Science Space Solar System Mars NASA's Mars Reconnaissance Orbiter snaps 100,000th image A high school student suggested the steep sand dunes of Syrtis Major for the milestone image. Breakthroughs, discoveries, and DIY tips sent every weekday. NASA's Mars Reconnaissance Orbiter (MRO) officially went into service above the Red Planet in November 2006. The spacecraft has since spent nearly 20 years circling Earth's closest neighbor, studying its geology and identifying icy evidence of a once watery world . After already sending back more than 450 terabits of data over the course of its ongoing mission, the orbiter recently passed a major milestone: its 100,000th image of the Martian surface.

Precise neutrino energy reconstruction is essential for next-generation long-baseline oscillation experiments, yet current methods remain limited by large uncertainties in neutrino-nucleus interaction modeling. Even so, it is well established that different interaction channels produce systematically varying amounts of missing energy and therefore yield different reconstruction performance--information that standard calorimetric approaches do not exploit. We introduce a strategy that incorporates this structure by classifying events according to their underlying interaction type prior to energy reconstruction. Using supervised machine-learning techniques trained on labeled generator events, we leverage intrinsic kinematic differences among quasi-elastic scattering, meson-exchange current, resonance production, and deep-inelastic scattering processes. A cross-generator testing framework demonstrates that this classification approach is robust to microphysics mismodeling and, when applied to a simulated DUNE $ν_μ$ disappearance analysis, yields improved accuracy and sensitivity. These results highlight a practical path toward reducing reconstruction-driven systematics in future oscillation measurements.

Seasonal frosting and defrosting on the surface of Mars is hypothesized to drive both climate processes and the formation and evolution of geomorphological features such as gullies. Past studies have focused on manually analyzing the behavior of the frost cycle in the northern mid-latitude region of Mars using high-resolution visible observations from orbit. Extending these studies globally requires automating the detection of frost using data science techniques such as convolutional neural networks. However, visible indications of frost presence can vary significantly depending on the geologic context on which the frost is superimposed. In this study, we (1) present a novel approach for spatially partitioning data to reduce biases in model performance estimation, (2) illustrate how geologic context affects automated frost detection, and (3) propose mitigations to observed biases in automated frost detection.

Image segmentation aims to partition an image according to the objects in the scene and is a fundamental step in analysing very high spatial-resolution (VHR) remote sensing imagery. Current methods struggle to effectively consider land objects with diverse shapes and sizes. Additionally, the determination of segmentation scale parameters frequently adheres to a static and empirical doctrine, posing limitations on the segmentation of large-scale remote sensing images and yielding algorithms with limited interpretability. To address the above challenges, we propose a deep-learning-based region merging method dubbed DeepMerge to handle the segmentation of complete objects in large VHR images by integrating deep learning and region adjacency graph (RAG). This is the first method to use deep learning to learn the similarity and merge similar adjacent super-pixels in RAG. We propose a modified binary tree sampling method to generate shift-scale data, serving as inputs for transformer-based deep learning networks, a shift-scale attention with 3-Dimension relative position embedding to learn features across scales, and an embedding to fuse learned features with hand-crafted features. DeepMerge can achieve high segmentation accuracy in a supervised manner from large-scale remotely sensed images and provides an interpretable optimal scale parameter, which is validated using a remote sensing image of 0.55 m resolution covering an area of 5,660 km^2. The experimental results show that DeepMerge achieves the highest F value (0.9550) and the lowest total error TE (0.0895), correctly segmenting objects of different sizes and outperforming all competing segmentation methods.

Before the seasonal polar ice cap starts to expand towards lower latitudes on Mars, small frost patches may condensate out during the cold night and they may remain on the surface even during the day in shady areas. If ice in these areas can persist before the arrival of the contiguous ice cap, they may remain after the recession of it too, until the irradiation increases and the ice is met with direct sunlight. In case these small patches form periodically at the same location, slow chemical changes might occur as well. To see the spatial and temporal occurrence of such ice patches, large number of optical images should be searched for and checked. The aim of this study is to survey the ice condensation period on the surface with an automatized method using a Convolutional Neural Network (CNN) applied to High-Resolution Imaging Science Experiment (HiRISE) imagery from the Mars Reconnaissance Orbiter mission. The CNN trained to recognise small ice patches is automatizing the search, making it feasible to analyse large datasets. Previously a manual image analysis was conducted on 110 images from the southern hemisphere, captured by the HiRISE camera. Out of these, 37 images were identified with smaller ice patches, which were used to train the CNN. This approach is applied now to find further images with potential water ice patches in the latitude band between -40{\deg} and -60{\deg}, but contrarily to the training dataset recorded between 140-200{\deg} solar longitude, the images were taken from the condensation period between Ls = 0{\deg} to 90{\deg}. The model was ran on 171 new HiRISE images randomly picked from the given period between -40{\deg} and -60{\deg} latitude band, creating 73155 small image chunks. The model classified 2 images that show small, probably recently condensed frost patches and 327 chunks were predicted to show ice with more than 60% probability.

Existing information retrieval (IR) models often assume a homogeneous format, limiting their applicability to diverse user needs, such as searching for images with text descriptions, searching for a news article with a headline image, or finding a similar photo with a query image. To approach such different information-seeking demands, we introduce UniIR, a unified instruction-guided multimodal retriever capable of handling eight distinct retrieval tasks across modalities. UniIR, a single retrieval system jointly trained on ten diverse multimodal-IR datasets, interprets user instructions to execute various retrieval tasks, demonstrating robust performance across existing datasets and zero-shot generalization to new tasks. Our experiments highlight that multi-task training and instruction tuning are keys to UniIR's generalization ability. Additionally, we construct the M-BEIR, a multimodal retrieval benchmark with comprehensive results, to standardize the evaluation of universal multimodal information retrieval.

The search for ephemeral liquid water on Mars is an ongoing activity. After the recession of the seasonal polar ice cap on Mars, small water ice patches may be left behind in shady places due to the low thermal conductivity of the Martian surface and atmosphere. During late spring and early summer, these patches may be exposed to direct sunlight and warm up rapidly enough for the liquid phase to emerge. To see the spatial and temporal occurrence of such ice patches, optical images should be searched for and checked. Previously a manual image analysis was conducted on 110 images from the southern hemisphere, captured by the High Resolution Imaging Science Experiment (HiRISE) camera onboard the Mars Reconnaissance Orbiter space mission. Out of these, 37 images were identified with smaller ice patches, which were distinguishable by their brightness, colour and strong connection to local topographic shading. In this study, a convolutional neural network (CNN) is applied to find further images with potential water ice patches in the latitude band between -40{\deg} and -60{\deg}, where the seasonal retreat of the polar ice cap happens. Previously analysed HiRISE images were used to train the model, where each image was split into hundreds of pieces (chunks), expanding the training dataset to 6240 images. A test run conducted on 38 new HiRISE images indicates that the program can generally recognise small bright patches, however further training might be needed for more precise identification. This further training has been conducted now, incorporating the results of the previous test run. To retrain the model, 18646 chunks were analysed and 48 additional epochs were ran. In the end the model produced a 94% accuracy in recognising ice, 58% of these images showed small enough ice patches on them. The rest of the images was covered by too much ice or showed CO2 ice sublimation in some places.

In this work we investigate different machine learning based strategies for denoising raw simulation data from ProtoDUNE experiment. Proto-DUNE detector is hosted by CERN and it aims to test and calibrate the technologies for DUNE, a forthcoming experiment in neutrino physics. Our models leverage deep learning algorithms to make the first step in the reconstruction workchain, which consists in converting digital detector signals into physical high level quantities. We benchmark this approach against traditional algorithms implemented by the DUNE collaboration. We test the capabilities of graph neural networks, while exploiting multi-GPU setups to accelerate training and inference processes.

A dry decade in the Navajo Nation has killed vegetation, dessicated soils, and released once-stable sand into the wind. This sand now covers one-third of the Nation's land, threatening roads, gardens and hundreds of homes. Many arid regions have similar problems: global warming has increased dune movement across farmland in Namibia and Angola, and the southwestern US. Current dune models, unfortunately, do not scale well enough to provide useful forecasts for the $\sim$5\% of land surfaces covered by mobile sand. We test the ability of two deep learning algorithms, a GAN and a CNN, to model the motion of sand dunes. The models are trained on simulated data from community-standard cellular automaton model of sand dunes. Preliminary results show the GAN producing reasonable forward predictions of dune migration at ten million times the speed of the existing model.

NASA has released several stunning new images of Mars captured by the InSight lander's robotic arm as it snapped a photos of its new workspace. A winter wonderland sits amid a sandy Martian surface -- at least, that's the story new images released by the European Space Agency (ESA) from the Red Planet seem to tell. The stunning photos, which reveal a 50-mile-wide crater filled with ice, were shared by the ESA's Mars Express spacecraft on Thursday. The Korolev crater is located on the northern lowlands of Mars, and it's consistently covered in a blanket of ice about a mile thick, the ESA said in a recent news release. "A beautiful #winter wonderland... on #Mars!" the ESA announced in a tweet, which was shared nearly 10,000 times as of Friday afternoon.