Can denoising diffusion probabilistic models generate realistic astrophysical fields?

Score-based generative models have emerged as alternatives to generative adversarial networks (GANs) and normalizing flows for tasks involving learning and sampling from complex image distributions. In this work we investigate the ability of these models to generate fields in two astrophysical contexts: dark matter mass density fields from cosmological simulations and images of interstellar dust. We examine the fidelity of the sampled cosmological fields relative to the true fields using three different metrics, and identify potential issues to address. We demonstrate a proof-of-concept application of the model trained on dust in denoising dust images. To our knowledge, this is the first application of this class of models to the interstellar medium.Comment: 8 pages, 3 figures, Accepted at the Machine Learning and the Physical Sciences workshop, NeurIPS 202

Found: a 'flaw' in the Taj dome not perfectly symmetrical, say scientists

Satellite imagery specialist Mandyam Rajani recalls almost flinching when her senior colleague Dilip Ahuja proposed she might find a flaw in one of India's most treasured architectural showpieces, the Taj Mahal. Ahuja, unable to dismiss something he had sensed nearly three decades ago during his second visit to the Taj, asked Rajani whether she could apply her skills in analysing images to measurements of the monument's central dome

Tiny satellites mooted to watch suspicious activity along border

Small military satellites could survey troop movement and terror training bases, says NIAS Tracking troop movements across international borders, or monitoring new or existing terror training camps is not easy. Satellites passing over target areas for only a few hours can relay time-limited intelligence, while ground-based intelligence gathering cannot always be reliable. But consider this: A series of eyes in the sky to snoop on unfriendly neighbours or hostile groups 24/7, using space-based electronic equipment on board military satellites to relay intelligence. This is a suggestion through a study by a Bengaluru-based scientific think-tank to the Indian Space Research Organisation (ISRO). And sources in the space agency say they are giving serious thought to the plan

Cosmological Field Emulation and Parameter Inference with Diffusion Models

Cosmological simulations play a crucial role in elucidating the effect of physical parameters on the statistics of fields and on constraining parameters given information on density fields. We leverage diffusion generative models to address two tasks of importance to cosmology -- as an emulator for cold dark matter density fields conditional on input cosmological parameters $\Omega_m$ and $\sigma_8$, and as a parameter inference model that can return constraints on the cosmological parameters of an input field. We show that the model is able to generate fields with power spectra that are consistent with those of the simulated target distribution, and capture the subtle effect of each parameter on modulations in the power spectrum. We additionally explore their utility as parameter inference models and find that we can obtain tight constraints on cosmological parameters.Comment: 7 pages, 5 figures, Accepted at the Machine Learning and the Physical Sciences workshop, NeurIPS 202

Differentiable Subdivision Surface Fitting

In this paper, we present a powerful differentiable surface fitting technique to derive a compact surface representation for a given dense point cloud or mesh, with application in the domains of graphics and CAD/CAM. We have chosen the Loop subdivision surface, which in the limit yields the smooth surface underlying the point cloud, and can handle complex surface topology better than other popular compact representations, such as NURBS. The principal idea is to fit the Loop subdivision surface not directly to the point cloud, but to the IMLS (implicit moving least squares) surface defined over the point cloud. As both Loop subdivision and IMLS have analytical expressions, we are able to formulate the problem as an unconstrained minimization problem of a completely differentiable function that can be solved with standard numerical solvers. Differentiability enables us to integrate the subdivision surface into any deep learning method for point clouds or meshes. We demonstrate the versatility and potential of this approach by using it in conjunction with a differentiable renderer to robustly reconstruct compact surface representations of spatial-temporal sequences of dense meshes

Stellar Reddening Based Extinction Maps for Cosmological Applications

Cosmological surveys must correct their observations for the reddening of extragalactic objects by Galactic dust. Existing dust maps, however, have been found to have spatial correlations with the large-scale structure of the Universe. Errors in extinction maps can propagate systematic biases into samples of dereddened extragalactic objects and into cosmological measurements such as correlation functions between foreground lenses and background objects and the primordial non-gaussianity parameter $f_{NL}$. Emission-based maps are contaminated by the cosmic infrared background, while maps inferred from stellar-reddenings suffer from imperfect removal of quasars and galaxies from stellar catalogs. Thus, stellar-reddening based maps using catalogs without extragalactic objects offer a promising path to making dust maps with minimal correlations with large-scale structure. We present two high-latitude integrated extinction maps based on stellar reddenings, with a point spread function of full-width half-maximum 6.1' and 15'. We employ a strict selection of catalog objects to filter out galaxies and quasars and measure the spatial correlation of our extinction maps with extragalactic structure. Our galactic extinction maps have reduced spatial correlation with large scale structure relative to most existing stellar-reddening based and emission-based extinction maps.Comment: 21 pages, 10 figure

Prototype-Sample Relation Distillation: Towards Replay-Free Continual Learning

In Continual learning (CL) balancing effective adaptation while combating catastrophic forgetting is a central challenge. Many of the recent best-performing methods utilize various forms of prior task data, e.g. a replay buffer, to tackle the catastrophic forgetting problem. Having access to previous task data can be restrictive in many real-world scenarios, for example when task data is sensitive or proprietary. To overcome the necessity of using previous tasks data, in this work, we start with strong representation learning methods that have been shown to be less prone to forgetting. We propose a holistic approach to jointly learn the representation and class prototypes while maintaining the relevance of old class prototypes and their embedded similarities. Specifically, samples are mapped to an embedding space where the representations are learned using a supervised contrastive loss. Class prototypes are evolved continually in the same latent space, enabling learning and prediction at any point. To continually adapt the prototypes without keeping any prior task data, we propose a novel distillation loss that constrains class prototypes to maintain relative similarities as compared to new task data. This method yields state-of-the-art performance in the task-incremental setting where we are able to outperform other methods that both use no data as well as approaches relying on large amounts of data. Our method is also shown to provide strong performance in the class-incremental setting without using any stored data points