Evaluation of climate changes and their accounting for developing the reclamation measures in western Ukraine

In modern conditions, there are cardinal climate changes on the Earth as at the planetary scale, as at the regional level. According to numerous hydrometeorological characteristics and indicators, climatologists specialists concluded that Ukraine also take place significant climatic changes in the last 10–25 years. In complicated natural-technical systems, which include irrigation and drainage systems (IDS) on drained lands, the selection of regime-technological and technical solutions on different levels of the decision including the time, should be based on the appropriate meteorological information for selecting climatologically optimal management strategies for such systems in the long-term and annual periods. The decisive influence on the formation of water and the overall natural reclamation modes of reclaimed land and harvest crops in many cases depends exactly from climate or weather conditions. Thus, it is necessary to have available data about their implementation to the relevant object as for number of previous years retrospective observations and the forecast period of functioning of the object. Therefore, forecasting of weather and climate conditions become an indispensable condition for implementation of assessing the overall effectiveness of IDS operation. To solve this problem we performed large-scale computer experiment for multi-year retrospective and current data observations in the area of Zhytomyr Polissya. Were planned and implemented the following variants of studies – «Base», «Transitional», «Recent», «CCCM», «UKMO». The forecast was done for five years of typical groups of vegetation periods regarding conditions of heat and moisture provision (very wet – 10%, wet – 30%, average – 50%, dry – 70%, very dry – 90%) on such basic meteorological characteristics: air temperature; precipitation; relative air humidity; defi cit of air humidity; photosynthetically active radiation (PAR); coeffi cient of moisture provision (the ratio of precipitation to evapotranspiration). Obtained results of comparative assess-ment of climatic conditions in Zhytomyr Polissya zone, suggests that for most of the basic meteorological parameters, already there are changes that in the short term may exceed 10% of the critical ecological threshold, which will lead to relevant irreversible changes in the state of the environment in the region

A search for technosignatures from 14 planetary systems in the Kepler field with the Green Bank Telescope at 1.15-1.73 GHz

Analysis of Kepler mission data suggests that the Milky Way includes billions of Earth-like planets in the habitable zone of their host star. Current technology enables the detection of technosignatures emitted from a large fraction of the Galaxy. We describe a search for technosignatures that is sensitive to Arecibo-class transmitters located within ~420 ly of Earth and transmitters that are 1000 times more effective than Arecibo within ~13 000 ly of Earth. Our observations focused on 14 planetary systems in the Kepler field and used the L-band receiver (1.15-1.73 GHz) of the 100 m diameter Green Bank Telescope. Each source was observed for a total integration time of 5 minutes. We obtained power spectra at a frequency resolution of 3 Hz and examined narrowband signals with Doppler drift rates between +/-9 Hz/s. We flagged any detection with a signal-to-noise ratio in excess of 10 as a candidate signal and identified approximately 850 000 candidates. Most (99%) of these candidate signals were automatically classified as human-generated radio-frequency interference (RFI). A large fraction (>99%) of the remaining candidate signals were also flagged as anthropogenic RFI because they have frequencies that overlap those used by global navigation satellite systems, satellite downlinks, or other interferers detected in heavily polluted regions of the spectrum. All 19 remaining candidate signals were scrutinized and none were attributable to an extraterrestrial source.Comment: 15 pages, 5 figures, accepted for publication in the Astronomical Journa

A search for technosignatures from TRAPPIST-1, LHS 1140, and 10 planetary systems in the Kepler field with the Green Bank Telescope at 1.15-1.73 GHz

As part of our ongoing search for technosignatures, we collected over three terabytes of data in May 2017 with the L-band receiver (1.15-1.73 GHz) of the 100 m diameter Green Bank Telescope. These observations focused primarily on planetary systems in the Kepler field, but also included scans of the recently discovered TRAPPIST-1 and LHS 1140 systems. We present the results of our search for narrowband signals in this data set with techniques that are generally similar to those described by Margot et al. (2018). Our improved data processing pipeline classified over $98\%$ of the $\sim$ 6 million detected signals as anthropogenic Radio Frequency Interference (RFI). Of the remaining candidates, 30 were detected outside of densely populated frequency regions attributable to RFI. These candidates were carefully examined and determined to be of terrestrial origin. We discuss the problems associated with the common practice of ignoring frequency space around candidate detections in radio technosignature detection pipelines. These problems include inaccurate estimates of figures of merit and unreliable upper limits on the prevalence of technosignatures. We present an algorithm that mitigates these problems and improves the efficiency of the search. Specifically, our new algorithm increases the number of candidate detections by a factor of more than four compared to Margot et al. (2018).Comment: 17 pages, 9 figure

A Search for Technosignatures Around 31 Sun-like Stars with the Green Bank Telescope at 1.15-1.73 GHz

We conducted a search for technosignatures in April of 2018 and 2019 with the L-band receiver (1.15-1.73 GHz) of the 100 m diameter Green Bank Telescope. These observations focused on regions surrounding 31 Sun-like stars near the plane of the Galaxy. We present the results of our search for narrowband signals in this data set as well as improvements to our data processing pipeline. Specifically, we applied an improved candidate signal detection procedure that relies on the topographic prominence of the signal power, which nearly doubles the signal detection count of some previously analyzed data sets. We also improved the direction-of-origin filters that remove most radio frequency interference (RFI) to ensure that they uniquely link signals observed in separate scans. We performed a preliminary signal injection and recovery analysis to test the performance of our pipeline. We found that our pipeline recovers 93% of the injected signals over the usable frequency range of the receiver and 98% if we exclude regions with dense RFI. In this analysis, 99.73% of the recovered signals were correctly classified as technosignature candidates. Our improved data processing pipeline classified over 99.84% of the ~26 million signals detected in our data as RFI. Of the remaining candidates, 4539 were detected outside of known RFI frequency regions. The remaining candidates were visually inspected and verified to be of anthropogenic nature. Our search compares favorably to other recent searches in terms of end-to-end sensitivity, frequency drift rate coverage, and signal detection count per unit bandwidth per unit integration time.Comment: 20 pages, 8 figures, in press at the Astronomical Journal (submitted on Sept. 9, 2020; reviews received Nov. 6; re-submitted Nov. 6; accepted Nov. 17

A Search for Technosignatures Around 11,680 Stars with the Green Bank Telescope at 1.15-1.73 GHz

We conducted a search for narrowband radio signals over four observing sessions in 2020-2023 with the L-band receiver (1.15-1.73 GHz) of the 100 m diameter Green Bank Telescope. We pointed the telescope in the directions of 62 TESS Objects of Interest, capturing radio emissions from a total of ~11,680 stars and planetary systems in the ~9 arcminute beam of the telescope. All detections were either automatically rejected or visually inspected and confirmed to be of anthropogenic nature. In this work, we also quantified the end-to-end efficiency of radio SETI pipelines with a signal injection and recovery analysis. The UCLA SETI pipeline recovers 94.0% of the injected signals over the usable frequency range of the receiver and 98.7% of the injections when regions of dense RFI are excluded. In another pipeline that uses incoherent sums of 51 consecutive spectra, the recovery rate is ~15 times smaller at ~6%. The pipeline efficiency affects calculations of transmitter prevalence and SETI search volume. Accordingly, we developed an improved Drake Figure of Merit and a formalism to place upper limits on transmitter prevalence that take the pipeline efficiency and transmitter duty cycle into account. Based on our observations, we can state at the 95% confidence level that fewer than 6.6% of stars within 100 pc host a transmitter that is detectable in our search (EIRP > 1e13 W). For stars within 20,000 ly, the fraction of stars with detectable transmitters (EIRP > 5e16 W) is at most 3e-4. Finally, we showed that the UCLA SETI pipeline natively detects the signals detected with AI techniques by Ma et al. (2023).Comment: 22 pages, 9 figures, submitted to AJ, revise

Maximizing Signal Detection and Improving Radio Frequency Interference Identification in the Search for Radio Technosignatures

In this work, I describe significant advancements to the signal detection and Radio Frequency Interference (RFI) identification capabilities of modern radio technosignature detection algorithms. These improvements are presented alongside the results of the analysis of four annual UCLA radio technosignatures searches spanning 2016-2019. First, I describe the UCLA SETI Group’s initial versions of the signal detection and RFI identification algorithms, which were able to detect approximately 850,000 candidate signals within a frequency range of 1.15-1.73 GHz over ~2 hours of observations with the 100 m diameter Green Bank Telescope in 2016. Next, I describe an improved candidate signal detection algorithm that detected approximately 6 million signals in a 2017 search for technosignatures with identical observational parameters. Importantly, I show that the common practice of ignoring frequency space around candidate detections can reduce the number of signals detected by a factor of four or more and presents significant problems when estimating figures of merit or upper limits on the prevalence of technosignatures. I then present further improvements to these detection algorithms, which introduce the use of the topographic prominence for detection purposes and nearly double the signal detection count of some previously analyzed data sets. I also describe improvements to direction-of-origin filter algorithms, which are designed to remove most of the signals attributable to RFI from the data. The updated algorithms ensure a unique link between signals observed in separate scans. Finally, I present a novel machine-learning-based RFI mitigation algorithm, which helps address a major remaining challenge in the search for radio technosignatures. Specifically, I describe the design and deployment of a Convolutional Neural Network (CNN) that can determine whether or not a signal detected in one scan is also present in another scan. This CNN-based filter outperforms both a baseline 2D correlation model as well as existing filters over a range of metrics and reduces the number of signals requiring visual inspection after the application of traditional filters by a factor of 6-16 in nominal situations

Maximizing Signal Detection and Improving Radio Frequency Interference Identification in the Search for Radio Technosignatures

In this work, I describe significant advancements to the signal detection and Radio Frequency Interference (RFI) identification capabilities of modern radio technosignature detection algorithms. These improvements are presented alongside the results of the analysis of four annual UCLA radio technosignatures searches spanning 2016-2019. First, I describe the UCLA SETI Group’s initial versions of the signal detection and RFI identification algorithms, which were able to detect approximately 850,000 candidate signals within a frequency range of 1.15-1.73 GHz over ~2 hours of observations with the 100 m diameter Green Bank Telescope in 2016. Next, I describe an improved candidate signal detection algorithm that detected approximately 6 million signals in a 2017 search for technosignatures with identical observational parameters. Importantly, I show that the common practice of ignoring frequency space around candidate detections can reduce the number of signals detected by a factor of four or more and presents significant problems when estimating figures of merit or upper limits on the prevalence of technosignatures. I then present further improvements to these detection algorithms, which introduce the use of the topographic prominence for detection purposes and nearly double the signal detection count of some previously analyzed data sets. I also describe improvements to direction-of-origin filter algorithms, which are designed to remove most of the signals attributable to RFI from the data. The updated algorithms ensure a unique link between signals observed in separate scans. Finally, I present a novel machine-learning-based RFI mitigation algorithm, which helps address a major remaining challenge in the search for radio technosignatures. Specifically, I describe the design and deployment of a Convolutional Neural Network (CNN) that can determine whether or not a signal detected in one scan is also present in another scan. This CNN-based filter outperforms both a baseline 2D correlation model as well as existing filters over a range of metrics and reduces the number of signals requiring visual inspection after the application of traditional filters by a factor of 6-16 in nominal situations

A Machine Learning-based Direction-of-origin Filter for the Identification of Radio Frequency Interference in the Search for Technosignatures

Abstract Radio frequency interference (RFI) mitigation remains a major challenge in the search for radio technosignatures. Typical mitigation strategies include a direction-of-origin (DoO) filter, where a signal is classified as RFI if it is detected in multiple directions on the sky. These classifications generally rely on estimates of signal properties, such as frequency and frequency drift rate. Convolutional neural networks (CNNs) offer a promising complement to existing filters because they can be trained to analyze dynamic spectra directly, instead of relying on inferred signal properties. In this work, we compiled several data sets consisting of labeled pairs of images of dynamic spectra, and we designed and trained a CNN that can determine whether or not a signal detected in one scan is also present in another scan. This CNN-based DoO filter outperforms both a baseline 2D correlation model and existing DoO filters over a range of metrics, with precision and recall values of 99.15% and 97.81%, respectively. We found that the CNN reduces the number of signals requiring visual inspection after the application of traditional DoO filters by a factor of 6–16 in nominal situations

Analysis of Four-band WISE Observations of Asteroids

Abstract We analyzed 82,548 carefully curated observations of 4420 asteroids with Wide-field Infrared Survey Explorer (WISE) four-band data to produce estimates of diameters and infrared emissivities. We also used these diameter values in conjunction with absolute visual magnitudes to infer estimates of visible-band geometric albedos. We provide solutions to 131 asteroids not analyzed by the NEOWISE team and to 1778 asteroids not analyzed with four-band data by the NEOWISE team. Our process differs from the NEOWISE analysis in that it uses an accurate solar flux, integrates the flux with actual bandpass responses, obeys Kirchhoff’s law, and does not force emissivity values in all four bands to an arbitrary value of 0.9. We used a regularized model-fitting algorithm that yields improved fits to the data. Our results more closely match stellar-occultation diameter estimates than the NEOWISE results by a factor of ∼2. Using 24 high-quality stellar-occultation results as a benchmark, we found that the median error of four-infrared-band diameter estimates in a carefully curated data set is 9.3%. Our results also suggest the presence of a size-dependent bias in the NEOWISE diameter estimates, which may pollute estimates of asteroid size distributions and slightly inflate impact-hazard risk calculations. For more than 90% of asteroids in this sample, the primary source of error on the albedo estimate is the error on absolute visual magnitude