Related papers: Flexible Simulation Based Inference for Galaxy Pho…
Simulation-based inference (SBI) enables amortized Bayesian inference for simulators with implicit likelihoods. But when we are primarily interested in the quality of predictive simulations, or when the model cannot exactly reproduce the…
Amortized variational inference is an often employed framework in simulation-based inference that produces a posterior approximation that can be rapidly computed given any new observation. Unfortunately, there are few guarantees about the…
To achieve a fuller understanding of galaxy evolution, SED fitting can be used to recover quantities beyond stellar masses (M$_*$) and star formation rates (SFRs). We use Star Formation Histories (SFHs) reconstructed via the Dense Basis…
Foundation models provide robust embeddings for diverse tasks, including medical imaging. We evaluate embeddings from seven general and medical-specific foundation models (e.g., DenseNet121, BiomedCLIP, MedImageInsight, Rad-DINO,…
Due to the difficulty of replicating the real conditions during training, supervised algorithms for spacecraft pose estimation experience a drop in performance when trained on synthetic data and applied to real operational data. To address…
Labelling data is expensive and time consuming especially for domains such as medical imaging that contain volumetric imaging data and require expert knowledge. Exploiting a larger pool of labeled data available across multiple centers,…
Detection of small objects and objects far away in the scene is a major challenge in surveillance applications. Such objects are represented by small number of pixels in the image and lack sufficient details, making them difficult to detect…
We introduce a new physically-motivated spectral template set for fitting the spectral energy distributions (SEDs) of high-z galaxies. We use the public galaxy formation code ARES to generate star formation histories of thirteen…
Aims:The Gaia astrometric survey mission will, as a consequence of its scanning law, obtain low resolution optical (330-1000 nm) spectrophotometry of several million unresolved galaxies brighter than V=22. We present the first steps in a…
In this work, we present a scalable approach for inferring the dark energy equation-of-state parameter ($w$) from a population of strong gravitational lens images using Simulation-Based Inference (SBI). Strong gravitational lensing offers…
We explore the ability of four different inverse population synthesis codes to recover the physical properties of galaxies from their spectra by SED fitting. Three codes, DynBaS, TGASPEX, and GASPEX, have been implemented by the authors and…
We derive a simple empirical photometric redshift estimator using a training set of galaxies with multiband photometry and measured redshifts in the Hubble Deep Field (HDF). This estimator is model-independent; it does not use spectral…
Data gridding is a common task in astronomy and many other science disciplines. It refers to the resampling of irregularly sampled data to a regular grid. We present cygrid, a library module for the general purpose programming language…
Recent generative models produce near-photorealistic images, challenging the trustworthiness of photographs. Synthetic image detection (SID) has thus become an important area of research. Prior work has highlighted how synthetic images…
Deploying vision models across devices with varying resource constraints, or even on a single device where available compute fluctuates due to battery state, thermal throttling, or latency deadlines, typically requires training and…
Large-scale structure surveys measure the shapes and positions of millions of galaxies in order to constrain the cosmological model with high precision. The resulting large data volume poses a challenge for the analysis of the data, from…
BayesicFitting is a comprehensive, general-purpose toolbox for simple and standardized model fitting. Its fitting options range from simple least-squares methods, via maximum likelihood to fully Bayesian inference, working on a multitude of…
Synthetic data generation is an important application of machine learning in the field of medical imaging. While existing approaches have successfully applied fine-tuned diffusion models for synthesizing medical images, we explore potential…
Hyperspectral imaging shows promise for surgical applications to non-invasively provide spatially-resolved, spectral information. For calibration purposes, a white reference image of a highly-reflective Lambertian surface should be obtained…
Supervisory controllers control cyber-physical systems to ensure their correct and safe operation. Synthesis-based engineering (SBE) is an approach to largely automate their design and implementation. SBE combines model-based engineering…