TOWARD DEEP LEARNING EMULATORS FOR MODELING THE LARGE-SCALE STRUCTURE OF THE UNIVERSE

Multi-billion dollar cosmological surveys are being conducted almost every decade in today’s era of precision cosmology. These surveys scan vast swaths of sky and generate tons of observational data. In order to extract meaningful information from this data and test these observations against theory, rigorous theoretical predictions are needed. In the absence of an analytic method, cosmological simulations become the most widely used tool to provide these predictions in order to test against the observations. They can be used to study covariance matrices, generate mock galaxy catalogs and provide ready-to-use snapshots for detailed redshift analyses. But cosmological simulations of matter formation in the universe are one of the most computationally intensive tasks. Faster but equally reliable tools that could approximate these simulations are thus desperately needed. Recently, deep learning has come up as an innovative and novel tool that can generate numerous cosmological simulations orders of magnitude faster than traditional simulations. Deep learning models of structure formation and evolution in the universe are unimaginably fast and retain most of the accuracy of conventional simulations, thus providing a fast, reliable, efficient, and accurate method to study the evolution of the universe and reducing the computational burden of current simulation methods. In this dissertation, we will focus on deep learning-based models that could mimic the process of structure formation in the universe. In particular, we focus on developing deep convolutional neural network models that could learn the present 3D distribution of the cold dark matter and generate 2D dark matter cosmic mass maps. We employ summary statistics most commonly employed in cosmology and computer vision to quantify the robustness of our models

A Novel RID Algorithm of Muon Trajectory Reconstruction in Water Cherenkov Detectors

Cosmic rays that strike the top of the Earth’s atmosphere generate a shower of secondary particles that move toward the surface with relativistic speeds. Water Cherenkov detectors (WCDs) on the ground can detect charged muons, which are one of the many particles generated in the shower, with the Cherenkov imaging technique. A large number of these muons travel in WCD tanks near the speed of light in a vacuum, faster than the speed of light in water, and so trigger isotropic Cherenkov radiation, which is detected by the photomultiplier tubes (PMTs) placed inside the tanks. When the radial component of the speed of the muon toward a PMT drops from superluminal to subluminal, the PMT records Cherenkov light from an optical phenomenon known as relativistic image doubling (RID), which causes two Cherenkov images of the same muon to appear suddenly, with both images moving in geometrically opposite directions on the original muon track. The quantities associated with the RID effect can be measured experimentally with a variety of detector types and can be used to find various points on the original trajectory of the muon. In this paper, a detailed study of reconstructing the trajectory of a muon entering a WCD using the RID technique has been presented. It is found that the measurements of standard RID observables enables a complete reconstruction of the trajectory of the muon to a high degree of accuracy with less than 1% error

The CAMELS Project: Public Data Release

The Cosmology and Astrophysics with Machine Learning Simulations (CAMELS) project was developed to combine cosmology with astrophysics through thousands of cosmological hydrodynamic simulations and machine learning. CAMELS contains 4233 cosmological simulations, 2049 N-body simulations, and 2184 state-of-the-art hydrodynamic simulations that sample a vast volume in parameter space. In this paper, we present the CAMELS public data release, describing the characteristics of the CAMELS simulations and a variety of data products generated from them, including halo, subhalo, galaxy, and void catalogs, power spectra, bispectra, Lyα spectra, probability distribution functions, halo radial profiles, and X-rays photon lists. We also release over 1000 catalogs that contain billions of galaxies from CAMELS-SAM: a large collection of N-body simulations that have been combined with the Santa Cruz semianalytic model. We release all the data, comprising more than 350 terabytes and containing 143,922 snapshots, millions of halos, galaxies, and summary statistics. We provide further technical details on how to access, download, read, and process the data at https://camels.readthedocs.io

Analyses of scissors cutting paper at superluminal speeds

A popular physics legend holds that scissors can cut paper with a speed faster than light. Here this counter-intuitive myth is investigated theoretically using four simple examples of scissors. For simplicity, all cases will involve a static lower scissors blade that remains horizontal just under the paper. In the first case, the upper blade will be considered perfectly rigid as it rotates around and through the paper, while in the second case, a rigid upper blade will drop down to cut the paper like a guillotine. In the third case, the paper is cut with a laser rotating with a constant angular speed that is pointed initially perpendicular to the paper at the closest point, while in the fourth case, the uniformly rotating laser is pointed initially parallel to the paper. Although details can be surprising and occasionally complex, all cases allow sections of the paper to be cut faster than light without violating special relativity. Therefore, the popular legend is confirmed, in theory, to be true

Reply to Comment on \u27Analysis of scissors cutting paper at super luminal speeds\u27

A brief reply to Chandru Iyer\u27s comment follows

Toward the Detection of Relativistic Image Doubling in Water Cerenkov Detectors

When a gamma or cosmic ray strikes the top of Earth\u27s atmosphere, a shower of secondary particles moves toward the surface. Some of these secondary particles are charged muons that subsequently enter water Cerenkov detectors (WCDs) on the ground. Many of these muons, traveling near the speed of light in vacuum, are moving faster than the speed of light in water and so trigger isotropic Cerenkov radiation in the WCDs. Inside many WCDs are photomultiplier tubes (PMTs) that detect this Cerenkov radiation. When the radial component of the speed of a muon toward a PMT drops from superluminal to subluminal, the PMT will record Cerenkov light from a little-known optical phenomenon called Relativistic Image Doubling (RID). Were the RID-detecting PMTs replaced by high resolution video recorders, they would see two Cerenkov images of the muon suddenly appear inside the tank, with one image moving with a velocity component toward the recorders, the other away. Even without a video, the RID phenomenon will cause different PMTs to record markedly different light curves for the same muon. In this paper, we present a study hoping to inspire the explicit detection and reporting of RID effects in WCDs. We consider three example cases of muon RIDs in High-Altitude Water Cerenkov (HAWC)-like systems: vertical, horizontal, and oblique. Monte Carlo simulations show that RID effects in HAWC-like systems are not rare - they occur for over 85% of all muon tracks

Erratum: Toward the detection of relativistic image doubling in water cerenkov detectors (Astrophysical Journal (2020) 898 (53) DOI: 10.3847/1538-4357/ab98fa)

In our published article, two errors were included inadvertently. 1. The formula Equation (5) for the angular locations of the two images as seen from the detector has a typographical error—it should have an arccos function on the right-hand side argument. The correct form reads (Equation Presented) In Section 3.1, example Equation (7) and the sentence including it are ill posed and should be discarded. These errors, however, do not affect the results, graphs, and other conclusions of our paper in any way. The computer programs and generalized equations of Section 2 were actually used to generate subsequent results, and not the typographically marred Equation (5) nor the ill-posed example Equation (7). We apologize for any possible inconvenience this may have caused

Toward the detection of relativistic image doubling in imaging atmospheric Cherenkov telescopes

Cosmic gamma-ray photons incident on the upper atmosphere create air showers that move to the Earth\u27s surface with superluminal speed, relative to the air. Even though many of these air showers remain superluminal all along their trajectories, the shower\u27s velocity component toward a single Imaging Atmospheric Cherenkov Telescope (IACT) may drop from superluminal to subluminal. When this happens, an IACT that is able to resolve the air shower both in time and angle should be able to document an unusual optical effect known as relativistic image doubling (RID). The logic of RID is that the shower appears to precede its own Cherenkov radiation when its speed component toward the IACT is superluminal, but appears to trail its own Cherenkov radiation when its speed component toward the IACT is subluminal. The result is that the IACT will see the shower start not at the top of the atmosphere but in the middle—at the point along the shower\u27s path where its radial velocity component drops to subluminal. Images of the shower would then be seen by the IACT to go both up and down simultaneously. A simple simulation demonstrating this effect is presented. Clear identification of RID would confirm in the atmosphere a novel optical imaging effect caused not by lenses but solely by relativistic kinematics, and may aid in the accuracy of path and speed reconstructions of the relativistic air shower

NECOLA: Toward a Universal Field-level Cosmological Emulator

We train convolutional neural networks to correct the output of fast and approximate N-body simulations at the field level. Our model, Neural Enhanced COLA (NECOLA), takes as input a snapshot generated by the computationally efficient COLA code and corrects the positions of the cold dark matter particles to match the results of full N-body Quijote simulations. We quantify the accuracy of the network using several summary statistics, and find that NECOLA can reproduce the results of the full N-body simulations with subpercent accuracy down to k ≃ 1 hMpc-1. Furthermore, the model that was trained on simulations with a fixed value of the cosmological parameters is also able to correct the output of COLA simulations with different values of ωm, ωb, h, n s , σ 8, w, and M ν with very high accuracy: the power spectrum and the cross-correlation coefficients are within ≃1% down to k = 1 hMpc-1. Our results indicate that the correction to the power spectrum from fast/approximate simulations or field-level perturbation theory is rather universal. Our model represents a first step toward the development of a fast field-level emulator to sample not only primordial mode amplitudes and phases, but also the parameter space defined by the values of the cosmological parameters

NECOLA: Toward a Universal Field-level Cosmological Emulator

We train convolutional neural networks to correct the output of fast and approximate N-body simulations at the field level. Our model, Neural Enhanced COLA (NECOLA), takes as input a snapshot generated by the computationally efficient COLA code and corrects the positions of the cold dark matter particles to match the results of full N-body Quijote simulations. We quantify the accuracy of the network using several summary statistics, and find that NECOLA can reproduce the results of the full N-body simulations with subpercent accuracy down to k ≃ 1 hMpc-1. Furthermore, the model that was trained on simulations with a fixed value of the cosmological parameters is also able to correct the output of COLA simulations with different values of ωm, ωb, h, n s , σ 8, w, and M ν with very high accuracy: the power spectrum and the cross-correlation coefficients are within ≃1% down to k = 1 hMpc-1. Our results indicate that the correction to the power spectrum from fast/approximate simulations or field-level perturbation theory is rather universal. Our model represents a first step toward the development of a fast field-level emulator to sample not only primordial mode amplitudes and phases, but also the parameter space defined by the values of the cosmological parameters