the Gaia Color-Magnitude Diagram with Bayesian Neural Flows to Constrain Distance Estimates
We demonstrate an algorithm for learning a flexible color-magnitude diagram from noisy parallax and photometry measurements using a normalizing flow, a deep neural network capable of learning an arbitrary multi-dimensional probability distribution.
HIGAN: Cosmic Neutral Hydrogen with Generative Adversarial Networks
One of the most promising ways to observe the Universe is by detecting the 21cm emission from cosmic neutral hydrogen (HI) through radio-telescopes.
The Role of Machine Learning in the Next Decade of Cosmology
In recent years, machine learning (ML) methods have remarkably improved how cosmologists can interpret data.
Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics
From Dark Matter to Galaxies with Convolutional Networks
In combination with current and upcoming data from cosmological observations, our method has the potential to answer fundamental questions about our Universe with the highest accuracy.
Learning to Predict the Cosmological Structure Formation
We build a deep neural network, the Deep Density Displacement Model (hereafter D$^3$M), to predict the non-linear structure formation of the Universe from simple linear perturbation theory.
The Simons Observatory: Science goals and forecasts
With up to an order of magnitude lower polarization noise than maps from the Planck satellite, the high-resolution sky maps will constrain cosmological parameters derived from the damping tail, gravitational lensing of the microwave background, the primordial bispectrum, and the thermal and kinematic Sunyaev-Zel'dovich effects, and will aid in delensing the large-angle polarization signal to measure the tensor-to-scalar ratio.
Cosmology and Nongalactic Astrophysics
CosmoFlow: Using Deep Learning to Learn the Universe at Scale
Deep learning is a promising tool to determine the physical model that describes our universe.
Estimating Cosmological Parameters from the Dark Matter Distribution
A major approach to estimating the cosmological parameters is to use the large-scale matter distribution of the Universe.
The clustering of galaxies in the completed SDSS-III Baryon Oscillation Spectroscopic Survey: tomographic BAO analysis of DR12 combined sample in configuration space
Splitting the sample into multiple overlapping redshift slices to extract the redshift information of galaxy clustering, we obtain a measurement of $D_A(z)/r_d$ and $H(z)r_d$ at nine effective redshifts with the full covariance matrix calibrated using MultiDark-Patchy mock catalogues.
Cosmology and Nongalactic Astrophysics
The clustering of galaxies in the completed SDSS-III Baryon Oscillation Spectroscopic Survey: cosmological analysis of the DR12 galaxy sample
When combined with supernova Ia data, we find H0 = 67. 3+/-1. 0 km/s/Mpc even for our most general dark energy model, in tension with some direct measurements.
Cosmology and Nongalactic Astrophysics
Detecting Damped Lyman-$α$ Absorbers with Gaussian Processes
We develop an automated technique for detecting damped Lyman-$\alpha$ absorbers (DLAs) along spectroscopic lines of sight to quasi-stellar objects (QSOs or quasars).
Cosmology and Nongalactic Astrophysics Data Analysis, Statistics and Probability
Evidence for the kinematic Sunyaev-Zeľdovich effect with ACTPol and velocity reconstruction from BOSS
We use microwave temperature maps from two seasons of data from the Atacama Cosmology Telescope (ACTPol) at 146 GHz, together with the Constant Mass CMASS galaxy sample from the Baryon Oscillation Spectroscopic Survey to measure the kinematic Sunyaev-Ze\v{l}dovich (kSZ) effect over the redshift range z = 0. 4 - 0. 7.
Cosmology and Nongalactic Astrophysics
Large Covariance Matrices: Smooth Models from the 2-Point Correlation Function
We introduce a new method for estimating the covariance matrix for the galaxy correlation function in surveys of large-scale structure.
Cosmology and Nongalactic Astrophysics
SDSS-III Baryon Oscillation Spectroscopic Survey Data Release 12: galaxy target selection and large scale structure catalogues
We also present the algorithms used to create large scale structure catalogues for the final Data Release (DR12) samples and the associated random catalogues that quantify the survey mask.
Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies
Optimal Ridge Detection using Coverage Risk
We introduce the concept of coverage risk as an error measure for density ridge estimation.
The one-dimensional Ly-alpha forest power spectrum from BOSS
We have developed two independent methods to measure the one-dimensional power spectrum of the transmitted flux in the Lyman-$\alpha$ forest.
Cosmology and Nongalactic Astrophysics
A First Look at creating mock catalogs with machine learning techniques
We investigate machine learning (ML) techniques for predicting the number of galaxies (N_gal) that occupy a halo, given the halo's properties.
Cosmology and Nongalactic Astrophysics
