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1. Evidence of Ultrafaint Radio Frequency Interference in Deep 21 cm Epoch of Reionization Power Spectra with the Murchison Wide-field Array

   https://doi.org/10.3847/1538-4357/acffbd  

   Wilensky, Michael J. ; Morales, Miguel F. ; Hazelton, Bryna J. ; Star, Pyxie L. ; Barry, Nichole ; Byrne, Ruby ; Jordan, C. H. ; Jacobs, Daniel C. ; Pober, Jonathan C. ; Trott, C. M. ( November 2023 , The Astrophysical Journal)

   We present deep upper limits from the 2014 Murchison Widefield Array Phase I observing season, with a particular emphasis on identifying the spectral fingerprints of extremely faint radio frequency interference (RFI) contamination in the 21 cm power spectra (PS). After meticulous RFI excision involving a combination of theSSINSRFI flagger and a series of PS-based jackknife tests, our lowest upper limit on the Epoch of Reionization (EoR) 21 cm PS signal is Δ²≤ 1.61 × 10⁴mK²atk= 0.258h Mpc⁻¹at a redshift of 7.1 using 14.7 hr of data. By leveraging our understanding of how even fainter RFI is likely to contaminate the EoR PS, we are able to identify ultrafaint RFI signals in the cylindrical PS. Surprisingly this signature is most obvious in PS formed with less than 1 hr of data, but is potentially subdominant to other systematics in multiple-hour integrations. Since the total RFI budget in a PS detection is quite strict, this nontrivial integration behavior suggests a need to more realistically model coherently integrated ultrafaint RFI in PS measurements so that its potential contribution to a future detection can be diagnosed.

    

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2. Quantifying excess power from radio frequency interference in Epoch of Reionization measurements

   https://doi.org/10.1093/mnras/staa2442  

   Wilensky, Michael J ; Barry, Nichole ; Morales, Miguel F ; Hazelton, Bryna J ; Byrne, Ruby ( October 2020 , Monthly Notices of the Royal Astronomical Society)

   null (Ed.)

   ABSTRACT We quantify the effect of radio frequency interference (RFI) on measurements of the 21-cm power spectrum during the Epoch of Reionization (EoR). Specifically, we investigate how the frequency structure of RFI source emission generates contamination in higher order wave modes, which is much more problematic than smooth-spectrum foreground sources. Using a relatively optimistic EoR model, we find that even a single relatively dim RFI source can overwhelm the EoR power spectrum signal of $\sim 10\, {\rm mK}^2$ for modes $0.1 \ \lt k \lt 2 \, h\, {\rm Mpc}^{-1}$. If the total apparent RFI flux density in the final power spectrum integration is kept below 1 mJy, an EoR signal resembling this optimistic model should be detectable for modes $k \lt 0.9\, h\, {\rm Mpc}^{-1}$, given no other systematic contaminants and an error tolerance as high as 10 per cent. More pessimistic models will be more restrictive. These results emphasize the need for highly effective RFI mitigation strategies for telescopes used to search for the EoR. 

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3. Simulating Spectral Kurtosis Mitigation Against Realistic RFI Signals

   Smith, E. T.' ; Pisano, D. J. ( September 2022 , The RFI2022 Workshop at ECMWF)

   We explore the statistical radio frequency interference (RFI) mitigation technique spectral kurtosis (SK) in the context of simulated realistic RFI signals. SK is a per-channel RFI detection metric that estimates the kurtosis of a collection of M power values in a single channel to discern between human-made RFI and incoherent astronomical signals of interest. We briefly test the ability of SK to flag signals with various representative modulation types, data rates, and duty cycles, as well as accumulation lengths M and multi-scale SK bin shapes. Multi-scale SK uses a rolling window to combine information from adjacent time-frequency pixels to mitigate weaknesses in single-scale SK. High data rate RFI signals with significant sidelobe emission are harder to flag, as well as signals with a 50% effective duty cycle. Multi-scale SK using at least one extra channel can detect both the center channel and side-band interference, flagging most of the signal at the expense of larger false positive rates. 

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4. FETCH: A deep-learning based classifier for fast transient classification

   https://doi.org/10.1093/mnras/staa1856  

   Agarwal, Devansh ; Aggarwal, Kshitij ; Burke-Spolaor, Sarah ; Lorimer, Duncan R ; Garver-Daniels, Nathaniel ( September 2020 , Monthly Notices of the Royal Astronomical Society)

   ABSTRACT With the upcoming commensal surveys for Fast Radio Bursts (FRBs), and their high candidate rate, usage of machine learning algorithms for candidate classification is a necessity. Such algorithms will also play a pivotal role in sending real-time triggers for prompt follow-ups with other instruments. In this paper, we have used the technique of Transfer Learning to train the state-of-the-art deep neural networks for classification of FRB and Radio Frequency Interference (RFI) candidates. These are convolutional neural networks which work on radio frequency-time and dispersion measure-time images as the inputs. We trained these networks using simulated FRBs and real RFI candidates from telescopes at the Green Bank Observatory. We present 11 deep learning models, each with an accuracy and recall above 99.5 per cent on our test data set comprising of real RFI and pulsar candidates. As we demonstrate, these algorithms are telescope and frequency agnostic and are able to detect all FRBs with signal-to-noise ratios above 10 in ASKAP and Parkes data. We also provide an open-source python package fetch (Fast Extragalactic Transient Candidate Hunter) for classification of candidates, using our models. Using fetch, these models can be deployed along with any commensal search pipeline for real-time candidate classification. 

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5. Deep Learning Based RFI Detection and Mitigation for SMAP Using Convolutional Neural Networks

   Ahmed Manavi Alam*, Ali Cafer ( January 2022 , RFI Workshop 2022)

   Passive Remote Sensing services are indispensable in modern society because of the applications related to climate studies and earth science. Among those, NASA’s Soil Moisture Active and Passive (SMAP) mission provides an essential climate variable such as the moisture content of the soil by using microwave radiation within protected band over 1400-1427 MHz. However, because of the increasing active wireless technologies such as Internet of Things (IoT), unmanned aerial vehicles (UAV), and 5G wireless communication, the SMAP’s passive observations are expected to experience an increasing number of Radio Frequency Interference (RFI). RFI is a well-documented issue and SMAP has a ground processing unit dedicated to tackling this issue. However, advanced techniques are needed to tackle the increasing RFI problem for passive sensing systems and to jointly coexist communication and sensing systems. In this paper, we apply a deep learning approach where a novel Convolutional Neural Network (CNN) architecture for both RFI detection and mitigation is employed. SMAP Level 1A spectrogram of antenna counts and various moments data are used as the inputs to the deep learning architecture. We simulate different types of RFI sources such as pulsed, CW or wideband anthropogenic signals. We then use artificially corrupted SMAP Level 1B antenna measurements in conjunction with RFI labels to train the learning architecture. While the learned detection network classifies input spectrograms as RFI or no-RFI cases, the mitigation network reconstructs the RFI mitigated antenna temperature images. The proposed learning framework both takes advantage of the existing SMAP data and the simulated RFI scenarios. Future remote sensing systems such as radiometers will suffer an increasing RFI problem and spectrum sharing and techniques that will allow coexistance of sensing and communication systems will be utmost importance for both parties. RFI detection and mitigation will remain a prerequisite for these radiometers and the proposed deep learning approach has the potential to provide an additional perspective to existing solutions. We will present detailed analysis on the selected deep learning architecture, obtained RFI detection accuracy levels and RFI mitigation performance. 

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