Determining the size distribution of asteroids is key for understanding the collisional history and evolution of the inner Solar System. We aim at improving our knowledge on the size distribution of small asteroids in the Main Belt by determining the parallaxes of newly detected asteroids in the Hubble Space Telescope (HST) Archive and hence their absolute magnitudes and sizes. Asteroids appear as curved trails in HST images due to the parallax induced by the fast orbital motion of the spacecraft. The parallax effect can be computed to obtain the distance to the asteroids by fitting simulated trajectories to the observed trails. Using distance, we can obtain the object's absolute magnitude and size estimation assuming an albedo value, along with some boundaries for its orbital parameters. In this work we analyse a set of 632 serendipitously imaged asteroids found in the ESA HST Archive. An object-detection machine learning algorithm was used to perform this task during previous work. Our raw data consists of 1,031 asteroids trails from unknown objects (not matching any entries in the MPC database). We also found 670 trails from known objects (objects featuring matching entries in the MPC). After an accuracy assessment and filtering process, our analysed HST set consists of 454 unknown objects and 178 known objects. We obtain a sample dominated by potential Main Belt objects featuring absolute magnitudes (H) mostly between 15 and 22 mag. The absolute magnitude cumulative distribution confirms the previously reported slope change for 15 < H < 18, from 0.56 to 0.26, maintained in our case down to absolute magnitudes around H = 20, hence expanding the previous results by approximately two magnitudes. HST archival observations can be used as an asteroid survey since the telescope pointings are statistically randomly oriented in the sky and they cover long periods of time.

Context . Determining the size distribution of asteroids is key to understanding the collisional history and evolution of the inner Solar System. Aims . We aim to improve our knowledge of the size distribution of small asteroids in the main belt by determining the parallaxes of newly detected asteroids in the Hubble Space Telescope (HST) archive and subsequently their absolute magnitudes and sizes. Methods . Asteroids appear as curved trails in HST images because of the parallax induced by the fast orbital motion of the spacecraft. Taking into account the trajectory of this latter, the parallax effect can be computed to obtain the distance to the asteroids by fitting simulated trajectories to the observed trails. Using distance, we can obtain the absolute magnitude of an object and an estimation of its size assuming an albedo value, along with some boundaries for its orbital parameters. Results . In this work, we analyse a set of 632 serendipitously imaged asteroids found in the ESA HST archive. Images were captured with the ACS/WFC and WFC3/UVIS instruments. A machine learning algorithm (trained with the results of a citizen science project) was used to detect objects in these images as part of a previous study. Our raw data consist of 1031 asteroid trails from unknown objects, not matching any entries in the Minor Planet Center (MPC) database using their coordinates and imaging time. We also found 670 trails from known objects (objects featuring matching entries in the MPC). After an accuracy assessment and filtering process, our analysed HST asteroid set consists of 454 unknown objects and 178 known objects. We obtain a sample dominated by potential main belt objects featuring absolute magnitudes (H) mostly between 15 and 22 mag. The absolute magnitude cumulative distribution log N ( H > H 0 ) ∝ α log( H 0 ) confirms the previously reported slope change for 15 < H < 18, from α ≈ 0.56 to α ≈ 0.26, maintained in our case down to absolute magnitudes of around H ≈ 20, and therefore expanding the previous result by approximately two magnitudes. Conclusions . HST archival observations can be used as an asteroid survey because the telescope pointings are statistically randomly oriented in the sky and cover long periods of time. They allow us to expand the current best samples of astronomical objects at no extra cost in regard to telescope time.

The recent launch of low Earth orbit satellite constellations is creating a growing threat for astronomical observations with ground-based telescopes 1–10 that has alarmed the astronomical community 11–16 . Observations affected by artificial satellites can become unusable for scientific research, wasting a growing fraction of the research budget on costly infrastructures and mitigation efforts. Here we report the first measurements, to our knowledge, of artificial satellite contamination on observations from a low Earth orbit made with the Hubble Space Telescope. With the help of volunteers on a citizen science project ( www.asteroidhunter.org ) and a deep learning algorithm, we scanned the archive of Hubble Space Telescope images taken between 2002 and 2021. We find that a fraction of 2.7% of the individual exposures with a typical exposure time of 11 minutes are crossed by satellites and that the fraction of satellite trails in the images increases with time. This fraction depends on the size of the field of view, exposure time, filter used and pointing. With the growing number of artificial satellites currently planned, the fraction of Hubble Space Telescope images crossed by satellites will increase in the next decade and will need further close study and monitoring.

This repository contains the Hubble Space Telescope (HST) observations with satellite classifications, released in the paper "The Impact of Satellite Trails on Hubble Space Telescope Observations" (DOI: 10.1038/s41550-023-01903-3). This table contains 114 607 individual HST images taken in the last 19 years and publicly released in the eHST archive by 3 October 2021, with satellite trail classifications made with machine learning and citizen science. We processed the individual images by adding the two ACS/WFC (WFC3/UVIS, respectively) apertures side-by-side, without correcting for geometric distortions and without the gap between the two detectors (hence why the satellite trails can appear discontinuous in the images). Note that these are not original images from the eHST archive, and therefore are not meant for other scientific analysis. The dataset contains 3072 HST images with satellites (2.7% of the dataset) and 3228 satellite trails in total. The classifications were visually inspected and vetted by the authors (images are flagged with the 'satellite' flag). The table contains the following columns: observation IDs: both individual exposures (simple_id) and composite images, multiple individual exposures processed and stacked (composite_id); instrument: ACS/WFC or WFC3/UVIS; start time and end time of exposure; exposure duration; right ascension (ra) and declination (dec); 'satellite' flag [empty otherwise]; no_sat: number of satellites in the image; image URL - URL for the individual HST observations used for satellite classification; additional metadata columns, as available in the eHST archive. The satellites were classified by volunteers on the Hubble Asteroid Hunter citizen science project and with a machine learning classifier. Please cite the paper (Kruk et al., https://www.nature.com/articles/s41550-023-01903-3) when using the data in this repository.

Context. Large and publicly available astronomical archives open up new possibilities to search for and study Solar System objects. However, advanced techniques are required to deal with the large amounts of data. These unbiased surveys can be used to constrain the size distribution of minor bodies, which represents a piece of the puzzle for the formation models of the Solar System. Aims. We aim to identify asteroids in archival images from the ESA Hubble Space Telescope (HST) Science data archive using data mining. Methods. We developed a citizen science project on the Zooniverse platform, Hubble Asteroid Hunter, and have asked members of the public to identify asteroid trails in archival HST images. We used the labels provided by the volunteers to train an automated deep learning model built with Google Cloud AutoML Vision to explore the entire HST archive to detect asteroids crossing the field-of-view. Results. We report the detection of 1701 new asteroid trails identified in archival HST data via our citizen science project and the subsequent machine learning exploration of the ESA HST science data archive. We detect asteroids to a magnitude of 24.5, which are statistically fainter than the populations of asteroids identified from ground-based surveys. The majority of asteroids are distributed near the ecliptic plane, as expected, where we find an approximate density of 80 asteroids per square degree. We matched 670 trails (39% of the trails found) with 454 known Solar System objects in the Minor Planet Center database; however, no matches were found for 1031 (61%) trails. The unidentified asteroids are faint, on average 1.6 magnitudes fainter than the asteroids we succeeded in identifying. They probably correspond to previously unknown objects. Conclusions. Citizen science and machine learning are very useful techniques for the systematic search for Solar System objects in existing astronomy science data archives. This work describes a method for finding new asteroids in astronomical archives that span decades; it could be effectively applied to other datasets, increasing the overall sample of well-characterised small bodies in the Solar System and refining their ephemerides.

Simulating quantum field theories on a quantum computer is one of the most exciting fundamental physics applications of quantum information science. Dynamical time evolution of quantum fields is a challenge that is beyond the capabilities of classical computing, but it can teach us important lessons about the fundamental fabric of space and time. Whether we may answer scientific questions of interest using near-term quantum computing hardware is an open question that requires a detailed simulation study of quantum noise. Here we present a large scale simulation study powered by a multi-node implementation of qsim using the Google Cloud Platform. We additionally employ newly-developed GPU capabilities in qsim and show how Tensor Processing Units — Application-specific Integrated Circuits (ASICs) specialized for Machine Learning — may be used to dramatically speed up the simulation of large quantum circuits. We demonstrate the use of high performance cloud computing for simulating ℤ 2 quantum field theories on system sizes up to 36 qubits. We find this lattice size is not able to simulate our problem and observable combination with sufficient accuracy, implying more challenging observables of interest for this theory are likely beyond the reach of classical computation using exact circuit simulation.

We introduce multinode quantum trajectory simulations with qsim, an open source high performance simulator of quantum circuits. qsim can be used as a backend of Cirq, a Python software library for writing quantum circuits. We present a novel delayed inner product algorithm for quantum trajectories which can result in an order of magnitude speedup for low noise simulation. We also provide tools to use this framework in Google Cloud Platform, with high performance virtual machines in a single mode or multinode setting. Multinode configurations are well suited to simulate noisy quantum circuits with quantum trajectories. Finally, we introduce an approximate noise model for Google's experimental quantum computing platform and compare the results of noisy simulations with experiments for several quantum algorithms on Google's Quantum Computing Service.

ABSTRACTBreast Cancers are among the most common forms of cancers impacting women with over 1 million diagnoses every year worldwide. They are complex cancers characterized by distinct clinical outcomes, morphological and molecular features. As high-throughput technologies generating data at the mRNA and protein levels become cheaper and more accessible, researchers are now able to study these entities in concert with clinical features to gain a more holistic picture of Breast Cancer and other complex diseases. In this poster, we aimed at identifying the concordance or discordance of mRNA and protein expressions that are significantly associated with Breast Cancer histological subtypes and other relevant clinical features. We employed a novel cloud-based approach to analyze these statistical associations using available genomic, proteomic, and clinical cancer data on the Google Cloud through the ISB-CGC, one of the National Cancer Institute's (NCI) Cloud Resources. Our results indicate that, considering all available clinical features, a considerable number of molecules (genes and proteins) are significantly associated with the Breast Cancer histological subtypes of infiltrating ductal carcinoma and infiltrating lobular carcinoma, two common forms associated with invasive Breast Cancer. Moreover, statistically significant associations were overrepresented for molecules involved in PI3K/AKT signaling, negative regulation of the PI3K/AKT network and extra-nuclear estrogen signaling. Taken together, these results demonstrate how powerful cloud-based analytics can be in identifying novel molecular relationships relevant for Breast Cancer. text here.

Commodity cloud computing makes it possible for science projects to easily procure highly-reliable data management infrastructure on demand. In this paper we argue that astronomy should outsource our data management needs to the commercial cloud thereby allowing us to focus on our core competencies of data calibration and science exploitation.

Commodity cloud computing, as provided by commercial vendors such as Amazon, Google, and Microsoft, has revolutionized computing in many sectors. With the advent of a new class of big data, public access astronomical facility such as LSST, DKIST, and WFIRST, there exists a real opportunity to combine these missions with cloud computing platforms and fundamentally change the way astronomical data is collected, processed, archived, and curated. Making these changes in a cross-mission, coordinated way can provide unprecedented economies of scale in personnel, data collection and management, archiving, algorithm and software development and, most importantly, science.

Many astronomy data centres still work on filesystems. Industry has moved on; current practice in computing infrastructure is to achieve Big Data scalability using object stores rather than POSIX file systems. This presents us with opportunities for portability and reuse of software underlying processing and archive systems but it also causes problems for legacy implementations in current data centers.

Flow induced by a random acceleration field (g-jitter) is considered in two related situations that are of interest for microgravity fluid experiments: the random motion of an isolated buoyant particle and coarsening of a solid-liquid mixture. We start by analyzing in detail actual accelerometer data gathered during a recent microgravity mission, and obtain the values of the parameters defining a previously introduced stochastic model of this acceleration field. We then study the motion of a solid particle suspended in an incompressible fluid that is subjected to such random accelerations. The displacement of the particle is shown to have a diffusive component if the correlation time of the stochastic acceleration is finite or zero, and mean squared velocities and effective diffusion coefficients are obtained explicitly. Finally, the effect of g-jitter on coarsening of a solid-liquid mixture is considered. Corrections due to the induced fluid motion are calculated, and estimates are given for coarsening of Sn-rich particles in a Sn-Pb eutectic fluid, experiment to be conducted in microgravity in the near future.