Contents
1
Introduction ........................................................................................................................................... 2
2
Science Background.............................................................................................................................. 3
3
Radio Frequency Interference ............................................................................................................... 4
4
Pulsar Timing and Real-time RFI Excision and Cancellation .............................................................. 6
5
Pulsar Searching and RFI Identification using Machine Learning ..................................................... 10
6
Relevance to NASA and Jurisdiction.................................................................................................. 12
7
NASA Interactions .............................................................................................................................. 13
8
Project Partners, Management and Personnel ..................................................................................... 14
9
Tasks and Schedule ............................................................................................................................. 15
10
Partnerships and Sustainability ....................................................................................................... 16
11
Dissemination ................................................................................................................................. 17
12
Prior NASA Research Support ....................................................................................................... 17
13
References ...................................................................................................................................... 18
1
1 Introduction
The National Radio Astronomy Observatory, in collaboration with West Virginia University
(WVU), Brigham Young University (BYU), and JPL, proposes a research program to develop
state-of-the-art radio frequency interference (RFI) mitigation techniques. These techniques will
enable sensitive pulsar searches and timing that will assist NASA in addressing key science
questions in the targeted research program “Physics of the Cosmos (PCOS)”. PCOS is a focused
program within NASA’s Astrophysics Division which seeks to understand the basic building
blocks of our existence - matter, energy, space, and time - and how they behave under extreme
physical conditions. PCOS addresses central questions about the nature of complex astrophysical
phenomena such as black holes, neutron stars, dark energy, and gravitational waves.
Pulsar observations with the Robert C. Byrd Green Bank Telescope (GBT), located in Green
Bank, WV, can directly answer these questions. Pulsars, rapidly rotating neutron stars with
clock-like timing precision, provide insights into a rich variety of physics and astrophysics.
Specifically, high precision pulsar timing observations address three of the five key science goals
of the PCOS Program (http://pcos.gsfc.nasa.gov/science), namely to: (i) test the validity of
Einstein's theory of General Relativity and investigate the nature of space-time; (ii) understand
the formation and growth of supermassive black holes and their role in the evolution of galaxies;
and (iii) explore the behavior of matter and energy in their most extreme environments.
The broader bandwidths made possible by newly developed pulsar instrumentation present the
opportunity to dramatically increase pulsar search sensitivity and timing precision. These will
lead to dramatic advances in all of these areas. However, taking advantage of broad-band
observations requires the development of improved techniques to remove RFI, which becomes a
larger problem as bandwidths increase.
The goal of this proposal is to develop the advanced radio frequency interference excision and
mitigation techniques necessary to allow the most sensitive pulsar observations with the GBT.
Research and development will occur in two primary areas:

Active Cancellation: Active cancellation of an RFI signal may be accomplished by
receiving an interfering signal with a secondary antenna, and then using this signal to
cancel out its effects on the radio astronomy signal. A related approach uses a parametric
estimation/subtraction technique that exploits known properties of the RFI modulation.
We will experiment with combining these two approaches.

Neural Networks: Machine learning algorithms, especially neural networks, have shown
great promise in automating astronomical data processing. Our research will focus on the
application of neural networks to identify RFI in pulsar data
This work is synergistic with NASA's Fermi gamma-ray telescope, which is revolutionizing our
view of the Galactic neutron star population. Over the past four years, 44 millisecond pulsars
have been found via targeted searches of Fermi sources. The RFI mitigation techniques to be
developed in this proposal will allow us to find fainter pulsars associated with Fermi sources. In
addition to providing astrophysical laboratories to study neutron stars and their environments, the
long-term timing of some of these pulsars will be used to add further millisecond pulsars to
timing array experiments currently being carried out to detect low-frequency gravitational waves.
2
2 Science Background
Pulsars are rapidly rotating, highly magnetized neutron stars. Neutron stars can have magnetic
field strengths exceeding 1012 G, rotation rates approaching 1000 Hz, central densities exceeding
1014 g cm-3, and normalized gravitational strengths of order 0.4. Due to their large moments of
intertia, their pulses provide a highly regular clock when they are detected as radio pulsars. This
timing precision makes them natural physical laboratories to test gravitational physics. For
example, precise measurements of the pulse arrival times of PSR B1913+16, one member of a
double neutron star system, over an interval of decades revealed that this system is losing energy
at a rate that is consistent with the emission of quadrupolar gravitational waves as predicted by
General Relativity (Taylor & Weisberg 1989). The discovery and subsequent timing of this
pulsar resulted in the 1993 Nobel Prize in Physics. More recently, the double pulsar PSR J07373039A/B, with two detectable neutron stars, is proving to be a spectacular gravitational and
plasma physics laboratory (Burgay et al. 2003; Lyne et al. 2004; McLaughlin et al. 2005a,b;
Kramer et al. 2006; Kramer & Stairs 2008). Another exotic pulsar system that demonstrates
pulsars as probes of fundamental physics is the pulsar J1744−2446ad, in the globular cluster
Terzan 5, which has a pulse period of 1.4 ms (implied rotation rate of 716 Hz; Hessels et al.
2006). In order for it to maintain its structural integrity, the nuclear equation of state must be
such that it can withstand the centrifugal force on its equator.
High precision pulsar timing observations specifically address three of the five key science goals
of the PCOS Program, namely to:
1) Test the validity of Einstein's theory of General Relativity and investigate the nature of
spacetime. Current GBT observations of the double pulsar system (Figure 1) provide the most
sensitive test of general relativity in the strong-field regime (Kramer et al. 2006), and future
discoveries expected of pulsar-black hole binaries and pulsars orbiting the black hole at the
center of the Milky Way are expected to make
further fundamental contributions in this area.
Figure 1: Using GBT timing observations, we constrain the
masses of the two pulsars and, at the same time, test the
predictions of General Relativity. The diagonal lines
labeled “R” represent the mass ratio based on the semimajor axes of the orbits of A and B. The shaded orange
region is forbidden. The other lines illustrate the relativistic
corrections to a Keplerian orbit that are measured. Two
lines are plotted for all parameters to illustrate the 1-sigma
errors. The masses of A and B correspond to the
intersection of all lines and are measured to be 1.3381 ±
0.0007 Msun for A and 1.2489 ± 0.0007 Msun for B. Because
all lines intersect at a common point, we can say that all
measurements of relativistic parameters thus far are
consistent with GR.
3
2) Understand the formation and growth of supermassive black holes and their role in the
evolution of galaxies. Gravitational wave detection via pulsar timing is just over the horizon
and will be accelerated by this work. Supermassive black hole binaries are the strongest
expected source of nHz gravitational waves (Hobbs et al. 2010). Gravitational wave
astronomy will allow us to study the cosmological population of these binaries and possibly
individual sources. Current limits based on GBT data (Figure 2) are already beginning to
constrain models for galaxy formation and
evolution (Demorest et al. 2012).
Figure 2: current upper limits on the gravitational
wave spectrum at low frequencies taken from
recently published measurements by Demorest et
al. (2012) as part of the North American Nanohertz
Observatory
for
Gravitational
Waves
(NANOGrav), which uses high-precision timing of
an array of millisecond pulsars. The current limits
are just above the expected regimes for
gravitational waves from cosmic strings (green
band) and an ensemble of supermassive black hole
binaries at cosmological distances (pink band).
3) Explore the behavior of matter and energy in their most extreme environments. Precision
measurements of neutron star masses in binary systems allow us to constrain the equation of
state of superdense matter (Demorest et al. 2010) and studies of pulsar magnetospheres allow
us to probe particle physics in high magnetic-field relativistic plasmas (Li et al. 2012).
Figure 3: mass-radius diagram showing
constraints on various neutron star equations-ofstate provided by Shapiro time delay measurements
of the binary millisecond pulsar J1614-2230
(Demorest et al. 2010). The red horizontal line
shows the mass determination of the pulsar (the
radius of which is currently unknown). The smooth
curves show various mass-radius relations for
different equations of state. A number of these
models are now excluded by the mass measurement
of PSR J1614-2230. The orange and yellow lines
indicate previous mass constraints.
4
Radio Frequency Interference
Radio frequency interference (RFI) is a growing problem over all radio astronomy bands due to
the proliferation of wireless applications, portable electronics, and new and expanded
communications technologies such as spread-spectrum digital transmission and satellite
downlinks. These technologies cause strong, unwanted radio signals that vary as a function of
time and frequency throughout key pulsar observing bands. Compounding the effects of the new
technologies is the fact that radio receivers and instruments have improved substantially and are
much more sensitive, with wider bandwidths, causing us to detect all of these signals in terribly
exquisite detail (see Figure 4).
Figure
4:
Radio
Frequency
Interference measured by the GBT
in the five receiver bands which
currently cover the frequency range
from 0.5 GHz to 2.5 GHz.
Most RFI is currently handled via analysis of the statistics of the raw data after it has been
recorded to disk. Outlying data samples with values far from those expected as a function of time
or observing frequency are removed from the analysis. Simply flagging statistically aberrant time
intervals or frequency channels can result in large fractions of the data being discarded, causing a
substantial loss of sensitivity, and so more sophisticated techniques are needed.
Pulsar observations are particularly susceptible to RFI. Remaining, unexcised RFI impacts pulsar
research in two ways. For pulsar timing, the presence of RFI can make the data unusable; more
insidiously, low levels of RFI subtly distort the pulse shape, leading to errors in the measured
time of arrival (Figure 5). For pulsar searching, the presence of RFI leads to a deluge of “false
positives”. The best recourse at present is extremely time-consuming human inspection of each
pulsar candidate to distinguish between genuine pulsars and spurious RFI artifacts.
The proposed research project will address both of these concerns via state-of-the-art but
realizable signal processing techniques. Pulsar timing will be enhanced via real-time RFI
excision and cancellation algorithms. Pulsar searching will be improved using neural networks,
to better distinguish genuine candidates from false positives. These two aspects of the proposal
are described separately below.
5
Figure 5: (a) and (c): radio frequency as a function of pulse phase for two different observations which show the
quadratic signature of pulse dispersion in the interstellar medium, as well as the contamination by RFI on different
parts of the spectrum. RFI is particularly prevalent in the observation in the left panel. (b) and (d): integrated pulse
profiles formed after these data are corrected for dispersion. The impact on profile fidelity for the observation
including strong RFI is clearly seen in (b) compared to the much cleaner pulse shape in (d).
3 Pulsar Timing and Real-time RFI Excision and Cancellation
High-precision pulsar timing is essential for fundamental physics experiments such as mass
measurements, tests of General Relativity, and the detection of low-frequency gravitational
waves. In addition, most of the Fermi pulsar detections have been enabled by folding gamma-ray
data with a radio-derived ephemeris. Continued timing of radio pulsars is essential for revealing
gamma-ray pulsations from more pulsars and for more sensitive gamma-ray detections of known
pulsars. Improved RFI excision will increase the sensitivity of our timing observations by
allowing us to use larger bandwidths and by removing spurious features in pulse profiles that can
increase time-of-arrival errors. The ability to time pulsars more precisely will allow us to not
only elucidate the geometries of neutron stars (by comparing the radio and gamma-ray pulse
phases), but also will provide sensitivity improvements for those pulsars that are being used in
timing arrays for the detection of low-frequency gravitational waves. This latter point is
extremely relevant given that gravitational-wave astronomy is one of the key science priorities
for NASA in the coming decade. The improvements discussed here will also result in many
fewer candidate signals resulting from pulsar searches, dramatically reducing the time needed to
inspect pulsar search output.
One important correction needed for high precision timing is the removal of frequencydependent dispersive and scattering delays incurred as radio pulses traverse the interstellar
medium. NRAO is in the process of developing a wide-band (approximately 0.6 – 2.4 GHz)
prime-focus receiver system and matching backend to be used primarily for high-precision pulsar
timing projects. Such a system will use all of the useful bandwidth available (i.e. at frequencies
above those with strong interstellar scattering effects) for high-precision pulsar timing, thereby
optimizing the GBT’s sensitivity for such science. In addition, it will enable cutting edge
removal of frequency-dependent propagation effects which can systematically limit some pulsar
timing observations. This crowded frequency range is especially prone to RFI since it includes
most cell phone bands, digital TV broadcasts, UHF commercial land mobile services, the
ubiquitous unlicensed industrial, scientific and medical (ISM) bands at 900 MHz and 2.4 GHz,
aeronautical traffic control radar and DME services, satellite downlink transmissions, GPS,
GLONASS, satellite phone, four large amateur radio bands, and many other high power licensed
6
services. A combination of active cancellation and real-time blanking will be essential to fully
realize the capabilities of the new receiver/backend system.
It is recently becoming apparent that an additional limitation to high-precision pulsar timing
experiments, and therefore the associated science that can be extracted, is that individual pulses
from a pulsar having varying pulse phases. Sophisticated techniques are now being developed to
minimize the impact of this “pulse jitter”. However, because they rely on the ability to detect
individual pulses and measure the pulse shapes with high precision, data must be essentially free
from RFI.
A detailed background on RFI mitigation techniques is provided by ITU-R RA.2126, Kesteven
(2010) and references therein. There are two main approaches to RFI mitigation applicable to the
GBT:
1. Excision, in the sense of “cutting out” RFI. For example, RFI consisting of brief pulses
might be mitigated by blanking the data when the pulse is present.
2. Cancellation, in the sense of “subtracting” RFI from the telescope output. Cancellation is
potentially superior to excision in that the RFI is removed with no impact on the
astronomical signal, providing a “look through” capability that is nominally free of the
artifacts associated with the simple “cutting out” of data.
We have considerable expertise in the application of both of these techniques. Jeffs and coworkers (Dong et al., 2005), have demonstrated the ability to blank pulses from the ARSR-3
FAA Air Surveillance Radar on Apple Orchard Mountain near Bedford, VA, 106 km southsoutheast of the GBT. This work was done by recording data to disk, and processing it after the
fact. Fisher (2004) has also addressed the similar problem of pulsed interference from aviation
distance measuring equipment (DME).
Active cancellation of an RFI signal is accomplished by using a high-gain antenna to receive the
interfering signal and using it as a reference for cancelling out its effects on the radio astronomy
signal. This technique was demonstrated for the first time in radio astronomy by Barnbaum and
Bradley (1998). The cancellation process must be done with an adaptive filter, since the signal
characteristics change with time. Barnbaum and Bradley used the popular least mean squares
(LMS) algorithm based on Wiener filter principles. A limitation of this algorithm is that it
requires an input interference to noise ratio (INR) > 1 in order to achieve significant benefit. To
achieve an output INR << 1 using this method, it is usually necessary to implement some means
to receive the RFI with INR greater than the INR perceived by the primary instrument. Since
most large dishes have approximately unity gain in the far sidelobes, the INR can be improved in
proportion to the gain of the auxiliary antenna used to receive the RFI. Thus, a yagi with 20 dB
gain could improve the INR available to the cancellation algorithm by about 20 dB, which could
then reduce INR at the telescope output by a comparable factor. This approach was
demonstrated by BYU Masters student Andrew Poulson (2003, 2005). Subsequent work (Jeffs et
al., 2005) describes the extension of this “reference signal” approach to achieve better
performance against RFI from satellites by using multiple auxiliary signals from dishes with
gains on the order of 30 dB.
Roshi (2002) has investigated techniques to suppress interference due to synchronization signals
in TV transmission. A combination of noise-free modeling of the synchronization signals and
7
adaptive filtering is used to suppress the interference. The measured lower limit on RFI rejection
using this technique on the TV synchronization signal is about 12 dB.
While all of these techniques show promise, none of them are in regular production use. Two
significant factors in this have been a) existing approaches have only been available to expert
users, and b) until recently, available digital signal processing hardware has only been able to
handle limited bandwidths, and time and frequency resolution. This situation is rapidly changing
with the advent of powerful multi-core CPU, GPU and FPGA-based radio astronomy backends.
Under the auspices of this grant, we will develop RFI blanking and cancellation techniques based
on extensions of the work cited above and implement them on the production digital backends in
use at the GBT. The focus of the work will be to: a) convert current off-line prototypes and
simulations into production real-time implementations; and b) continue research to advance the
state of the art for each of these approaches. The specific work will be as follows:
Perform an investigation of how badly low levels of RFI impact pulsar time of arrivals:
Paul Demorest has constructed an RFI-free software model of pulsar timing precision for
measurements in the 0.3 to 3 GHz frequency range using ranges of known pulsar parameters,
telescope sensitivity as function of frequency, and interstellar dispersion and scattering. This
model will be extended to include the loss of spectrum due to known RFI in this frequency range
and the improvements to pulsar timing that can be achieved by recovering various portions of
lost spectrum with RFI mitigation techniques. This model will also be extended to include the
effects of incomplete RFI subtraction on various types of known signals to guide the most
effective use of signal processing development and to determine when each specific mitigation
algorithm is “good enough”.
Implement radar and DME blankers using the approach of Fisher et al.: The parameters of
short-pulse radar signals and signals from aircraft-borne distance measuring equipment (DME)
have been well studied in our previous research. The main task here is to implement excision
algorithms in FPGA firmware to make them effective in real time and over an unprecedented
wide spectral bandwidth.
The spectrum allocated to the airborne portion of the DME service is 1.025 to 1.15 GHz, and is
divided into 1 MHz wide independent channels. The first signal processing implementation task
is to cleanly divide the spectrum into independent 1 MHz channels. There will be at least three
identical spectrometers, one for each of the two radio astronomy signals that process orthogonal
polarizations and at least one filter bank for a reference channel whose antenna gain is
maximized on the horizon from which most DME signals arrive. The next step is to implement a
matched filter in the reference channel for the double-pulse DME signature to maximize the
detectability of each pulse pair and provide a blanking gate to remove each pulse pair from the
two corresponding radio astronomy frequency channels with minimal loss of data. A few of the
1-MHz channels around 1.08 GHz will be discarded in this reconstruction to eliminate the
cacophony of radar transponder signals from all aircraft at this frequency within the DME band.
Blanking of pulses from ground-based radar is best done with a different strategy from DME
pulse blanking. The timing pulses from one or at most a few radar stations near radio astronomy
sites follows a repeating sequence. Any slow drifts in absolute times of individual pulse arrival
and the rotation rate of a sweeping radar antenna are easily tracked with modest signal
8
processing. For the older style short-pulse radar, blanking in the few MHz around each radar
transmitting frequency begins just before the expected pulse arrival time and continues for some
tens of microseconds to allow time for echoes from surrounding terrain an nearby aircraft to die
away. Newer radars use chirped pulses that occupy a larger fraction of the time between pulses
and sweep across a wider frequency bandwidth. The pulses can be de-chirped with the
appropriate algorithms in the radio telescope signal processing and removed with not much more
loss of science data than with short-pulse radar, but the signal processing design and
implementation is more challenging. After pulse removal the processed signal must be de-dechirped (re-chirped) to restore the high resolution pulsar timing information in the data stream.
Chirped radar blanking is a new area of development that will be supported by this research
grant. It has immediate relevance because the short-pulse radar that currently affects GBT
observing is slated to be replaced by a chirped radar within the period covered by this grant.
Implement an active canceller for GPS L1, L2C and GLONASS signals: Signals from
navigation satellites, such as GPS and GLONASS occupy a much wider bandwidth than is
required by the rate of information transmitted from satellite to navigation receiver. They use a
technique called spread spectrum that provides a great deal of immunity to interference and, in
the case of GPS, the ability to allow more than one satellite to use the same frequency band.
Ellingson et al (2001), using off-line software processing, have demonstrated that the GLONASS
signal can be de-spread using the published spreading code and the resulting narrow bandwidth
signal removed with a narrowband filter. Other information in the broader band of the original
GLONASS signal, including radio astronomy data, was recovered by re-spreading the filtered
signal with the original digital spreading function. This signal processing will be implemented in
FPGA firmware as part of this research grant and provided to the science and engineering
communities. Removal of GPS signals from radio astronomy data presents a greater challenge
because, unlike GLONASS where each satellite transmits on a different frequency band, all GPS
satellites use the same frequencies. For the GPS system to work, the spreading functions for the
different satellites are quite different, so that each satellite signal can be de-spread, have its
resulting narrow bandwidth signal removed, and re-spread in succession. We will conduct a
research study using real data to determine how effective this combined RFI excision technique
is. Our initial estimate is that the residual RFI will be of little consequence to pulsar timing.
Implement cancellation of TV signals below 700 MHz: UHF TV occupies the 470-806 MHz
frequency band with each channel occupying 6 MHz. With the advent of digital TV each TV
signal occupies its entire 6 MHz band with only small guard bands between channels. Unlike
GLONASS and GPS signals, the modulation (spreading) function is not deterministic. It depends
on the ever-changing picture and sound content, so de-spreading algorithms cannot be applied to
these signals. To remove these signals from radio astronomy data we must use adaptive
cancellation techniques where a relatively clean copy of each TV signal is acquired in a
reference channel with a relatively high gain antenna pointing in the direction of arrival. This
clean copy is then modified in phase and amplitude using LMS or a similar algorithm
implemented in DSP to match the same signal in the radio astronomy telescope channel and
subtracted. Since the relative phase and amplitude of the TV signal in the reference and
telescope channels is unknown and continuously changing, the subtractive process must be
adaptive with the signal processing objective of minimizing the TV signal in the radio astronomy
channel at all times. This technique has been well studied in experimental settings by several of
the co-PIs and collaborators, so the key tasks for this project will be to implement the technique
9
in FPGA firmware, study the unique propagation conditions for signals arriving at the GBT, and
optimize the adaptive algorithms accordingly.
4 Pulsar Searching and RFI Identification using Machine Learning
Finding more millisecond pulsars is the most important way to improve the sensitivity of pulsar
timing arrays for gravitational waves. Therefore, pulsar searches directly support one of NASA’s
key science priorities. In addition, as the Fermi mission continues, the need for radio
identification will continue to be important and, as Fermi detects more distant objects, more
sensitive radio surveys will be necessary. These are important for better understanding the pulsar
population in the Galaxy, crucial for interpreting the Fermi discoveries. In particular, new pulsar
discoveries will improve the distance model for pulsars in our galaxy. A refined distance model
will enable more accurate distances to Fermi pulsars to be calculated, enabling better
interpretation of their fluxes in the context of specific emission models.
All of the large-scale and targeted searches for pulsars currently being carried out are dominated
by RFI and generate many spurious false pulsar candidates. These candidates are typically
excised through a laborious manual review, which is currently the most significant limitation on
the speed of pulsar searches. The second thrust of the proposed project will automate this
candidate review process using machine learning pattern recognition techniques. This promises
to be a game-changing advance that significantly improves the speed and accuracy of pulsar
surveys.
The astronomy community has standardized pulsar search software that first uses a set of
statistical tests to clean the worst RFI out of the data. However, even with this initial RFI
filtering step, the candidate lists from real-world pulsar searches are dominated by RFI. For
example, the Pulsar Search Collaboratory (PSC, http://www.pulsarsearchcollaboratory.org/,
Rosen et al., 2010) database contains approximately 2 million candidates, out of which ~100 are
thought to be true pulsar signals and 700,000 have been labeled as RFI. These numbers are
typical of all current radio pulsar searches. The pulsar candidate data volumes are so large that
manually classifying RFI is impractical. While some research into automated methods to rank
candidates and/or remove likely RFI has been done, the diversity of RFI and the high noise of
these observations make it very difficult to develop reliable rules in advance. The human eye is
currently needed to interpret the subtle patterns that distinguish RFI from a real pulsar. For a
large pulsar survey, this requires many hours of work by trained observers; the relative slowness
of manual candidate inspection can cause discoveries to lag behind the original observation by
several years. Human errors and review fatigue often mean events are discovered in second or
third inspection rounds taking place years after the initial survey results (e.g. Mickaliger et al.,
2012).
Fortunately, modern pattern recognition methods may address this situation. Machine learning
algorithms, especially neural networks, have shown initial promise for automating astronomical
data processing (McCarty 2011), and in particular, pulsar search data processing (Eatough et al.,
2010). These techniques use examples (i.e. training data) to build a statistical model of RFI and
pulsar patterns and extrapolate this to classify new cases. The models can exploit arbitrary linear
or nonlinear relationships, finding distinguishing numerical features and exploiting patterns that
the human user need not notice or articulate. This holds particular promise for automating the
10
subtle pattern recognition problems of RFI labeling. The system can still defer ambiguous
events for human review to minimize the risk of missing any real pulsars.
Our research on automated RFI excision for pulsar searching will proceed in several stages. The
first step will be to study and catalogue the different kinds of RFI in pulsar search output. A solid
understanding of RFI population will ensure that the classifier design incorporates all the
relevant attributes and training data. A detailed characterization study will be performed on
observations known to contain or be free of RFI. We will employ the best available visualization
and data mining techniques to characterize these populations. Clustering and statistical analysis
will be used explore the data and identify feature sets. Some of the techniques we will use
include k-means clustering, Bayesian Networks (ref), Principal Component Analysis (Abdi et al.,
2010), and frequent pattern mining (Yang et al., 2003).
The next critical problem in any pattern recognition task is “feature selection,” e.g. designing a
numerical representation of the candidate events that encodes enough information to discriminate
RFI from pulsars (Keith et al. 2009). Each event has an associated “feature vector,” a list of
values serving as input to the classifier. We will start with attributes found by the initial study to
be statistically discriminative. We will also conduct interviews and discussions with pulsar
domain experts, working closely to understand the visual and statistical patterns that they use to
recognize RFI. This will undoubtedly indicate other informative features. For example, we can
incorporate the output from the PRESTO pulsar visualization software, such as the result of
standard statistical tests and filtering operations, and feed these directly into the classifier. In this
way we can seamlessly incorporate such derived attributes, automating – and building from – the
best existing expert knowledge. We are aware of similar efforts elsewhere (e.g. I. Stairs,
University of British Columbia) and will coordinate closely with them.
After developing the software to record, extract, and catalogue the features of candidate events,
we will train the pattern recognition engine. We initially favor a back-propagation neural
network, a classification method inspired by biological neural networks. Neural networks in
particular have the advantages of a high tolerance to noisy data and the ability to handle highdimensional feature spaces. The neural network is a classification model composed of a set of
input and output nodes in which each connection has an associated weight. These weights are
adjusted during a training phase to predict the correct class label of a given input. The results can
be as simple as a Boolean to identify the class label (e.g., whether or not the sample contained
RFI). More complex outputs produce the probability of a sample belonging to a given class; this
has the distinct advantage of making the output more interpretable and useful.
A major strength of the proposed effort is the large PSC database of human-labeled training data
already available for our use. Supervised classification methods such as neural networks require
a training and validation phase where the system learns from data that has been manually
labeled. The PSC database along with the associated raw observational data will be used to
create training and testing data sets for our work. Once constructed, the classifier will be applied
to testing data from the PSC database for validation. Finally, after validation, the neural network
classification system will be used to identify and flag RFI in an offline data analysis pipeline.
By applying machine learning techniques to the problem we will at minimum reduce the volume
of RFI candidates that must be inspected, leading to more efficient searches. Tuning the
confidence level using decision theory will allow us to accomplish this without dismissing pulsar
11
signals as RFI (false negatives). It may also be possible to improve the sensitivity of searches by
more reliably removing RFI and allowing lower significance candidates to be considered.
While we plan to focus on neural network classifiers in this work, alternative classification
methods exist that could also be suitable for this application. Most of our effort will go toward
constructing the training and test data sets, software pipeline, and integration into exist data
reduction pipelines. Our black box classification approach will allow us to trivially substitute
alternative classifiers as needed, making the RFI excision pipeline a testbed resource for future
research efforts in this area.
5 Relevance to NASA and Jurisdiction
The proposed work, to develop algorithms and techniques for the mitigation of interfering
signals, enables the detection of gravitational waves, tests of general relativity, and the study of
the extreme states of matter. As such, the proposed work is responsive to Strategic Goal 2 of the
2011 NASA Strategic Plan, specifically addressing, “Discover how the Universe works, explore
how it began and evolved, ….” Within the 2010 Science Plan for the Science Mission
Directorate, the proposed work is responsive to two aspects. Because merging galaxies should
produce supermassive black hole binaries that then generate gravitational waves, and the matter
within neutron stars is at extremely high densities, the proposed work addresses the Astrophysics
Science Questions “How do matter, energy, space, and time behave under the extraordinarily
diverse conditions of the cosmos?” and “How did the Universe . . . evolve to produce the
galaxies, stars, and planets that we see today?” From these questions, the specific Astrophysics
Science Objectives addressed are “Understand . . . the nature of black holes . . . and gravity” and
“Understand the many phenomena and processes associated with galaxy, stellar, and planetary
system . . . evolution from the earliest epochs to today.”
The proposed work is also directly responsive to Physics of the Cosmos (PCOS) program science
objectives, namely to "Test the validity of Einstein's General Theory of Relativity and investigate
the nature of spacetime," "Understand the formation and growth of massive black holes and their
role in the evolution of galaxies," and "Explore the behavior of matter and energy in its most
extreme environments."
Finally, with respect to the New Worlds, New Horizons Decadal Survey, the proposed work cuts
across all of the major science themes—Origins, Understanding the Cosmic Order, Frontiers of
Knowledge, and Discovery—addressing questions such as:






How do cosmic structures form and evolve?
What is the fossil record of galaxy assembly and evolution . . . ?
How do . . . black holes form?
How do black holes work and influence their surroundings?
What controls the masses, spins and radii of compact stellar remnants?
Gravitational wave astronomy
Nationally, there is much interest in research relating to access to the radio spectrum. Brigham
Young University has a robust research program in RFI mitigation, and such programs are of
direct benefit to NASA missions, but very little, if any research is occurring in West Virginia.
The proposed work will build research capacity at NRAO and West Virginia University in radio
12
frequency technologies, digital signal processing, and reconfigurable computing. This research
has broad applicability—it carries with it potential for technology development that will
command a wider audience than radio astronomy. As pressure—commercial and other— on the
spectrum increases, the need for active RFI mitigation will be critical for communication
technologies, not just for radio astronomy.
NRAO, throughout its history, has sought to provide astronomers with RFI-free data,
traditionally by prohibiting emissions around the Observatory (with radio quiet zones). With the
proliferation of land- and space based RF technologies, this is no longer sufficient. Astronomers
are left to excise corrupting RFI from increasingly large data sets through means they develop
individually. It is time to build research and development capacity in RFI mitigation and excision
techniques and to convert experimental techniques to a robust common–user implementation.
This project also affords an opportunity for a wider collaboration that builds needed research
capacity in a third EPSCoR jurisdiction—Puerto Rico. Astronomers using the Arecibo Telescope
encounter similar RFI problems (as is the case at all radio observatories).
6 NASA Interactions
This work will add value to existing collaborations between the partner organizations and
NASA. NRAO, WVU and JPL are all members of the North American Nanohertz Observatory
for Gravitational Waves (NANOGrav, http://nanograv.org/), which will directly benefit from the
results of this work.
JPL and NRAO already have an active collaboration in machine learning via the VLBA Fast
Radio Transients Experiment (V-FASTR, ref). Research and development in neural networks
will benefit this partnership which seeks to develop algorithms to detect transient signals in time
series data sets; transient searches are uniquely sensitive to RFI. The current proposal will further
strengthen our collaboration with NASA. The research team will be working closely with the
following researchers from JPL:
•
•
•
•
Dr Joseph Lazio, Scientist
Dr Walid Majid, Scientist
Dr Kiri Wagstaff, Machine Learning Researcher
Dr David Thompson, Machine Learning Researcher
The primary technical contact throughout the project will be Dr. Lazio, Please note the support
letter from Dr. Lazio which is in the attached proposal documents. The above researchers and the
appropriate members of our research team already hold bi-weekly telecons to discuss areas of
technical overlap. These will continue throughout the proposal period. In addition, we have
budgeted four person-weeks per year of travel from JPL to WV (NRAO or WVU), this will
allow two face-to-face meetings per year.
Additionally the proposed work will improve the data products of researchers who make joint
observations using the Fermi telescope and the GBT. NRAO has a cooperative agreement with
Fermi that commits observing time on NRAO telescopes for coordinated observations of Fermi
sources, to be awarded on a competitive basis. The scientific programs supported within this
13
agreement are those that are enhanced by the combination of Fermi observations with
investigations
using
the
radio
facilities
operated
by
NRAO
(see
http://fermi.gsfc.nasa.gov/ssc/proposals/nrao.html).
7 Project Partners, Management and Personnel
The proposed research is a collaboration between the National Radio Astronomy Observatory
(NRAO), and West Virginia University, both in West Virginia, and Brigham Young University
in Utah. WVU will be the lead, and BYU will be a sub-awardee; as a Federal Agency we assume
NRAO will be funded via inter agency transfer.
The personnel for the project are as follows:










Dr Majid Jaridi, PI, Professor, Director of the NASA WV ESPCoR, WVU
Dr Paul Demorest, Co-I/Science PI, Assistant Scientist, NRAO
Dr Richard Prestage, Co-I, Institutional-PI, NRAO
Dr Duncan Lorimer, Co-I, Professor, WVU
Dr Brian Jeffs, Co-I, Professor, WVU
Mr Michael McCarty, Software Engineer, NRAO
A Post-doctoral Fellow to be based at NRAO
An Electronic Engineer to be based at NRAO
Two PhD students (one each at WVU and BYU)
12 undergraduate summer interns (over the three year period) NRAO
In addition, key collaborators include Dr Maura McLaughlin (WVU), Dr Karl Warnick (BYU)
Drs Scott Ransom, Anish Roshi, Rick Fisher and Rich Bradley (NRAO) and the JPL machine
learning group. As part of its federal funding, NRAO will provide support for Prestage,
Demorest and McCarty. NRAO will also provide all of the support infrastructure necessary to
accomplish the project; this includes access to telescopes, the telescope backends to which
firmware upgrades will be made, the CASPER development support environment, and the
necessary electronics equipment and facilities. The post-doc and electronics engineer will be new
hires supported by this proposal.
The Principal Investigator will be Dr. Majid Jaridi, as director of the WV NASA EPSCoR
program. Dr. Jaridi will be responsible for coordinating the preparation and submission of all
reports. He will also assist in undergraduate student recruitment. Dr Richard Prestage will be the
Project Manager, and have overall technical responsibility for the program. He will coordinate
the technical work and manage the budget. He will also be responsible for ensuring good
communication and coordination between the project partners. Drs Lorimer and Jeffs will
provide guidance and supervision to the WVU and BYU graduate students. The program will be
split into four areas, as follows:



Characterization of the effects of RFI and the improvements provided by the various
mitigation strategies: Demorest, WVU graduate student.
Development of active cancellation strategies: Post-doc, BYU graduate student
Implementation of active cancellation algorithms in production backends: Electronics
Engineer assisted by Demorest.
14

Research and implementation of machine learning techniques: Demorest and McCarty in
collaboration with JPL (Majid, Thompson).
8 Diversity and Education.
A core part of NRAO’s mission is to mentor the next generation of STEM professionals, and the
organization has an outstanding fifty-year track record in the regard. Number of undergrad/grad
students have participated in research experiences at the NRAO. The Observatory uses a
spiraling apprenticeship model where students may participate in projects immediately, as a
novice, while building skills and knowledge that allow them to progress to higher levels within
the project as they gain expertise. In this project we will develop a co-op program that will serve
up to 12 undergraduate students over the 3-year grant period. Rather than a single-summer
research experience, we aim to have students return for a second summer, or school term. The
returning students will mentor the new cadre of students. To encourage greater participation
among underrepresented groups, we will partner with Bluefield State University (an historically
black college). Six students will be funded by the NSF Research Experiences for Undergraduate
program, and NRAO funds set aside for co-op programs.
Finally, we note that this project can potentially involve high-school students. Currently, more
than 600 high-school students from around the U.S. are engaged in the NRAO/WVU Pulsar
Search Collaboratory program, in which students search GBT data for new pulsars. Students can
flag RFI in the data thus creating a training data set for neural network development. In addition,
the RFI excision algorithms we develop will increase the sensitivity of the PSC searches, directly
benefiting this very successful outreach program.
9
Tasks, Schedule and Evaluation.
Task 1: Characterize effects of RFI and improvements provided by mitigation strategies.
Sub-Task 1A:
Extend model of pulsar timing precision (Yr 1).
Sub-Task 1B:
Characterize effectiveness of mitigation strategies (Yr 2,3).
Task 2: Development of active cancellation strategies.
Sub-Task 2A:
Develop radar and DME blankers (Yr 1).
Sub-Task 2B:
Develop active canceller for GPS and GLONASS signals (Yr 1,2).
Sub-Task 2C:
Develop cancellation techniques for digital TV signals (Yr 2,3).
Task 3: Implement active cancellation strategies.
Sub-Task 3A:
Implement radar and DME blankers (Yr 1).
Sub-Task 3B:
Implement active canceller for GPS and GLONASS signals (Yr 2).
Sub-Task 3C:
Implement cancellation techniques for digital TV signals (Yr 3).
Task 4: Research and Implementation of machine learning techniques.
Sub-Task 4A:
Identify training and testing data sets (Yr 1).
a. Decide appropriate stage of the data processing
b. Identify labeled training and testing data
Sub-Task 4B:
Characterize RFI found in training and testing data sets (Yr 1).
a. Using unsupervised clustering and statistical methods to identify features
b. Using frequent pattern mining of data known to be contaminated
c. By inspecting data with astronomers.
15
Sub-Task 4C:
Identify representative features found through the characterization study
that are applicable to Neural Net classification. (Yr 2).
Sub-Task 4D:
Implement preprocessing procedures to extract representative features
from training, testing, and observational data for offline data processing. (Yr 2).
Sub-Task 4E:
Implement Neural Network (Yr 3).
a. Design initial network topology (architecture).
b. Interface network with training and testing data sets.
c. Training, testing, and evaluation iterations.
Sub-Task 4F:
Incorporate classification results back into original data sets. (Yr 3).
We will evaluate the project through the following metrics:
Did Project participants accomplish project milestones on time and within budget?
 Tracking and reporting of major tasks accomplished.
Did the project effectively train a cadre of STEM professionals? Number of undergraduate
students mentored through the program
 Pre/post Surveys and interviews with undergraduate students;
 Number of publications with undergraduates as first and second authors.
Did the project develop new research capacity that enables the jurisdiction to seek outside
support?
 Successful grant applications to NSF, or non EPSCoR NASA CANs
 Sustained collaborations with West Virginia University and Bluefield State University
after project ends.
10 Partnerships and Sustainability
The project will strengthen the ties between two of West Virginia’s premier research institutions
– West Virginia University and the National Radio Astronomy Observatory in Green Bank, West
Virginia. This proposal aims to partner students from WVU with post-doctoral researchers and
engineers at NRAO to find novel ways of eliminating radio frequency interference from radio
astronomy data. The techniques themselves, using neural networks and FPGA-based
technologies to remove unwanted signals from datasets, are also new to West Virginia, opening a
potential new area of research to the state. This builds our capacity in West Virginia to compete
for research and development work within future NASA remote sensing missions, NSF
programs such as the new program “Enhancing Access to the Radio Spectrum”, and commercial
research and development contracts.
The proposed work will create, for the first time in West Virginia, a cadre of researchers, and
science and engineering students who are focused on research and development in active RFI
mitigation strategies.
NSF EARS, SAVI
16
11 Dissemination
The results of this work will have broad appeal beyond its implications for pulsar research and
will be disseminated widely:
 The Berkeley “Collaboration for Astronomy Signal Processing and Electronics
Research”. Hundreds of STEM professionals from Universities around the world meet
annually to develop and learn new digital signal processing implementations. The
CASPER group also maintains a development wiki for remote collaboration. We will
present this work at the annual conference and also disseminate project related
documentation to the CASPER wiki. (See https://casper.berkeley.edu/wiki/Projects).
 Remote Sensing and NASA Deep Space Network. We will visit JPL to present the work
at a colloquium and interact with potential users of the algorithms developed (for
example the NASA Deep Space Network, future Earth remote sensing satellites).
 URSI meetings
 Open Source. The implementations will be open source, and freely available to the
scientific/engineering community.
 Publications. The results of this work will be published in the scientific literature which
include astronomy journals, but also those with broader audiences such as Radio Science,
and the IEEE transactions.
12 Prior NASA Research Support
In the past five years, the following projects have been awarded to researchers from West
Virginia, and managed by Dr. Majid Jaridi, WV NASA EPSCoR Director.
1. Molecular and Cellular Mechanisms Underlying Skeletal Muscle and Cardiovascular
Adaptation to Simulated Microgravity; NASA Award Number NNX07AT54A; NASA Funding
$749,521.
2. Design, Simulation Validation and Flight Testing of Adaptive Fault Tolerant Flight Control
Systems; NASA Award Number NNX07AT53A; NASA Funding $750,000.
3. NASA EPSCoR Research Infrastructure Development; NASA Award Number
NNX07AL53A; NASA Funding $250,000.
4. NASA EPSCoR Augmentation; NASA Award Number NNX07AL53A; NASA Funding
$50,000.
5. Control of Steady and Unsteady Separation Through Dynamic Roughness; NASA Award
Number NNX09AW07A; NASA Funding $750,000.
6. NASA EPSCoR Research Infrastructure Development; NASA Award Number
NNX07AL53A; NASA Funding $250,000.
7. Remote Thermal Ion Measurements and Integrated Magnetospheric Modeling; NASA Award
Number NNX10AN08A; NASA Funding $748,994.
8. Spray Cooling Heat Transfer Mechanisms; NASA Award Number NNX10AN04A; NASA
Funding $750,000.
9. Coherent Terahertz Acoustic Phonons: A Novel Diagnostic for Erosion in Hall Thruster
Discharge Chamber Walls; NASA Award Number NNX11AM04A; NASA Funding $748,685
10. NASA EPSCoR Research Infrastructure Development Augmentation; NASA Award Number
NNX07AL53A; NASA Funding $50,000.
17
13 References
Abdi, H., & Williams, L. J., 2010. WIREs Comp Stat 2010, 2: 433-459. doi: 10.1002/wics.101
Abdo, A., et al. 2010, ApJS, 187, 460
Barnbaum, C. & Bradley, R. F. 1998, Astron. J. 116, 2598
Burgay, M. et al. 2003, Nature, 426, 531
Demorest, P. et al. 2010, Nature, 467, 1081
Demorest, P. et al. 2012, ApJ, in press, arXiv:1201.6641
Dong, W., et al., 2005, Radio Science, RS5S04, doi:10.1029/2004RS003130, vol. 40, no. 5
Eatough, R. et al. 2010, MNRAS, 407, 2443
Ellingson et al. 2001, ApJS, 135, 87
Fisher, J. 2004, NRAO Green Bank Electronics Division Internal Report No 313.
http://www.gb.nrao.edu/electronics/edir/edir313.pdf
Hessels, J. et al. 2006, Science, 311, 1901
Hobbs, G. et al. 2010, Classical and Quantum Gravity, Volume 27, Issue 8, pp. 084013
IRU-R RA.2126. http://www.itu.int/dms_pub/itu-r/opb/rep/R-REP-RA.2126-2007-PDF-E.pdf
Jeffs, B.D. et al., 2005, IEEE Transactions on Signal Processing, vol. 53, No. 2, pp. 439-451.
Keith, M. et al. 2009, MNRAS, 395, 837
Kesteven, M. et al. 2005, Radio Science, vol. 40, no. 5
Kesteven, M. et al, 2010, RFI mitigation workshop, Proceedings of Science
Kramer, M. et al. 2006, Science, 314, 97
Kramer, M. & Stairs, I. H. 2008, ARAA, 46, 541
Li, J. et al. 2012. ApJ , 746, 60
Lorimer, D. R. & Kramer, M. 2005. “Handbook of Pulsar Astronomy” Cambridge University
Press
Mickaliger, M. et al. 2012, ApJ, submitted, arXiv:1206.2895
Lyne, A. G. et al. 2004, Science, 303, 1153
McCarty, M., 2011. http://www.gb.nrao.edu/~mmccarty/ann_astronomy.pdf
McLaughlin, M. A. et al. 2004a, ApJ, 613, L57
McLaughlin, M. A. et al. 2004b, ApJ, 616, L131
Poulson, A. 2003. BYU Masters Thesis. http://ras.groups.et.byu.net/docs/poulsen_thesis.pdf
Poulson, A.J. et al., 2005, Astronomical Journal, vol. 130, no. 6, pp. 2916-2927
Rosen, R. et al. 2010, Astron. Education Review, 9, 010106
Roshi, A. 2002, NRAO Green Bank Electronics Division Technical Report No. 193
http://www.gb.nrao.edu/electronics/edtn/edtn193.pdf
Radhakrishnan, V. & Cooke, D. J., 1969, ApJ, 3, 225
Taylor, J. H. & Weisberg, J. M., 1989, ApJ, 345, 434
Yang, J. et al, 2003, , IEEE Transactions on Knowledge and Data Engineering, vol.15,
no.3, pp. 613- 628 doi: 10.1109/TKDE.2003.1198394
18