Astrophysics

AstroSat/UVIT carries two gratings in the FUV channel and a single grating in the NUV channel. These gratings are useful for low resolution, slitless spectroscopy in the far and near UV bands of a variety of cosmic sources such as hot stars, interacting binaries, active galactic nuclei, etc. We present the calibration of these gratings using observations of UV standards NGC40 and HZ4. We perform wavelength and flux calibration and derive effective areas for different grating orders. We find peak effective areas of 18.7cm^2 at 2325 Angstrom for the -1 order of NUV-Grating, 4.5cm^2 at 1390 Angstrom for the -2 order of FUV-Grating1, and 4.3cm^2 at 1500 Angstrom for the -2 order of FUV-Grating2. The FWHM spectral resolution of the FUV gratings is 14.6 Angstrom in the -2 order. The -1 order of NUV grating has an FWHM resolution of 33 Angstrom. We find excellent agreement in flux measurements between the FUV/NUV gratings and all broadband filters. We have generated spectral responses of the UVIT gratings and broadband filters that can directly be used in the spectral fitting packages such as XSPEC, Sherpa, and ISIS, thus allowing spectral analysis of UVIT data either separately or jointly with X-ray data from AstroSat or other missions.

This is a mission to explore the surface of Venus from low altitudes. The Calypso Venus Scout consists of a high-altitude balloon and a instrumented Descent Module (DM). The DM is deployed to an altitude of 10-25 km by means of a Tether where it obtains images, with meter and centimeter scale resolution, and rough IR spectra. It is reeled-in after several hours for a "cool down" cycle, then deployed again. The balloon remains at high-altitude with no need to be fortified to survive high-T and high-P of Venus' lower atmosphere.

Many astrophysical phenomena are time-varying, in the sense that their intensity, energy spectrum, and/or the spatial distribution of the emission suddenly change. This paper develops a method for modeling a time series of images. Under the assumption that the arrival times of the photons follow a Poisson process, the data are binned into 4D grids of voxels (time, energy band, and x-y coordinates), and viewed as a time series of non-homogeneous Poisson images. The method assumes that at each time point, the corresponding multi-band image stack is an unknown 3D piecewise constant function including Poisson noise. It also assumes that all image stacks between any two adjacent change points (in time domain) share the same unknown piecewise constant function. The proposed method is designed to estimate the number and the locations of all the change points (in time domain), as well as all the unknown piecewise constant functions between any pairs of the change points. The method applies the minimum description length (MDL) principle to perform this task. A practical algorithm is also developed to solve the corresponding complicated optimization problem. Simulation experiments and applications to real datasets show that the proposed method enjoys very promising empirical properties. Applications to two real datasets, the XMM observation of a flaring star and an emerging solar coronal loop, illustrate the usage of the proposed method and the scientific insight gained from it.

The Gaia DR2 radial velocity sample (GDR2RVS), which provides six-dimensional phase-space information on 7.2 million stars, is of great value for inferring properties of the Milky Way. Yet a quantitative and accurate modelling of this sample is hindered without knowledge and inclusion of a well-characterized selection function. Here we derive the selection function through estimates of the internal completeness, i.e. the ratio of GDR2RVS sources compared to all Gaia DR2 sources (GDR2all). We show that this selection function or "completeness" depends on basic observables, in particular the apparent magnitude GRVS and colour G-GRP, but also on the surrounding source density and on sky position, where the completeness exhibits distinct small-scale structure. We identify a region of magnitude and colour that has high completeness, providing an approximate but simple way of implementing the selection function. For a more rigorous and detailed description we provide python code to query our selection function, as well as tools and ADQL queries that produce custom selection functions with additional quality cuts.

Gravitational wave astronomy has been already a well-established research domain for many years. Moreover, after the detection by LIGO/Virgo collaboration, in 2017, of the first gravitational wave signal emitted during the collision of a binary neutron star system, that was accompanied by the detection of other types of signals coming from the same event, multi-messenger astronomy has claimed its rights more assertively. In this context, it is of great importance in a gravitational wave experiment to have a rapid mechanism of alerting about potential gravitational waves events other observatories capable to detect other types of signals (e.g. in other wavelengths) that are produce by the same event. In this paper, we present the first progress in the development of a neural network algorithm trained to recognize and characterize gravitational wave patterns from signal plus noise data samples. We have implemented two versions of the algorithm, one that classifies the gravitational wave signals into 2 classes, and another one that classifies them into 4 classes, according to the mass ratio of the emitting source. We have obtained promising results, with 100% training and testing accuracy for the 2-class network and approximately 95% for the 4-class network. We conclude that the current version of the neural network algorithm demonstrates the ability of a well-configured and calibrated Bidirectional Long-Short Term Memory software to classify with very high accuracy and in an extremely short time gravitational wave signals, even when they are accompanied by noise. Moreover, the performance obtained with this algorithm qualifies it as a fast method of data analysis and can be used as a low-latency pipeline for gravitational wave observatories like the future LISA Mission.

We demonstrate a fully functional implementation of (per-user) checkpoint, restore, and live migration capabilities for JupyterHub platforms. Checkpointing -- the ability to freeze and suspend to disk the running state (contents of memory, registers, open files, etc.) of a set of processes -- enables the system to snapshot a user's Jupyter session to permanent storage. The restore functionality brings a checkpointed session back to a running state, to continue where it left off at a later time and potentially on a different machine. Finally, live migration enables moving running Jupyter notebook servers between different machines, transparent to the analysis code and w/o disconnecting the user. Our implementation of these capabilities works at the system level, with few limitations, and typical checkpoint/restore times of O(10s) with a pathway to O(1s) live migrations. It opens a myriad of interesting use cases, especially for cloud-based deployments: from checkpointing idle sessions w/o interruption of the user's work (achieving cost reductions of 4x or more), execution on spot instances w. transparent migration on eviction (with additional cost reductions up to 3x), to automated migration of workloads to ideally suited instances (e.g. moving an analysis to a machine with more or less RAM or cores based on observed resource utilization). The capabilities we demonstrate can make science platforms fully elastic while retaining excellent user experience.

NASA should design missions to Mars for the purpose of generating "Aha!" discoveries to jolt scientists contemplating the molecular origins of life. These missions should be designed with an understanding of the privileged chemistry that likely created RNA prebiotically on Earth, and universal chemical principles that constrain the structure of Darwinian molecules generally.

This study investigate the effectiveness of using Deep Learning (DL) for the classification of planetary nebulae (PNe). It focusses on distinguishing PNe from other types of objects, as well as their morphological classification. We adopted the deep transfer learning approach using three ImageNet pre-trained algorithms. This study was conducted using images from the Hong Kong/Australian Astronomical Observatory/Strasbourg Observatory H-alpha Planetary Nebula research platform database (HASH DB) and the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS). We found that the algorithm has high success in distinguishing True PNe from other types of objects even without any parameter tuning. The Matthews correlation coefficient is 0.9. Our analysis shows that DenseNet201 is the most effective DL algorithm. For the morphological classification, we found for three classes, Bipolar, Elliptical and Round, half of objects are correctly classified. Further improvement may require more data and/or training. We discuss the trade-offs and potential avenues for future work and conclude that deep transfer learning can be utilized to classify wide-field astronomical images.

In this paper, we seek to answer the question "given a rotating core collapse gravitational wave signal, can we determine its nuclear equation of state?". To answer this question, we employ deep convolutional neural networks to learn visual and temporal patterns embedded within rotating core collapse gravitational wave (GW) signals in order to predict the nuclear equation of state (EOS). Using the 1824 rotating core collapse GW simulations by Richers et al. (2017), which has 18 different nuclear EOS, we consider this to be a classic multi-class image classification and sequence classification problem. We attain up to 72\% correct classifications in the test set, and if we consider the "top 5" most probable labels, this increases to up to 97\%, demonstrating that there is a moderate and measurable dependence of the rotating core collapse GW signal on the nuclear EOS.

Several attempts to detect extensive air showers (EAS) induced by ultrahigh-energy cosmic rays have been conducted in the last decade based on the molecular Bremsstrahlung radiation (MBR) at GHz frequencies from quasi-elastic collisions of ionisation electrons left in the atmosphere after the passage of the cascade of particles. These attempts have led to the detection of a handful of signals only, all of them forward-directed along the shower axis and hence suggestive of originating from geomagnetic and Askaryan emissions extending into GHz frequencies close to the Cherenkov angle. In this paper, the lack of detection of events is explained by the coherent suppression of the MBR in frequency ranges below the collision rate due to the destructive interferences impacting the emission amplitude of photons between the successive collisions of the electrons. The spectral intensity at the ground level is shown to be several orders of magnitude below the sensitivity of experimental setups. In particular, the spectral intensity at 10~km from the shower core for a vertical shower induced by a proton of 10 17.5 eV is 7-to-8 orders of magnitude below the reference value anticipated from a scaling law converting a laboratory measurement to EAS expectations. Consequently, the MBR cannot be seen as the basis of a new detection technique of EAS for the next decades.