Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userDeep learning insights into cosmological structure formation
122
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
While the evolution of linear initial conditions present in the early universe into extended halos of dark matter at late times can be computed using cosmological simulations, a theoretical understanding of this complex process remains elusive. Here, we build a deep learning framework to learn this non-linear relationship, and develop techniques to physically interpret the learnt mapping. A three-dimensional convolutional neural network (CNN) is trained to predict the mass of dark matter halos from the initial conditions. We find no change in the predictive accuracy of the model if we retrain the model removing anisotropic information from the inputs. This suggests that the features learnt by the CNN are equivalent to spherical averages over the initial conditions. Our results indicate that interpretable deep learning frameworks can provide a powerful tool for extracting insight into cosmological structure formation.
rate research
Read More
The JWST MIRI instrument will revolutionize extragalactic astronomy with unprecedented sensitivity and angular resolution in mid-IR. Here, we assess the potential of MIRI photometry to constrain galaxy properties in the Cosmic Evolution Early Release Science (CEERS) survey. We derive estimated MIRI fluxes from the spectral energy distributions (SEDs) of real sources that fall in a planned MIRI pointing. We also obtain MIRI fluxes for hypothetical AGN-galaxy mixed models varying the AGN fractional contribution to the total IR luminosity ($rm frac_{AGN}$). Based on these model fluxes, we simulate CEERS imaging (3.6-hour exposure) in 6 bands from F770W to F2100W using MIRISIM, and reduce these data using JWST PIPELINE. We perform PSF-matched photometry with TPHOT, and fit the source SEDs with X-CIGALE, simultaneously modeling photometric redshift and other physical properties. Adding the MIRI data, the accuracy of both redshift and $rm frac_{AGN}$ is generally improved by factors of $gtrsim 2$ for all sources at $zlesssim 3$. Notably, for pure-galaxy inputs ($rm frac_{AGN}=0$), the accuracy of $rm frac_{AGN}$ is improved by $sim 100$ times thanks to MIRI. The simulated CEERS MIRI data are slightly more sensitive to AGN detections than the deepest X-ray survey, based on the empirical $L_{rm X}$-$L_{rm 6mu m}$ relation. Like X-ray observations, MIRI can also be used to constrain the AGN accretion power (accuracy $approx 0.3$ dex). Our work demonstrates that MIRI will be able to place strong constraints on the mid-IR luminosities from star formation and AGN, and thereby facilitate studies of the galaxy/AGN co-evolution.
We present a concept for a machine-learning classification of hard X-ray (HXR) emissions from solar flares observed by the Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI), identifying flares that are either occulted by the solar limb or located on the solar disk. Although HXR observations of occulted flares are important for particle-acceleration studies, HXR data analyses for past observations were time consuming and required specialized expertise. Machine-learning techniques are promising for this situation, and we constructed a sample model to demonstrate the concept using a deep-learning technique. Input data to the model are HXR spectrograms that are easily produced from RHESSI data. The model can detect occulted flares without the need for image reconstruction nor for visual inspection by experts. A technique of convolutional neural networks was used in this model by regarding the input data as images. Our model achieved a classification accuracy better than 90 %, and the ability for the application of the method to either event screening or for an event alert for occulted flares was successfully demonstrated.
LCDM is remarkably successful in predicting the cosmic microwave background and large-scale structure, and LCDM parameters have been determined with only mild tensions between different types of observations. Hydrodynamical simulations starting from cosmological initial conditions are increasingly able to capture the complex interactions between dark matter and baryonic matter in galaxy formation. Simulations with relatively low resolution now succeed in describing the overall galaxy population. For example, the EAGLE simulation in volumes up to 100 cubic Mpc reproduces the observed local galaxy mass function nearly as well as semi-analytic models. It once seemed that galaxies are pretty smooth, that they generally grow in size as they evolve, and that they are a combination of disks and spheroids. But recent HST observations combined with high-resolution hydrodynamic simulations are showing that most star-forming galaxies are very clumpy; that galaxies often undergo compaction which reduces their radius and increases their central density; and that most lower-mass star-forming galaxies are not spheroids or disks but are instead elongated when their centers are dominated by dark matter. We also review LCDM challenges on smaller scales: cusp-core, too big to fail, and substructure issues. Although starbursts can rapidly drive gas out of galaxy centers and thereby reduce the dark matter density, it remains to be seen whether this or other baryonic physics can explain the observed rotation curves of the entire population of dwarf and low surface brightness galaxies. If not, perhaps more complicated physics such as self-interacting dark matter may be needed. But standard LCDM appears to be successful in predicting the dark matter halo substructure that is now observed via gravitational lensing and breaks in cold stellar streams, and any alternative theory must do at least as well.
The one-body reduced density matrix (1-RDM) of a many-body system at zero temperature gives direct access to many observables, such as the charge density, kinetic energy and occupation numbers. It would be desirable to express it as a simple functional of the density or of other local observables, but to date satisfactory approximations have not yet been found. Deep learning is the state-of the art approach to perform high dimensional regressions and classification tasks, and is becoming widely used in the condensed matter community to develop increasingly accurate density functionals. Autoencoders are deep learning models that perform efficient dimensionality reduction, allowing the distillation of data to its fundamental features needed to represent it. By training autoencoders on a large data-set of 1-RDMs from exactly solvable real-space model systems, and performing principal component analysis, the machine learns to what extent the data can be compressed and hence how it is constrained. We gain insight into these machine learned constraints and employ them to inform approximations to the 1-RDM as a functional of the charge density. We exploit known physical properties of the 1-RDM in the simplest possible cases to perform feature engineering, where we inform the structure of the models from known mathematical relations, allowing us to integrate existing understanding into the machine learning methods. By comparing various deep learning approaches we gain insight into what physical features of the density matrix are most amenable to machine learning, utilising both known and learned characteristics.
Hydrodynamical simulations of galaxy formation and evolution attempt to fully model the physics that shapes galaxies. The agreement between the morphology of simulated and real galaxies, and the way the morphological types are distributed across galaxy scaling relations are important probes of our knowledge of galaxy formation physics. Here we propose an unsupervised deep learning approach to perform a stringent test of the fine morphological structure of galaxies coming from the Illustris and IllustrisTNG (TNG100 and TNG50) simulations against observations from a subsample of the Sloan Digital Sky Survey. Our framework is based on PixelCNN, an autoregressive model for image generation with an explicit likelihood. We adopt a strategy that combines the output of two PixelCNN networks in a metric that isolates the fine morphological details of galaxies from the sky background. We are able to emph{quantitatively} identify the improvements of IllustrisTNG, particularly in the high-resolution TNG50 run, over the original Illustris. However, we find that the fine details of galaxy structure are still different between observed and simulated galaxies. This difference is driven by small, more spheroidal, and quenched galaxies which are globally less accurate regardless of resolution and which have experienced little improvement between the three simulations explored. We speculate that this disagreement, that is less severe for quenched disky galaxies, may stem from a still too coarse numerical resolution, which struggles to properly capture the inner, dense regions of quenched spheroidal galaxies.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
