Detection Of Transient Events Research Articles

Revisiting the OBS seafloor compliance signal removal with a stationarity and stacking-based approach: the BRUIT-FM toolbox

Summary This study focuses on improving the seafloor compliance noise removal method, which relies on estimates of the compliance transfer function frequency response (the deformation of the seafloor under long-period pressure waves). We first propose a new multi-scale deviation analysis of broadband ocean bottom seismometer data to evaluate stationarity properties that are key to the subsequent analysis. We then propose a new approach to removing the compliance noise from the vertical channel data, by stacking daily estimated transfer function frequency responses over a period of time. We also propose an automated transient event detection and data selection method based on a cross-correlation criterion. As an example, we apply the method to data from the Cascadia Initiative (network 7D2011). We find that the spectral extent of long-period forcing waves varies significantly over time so that standard daily transfer function calculation techniques poorly estimate the transfer function frequency response at the lowest frequencies, resulting in poor denoising performance. The proposed method more accurately removes noise at these lower frequencies, especially where coherence is low, reducing the mean deviation of the signal in our test case by 27 % or more. We also show that our calculated transfer functions can be transfered across time periods. The method should allow better estimates of seafloor compliance and help to remove compliance noise at stations with low pressure-acceleration coherence. Our results can be reproduced using the BRUIT-FM Python toolbox, available at https://gitlab.ifremer.fr/anr-bruitfm/bruit-fm-toolbox.

Transition of dome formation to sudden explosive eruptions at Popocatépetl, Mexico: magnetic indicators

Transitions from effusive to explosive activity can increase hazards making it crucial to define early indicators such as changes in the magnetic signals. After more than 80 cycles of crater-dome extrusion and destruction from 1996 on, Popocatépetl volcano (Mexico) experienced changes in its behavior from March 15 to 18 July 2019, when no lava domes were observed. Some of the domes behaved as contained lava flows within the crater floor (pancakes) while others were more irregular-shaped. Activity decreased considerably over this 2019 interval except for the unexpected explosions in March and June, that produced ash plumes reaching up to 14,000 m a.s.l. In order to investigate the causes of the transition from effusive to explosive behavior in March and June, we analyzed the time series from the magnetic monitoring network at Popocatépetl volcano between October 2018 and December 2019. The raw signals were analyzed by weighted differences (WD) based on the elimination of non-local changes from the total intensity values of the geomagnetic field and the discrete-time continuous wavelet transform was used to evaluate the local variations of energy within the time series. The high energy periods (linked to negative magnetic anomalies) are induced by magma ascent associated with movement within the conduit. They indicate that the sudden explosions were due to the ascent of several magma batches that were slowed during ascent and were not able to reach the surface. Changes in the rheology of the lava are linked to the influx of several batches of magma with different compositions as well as to compaction by gas loss when ascending andesitic magma pushed out overlying more viscous degassed magma clearing the conduit, which can explain why these sudden explosions were more energetic. Several geophysical data sets as well as tephra compositions were integrated to support this conclusion. The correlated multiparameters also confirm that geomagnetic volcano monitoring has been essential in understanding the processes that drive the observed changes in eruptive behavior. We present new evidence for the detection of transient events produced by magma ascent and changes in the feeding system of Popocatépetl volcano with wavelet analysis. Detailed vulcanomagnetic processing, especially when it is correlated with other monitoring parameters, provides information on ascending magma and several conduit processes that would otherwise be camouflaged. Ascending batches may precede an eruption but they can also ascend in several pulses indicating how dome growth occurs.

HR 10 as seen by CHEOPS and TESS. Revealing delta Scuti pulsations, granulation-like signal and hint for transients

HR 10 has only recently been identified as a binary system. Previously thought to be an A-type shell star, it appears that both components are fast-rotating A-type stars, each presenting a circumstellar envelope. Although showing complex photometric variability, spectroscopic observations of the metallic absorption lines reveal variation explained by the binarity, but not indicative of debris-disc inhomogeneities or sublimating exocomets. On the other hand, the properties of the two stars make them potential delta Scuti pulsators. The system has been observed in two sectors by the TESS satellite, and was the target of three observing visits by CHEOPS. Thanks to these new data, we aim to further characterise the stellar properties of the two components. In particular, we aim to decipher the extent to to which the photometric variability can be attributed to a stellar origin. In complement, we searched in the lightcurves for transient-type events that could reveal debris discs or exocomets. We analysed the photometric variability of both the TESS and CHEOPS datasets in detail. We first performed a frequency analysis to identify and list all the periodic signals that may be related to stellar oscillations or surface variability. The signals identified as resulting from the stellar variability were then removed from the lightcurves inorder to search for transient events in the residuals. We report the detection of delta Scuti pulsations in both the TESS and CHEOPS data, but we cannot definitively identify which of the components is the pulsating star. In both datasets, we find flicker noise with the characteristics of a stellar granulation signal. However, it remains difficult to firmly attribute it to actual stellar granulation from convection, given the very thin surface convective zones predicted for both stars. Finally, we report probable detection of transient events in the CHEOPS data, without clear evidence of their origin.

SNIa DETECTION ANALYSIS RESULTS FROM REAL AND SIMULATED IMAGES USING SPECIALIZED SOFTWARE

The detection of transient events, Type Ia supernovae in particular, has become an important research subject in today's astronomy. We use as a base tool the software suite for astronomical image processing called LSSTsp and adapt it to assemble a type Ia supernova detection pipe. We study some straightforward changes on the overall pipeline by selecting better quality inputs to perform a coaddition of reference images, we analyze the different residual sources detected on the difference images and, lastly, we build light curves by taking into account the features of detected difference image analysis sources. Finally, we build a catalog of supernova candidates by using a random forest classification, and check the relevance these additions. We reduced the overall source detection density with our changes while finding between 82% and 85% of the present Type Ia supernovae.

Reduction of non-tidal oceanographic fluctuations in ocean-bottom pressure records of DONET using principal component analysis to enhance transient tectonic detectability

Ocean bottom pressure-gauge (OBP) records play an essential role in seafloor geodesy. Oceanographic fluctuations in OBP data, however, pose as a significant noise source in seafloor transient crustal deformation observations, including slow slip events (SSEs), making it crucial to evaluate them quantitatively. To extract the significant fluctuation phenomena common to multiple observation networks, including oceanographic fluctuations and tectonic signals, we applied principal component analysis (PCA) to the 3-year Dense Oceanfloor Network System for Earthquakes and Tsunamis (DONET) OBP time series for 40 stations during 2016–2019. PCA could separate several oceanographic signals based on the characteristics of their spatial distributions, although evident transient tectonic signals could not be confirmed from the observed pressure records during this observed period. The spatial distribution of the first four principal components (PCs) reflected the common component, inclined component along sea depth, longitude component, and parabola-like pattern, respectively. By subtracting each PC (in particular, PC-2 and PC-4) from the time series, we could significantly reduce the sea depth dependence of OBP records, which has been highlighted in several previous studies and is also evident in this region. We interpreted PCs 2–4 as the reflection of the strength and meandering of ocean geostrophic currents based on a comparison with the PC spatial distribution of the numerical oceanographic models. In addition, to evaluate the ability of PCA to separate transient tectonic signal from OBP time series, including oceanographic fluctuations, we conducted a synthetic ramp assuming an SSE by rectangular fault and then applied PCA. The assumed synthetic tectonic signal could be separated from the oceanographic signals and included in the principal component independently depending on its amplitude, suggesting that the spatial distribution of each PC would change if the amplitude of the synthetic signal were sufficiently large. We propose a transient event-detection method based on the spatial distribution difference of a specific PC with or without a tectonic signal. We used the normalized inner product (NIP) between these PCs as the indicator of their similarities. This method can detect transient tectonic signals more significantly than the moment-magnitude scale of 5.9 from OBP records.Graphical

Improved detection of transient events in wide area sky survey using convolutional neural networks

The aim of data science is to catch up with the data-intensive life style as well as the demand for decision support, which becomes common in various domains such as medical, education and other smart solutions. As such, high quality of data analysis is greatly desired for accurate and effective downstreaming exploitations. This is also true for the domain of astronomical survey like GOTO (Gravitational-wave Optical Transient Observer), where large amount of raw data has been collected daily. This is one of recognised projects that search for transient events with the new breed of optical survey telescopes that can detect the sky faster and deeper. This is accomplished by comparing the night-specific data with the reference such that new bright sources are obtained for further study. However, the huge size of data makes it difficult to sift by naked eyes, thus requiring an automated system. Yet, many conventional machine-learning models have been sub-optimal for this task, as true positives can hardly be recognised due to the nature of imbalance data. This motivates the exploration of convolutional neural networks or CNN for this binary classification problem. Based on existing technologies, the paper reports the original application of basic CNN model to a representative data, which has been designed and generated within the GOTO project. In addition to the improvement over those previous works, this empirical study also includes details of parameter analysis, which will be useful for practice and further investigation.

Automatic detection of long-duration transients in Fermi-GBM data

Context. In the era of time-domain, multi-messenger astronomy, the detection of transient events on the high-energy electromagnetic sky has become more important than ever. Previous attempts to systematically search for onboard, untriggered events in the data of Fermi-GBM have been limited to short-duration signals with variability time scales smaller than ≈1 min. This is due to the dominance of background variations on longer timescales. Aims. In this study, we aim to achieve a detection of slowly rising or long-duration transient events with high sensitivity and a full coverage of the GBM spectrum. Methods. We made use of our earlier developed physical background model, which allows us to effectively decouple the signal from long-duration transient sources from the complex varying background seen with the Fermi-GBM instrument. We implemented a novel trigger algorithm to detect signals in the variations of the time series that is composed of simultaneous measures in the light curves of the different Fermi-GBM detectors in different energy bands. To allow for a continuous search in the data stream of the satellite, the new detection algorithm was embedded in a fully automatic data analysis pipeline. After the detection of a new transient source, we also performed a joint fit for spectrum and location using the BALROG algorithm. Results. The results from extensive simulations demonstrate that the developed trigger algorithm is sensitive down to sub-Crab intensities (depending on the search timescale) and has a near-optimal detection performance. During a two month test run on real Fermi-GBM data, the pipeline detected more than 300 untriggered transient signals. We verified, for one of these transient detections, that it originated from a known astrophysical source, namely, the Vela X-1 pulsar, showing pulsed emission for more than seven hours. More generally, this method enables a systematic search for weak or long-duration transients.

Periodic/Aperiodic parameterization of transient oscillations (PAPTO)–Implications for healthy ageing

Two techniques for analyzing human extracranial neurophysiological signals, namely the periodic/aperiodic parameterization of neural power spectra and the transient events framework of oscillatory activity, have recently emerged in the scientific literature. In this work, we integrate these two analysis perspectives to analyze extracranial neurophysiological signals as a series of transient rhythmic events disambiguated from the background aperiodic activity. We call this novel technique the periodic/aperiodic parametrization of transient oscillations (PAPTO). We demonstrate PAPTO by investigating resting-state sensorimotor magnetoencephalography recordings from the Cambridge Center for Ageing and Neuroscience cross-sectional study on healthy ageing (n = 600, ages 18–88). We show that PAPTO is more sensitive to neocortical transient beta rhythms compared to more conventional transient event detection algorithms and captures more variance in the resting-state occurrence rate of beta events across participants. The improved sensitivity of PAPTO reveals that the beta occurrence rate almost doubles over the adult lifespan which we discuss in terms of thalamocortical beta generation in the somatosensory cortex and the age-related decline of sensory perception.

High Energy Modular Ensemble of Satellites Mission: Towards the final Full Constellation

The High Energy Modular Ensemble of Satellites (HERMES) project intends to build an all-sky monitor operating from keV to MeV for the detection and localisation of transient events, like gamma ray bursts. HERMES is a modular observatory composed by detectors on-board of nanosatellites. HERMES aims to revolutionise the world of multi-messenger astrophysics thanks to the innovative concept of a modular instrument based on small satellites and characterised by reduced design and development times and low costs, in the face of high technological content and scientific profile of the mission.A first part of the project, HERMES Technological Pathfinder, composed of three CubeSats, aims to demonstrate the feasibility of detecting transient phenomena in high energy with small satellites. The next phase of the project, HERMES Scientific Pathfinder, will expand the constellation up to six satellites, allowing routinely accurate triangulation measurements.This paper presents an analysis that aims to be propaedeutic for the design of the final HERMES Full Constellation, which will be an all-sky monitor made up of tens/hundreds of nanosatellites in Low Earth Orbit with a total effective area of ∼m2. An adequate number of nanosatellites, simultaneously detecting a transient, provides a source localisation accuracy of the order of magnitude of few arcmin and large effective area. The objective of this work is to define possible strategies of injection in orbit of the complete constellation. In this first analysis we used only the number of triangulable Gamma Ray Bursts (simultaneously detected by at least three nanosatellites) as discriminating factor in order to maximise the constellation performance. By achieving the goal of at least a mean number of 20 Gamma Ray Bursts triangulated per nanosatellite over the two years life-time, optimal configurations for the complete constellation, consisting of seven to fourteen CubeSats, have been identified.

Volcanic Tremor Extraction and Earthquake Detection Using Music Information Retrieval Algorithms

Abstract Volcanic tremor signals are usually observed before or during volcanic eruptions and must be monitored to evaluate the volcanic activity. A challenge in studying seismic signals of volcanic origin is the coexistence of transient signal swarms and long-lasting volcanic tremor signals. Separating transient events from volcanic tremors can, therefore, contribute to improving upon our understanding of the underlying physical processes. Exploiting the idea of harmonic–percussive separation in musical signal processing, we develop a method to extract the harmonic volcanic tremor signals and to detect transient events from seismic recordings. Based on the similarity properties of spectrogram frames in the time–frequency domain, we decompose the signal into two separate spectrograms representing repeating (harmonic) and nonrepeating (transient) patterns, which correspond to volcanic tremor signals and earthquake signals, respectively. We reconstruct the harmonic tremor signal in the time domain from the complex spectrogram of the repeating pattern by only considering the phase components for the frequency range in which the tremor amplitude spectrum is significantly contributing to the energy of the signal. The reconstructed signal is, therefore, clean tremor signal without transient events. Furthermore, we derive a characteristic function suitable for the detection of transient events (e.g., earthquakes) by integrating amplitudes of the nonrepeating spectrogram over frequency at each time frame. Considering transient events like earthquakes, 78% of the events are detected for signal-to-noise ratio = 0.1 in our semisynthetic tests. In addition, we compared the number of detected earthquakes using our method for one month of continuous data recorded during the Holuhraun 2014–2015 eruption in Iceland with the bulletin presented in Ágústsdóttir et al. (2019). Our single station event detection algorithm identified 84% of the bulletin events. Moreover, we detected a total of 12,619 events, which is more than twice the number of the bulletin events.

Balloon flight test of a CeBr3 detector with silicon photomultiplier readout

Recent advances in silicon photomultiplier (SiPM) technology and new scintillator materials allow for the creation of compact high-performance gamma-ray detectors which can be deployed on small low-cost satellites. A small number of such satellites can provide full sky coverage and complement, or in some cases replace the existing gamma-ray missions in detection of transient gamma-ray events. The aim of this study is to test gamma-ray detection using a novel commercially available CeBr3 scintillator combined with SiPM readout in a near-space environment and inform further technology development for a future space mission. A prototype gamma-ray detector was built using a CeBr3 scintillator and an array of 16 J-Series SiPMs by ON Semiconductor. SiPM readout was performed using SIPHRA, a radiation-tolerant low-power integrated circuit developed by IDEAS. The detector was flown as a piggyback payload on the Advanced Scintillator Compton Telescope balloon flight from Columbia Scientific Balloon Facility. The payload included the detector, a Raspberry Pi on-board computer, a custom power supply board, temperature and pressure sensors, a Global Navigation Satellite System receiver and a satellite modem. The balloon delivered the detector to 37 km altitude where its detection capabilities and readout were tested in the radiation-intense near-space environment. The detector demonstrated continuous operation during the 8-hour flight and after the landing. It performed spectral measurements in an energy range of 100 keV to 8 MeV and observed the 511 keV gamma-ray line arising from positron annihilation in the atmosphere with full width half maximum of 6.8%. During ascent and descent, the detector count rate peaked at an altitude of 16 km corresponding to the point of maximum radiation intensity in the atmosphere. Despite several engineering issues discovered after the flight test, the results of this study confirm the feasibility of using CeBr3 scintillator, SiPMs, and SIPHRA in future space missions.

AutoScanJ: A Suite of ImageJ Scripts for Intelligent Microscopy.

We developed AutoscanJ, a suite of ImageJ scripts enabling to image targets of interest by automatically driving a motorized microscope at the corresponding locations. For live samples, our software can sequentially detect biological events from their onset and further image them at high resolution, an action that would be impractical by user operation. For fixed samples, the software can dramatically reduce the amount of data acquired and the acquisition duration in situations where statistically few targets of interest are observed per field of view. AutoScanJ is compatible with motorized fluorescence microscopes controlled by Leica LAS AF/X or Micro-Manager. The software is straightforward to set up and new custom image analysis workflows to detect targets of interest can be simply implemented and shared with minimal efforts as independent ImageJ macro functions. We illustrate five different application scenarios with the system ranging from samples fixed on micropatterned surfaces to live cells undergoing several rounds of division. The target detection functions for these applications are provided and can be used as a starting point and a source of inspiration for new applications. Overall, AutoScanJ helps to optimize microscope usage by autonomous operation, and it opens up new experimental avenues by enabling the real-time detection and selective imaging of transient events in live microscopy.

Revisiting the Detection of Optical Lightning Superbolts

AbstractThis study uses Fast On‐Orbit Detection of Transient Events (FORTE) satellite observations to identify superbolt‐class optical lightning events and evaluate their origins. Superbolts have been defined by Turman (1977, https://doi.org/10.1029/JC082i018p02566) as lightning pulses whose peak optical power exceeds 1011 W. However, it has been unclear whether superbolts resulted from particular types of high‐energy lightning process or whether they were the result of measurement bias. In the latter case, any decently bright lightning process could be recorded as a superbolt if the sensor had a particularly clear sight line to the hot channel without thick clouds diluting the optical signals. Our 12‐year analysis of FORTE superbolt detections indicates that the lower optical superbolt energy range (~100 GW) is dominated by normal lightning, but brighter cases are predominantly strong +CG strokes that originate from specific types of storms. Oceanic storm systems, particularly during the winter, and especially those located around Japan are shown to produce these intense superbolts. We suggest that some optical superbolts result from favorable viewing conditions and would not be identified as such by another instrument located elsewhere and that others are associated with a unique set of physics that may merit the “superbolt” distinction.

A Machine-Learning Based Nonintrusive Smart Home Appliance Status Recognition

In a smart home, the nonintrusive load monitoring recognition scheme normally achieves high appliance recognition performance in the case where the appliance signals have widely varying power levels and signature characteristics. However, it becomes more difficult to recognize appliances with equal or very close power specifications, often with almost identical signature characteristics. In literature, complex methods based on transient event detection and multiple classifiers that operate on different hand crafted features of the signal have been proposed to tackle this issue. In this paper, we propose a deep learning approach that dispenses with the complex transient event detection and hand crafting of signal features to provide high performance recognition of close tolerance appliances. The appliance classification is premised on the deep multilayer perceptron having three appliance signal parameters as input to increase the number of trainable samples and hence accuracy. In the case where we have limited data, we implement a transfer learning-based appliance classification strategy. With the view of obtaining an appropriate high performing disaggregation deep learning network for the said problem, we explore individually three deep learning disaggregation algorithms based on the multiple parallel structure convolutional neural networks, the recurrent neural network with parallel dense layers for a shared input, and the hybrid convolutional recurrent neural network. We disaggregate a total of three signal parameters per appliance in each case. To evaluate the performance of the proposed method, some simulations and comparisons have been carried out, and the results show that the proposed method can achieve promising performance.

Oscillatory Bursting as a Mechanism for Temporal Coupling and Information Coding.

Even the simplest cognitive processes involve interactions between cortical regions. To study these processes, we usually rely on averaging across several repetitions of a task or across long segments of data to reach a statistically valid conclusion. Neuronal oscillations reflect synchronized excitability fluctuations in ensembles of neurons and can be observed in electrophysiological recordings in the presence or absence of an external stimulus. Oscillatory brain activity has been viewed as sustained increase in power at specific frequency bands. However, this perspective has been challenged in recent years by the notion that oscillations may occur as transient burst-like events that occur in individual trials and may only appear as sustained activity when multiple trials are averaged together. In this review, we examine the idea that oscillatory activity can manifest as a transient burst as well as a sustained increase in power. We discuss the technical challenges involved in the detection and characterization of transient events at the single trial level, the mechanisms that might generate them and the features that can be extracted from these events to study single-trial dynamics of neuronal ensemble activity.

First 10 Months of TGF Observations by ASIM

AbstractThe Atmosphere‐Space Interactions Monitor (ASIM) was launched to the International Space Station on 2 April 2018. The ASIM payload consists of two main instruments, the Modular X‐ray and Gamma‐ray Sensor (MXGS) for imaging and spectral analysis of Terrestrial Gamma‐ray Flashes (TGFs) and the Modular Multi‐spectral Imaging Array for detection, imaging, and spectral analysis of Transient Luminous Events and lightning. ASIM is the first space mission designed for simultaneous observations of Transient Luminous Events, TGFs, and optical lightning. During the first 10 months of operation (2 June 2018 to 1 April 2019) the MXGS has observed 217 TGFs. In this paper we report several unprecedented measurements and new scientific results obtained by ASIM during this period: (1) simultaneous TGF observations by Fermi Gamma‐ray Burst Monitor and ASIM MXGS revealing the very good detection capability of ASIM MXGS and showing substructures in the TGF, (2) TGFs and Elves produced during the same lightning flash and even simultaneously have been observed, (3) first imaging of TGFs giving a unique source location, (4) strong statistical support for TGFs being produced during the upward propagation of a leader just before a large current pulse heats up the channel and emits a strong optical pulse, and (5) the t50 duration of TGFs observed from space is shorter than previously reported.

Optical high-voltage sensor based on fiber Bragg gratings and stacked piezoelectric actuators for a.c. measurements.

In this paper, we present the design and fabrication of a compact, modular optical high-voltage sensor (OHVS) based on fiber Bragg gratings (FBG) for a.c. distribution and transmission lines. The proposed OHVS is composed by a stack of piezoelectric transducers that transfer mechanical strain to a sensing FBG. A prototype was tested in the laboratory and showed a maximum linearity error of less than 3% of full-scale range (FSR) for input voltages up to ${14\,\,{\rm kV}_{\rm rms}}$14kVrms with a signal-to-noise ratio of 55 dB, allowing measurements with a resolution of less than 0.2% of FSR. Transient response of the developed OHVS was preliminarily investigated, and a response time of less than 1 ms was observed. The results obtained allow us to conclude that the developed OHVS may also be used for the detection of transient events.

Slow Slip Event Detection in Cascadia Using Vertical Derivatives of Horizontal Stress Rates

AbstractWe present a new approach assuming elastic behavior at the Earth's surface to invert continuous GPS time series for Vertical Derivatives of Horizontal Stress rates. This approach enables detection of transient slow slip events at the Cascadia subduction zone. Vertical Derivatives of Horizontal Stress rates are the most spatially compact expression of surface deformation due to subsurface deformation sources (compared to GPS displacement/velocity fields, and strain rates), and enable identification of transient slip events and temporal evolution of locking on the interface throughout the slow slip cycle. Slow slip events (SSEs) identified by our method coincide spatially and temporally with tremor episodes, and agree well with SSEs identified by previous geodetic studies. We present daily slip models that fit the Vertical Derivatives of Horizontal Stress rates, showing phases of locking, unlocking, and SSEs on the subduction interface for the 2009–2015 period. Similar to previous studies, our results highlight along‐strike variations in SSEs and locking behavior from northern Cascadia to southern Cascadia. Between events, the SSE source zone in northern Cascadia appears to be completely locked. In contrast, we suggest that some fraction of plate motion in the SSE zone in central and southern Cascadia is accommodated by steady creep (or very small, frequent SSEs) between large SSE events. We observe rapid relocking of the interface (within weeks) following the end of most SSEs, with implications for the time scales of fault healing following deformation events. Our results present a promising approach to transient deformation detection, with a fine temporal and spatial resolution of the surface expression of deformation.

OBSERVACIONES DE EVENTOS TRANSITORIOS CON EL SISTEMA DE MONITOREO DE CAMPO ANCHO MINI-MEGATORTORA CON RESOLUCIÓN TEMPORAL SUB-SEGUNDO

Here we present the summary of operation of a novel 9-channel wide-field optical monitoring system with subsecond temporal resolution, Mini-MegaTORTORA, which systematically surveys the sky since 2014 at Special Astrophysical Observatory on Russian Caucasus. The system is able to observe the sky simultaneously in either wide (∼900 square degrees) or narrow (∼100 square degrees fields of view, either in clear light or with any combination of color (Johnson-Cousins B, V or R) and polarimetric filters installed, with exposure times ranging from 0.1 s to hundreds of seconds. The real-time system data analysis pipeline performs automatic detection of rapid transient events, both near-Earth and extragalactic. The objects routinely detected by Mini-MegaTORTORA also include faint meteors and artificial satellites. The imaging survey performed by Mini-MegaTORTORA also allows to look for a slower variability of various kinds of objects.

Robbie: A batch processing work-flow for the detection of radio transients and variables

Abstract We present Robbie : a general work-flow for the detection and characterization of radio variability and transient events in the image domain. Robbie is designed to work in a batch processing paradigm with a modular design so that components can be swapped out or upgraded to adapt to different input data, whilst retaining a consistent and coherent methodological approach. Robbie is based on commonly used and open software, and is encapsulated in a Makefile to aid portability and reproducibility. In this paper we describe the methodology behind Robbie , and demonstrate its use on real and simulated data. Robbie is available on GitHub .