1. Data Analytics and technologies used in Deep Space Exploration
Case Study: New Horizons mission to Pluto
Mission Duration: (2006-2015) 9 years.
Course: Business Intelligence
Prof. Young Kwark
Submitted by: Swapnil Kambli

2. Overview and Challenges:
Data Analytics and Business Intelligence technologies are prevalent in almost all the sectors
ranging from Retail, Transport, Hospitality, Education, Healthcare to name a few. The recent
success in NASA’s New Horizons project which took the collaborating team for John Hopkins
University, Stanford University and NASA for the spacecraft to reach Pluto a staggering 9 years.
Imagine the amount of data analytics that must have been involved to deploy a space shuttle to
a planet which is 5.9 billion Kms (3.7 billion miles). The challenge faced by the team was to fix a
trajectory, calculate the estimated route, path, time estimation, acceleration and velocity of the
space craft to complete the 9 years’ journey to reach its destination, ‘Pluto’. The most
interesting fact here is that the destination is not stationery! Pluto orbits the sun every 248
years and it has the most elliptical orbit of the 9 planets ranging from 2.8 billion miles to 4.6
billion miles from the sun. The latest close approach to the sun was in 1989 and in the years
1979-1999 it was closer to the sun than Neptune. A typical day on Pluto is 153 hours as
compared to 24-hour day on the earth. Its orbit is tilted 17 degrees from the ecliptic plane as
compared to the plane of the other planets. Its rotational north pole is 118 degrees from
celestial north and 28 degrees below ecliptic plane. To add to the dilemma, Pluto and Charon
both rotate every 6.4 days and Pluto’s smaller moons are roughly two to three times farther
from Pluto than Charon, and roughly 10-20% of Charon’s size
Cost: Approximately $700 million (including spacecraft and instrument development, launch
vehicle, mission operations, data analysis, and education/public outreach) over the period
2001–2016.
Fig: Visualization of journey from Earth to Pluto

3. Design, Data Storage and Communications:
The team did not previously determine before launch exactly how close New Horizons (the
spacecraft) will come to Pluto and the pre-launch planning is in the range of 10,000 kilometers
(6,200 miles). To fuel the spacecraft for a practically impossible voyage was out of question and
the team designed a power system and instruments that uses only 2-10 watts of energy to
operate.
The spacecraft’s high-gain antenna dish is linked to advanced electronics and is shaped to
receive even the faintest radio signals from home – a necessity when the mission’s main target
is more than 3 billion miles from Earth and round-trip transmission time is 9 hours.
For data storage, it carries 2 low-power solid-state recorders that holds up to 8 gigabytes each.
The main processor collects, compresses, reformats, sorts and stores science and housekeeping
data on the recorder – similar to a flash memory card for a digital camera – for transmission to
Earth through the telecommunications subsystem.

4. Space Data Analytics for Spacecraft’s guidance and control:
Challenge: New Horizons space craft had to be oriented in a particular direction to collect data
with its scientific instruments, communicate with Earth, or maneuver through space.
Solution:
The spacecraft was installed with star-tracking cameras and a map of about 3,000 stars. The
cameras on board snaps 10 times per second wide-angle picture of space, and compares the
locations of the stars to its map, and calculates the spacecraft’s orientation. The IMU feeds
motion information 100 times a second. If data shows New Horizons is outside a predetermined
position, small hydrazine thrusters fire to re-orient the spacecraft. The Sun sensors back up the
star trackers; they would find and point New Horizons toward the Sun if the other sensors
couldn’t find home in an emergency.
Communications:
New Horizons’ X-band communications system is the spacecraft’s link to Earth, that returns
science data, exchanges commands and status information, and allows precise radiometric
tracking through NASA’s Deep Space Network of antenna stations.
Launch Decision Analytics Launch Challenge:
The 35-day launch period was extended from Jan. first week to 11-Feb. 14, 2006. The mission
designers had to analyze specific dates based on existing historical data gathered which tracked
the position of the Pluto and stored coordinates. Since it takes about 248 years for Pluto to
revolve around the sun, tracking exact coordinates and storing them using traditional
technology was a tedious task and there was no reliable data to predict its exact position after 9
years after the spacecraft reaches in the proximity of Pluto.
Analyzing existing Pluto’s position which was not documented precisely due to lack of existing
technologies and multiple documentations and since it was historical data, the accuracy of the
data was debated.
The scientists decided and picked dates as Earth was along the line of sight to Pluto during 2006
and after undergoing various regression analysis predicted a near value for Pluto’s future
position when the spacecraft would approach the target planet.
Speed, Data Transmission and Communication Challenge:
Distance: 3 billion miles from earth.
Time: The transmission from the spacecraft takes place at 700 bits/sec which was designed in
2006. It takes about 9 months for the transmitted data to reach earth and to transmit any
orientation commands and coordinates from the NASA command center to the spacecraft it
takes about 4 hours for the radio signals to reach the spacecraft.

5. Technologies used by NASA:
A.) Neural networks: Neural networks is applied in different problems of space physics.
Some of them are:
• Modeling of pseudo-indeterministic processes. Statistic models of a process may be
constructed using neural networks as building blocks. Models may then be used, for
example, to predict data that may be collected under specific, seldom observed
conditions.
• Automatic, objective categorization of complex, multivariate data.
• Reconstruction of missing data.
B.) Causal modeling:
In complex multivariate processes it is usually difficult, or even impossible, to establish cause-
effect relations between observed variables. The physical theory behind the observations may
help us to guess about those relations, but until recently it was impossible to present a statistical
proof for a given causal model of the process. However, a powerful software TETRAD, searches
for causal relations in multivariate data, which is developed by Carnegie Mellon University. The
present project is aimed to find new applications of causal modeling in space physics. A possibility
to use TETRAD to analyze different kinds of spectral data was investigated.
In case of particle energy spectra, the method may be used to automatically detect acceleration
or deceleration processes.

6. C.) Multivariate time series analysis:
An important group of tools to study multivariate time series, like multisensory data or time
sequences of different kinds of spectra, are decomposition techniques. A linear decomposition
technique, based upon the principal component method, is applied to particle energy spectra,
FFT frequency spectra and to wavelet spectra. The method separates different particle
populations and is used to remove instrumental effects on the observed particle energy
spectrum.
In studies of data in the form of point processes, as particle or photon counts, there is an
important question, whether the observed process may be fully described by a Poisson
distribution, or if it is carrying deterministic information. A method, described in two recent
scientific reports, has been developed, to extract the true temporal variations of the photon flux
from the the photon event history observed by the X-ray satellite ROSAT. Also wavelet technique
has been used to study the photon flux variations.
It has been found that introducing the concepts of the non-linear statistics into the signal
processing of infra-acoustic waves, considerable improvements of the results may be obtained.
At present, the angle-of -arrival software was rewritten in such a way that the use of cross-
correlation function was replaced with the entropy based mutual information function.
Another promising technique is filtering of multivariate time series, for example time sequences
of spectra, in the principal component space. The present technique does not distort periodic
or quasi-periodic phenomena present in the data in the same way as the conventional filtering.
NASA’s Big Data Challenge:
NASA’s big data challenge is not just a terrestrial one and it goes beyond the stereotypical
challenge. Many of their “big data” sets are described by significant metadata, but on a scale that
challenges current and future data management practice. Several missions require to regularly
stream data from spacecraft on Earth and in space, faster than we can store, manage, and interpret
it. NASA has two very different types of spacecraft. A deep space spacecraft that sends back data in
the order of MB/s. and earth orbiters that send back data in GB/s. In NASA’s current missions, data
is transferred with radio frequency, which is relatively slow. In the future, NASA will employ
technology such as optical (laser) communication to increase the download and mean a 1000x
increase in the volume of data. This is much more data NASA can handle today and is paring to
tackle future challenges. Currently NASA is planning missions that will easily stream more then
24TB’s a day. That’s roughly 2.4 times the entire Library of Congress – EVERY DAY. For one mission!

7. Analogy:
It’s very expensive to transfer just one bit down from a spacecraft so NASA has to make sure
they collect what is most important. Once the data makes its way to NASA’s data centers,
storing, managing, visualizing and analyzing it becomes an issue. To give an idea of what NASA
is dealing with, the size of the Climate Change data repositories alone are projected to grow to
nearly 350 Petabytes by 2030. 5 PB’s is equivalent to the total number of letters delivered by
the US Postal Service in one year!
NASA’s (Pleidas) Supercomputer for Data Analysis:
NASA’s Pleiades supercomputer is used to analyze the challenging projects, from solar flare and
space weather scenarios to detailed space vehicle designs. Pleiades was recently used to
process massive amounts of star data gathered from NASA’s Kepler spacecraft, leading to the
discovery of new Earth-sized planets in the Milky Way galaxy. More than 1,200 users across the
country rely on the system to perform large, complex calculations. It was also used to generate
the Bolshoi cosmological simulation which explores how galaxies and the large-scale structure
of the universe has formed over billions of years.
Fig: Pleidas, NASA Supercomputer
Visualization
The NASA Earth Exchange (NEX) is a virtual laboratory that integrates supercomputer, data
system, data visualization, large amount of online data, models and algorithms, with social
network and collaborative technology. Prior to NEX, scientists were required to invest
tremendous amounts of time and effort to develop high-end computational methods rather
than focus on important scientific problems. Now, scientists use the supercomputer to visualize
large Earth science data sets as well as run and share modeling algorithms and collaborate on
new or existing projects.

8. Work Cited and References:
IRF UMEA Swedish Institute of Space Physics
http://www.umea.irf.se/ume/homep2/
NASA
https://nex.nasa.gov
https://open.nasa.gov
http://www.nas.nasa.gov/hecc/resources/pleiades.html