Astrophysics

We search for optical transient sources with durations of ??.1 to ??.3 s using a dataset obtained in the Organized Autotelescopes for Serendipitous Event Survey (OASES) observation campaign. Since the OASES observations were carried out using two independent wide-field and high-cadence observation systems monitored the same field simultaneously, the obtained dataset provides a unique opportunity to develop a robust detection method for sub-second optical transients. In the dataset of a selected field around the ecliptic and the Galactic plane, we find no astronomical event candidate that satisfies our detection criteria. From the non-detection result, we derive an upper limit on the event rate of sub-second transients around the ecliptic and the Galactic plane for the first time, obtaining ??.090 and ??.38 h r ?? de g ?? for m=12 and 13 Vmag, respectively. In addition, future prospects of the sub-second scale transient event surveys are discussed.

To search for optical counterparts to gravitational waves, it is crucial to develop an efficient follow-up method that allows for both a quick telescopic scan of the event localization region and search through the resulting image data for plausible optical transients. We present a method to detect these transients based on an artificial neural network. We describe the architecture of two networks capable of comparing images of the same part of the sky taken by different telescopes. One image corresponds to the epoch in which a potential transient could exist; the other is a reference image of an earlier epoch. We use data obtained by the Dr. Cristina V. Torres Memorial Astronomical Observatory and archival reference images from the Sloan Digital Sky Survey. We trained a convolutional neural network and a dense layer network on simulated source samples and tested the trained networks on samples created from real image data. Autonomous detection methods replace the standard process of detecting transients, which is normally achieved by source extraction of a difference image followed by human inspection of the detected candidates. Replacing the human inspection component with an entirely autonomous method would allow for a rapid and automatic follow-up of interesting targets of opportunity. The method will be further tested on telescopes participating in the Transient Optical Robotic Observatory of the South Collaboration.

The detection of gravitational waves is considered to be one of the most magnificent discoveries of the century. Due to the high computational cost of matched filtering pipeline, there is a hunt for an alternative powerful system. I present, for the first time, the use of 1D residual neural network for detection of gravitational waves. Residual networks have transformed many fields like image classification, face recognition and object detection with their robust structure. With increase in sensitivity of LIGO detectors we expect many more sources of gravitational waves in the universe to be detected. However, deep learning networks are trained only once. When used for classification task, deep neural networks are trained to predict only a fixed number of classes. Therefore, when a new type of gravitational wave is to be detected, this turns out to be a drawback of deep learning. Shallow neural networks can be used to learn data with simple patterns but fail to give good results with increase in complexity of data. Remodelling the neural network with detection of each new type of GW is highly infeasible. In this letter, I also discuss ways to reduce the time required to adapt to such changes in detection of gravitational waves for deep learning methods. Primarily, I aim to create a custom residual neural network for 1-dimensional time series inputs, which can learn a ton of features from dataset without giving up on increasing the number of classes or increasing the complexity of data. I use the two class of binary coalescence signals (Binary Black Hole Merger and Binary Neutron Star Merger signals) detected by LIGO to check the performance of residual structure on gravitational waves detection.

The identification of extraterrestrial life is one the most exciting and challenging endeavors in space research. The existence of extinct or extant life can be inferred from biogenic elements, isotopes, and molecules, but accurate and sensitive instruments are needed. In this whitepaper we show that Laser-based Mass Spectrometers are promising instrument for the in situ identification of atomic, isotopic, and molecular biosignatures. An overview of Laser ablation/Ionization Mass Spectrometry (LIMS) and Laser Desorption/Ionization Mass Spectrometry (LD-MS) instruments developed for space exploration is given. Their uses are discussed in the context of a Mars scenario and a Europa scenario. We show that Laser-based Mass Spectrometers are versatile and technologically mature instruments with many beneficial characteristics for the detection of life. Future planetary lander and rover missions should be encouraged to make use of Laser-based Mass Spectrometry instruments in their scientific payload.

We propose a new model of Bayesian Neural Networks to not only detect the events of compact binary coalescence in the observational data of gravitational waves (GW) but also identify the full length of the event duration including the inspiral stage. This is achieved by incorporating the Bayesian approach into the CLDNN classifier, which integrates together the Convolutional Neural Network (CNN) and the Long Short-Term Memory Recurrent Neural Network (LSTM). Our model successfully detect all seven BBH events in the LIGO Livingston O2 data, with the periods of their GW waveforms correctly labeled. The ability of a Bayesian approach for uncertainty estimation enables a newly defined `awareness' state for recognizing the possible presence of signals of unknown types, which is otherwise rejected in a non-Bayesian model. Such data chunks labeled with the awareness state can then be further investigated rather than overlooked. Performance tests with 40,960 training samples against 512 chunks of 8-second real noise mixed with mock signals of various optimal signal-to-noise ratio 0≤ ρ opt ≤18 show that our model recognizes 90% of the events when ρ opt >7 (100% when ρ opt >8.5 ) and successfully labels more than 95% of the waveform periods when ρ opt >8 . The latency between the arrival of peak signal and generating an alert with the associated waveform period labeled is only about 20 seconds for an unoptimized code on a moderate GPU-equipped personal computer. This makes our model possible for nearly real-time detection and for forecasting the coalescence events when assisted with deeper training on a larger dataset using the state-of-art HPCs.

The investigation of pulsars between millimetre and optical wavelengths is challenging due to the faintness of the pulsar signals and the relative low sensitivity of the available facilities compared to 100-m class telescopes operating in the centimetre band. The Kinetic Inductance Detector (KID) technology offers large instantaneous bandwidths and a high sensitivity that can help to substantially increase the ability of existing observatories at short wavelengths to detect pulsars and transient emission. To investigate the feasibility of detecting pulsars with KIDs, we observed the anomalous X-ray pulsar XTE J1810-197 with the New IRAM KIDs Array-2 (NIKA2) camera installed at the IRAM 30-m Telescope in Spain. We detected the pulsations from the pulsar with NIKA2 at its two operating frequency bands, 150 and 260 GHz ( λ =2.0 and 1.15 mm, respectively). This is the first time that a pulsar is detected with a receiver based on KID technology in the millimetre band. In addition, this is the first report of short millimetre emission from XTE J1810-197 after its reactivation in December 2018, and it is the first time that the source is detected at 260 GHz, which gives us new insights into the radio emission process of the star.

LiteBIRD is a JAXA-led strategic Large-Class satellite mission designed to measure the polarization of the cosmic microwave background and cosmic foregrounds from 34 to 448 GHz across the entire sky from L2 in the late 2020's. The primary focus of the mission is to measure primordially generated B-mode polarization at large angular scales. Beyond its primary scientific objective LiteBIRD will generate a data-set capable of probing a number of scientific inquiries including the sum of neutrino masses. The primary responsibility of United States will be to fabricate the three flight model focal plane units for the mission. The design and fabrication of these focal plane units is driven by heritage from ground based experiments and will include both lenslet-coupled sinuous antenna pixels and horn-coupled orthomode transducer pixels. The experiment will have three optical telescopes called the low frequency telescope, mid frequency telescope, and high frequency telescope each of which covers a portion of the mission's frequency range. JAXA is responsible for the construction of the low frequency telescope and the European Consortium is responsible for the mid- and high- frequency telescopes. The broad frequency coverage and low optical loading conditions, made possible by the space environment, require development and adaptation of detector technology recently deployed by other cosmic microwave background experiments. This design, fabrication, and characterization will take place at UC Berkeley, NIST, Stanford, and Colorado University, Boulder. We present the current status of the US deliverables to the LiteBIRD mission.

The development of bare fiber or air-gapped microlens-fiber coupled Integral Field Units (IFUs) for astronomical applications requires careful treatment of the fiber end-faces (terminations). Previous studies suggest that minimization of fiber end face irregularity leads to better optical performance in terms of the diminishing effect of focal ratio degradation. Polishing has typically been performed using commercial rotary polishers with multiple gradually decreasing grit sizes. These polishers generally lack the ability to carefully adjust angular position and polishing force. Control of these parameters vastly help in getting a repeatable and controllable polish over a variety of glass/epoxy/metal matrices that make up integral filed units and fiber slits. A polishing arm is developed to polish the fiber terminations (IFU, mini-bundles and v-grooves) of the NIR Fiber System for the RSS spectrograph at SALT. The polishing arm angular adjustments ensure the correct position and orientation of each termination on the polishing surface during the polish. Various studies have indicated that the fiber focal ratio also degrades if the fiber end face comes under excessive stress. The polishing arm is fitted with a load cell to enable control of the polishing force. We have explored the minimal applicable end stress by applying different loads while polishing. The arm is modular to hold a variety of fiber termination styles. The polishing arm is also designed to access a fiber inspection microscope without removing the fiber termination from the arm. This enables inspection of the finish quality at various stages through polishing process.

The design and implementation of astronomical cameras based on the large-format CCD and CMOS detectors is described in this paper. The Dinacon-5 controller is used for work with the CCDs and to achieve high performance and low noise. A new controller is designed for CMOS sensors. The main characteristics of the provided systems are estimated on the basis of experimental data. The spatial autocorrelation analysis is applied for PSF estimation. The obtained test results are presented.

We present a case study of a cloud-based computational workflow for processing large astronomical data sets from the Murchison Widefield Array (MWA) cosmology experiment. Cloud computing is well-suited to large-scale, episodic computation because it offers extreme scalability in a pay-for-use model. This facilitates fast turnaround times for testing computationally expensive analysis techniques. We describe how we have used the Amazon Web Services (AWS) cloud platform to efficiently and economically test and implement our data analysis pipeline. We discuss the challenges of working with the AWS spot market, which reduces costs at the expense of longer processing turnaround times, and we explore this tradeoff with a Monte Carlo simulation.