Related papers: Measuring the intracluster light fraction with mac…

We study the fraction of the intra-cluster light (ICL) formed in-situ in the three most massive clusters of the TNG50 simulation, with virial masses $\sim 10^{14}$ M$_{\odot}$. We find that a significant fraction of ICL stars ($8\%$-$28\%$)…

In-Context Learning (ICL) enables large language models (LLMs) to achieve rapid task adaptation by learning from demonstrations. With the increase in available context length of LLMs, recent experiments have shown that the performance of…

A deep clustering model conceptually consists of a feature extractor that maps data points to a latent space, and a clustering head that groups data points into clusters in the latent space. Although the two components used to be trained…

The diffuse stellar component of galaxy clusters known as intracluster light (ICL) has been proposed as an observable tracer of the cluster's dark matter (DM) halo. Assessing its reliability as a DM tracer requires understanding how the…

The intra-halo light (IHL) is the diffuse stellar component that surrounds galaxies, groups, and clusters. Its formation is intimately linked to the hierarchical assembly of the system, making it a key tracer of galaxy evolution. However,…

Within a cluster, gravitational effects can lead to the removal of stars from their parent galaxies. Gas hydrodynamical effects can additionally strip gas and dust from galaxies. The properties of the ICL can therefore help constrain the…

In-Context Learning (ICL) empowers Large Language Models (LLMs) for rapid task adaptation without Fine-Tuning (FT), but its reliance on demonstration selection remains a critical challenge. While many-shot ICL shows promising performance…

Contrastive Language Image Pre-training (CLIP) has recently demonstrated success across various tasks due to superior feature representation empowered by image-text contrastive learning. However, the instance discrimination method used by…

We are undertaking a program to measure the characteristics of the intracluster light (total flux, profile, color, and substructure) in a sample of 10 galaxy clusters with a range of cluster mass, morphology, and redshift. We present here…

The goal of this paper is to develop a machine learning model to analyze the main gravitational lens and detect dark substructure (subhalos) within simulated images of strongly lensed galaxies. Using the technique of image segmentation, we…

Large language models (LLMs) excel at few-shot in-context learning (ICL) -- learning from a few examples provided in context at inference, without any weight updates. Newly expanded context windows allow us to investigate ICL with hundreds…

Compressed Learning (CL) is a joint signal processing and machine learning framework for inference from a signal, using a small number of measurements obtained by linear projections of the signal. In this paper we present an end-to-end deep…

We propose In-Context Clustering (ICC), a flexible LLM-based procedure for clustering data from diverse distributions. Unlike traditional clustering algorithms constrained by predefined similarity measures, ICC flexibly captures complex…

Fine-tuning and in-context learning (ICL) are two prevalent methods in imbuing large language models with task-specific knowledge. It is commonly believed that fine-tuning can surpass ICL given sufficient training samples as it allows the…

The emergent ability of Large Language Models to use a small number of examples to learn to perform in novel domains and tasks, also called in-context learning (ICL). In this work, we show that a much smaller model can be trained to perform…

In this two-part series, we present a multi-probe mass modelling method for massive galaxy clusters, designed to disentangle the contributions of individual mass components (Dark matter, intra-cluster gas, stellar masses). In this first…

Strong lensing galaxy clusters provide a unique and powerful way to test simulation-derived structure predictions that follow from $\Lambda$ Cold Dark Matter ($\Lambda$CDM) cosmology. Specifically, the relative alignments of the dark matter…

Supervised learning is ubiquitous in medical image analysis. In this paper we consider the problem of meta-learning -- predicting which methods will perform well in an unseen classification problem, given previous experience with other…

Galaxy-scale strong lenses in galaxy clusters provide a unique tool to investigate their inner mass distribution and the sub-halo density profiles in the low-mass regime, which can be compared with the predictions from cosmological…

Many-shot in-context learning (ICL) has emerged as a unique setup to both utilize and test the ability of large language models to handle long context. This paper delves into long-context language model (LCLM) evaluation through many-shot…