Gammapy: a python package for (not only) gamma-ray astronomy

The recent discovery of high-energy astrophysical neutrinos has opened a new window to the Universe. However, the sources of those neutrinos are still unknown. Among the plausible candidates are gamma-ray bursts, active galactic nuclei and supernovae. Combining neutrino data with electromagnetic measurements in a multimessenger approach will increase our ability to identify the neutrino sources and help to solve long-standing problems in astrophysics such as the origin of cosmic rays. Neutrino observations may also contribute to future detections of gravitational wave signals, and enable the study of their source progenitors. I will review the recent progress in multimessenger astronomy using neutrino data.

High dimensionality data have become common in neuroimaging fields, especially group‐level functional magnetic resonance imaging (fMRI) datasets. fMRI connectivity analysis is a widely used, powerful technique for studying functional brain networks to probe underlying mechanisms of brain function and neuropsychological disorders. However, data‐driven technique like independent components analysis (ICA), can yield unstable and inconsistent results, confounding the true effects of interest and hindering the understanding of brain functionality and connectivity. A key contributing factor to this instability is the information loss that occurs during fMRI data reduction. Data reduction of high dimensionality fMRI data in the temporal domain to identify the important information within group datasets is necessary for such analyses and is crucial to ensure the accuracy and stability of the outputs. In this study, we describe an fMRI data reduction strategy based on an adapted neighborhood preserving embedding (NPE) algorithm. Both simulated and real data results indicate that, compared with the widely used data reduction method, principal component analysis, the NPE‐based data reduction method (a) shows superior performance on efficient data reduction, while enhancing group‐level information, (b) develops a unique stratagem for selecting components based on an adjacency graph of eigenvectors, (c) generates more reliable and reproducible brain networks under different model orders when the outputs of NPE are used for ICA, (d) is more sensitive to revealing task‐evoked activation for task fMRI, and (e) is extremely attractive and powerful for the increasingly popular fast fMRI and very large datasets.

We present results of an archival coincidence analysis between Fermi Large Area Telescope (LAT) gamma-ray data and public neutrino data from the IceCube neutrino observatory’s 40-string (IC 40) and 59-string (IC 59) observing runs. Our analysis has the potential to detect either a statistical excess of neutrino + gamma-ray (ν + γ) emitting transients or, alternatively, individual high gamma-multiplicity events, as might be produced by a neutrino observed by IceCube coinciding with a LAT-detected gamma-ray burst. Dividing the neutrino data into three data sets by hemisphere (IC 40, IC 59-North, and IC 59-South), we construct uncorrelated null distributions by Monte Carlo scrambling of the neutrino data sets. We carry out signal-injection studies against these null distributions, demonstrating sensitivity to individual ν + γ events of sufficient gamma-ray multiplicity, and to ν + γ transient populations responsible for >13% (IC 40), >9% (IC 59-North), or >8% (IC 59-South) of the gamma-coincident neutrinos observed in these data sets, respectively. Analyzing the unscrambled neutrino data, we identify no individual high-significance neutrino + high gamma-multiplicity events and no significant deviations from the test statistic null distributions. However, we observe a similar and unexpected pattern in the IC 59-North and IC 59-South residual distributions that we conclude reflects a possible correlation (p = 7.0%) between IC 59 neutrino positions and persistently bright portions of the Fermi gamma-ray sky. This possible correlation should be readily testable using eight years of further data already collected by IceCube. We are currently working with Astrophysical Multimessenger Observatory Network (AMON) partner facilities to generate low-latency ν + γ alerts from Fermi-LAT gamma-ray and IceCube and ANTARES neutrino data and distribute these in real time to AMON follow-up partners.

ImageJ is a graphical user interface (GUI) driven, public domain, Java-based, software package for general image processing traditionally used mainly in life sciences fields. The image processing capabilities of ImageJ are useful and extendable to other scientific fields. Here we present AstroImageJ (AIJ), which provides an astronomy specific image display environment and tools for astronomy specific image calibration and data reduction. Although AIJ maintains the general purpose image processing capabilities of ImageJ, AIJ is streamlined for time-series differential photometry, light curve detrending and fitting, and light curve plotting, especially for applications requiring ultra-precise light curves (e.g., exoplanet transits). AIJ reads and writes standard Flexible Image Transport System (FITS) files, as well as other common image formats, provides FITS header viewing and editing, and is World Coordinate System aware, including an automated interface to the astrometry.net web portal for plate solving images. AIJ provides research grade image calibration and analysis tools with a GUI driven approach, and easily installed cross-platform compatibility. It enables new users, even at the level of undergraduate student, high school student, or amateur astronomer, to quickly start processing, modeling, and plotting astronomical image data with one tightly integrated software package.

Background: Premature ovarian insufficiency (POI) is a disorder of increasing occurrence and has multiple long-term impacts on affected females’ health; one of which is sexual dysfunction. Testosterone has been shown to improve hypoactive sexual desire disorder in postmenopausal women. Traditional hormone replacement therapy (estrogen and progesterone) is known for its beneficial effects in these cases, but there is still no scientific evidence to support the use of androgen in this situation. Objective: Critically review and assess the literature on the use of androgen preparations in women with premature ovarian insufficiency (POI). Search Strategy: Database search included randomized controlled trials, prospective comparative, observational, non-randomized studies, and case series of 10 or more subjects with no time limit restrictions. Selection criteria: Eligible trials included females below the age of 40, with amenorrhea >4 months, FSH >40 IU/L on two occasions separated by six weeks. Results: 539 abstracts were double screened, identifying 6 eligible studies; 3 randomized prospective double blinded studies, one randomized placebo controlled, one prospective observational, and one retrospective study. Discrepancies were arbitrated by a third reviewer. Androgen preparations included DHEA, danazol, and testosterone patch. Trials studied the impact of androgen on menstrual pattern, ovarian reserve markers, fertility outcomes, mood, self-esteem, and bone mineral density (BMD). Conclusion: There was no significant effect of androgen supplementation on mood, self-esteem, or bone mineral density. Although one study demonstrated an increase in ovarian volume and antral follicular count, there was no significant change in ovarian reserve, ovarian function, fertility outcome or menstrual pattern. Clinical Trial Registration: Trial was registered with open science framework (OSF), DOI: 10.17605/OSF. IO/2MYUV

The Internet of Things (IoT) is expected to grow faster than any other category of connected devices. IoT allows any device with an on-and-off switch to connect to the internet—a concept that has the ability to greatly change our lives and work. These modern systems collect inherently complex data streams due to the volume, velocity, value, variety, variability, and veracity of data, which leads to incremental growth in data traffic on networks and in the cloud. To fulfill the requirements of IoT, including geodistribution, low latency, location awareness, and mobility support, a new paradigm is proposed: edge computing. In edge computing, substantial computing and storage resources are placed at the edge of the network in mobile devices or sensors. The term “edge” is taken from network diagrams; normally, the edge of a network diagram represents the point at which data traffic enters or leaves the workable network. Using the concept of edge computing, an organization can shift huge amounts of data into processed data near the data origin, which helps to reduce data traffic in the network’s central repository (called the “cloud”). Edge computing uses a variety of data reduction techniques close to the data source at the network edge, including data pre-processing, local storage, and filtering. This approach can prevent some critical issues, such as I/O bottlenecks, storage and bandwidth limitations, data traffic increments, and high energy costs. A major advantage of edge computing is improvement of the request-response delay to milliseconds. Edge computing also supports security and network challenges. However, two major obstacles exist toward achieving the benefit of network-edge computing. First, the most efficient algorithms for data reduction in time series (one of the most common types of data in IoT) were developed to work posteriori upon big datasets, but they cannot make decisions for each incoming data item. Secondly, the state of the art lacks systems that can apply any of the possible data reduction methods without adding significant delays or major reconfigurations. Edge computing has also inherited some of the challenges of cloud computing, including data abstraction, naming, and programmability. This chapter presents a detailed taxonomic discussion of edge computing, along with its challenges, opportunities, and data reduction methods.

Computational time in optimization models scales with the number of time steps. To save time, solver time resolution can be reduced and input data can be down-sampled into representative periods such as one or a few representative days per month. However, such data reduction can come at the expense of solution accuracy. In this work, the impact of reduction of input data is systematically isolated considering an optimization which solves an energy system using representative days. A new data reduction method aggregates annual hourly demand data into representative days which preserve demand peaks in the original profiles. The proposed data reduction approach is tested on a real energy system and real annual hourly demand data where the system is optimized to minimize total annual costs. Compared to the full-resolution optimization of the energy system, the total annual energy cost error is found to be equal or less than 0.22% when peaks in customer demand are preserved. Errors are significantly larger for reduction methods that do not preserve peak demand. Solar photovoltaic data reduction effects are also analyzed. This paper demonstrates a need for data reduction methods which consider demand peaks explicitly.

As the number of pre-trained machine learning (ML) models is growing exponentially, data reduction tools are not catching up. Existing data reduction techniques are not specifically designed for pre-trained model (PTM) dataset files. This is largely due to a lack of understanding of the patterns and characteristics of these datasets, especially those relevant to data reduction and compressibility. This paper presents the first, exhaustive analysis to date of PTM datasets on storage compressibility. Our analysis spans different types of data reduction and compression techniques, from hash-based data deduplication, data similarity detection, to dictionary-coding compression. Our analysis explores these techniques at three data granularity levels, from model layers, model chunks, to model parameters. We draw new observations that indicate that modern data reduction tools are not effective when handling PTM datasets. There is a pressing need for new compression methods that take into account PTMs' data characteristics for effective storage reduction. Motivated by our findings, we design Elf, a simple yet effective, error-bounded, lossy floating-point compression method. Elf transforms floating-point parameters in such a way that the common exponent field of the transformed parameters can be completely eliminated to save storage space. We develop Elves, a compression framework that integrates Elf along with several other data reduction methods. Elves uses the most effective method to compress PTMs that exhibit different patterns. Evaluation shows that Elves achieves an overall compression ratio of 1.52×, which is 1.31×, 1.32× and 1.29× higher than a general-purpose compressor (zstd), an error-bounded lossy compressor (SZ3), and the uniform model quantization, respectively, with negligible model accuracy loss.

Die Serie Communications in Asteroseismology (CoAst) stellt eine international gefragte Plattform für wissenschaftliche Publikationen der neuesten Forschungsergebnisse auf dem Gebiet der Asteroseismologie dar. Forschungsgegenstand der Asteroseismologie sind veränderliche und pulsierende Sterne. Neben den halbjährlich erscheinenden regulären Ausgaben mit Beiträgen aus der laufenden Forschung werden auch Sonderausgaben für Konferenzberichte bzw. Programmhandbücher herausgegeben. Die in Volume 156 publizierten Arbeiten zu pulsierenden Sternen behandeln eine große Zahl verschiedener Sterntypen in unterschiedlichen Entwicklungszuständen, welche mithilfe von photometrischen sowie numerischen Techniken analysiert wurden. Unter anderem berichtet eine Publikation über die ersten Entdeckungen der CoRoT-Satelliten-Mission, die vor knapp einem Jahr ihren wissenschaftlichen Betrieb aufgenommen hat. Den Abschluss von Volume 156 bilden Berichte des von der EU eingerichtete Forschungsnetzwerk HELAS – European Helio- and Asteroseismology Network – über die Aktivitäten und die Forschungsvorhaben der europäischen Helioseismologie.

Persistent homology is used for computing topological features of a space at different spatial resolutions. It is one of the main tools from computational topology that is applied to the problems of data analysis. Despite several attempts to reduce its complexity, persistent homology remains expensive in both time and space. These limits are such that the largest data sets to which the method can be applied have the number of points of the order of thousands in ℝ3. This paper explores a technique intended to reduce the number of data points while preserving the salient topological features of the data. The proposed technique enables the computation of persistent homology on a reduced version of the original input data without affecting significant components of the output. Since the run time of persistent homology is exponential in the number of data points, the proposed data reduction method facilitates the computation in a fraction of the time required for the original data. Moreover, the data reduction method can be combined with any existing technique that simplifies the computation of persistent homology. The data reduction is performed by creating small groups of similar data points, called nano-clusters, and then replacing the points within each nano-cluster with its cluster center. The persistence homology of the reduced data differs from that of the original data by an amount bounded by the radius of the nano-clusters. The theoretical analysis is backed by experimental results showing that persistent homology is preserved by the proposed data reduction technique.

Data reduction has become greatly significant prior to applying instance-based machine learning algorithms in the Big Data era. Data reduction is used to reduce the size of data sets while retaining representative data. Existing algorithms, however, suffer from heavy computational cost and in having tradeoff in size reduction rate and learning accuracy. In this paper, we propose a fast data reduction approach by using granular computing to label important instances, i.e., instances with higher contributions to the learning task. The original data set is first granulated into $K$ granules by applying $K$ -means to a mapped lower-dimension space. Then, the importance of each instance in every granule is labeled based on its Hausdorff distance. Those instances whose importance values are lower than an experimentally tuned threshold are eliminated. The presented algorithm is applied to $k$ NN classification tasks with eighteen different sizes of data sets from the UCI repository, and its outstanding performance in classification accuracy, size reduction rate, and runtime is illustrated by comparing with seven data reduction methods. The experimental results demonstrate that the proposed algorithm can greatly reduce the computational cost and achieve a higher classification accuracy when the reduction size is the same for all the compared algorithms.

The FORS1 instrument on the ESO Very Large Telescope was used to obtain low-resolution circular polarised spectra of nearly a thousand different stars, with the aim of measuring their mean longitudinal magnetic fields. A catalogue of FORS1 magnetic measurements would provide a valuable resource with which to better understand the strengths and limitations of this instrument and of similar low-dispersion, Cassegrain spectropolarimeters. However, FORS1 data reduction has been carried out by a number of different groups using a variety of reduction and analysis techniques. Our understanding of the instrument and our data reduction techniques have both improved over time. A full re-analysis of FORS1 archive data using a consistent and fully documented algorithm would optimise the accuracy and usefulness of a catalogue of field measurements. Based on the ESO FORS pipeline, we have developed a semi-automatic procedure for magnetic field determinations, which includes self-consistent checks for field detection reliability. We have applied our procedure to the full content of circular spectropolarimetric measurements of the FORS1 archive. We have produced a catalogue of spectro-polarimetric observations and magnetic field measurements for about 1400 observations of about 850 different objects. The spectral type of each object has been accurately classified. We have also been able to test different methods for data reduction is a systematic way. The resulting catalogue has been used to produce an estimator for an upper limit to the uncertainty in a field strength measurement of an early type star as a function of the signal-to-noise ratio of the observation. While FORS1 is not necessarily an optimal instrument for the discovery of weak magnetic fields, it is very useful for the systematic study of larger fields, such as those found in Ap/Bp stars and in white dwarfs.

This paper focuses on nowcasts of tail risk to GDP growth, with a potentially wide array of monthly and weekly information. We consider different models (Bayesian mixed frequency regressions with stochastic volatility, as well as classical and Bayesian quantile regressions) and also different methods for data reduction (either forecasts from models that incorporate data reduction or the combination of forecasts from smaller models). Our results show that, within some limits, more information helps the accuracy of nowcasts of tail risk to GDP growth. Accuracy typically improves as time moves forward within a quarter, making additional data available, with monthly data more important to accuracy than weekly data. Accuracy also typically improves with the use of financial indicators in addition to a base set of macroeconomic indicators. The better-performing models or methods include the Bayesian regression model with stochastic volatility, Bayesian quantile regression, some approaches to data reduction that make use of factors, and forecast averaging. In contrast, simple quantile regression performs relatively poorly.

In recent years, Deep Learning has gained popularity for its ability to solve complex classification tasks, increasingly delivering better results thanks to the development of more accurate models, the availability of huge volumes of data and the improved computational capabilities of modern computers. However, these improvements in performance also bring efficiency problems, related to the storage of datasets and models, and to the waste of energy and time involved in both the training and inference processes. In this context, data reduction can help reduce energy consumption when training a deep learning model. In this paper, we present up to eight different methods to reduce the size of a tabular training dataset, and we develop a Python package to apply them. We also introduce a representativeness metric based on topology to measure how similar are the reduced datasets and the full training dataset. Additionally, we develop a methodology to apply these data reduction methods to image datasets for object detection tasks. Finally, we experimentally compare how these data reduction methods affect the representativeness of the reduced dataset, the energy consumption and the predictive performance of the model.

The analysis and combination of data from different gamma-ray instruments involves the use of collaboration proprietary software and case-by-case methods. The effort of defining a common data format for high-level data, namely event lists and instrument response functions (IRFs), has recently started for very-high-energy gamma-ray instruments, driven by the upcoming Cherenkov Telescope Array (CTA). In this work we implemented this prototypical data format for a small set of MAGIC, VERITAS, FACT, and H.E.S.S. Crab nebula observations, and we analyzed them with the open-source gammapy software package. By combining data from Fermi-LAT, and from four of the currently operating imaging atmospheric Cherenkov telescopes, we produced a joint maximum likelihood fit of the Crab nebula spectrum. Aspects of the statistical errors and the evaluation of systematic uncertainty are also commented upon, along with the release format of spectral measurements. The results presented in this work are obtained using open-access on-line assets that allow for a long-term reproducibility of the results.