Tableau vs Python. Begun the visualization wars have.

Example of Tableau vs Python shown below. Can you tell which one is which?

NASA confirmed exoplanet table parameters and definitions

	Database Column Name	Table Label	Description
0	pl_hostname	Host Star Name	Stellar name most commonly used in the literature.
1	pl_letter	Planet Letter	Letter assigned to the planetary component of a planetary system.
2	pl_name	Planet Name	Planet name most commonly used in the literature.
3	pl_discmethod	Discovery Method	Method by which the planet was first identified.
4	pl_controvflag	Controversial Flag	Flag indicating whether the confirmation status of a planet has been questioned in the published literature (1=yes, 0=no)
5	pl_pnum	Number of Planets in System	Number of planets in the planetary system.
6	pl_orbper	Orbital Period (days)	Time the planet takes to make a complete orbit around the host star or system.
7	pl_orbsmax	Orbit Semi-Major Axis (AU)	The longest radius of an elliptic orbit, or, for exoplanets detected via gravitational microlensing or direct imaging, the projected separation in the plane of the sky.
8	pl_orbeccen	Eccentricity	Amount by which the orbit of the planet deviates from a perfect circle.
9	pl_orbincl	Inclination (deg)	Angular distance of the orbital plane from the line of sight.
10	pl_bmassj	Planet Mass or M*sin(i) [Jupiter mass]	Best planet mass estimate available, in order of preference: Mass, M*sin(i)/sin(i), or M*sin(i), depending on availability, and measured in Jupiter masses. See Planet Mass M*sin(i) Provenance (pl_bmassprov) to determine which measure applies.
11	pl_bmassprov	Planet Mass or M*sin(i) Provenance	Provenance of the measurement of the best mass. Options are: Mass, M*sin(i)/sin(i), and M*sini.
12	pl_radj	Planet Radius (Jupiter radii)	Length of a line segment from the center of the planet to its surface, measured in units of radius of Jupiter.
13	pl_dens	Planet Density (g/cm**3)	Amount of mass per unit of volume of the planet.
14	pl_ttvflag	TTV Flag	Flag indicating if the planet orbit exhibits transit timing variations from another planet in the system (1=yes, 0=no).Note:Non-transiting planets discovered via the transit timing variations of another planet in the system will not have their TTV flag set, since they do not themselves demonstrate TTVs.
15	pl_kepflag	Kepler Field Flag	Flag indicating if the planetary system signature is present in data taken with the Kepler mission (1=yes, 0=no).
16	pl_k2flag	K2 Mission Flag	Flag indicating if the planetary system signature is present in data taken with the K2 Mission (1=yes, 0=no).
17	pl_nnotes	Number of Notes	Number of Notes associated with the planet. View all notes in the Confirmed Planet Overview page.
18	ra_str	RA (sexagesimal)	Right Ascension of the planetary system in sexagesimal format.
19	dec_str	Dec (sexagesimal)	Declination of the planetary system in sexagesimal notation.
20	ra	RA (decimal degrees)	Right Ascension of the planetary system in decimal degrees.
21	dec	Dec (decimal degrees)	Declination of the planetary system in decimal degrees.
22	st_dist	Distance (pc)	Distance to the planetary system in units of parsecs.
23	gaia_dist	GaiaDistance [pc]	Distance computed fromGaiaparallax.
24	st_optmag	Optical Magnitude [mag]	Brightness of the host star as measured using the V (Johnson) or the Kepler-band in units of magnitudes.
25	st_optband	Optical Magnitude Band	Band corresponding to the Optical Magnitude. Options are: V (Johnson) or Kepler-band.
26	gaia_gmag	G-band (Gaia) [mag]	Brightness of the host star as measuring using theGaiaband in units of magnitudes. Objects matched toGaiausing the Hipparcos or 2MASS IDs provided inGaiaDR2.
27	st_teff	Effective Temperature (K)	Temperature of the star as modeled by a black body emitting the same total amount of electromagnetic radiation.
28	st_mass	Stellar Mass (solar mass)	Amount of matter contained in the star, measured in units of masses of the Sun.
29	st_rad	Stellar Radius (solar radii)	Length of a line segment from the center of the star to its surface, measured in units of radius of the Sun.
30	rowupdate	Date of Last Update	Date of last update of the planet parameters.
31	pl_facility	Discovery Facility	Name of facility of planet discovery observations
33	pl_tranflag	Planet Transit Flag	Flag indicating if the planet transits its host star (1=yes, 0=no)
34	pl_rvflag	Planet RV Flag	Flag indicating if the planet host star exhibits radial velocity variations due to the planet (1=yes, 0=no)
35	pl_imgflag	Planet Imaging Flag	Flag indicating if the planet has been observed via imaging techniques (1=yes, 0=no)
36	pl_astflag	Planet Astrometry Flag	Flag indicating if the planet host star exhibits astrometrical variations due to the planet (1=yes, 0=no)
37	pl_omflag	Orbital Modulation Flag	Flag indicating whether the planet exhibits orbital modulations on the phase curve (1=yes, 0=no)
38	pl_cbflag	Circumbinary Flag	Flag indicating whether the planet orbits a binary system (1=yes, 0=no)
39	pl_angsep	Calculated Angular Separation [mas]	The calculated angular separation (semi-major axis/distance) between the star and the planet. This value is only calculated for systems with both a semi-major axis and a distance value.
40	pl_orbtper	Time of Periastron (Julian Days)	The time at which the orbiting body is at its closest approach to the star it orbits (i.e. is at periastron).
41	pl_orblper	Longitude of Periastron (deg)	The angular separation between the ascending node of the orbit and the location in the orbit of periastron.
42	pl_rvamp	Radial Velocity Amplitude [m/s]	Half the peak-to-peak amplitude of variability in the stellar radial velocity.
43	pl_eqt	Planet Equilibrium Temperature [K]	The equilibrium temperature of the planet as modeled by a black body heated only by its host star, or for directly imaged planets, the effective temperature of the planet required to match the measured luminosity if the planet were a black body.
44	pl_insol	Insolation Flux [Earth flux]	Insolation flux is another way to give the equilibrium temperature. It's given in units relative to those measured for the Earth from the Sun.
45	pl_massj	Planet Mass (Jupiter mass)	Amount of matter contained in the planet, measured in units of masses of Jupiter.
46	pl_msinij	Planet M*sin(i) (Jupiter mass)	Minimum mass of a planet as measured by radial velocity, measured in units of masses of Jupiter.
47	pl_masse	Planet Mass (Earth mass)	Amount of matter contained in the planet, measured in units of masses of the Earth.
48	pl_msinie	Planet M*sini(i) [Earth mass]	Minimum mass of a planet as measured by radial velocity, measured in units of masses of Earth.
49	pl_bmasse	Planet Mass or M*sin(i) [Earth mass]	Best planet mass estimate available, in order of preference: Mass, M*sin(i)/sin(i), or M*sin(i), depending on availability, and measured in Earth masses. See Planet Mass M*sin(i) Provenance (pl_bmassprov) to determine which measure applies.
50	pl_rade	Planet Radius (Earth radii)	Length of a line segment from the center of the planet to its surface, measured in units of radius of the Earth.
51	pl_rads	Planet Radius (solar)	Length of a line segment from the center of the planet to its surface, measured in units of radius of the Sun.
52	pl_trandep	Transit Depth (percentage)	The size of the relative flux decrement caused by the orbiting body transiting in front of the star.
53	pl_trandur	Transit Duration (days)	The length of time from the moment the planet begins to cross the stellar limb to the moment the planet finishes crossing the stellar limb.
54	pl_tranmid	Transit Midpoint (Julian days)	The time given by the average of the time the planet begins to cross the stellar limb and the time the planet finishes crossing the stellar limb.
55	pl_tsystemref	Time System Reference
56	pl_imppar	Impact Parameter	The sky-projected distance between the center of the stellar disc and the center of the planet disc at conjunction, normalized by the stellar radius.
57	pl_occdep	Occultation Depth	Depth of occultation of secondary eclipse
58	pl_ratdor	Planet-Star Distance over Star Radius	The distance between the planet and the star at mid-transit divided by the stellar radius. For the case of zero orbital eccentricity, the distance at mid-transit is the semi-major axis of the planetary orbit.
59	pl_ratror	Planet-Star Radius Ratio	The planet radius divided by the stellar radius
60	pl_def_refname	Default Reference	Reference for publication used for default parameter
61	pl_disc	Year of Discovery	Year the planet was discovered
62	pl_disc_refname	Discovery Reference	Reference name for discovery publication
63	pl_locale	Discovery Locale	Location of observation of planet discovery (Ground or Space)
64	pl_telescope	Discovery Telescope	Name of telescope of planet discovery observations
65	pl_instrument	Discovery Instrument	Name of instrument of planet discovery observations
66	pl_status	Status	Status of the planet (1 = announced, 2 = submitted, 3 = accepted, 0 = retracted).
67	pl_mnum	Number of Moons in System	Number of moons detected in the planetary system.
68	pl_st_npar	Number of Stellar and Planet Parameters	Number of Stellar and Planet Parameters
69	pl_st_nref	Number of Stellar and Planet References	Number of Stellar and Planet References
70	pl_pelink	Link to Exoplanet Encyclopaedia	It links to the planet page in the Exoplanet Encyclopaedia.
71	pl_edelink	Link to Exoplanet Data Explorer	It links to the planet page in Exoplanet Data Explorer.
72	pl_publ_date	Publication Date	Publication Date of the planet discovery referee publication.
74	hd_name	HD Name	Name of the star as given by the Henry Draper Catalog.
75	hip_name	HIP Name	Name of the star as given by the Hipparcos Catalog.
76	st_rah	RA (hours)	Right Ascension of the planetary system in decimal hours.
77	st_glon	Galactic Longitude (deg)	Galactic longitude of the planetary system in units of decimal degrees.
78	st_glat	Galactic Latitude (deg)	Galactic latitude of the planetary system in units of decimal degrees.
79	st_elon	Ecliptic Longitude (deg)	Ecliptic longitude of the planetary system in units of decimal degrees.
80	st_elat	Ecliptic Latitude (deg)	Ecliptic latitude of the planetary system in units of decimal degrees.
81	st_plx	Parallax (mas)	Difference in the angular position of a star as measured at two opposite positions within the Earth's orbit.
82	gaia_plx	GaiaParallax [mas]	GaiaDR2 difference in the angular position of a star as measured at two opposite positions within the Earth's orbit.
83	st_pmra	RA Proper Motion (mas/yr)	Angular change in right ascension over time as seen from the center of mass of the Solar System.
84	st_pmdec	Dec Proper Motion (mas/yr)	Angular change in declination over time as seen from the center of mass of the Solar System.
85	st_pm	Total Proper Motion (mas/yr)	Angular change in position over time as seen from the center of mass of the Solar System.
86	gaia_pmra	GaiaProper Motion (RA) [mas/yr]	GaiaDR2 angular change in right ascension over time as seen from the center of mass of the Solar System.
87	gaia_pmdec	GaiaProper Motion (Dec) [mas/yr]	GaiaDR2 angular change in declination over time as seen from the center of mass of the Solar System.
88	gaia_pm	GaiaTotal Proper Motion [mas/yr]	GaiaDR2 total proper motion computed from the RA and Dec.
89	st_radv	Radial Velocity (km/sec)	Velocity of the star in the direction of the line of sight.
90	st_spstr	Spectral Type	Classification of the star based on their spectral characteristics following the Morgan-Keenan system.
91	st_logg	Stellar Surface Gravity	Gravitational acceleration experienced at the stellar surface.
92	st_lum	Stellar Luminosity [log(solar)]	Amount of energy emitted by a star per unit time, measured in units of solar luminosities.
93	st_dens	Stellar Density [g/cm**3]	Amount of mass per unit of volume of the star.
94	st_metfe	Stellar Metallicity (dex)	Measurement of the metal content of the photosphere of the star as compared to the hydrogen content.
95	st_metratio	Metallicity Ratio	Ratio for the Metallicity Value ([Fe/H] denotes iron abundance, [M/H] refers to a general metal content)
96	st_age	Stellar Age [Gyr]	The age of the host star.
97	st_vsini	Rotational Velocity v*sin(i) [km/s]	Rotational velocity at the equator of the star multiplied by the sine of the inclination.
98	st_acts	Stellar Activity Index (S-Index)	Chromospheric activity as measured by the S-index (ratio of the emission of the H and K Ca lines to that in nearby continuum).
99	st_actr	Stellar Activity Log (R'HK)	Chromospheric activity as measured by the log(R' HK) index, with is based on the S-index, but excludes the photospheric component in the Ca lines.
100	st_actlx	x-ray Activity (Lx)"	Stellar activity as measured by the total luminosity in X-rays.
101	swasp_id	SWASP Identifier	Name of the star as given by the SuperWASP (Wide Angle Search for Planets) project.
102	st_nts	Number of Time Series	Number of literature time series available for this star in the NASA Exoplanet Archive
103	st_nplc	Number of Planet Transit Light Curves	Number of literature transit light curves available for this star in the NASA Exoplanet Archive.
104	st_nglc	Number of General Light Curves	Number of Hipparcos light curves available for this star in the NASA Exoplanet Archive.
105	st_nrvc	Number of Radial Velocity Time Series	Number of literature radial velocity curves available for this star in the NASA Exoplanet Archive.
106	st_naxa	Number of Amateur Light Curves	Number of literature amateur light curves available for this star in the NASA Exoplanet Archive.
107	st_nimg	Number of Images	Number of literature images available for this star in the NASA Exoplanet Archive.
108	st_nspec	Number of Spectra	Number of literature of spectra available for this star in the NASA Exoplanet Archive.
110	st_uj	U-band (Johnson) [mag]	Brightness of the host star as measured using the U (Johnson) band in units of magnitudes.
111	st_vj	V-band (Johnson) [mag]	Brightness of the host star as measured using the V (Johnson) band in units of magnitudes.
112	st_bj	B-band (Johnson) [mag]	Brightness of the host star as measured using the B (Johnson) band in units of magnitudes.
113	st_rc	R-band (Cousins) [mag]	Brightness of the host star as measured using the R (Cousins) band in units of magnitudes.
114	st_ic	I-band (Cousins) [mag]	Brightness of the host star as measured using the I (Cousins) band in units of magnitudes.
115	st_j	J-band (2MASS) [mag]	Brightness of the host star as measured using the J (2MASS) band in units of magnitudes.
116	st_h	H-band (2MASS) [mag]	Brightness of the host star as measured using the H (2MASS) band in units of magnitudes.
117	st_k	Ks-band (2MASS) [mag]	Brightness of the host star as measured using the K (2MASS) band in units of magnitudes.
118	st_wise1	WISE 3.4um [mag]	Brightness of the host star as measured using the 3.4um (WISE) band in units of magnitudes.
119	st_wise2	WISE 4.6um [mag]	Brightness of the host star as measured using the 4.6um (WISE) band in units of magnitudes.
120	st_wise3	WISE 12.um [mag]	Brightness of the host star as measured using the 12.um (WISE) band in units of magnitudes.
121	st_wise4	WISE 22.um [mag]	Brightness of the host star as measured using the 22.um (WISE) band in units of magnitudes.
122	st_irac1	IRAC 3.6um [mag]	Brightness of the host star as measured using the 3.6um (IRAC) band in units of magnitudes.
123	st_irac2	IRAC 4.5um [mag]	Brightness of the host star as measured using the 4.5um (IRAC) band in units of magnitudes.
124	st_irac3	IRAC 5.8um [mag]	Brightness of the host star as measured using the 5.8um (IRAC) band in units of magnitudes.
125	st_irac4	IRAC 8.0um [mag]	Brightness of the host star as measured using the 8.0um (IRAC) band in units of magnitudes.
126	st_mips1	MIPS 24um [mag]	Brightness of the host star as measured using the 24um (MIPS) band in units of magnitudes.
127	st_mips2	MIPS 70um [mag]	Brightness of the host star as measured using the 70um (MIPS) band in units of magnitudes.
128	st_mips3	MIPS 160um [mag]	Brightness of the host star as measured using the 160um (MIPS) band in units of magnitudes.
129	st_iras1	IRAS 12um [Jy]	Brightness of the host star as measured using the 12um (IRAS) band in units of Jy.
130	st_iras2	IRAS 25um [Jy]	Brightness of the host star as measured using the 25um (IRAS) band in units of Jy.
131	st_iras3	IRAS 60um [Jy]	Brightness of the host star as measured using the 60um (IRAS) band in units of Jy.
132	st_iras4	IRAS 100um [Jy]	Brightness of the host star as measured using the 100um (IRAS) band in units of Jy.
133	st_photn	Number of Photometry Measurements	Number of photometry measurements available for this star in the NASA Exoplanet Archive.
135	st_umbj	U-B (Johnson) [mag]	Color of the star as measured by the difference between U and B (Johnson) bands.
136	st_bmvj	B-V (Johnson) [mag]	Color of the star as measured by the difference between B and V (Johnson) bands.
137	st_vjmic	V-I (Johnson-Cousins) [mag]	Color of the star as measured by the difference between V (Johnson) and I (Cousins) bands.
138	st_vjmrc	V-R (Johnson-Cousins) [mag]	Color of the star as measured by the difference between V (Johnson) and R (Cousins) bands.
139	st_jmh2	J-H (2MASS) [mag]	Color of the star as measured by the difference between J and H (2MASS) bands.
140	st_hmk2	H-Ks (2MASS) [mag]	Color of the star as measured by the difference between H and K (2MASS) bands.
141	st_jmk2	J-Ks (2MASS) [mag]	Color of the star as measured by the difference between K and K (2MASS) bands.
142	st_bmy	b-y (Stromgren) [mag]	Color of the star as measured by the difference between b and y (Stromgren) bands.
143	st_m1	m1 (Stromgren) [mag]	Color of the star as measured by the m1 (Stromgren) system.
144	st_c1	c1 (Stromgren) [mag]	Color of the star as measured by the c1 (Stromgren) system.
145	st_colorn	Number of Color Measurements	Number of color measurements available for this star in the NASA Exoplanet Archive.
147

Visualizing confirmed exoplanet detection with Tableau.

Every human child has at one point in their lives dreamed of venturing to the stars and exploring other worlds. For some of those children those dreams manifest themselves in the form trance music played in cave on the southern hemisphere during the longest night of the year, the winter solstice.

For others it manifests as a desire to pursue a career in radio astronomy. I'm on the path of the latter.

Here is proof:

Can't stop this Sagan in the makin. Radio astronomy is awesome.

The search for Exoplanets.

In the last 20 years humanity has begun an incredible preliminary exploration of nearby solar systems that may contain worlds in orbit around other stars.

Using data visualization software Tableau Public as part of my semester break project I have compiled some interesting perspectives on the variety of confirmed exoplanets that exist outside our solar systems, thus producing a series of info-graphics I call the "Infographica Galactica" which is a play on words on the "Encyclopedia Galactica"

This blog post details the development of this project.

Now, radio astronomy and planetary science are two different disciplines. One deals with light at low frequency and the other deals with wandering bodies in orbit around stars but both are connected by the practice of data mining and the art of knowledge discovery and that is exactly the type of approach that will be taken in this project.

*literature review on the planets.

The search for planets beyond our solar system goes hand in hand with the search for extraterrestrial life. So far we only have one model to go on to study the emergence of life in the entire Universe and that is planet Earth in our own solar system. (The verdicts on Mars and Europa have yet to be decided). Therefore, the search for exoplanets boils down to the quest of looking for planets that are "Earth Size" and "Earth like"

We must then establish the distinction between planets that are "Earth Size" and "Earth Like"

NASA Exoplanet Database. the 4,000 worlds

NASA keeps an database archive of confirmed exoplanets. So far there have been 4,000 worlds known without a doubt to exist beyond our solar system. A full list of the planets can be downloaded which makes for great exercise in data mining depending on what you are looking for.

There is separate table available that details the definition of each parameter in the confirmed planets table:

https://exoplanetarchive.ipac.caltech.edu/cgi-bin/TblView/nph-tblView?app=ExoTbls&config=planets

There is separate table available that details the definition of each attribute in the confirmed planets table: 

https://exoplanetarchive.ipac.caltech.edu/docs/API_exoplanet_columns.html

As part of literature review I need to understand what each term does and whether it has any significance for visualization. There are 5 tables  detailing terms on the confirmed exoplanet table. Each table corresponds to a different characteristic.

• Default Column
• Planet Column
• Stellar Column
• Photometry Column
• Colour Column

I could do it manually by copying each term from the website into the excel table that I have on my laptop. But as any good programmer knows:

"Anything you have to do repetitively more than 3 times can be automated."

So I scraped it using Beautiful Soup. A cursory glance at the terms shows the following

The table can be found here: http://scienceepicyt.blogspot.com/2019/07/database-column-name-table-label.html

Code is available on github: https://github.com/coderXmachina2

The Draft of my Infographica

How Pulsar Timing Arrays Work

Best Explanation so far for how a Pulsar Timing Array Works, and it makes it look more analogous to an Interferometer which is sweet as.

Hippo Poo Vital for Health of East Africa's Rivers

Hippo poo vital for ecosystem balance

I have good shit

There was a piece of News recently about how hippo feces acted as a vital carrier of Nutrients for rivers in East Africa. The large (and adorable) land fauna would eat copious amount of grass and just chill out with the herd in communal hippo pools where they would excrete the grass they had eaten.

The hippo feces contains a large amount of Silicon extracted from the grass that is vital to the health of tiny bacteria called diatoms that live further downstream all the way to Lake Victoria, the largest lake in Africa. This tiny bacteria forms the foundational basis of the diet and food chain of the Mara River in Africa and other rivers that flow into Lake Victoria.

The diatom bacteria are also responsible for carbon sequestration in the environment, that is the act of removing CO2 from the environment. CO2 is a greenhouse gas. Something that if we have too much of in the atmosphere... we die.

I shit you not

Hippo Poo makes the Mara River go round

Without a thriving bacterial community the environmental balance  in the area would be disastrously upset. Fish all along the food chain can't get their nutrition and a dangerous type of life-suffocating bacteria called cyanobacteria will grow to excess causing the emergence of environmental dead zones. That sounds scary.

In the light of recent catastrophes in nearby Mozambique it paints a bleak picture of the future.

The hippo population has suffered a decrease in the last decade and the numbers are projected to further drop but the actions we carry out on now will decide the fate of the hippos and the Mara River. There is still time to stop the key from turning. Once it turns there is no turning back.

DIatoms in a Psychedelic Universe. We are all connected

It makes me think about this little image here. Credits to Annis Pratt who led me to it.

And the line from the Fourth Hindu Veda:

Whatever I dig from you, O Earth
May your mantle grow back again quickly
O Earth, Purifier, may we never injure you

Sincerely,
SonOfTerra92

Why should we bother looking for Gravitational Waves?

26/4/2019 mid sem Holiday is almost up. I Better get started on that ASTR 800 (Advanced Topics in Astrophysics) paper.

Break is over, better get started on that term paper.

So I want to talk about Gravitational Waves, more so the importance of finding gravitational waves.

Why do we need to find these elusive ripples of propagating space time called Gravitational Waves?

For a long time Gravitational Waves seemed to be secluded within the realm of theoretical physics. The idea was born out of the mind of Einstein while the experimentalists in the room which included Richard Feynman and Joe Weber could only dream of detecting them. Some of those early  experimentalists although daring in the quest for gravitational waves are no longer with us, were not able to make it to witness the progress we have made today.

But the first Ligo Gravitational Wave observation happened in 2016 and we can now pinpoint their origins to Supermassive Black Hole Binaries (SMBHB) and Neutron Star Binaries (NSB). Wherever there are really dense astronomical objects orbiting each other in an inspriling cosmic death dance. That is where the origin of gravitational waves can be pinpointed to.

In-fact these events are responsible for the formation of heavier elements in the Universe like Gold and Platinum. So you are essentially buying for your spouse a briproduct of the most violent collisions in the Universe.... just like my relationships.

But why do Astronomers have a vested interest in detecting these gravitational waves?

Well that is an interesting question actually. I mean are not observations in the EM domain enough to learn us all we can about the Universe that we live in and the Cosmos from which we spring?

Well, I can tell you that the answer to that is No. Because of something called Multi Messenger Astronomy.

We live in the age of Mutli-Messenger Astronomy (MMA) which is this idea that we can acquire much more information about the universe by looking at signals that reach us in different ways. Here are the 4 ways that information about the Universe can reach us.

• EM - Light. The classic Medium of Discovery. From Planets to Pulsars this is how its been done since back in the day.
• Gravitational Waves - Ripples of Propagating space time. These can tell us about the merging of really dense objects in the Universe. Black Holes and Netutron Stars
• Neutrinos - Elusive Tiny Particles travelling at the speed of light.
• Cosmic Rays - High Energy Particles. Can tell us about  Gamma Ray bursts

Some of things out there may be detectable by the many different media, others in singularly different ones. but there is a wealth of information that can be conveyed by analyzing the same cosmological phenomena in the different messenger media.

One example is how the Gravitational Waves detected by LIGO are quickly followed up by observations by Radio Telescopes. etc.

But what really convinced me about MMA  surrounds some discoveries that happened around the time of my birth (1992). That is the discovery of PSR B 1257 + 12 and the planets that surround it. Draugr, Poltergeist and Phobetor.

I wonder if it will have an atmosphere. Not that we really need one if we ever get to explore it. I figure we'll carry our own atmosphere with us.

These 3 planets orbit the husk of a star that went supernova a long time ago. That star is survived today as a pulsar that periodically emits beams of electromagnetic radiation  The planets were discovered by measuring the variation in Doppler shift of pulsation Period.

This planet marks the discovery of an extrasolar planet via means of Pulsar Timing.  One of the worlds was discovered to be twice the size of the moon at 0.02 Earth Mass, the smallest planet ever to be discovered. In all the planets discovered by the Kepler mission in the modern age. Pulsar Timing in 1992 (the year that I was born) revealed to us a world in a category of its own. A tiny little speck orbiting a Pulsar.

That remarkable discovery was made via an indirect observation by studying subtle changes in the propagation of light. we didn't see the planet optically but implied its existence by looking at the effect it had on the pulsar signal, thus confirming its existence.

Now extend that to Gravitational Waves and the information they carry, and the things we might come to learn of when we detect Grav Waves. What new wonders undreamt of in our own time would will we discover by studying gravitational waves? 

Lets find out...

AGNs and Cross Matching Algorithms

17/4/2019 Cross Matching in a nutshell.

We begin our story with Active Galactic Nuclei colloquially known as AGN. In a nutshell they are Supermassive Black Holes surrounded by accretion disks of matter that shoot out jets of radiation in the form of radio lobes that are pretty exotic things to see in the radio spectrum.

Here is an artists rendition:

This is what they look like in the Radio

They are primordial objects found in the early universe. To borrow a quote from Armand Delsemme "When the Quazars shone their dazzling brilliance"... that "dazzling brilliance" refers to the fact that AGNs can shine at much higher than normal luminosity in all over the EM spectrum.

And in fact they do.

The radiation from the accretion disk is brightest in Optical and Ultraviolet

The Gas and Dust that surrounds the Black Hole is visible in the Infrared

Hot gas surrounding the black hole gets heated up and emits X Rays

The jets emitted by the black hole are visible for many lightyears in radio frequencies

So we point many different telescopes in the same direction in order to see them. There's a diverse ecosystem of telescopes and eyes on the sky that humanity has at its disposal. All of which can be used to study Active Galactic Nuclei

But there is a problem. y and X-ray telescopes are located in space and orbit the Earth with different periodicity. Optical Telescopes are placed on mountain tops all over the world, and radio telescopes are situated in barren deserts tucked far away from any radio frequency interference. 

How do we study the same brilliant dazzling objects with such different equipment?

Coordinating these instruments to look at the same object in the sky require a herculean task of cooperation and cross border collaboration that demonstrate capacity for humans to let go of their differences and unite in the effort of studying the Cosmos, ancient and vast from which we spring.

The solution to this problem is: Cross Matching Algorithms 

Here is a diagram of what cross matching implies:

Cross Matching Astronomical Objects from different Catalogs. VLA, SDDS, Hubble

This is what a naive cross matcher (pseudocode) would look like:

for l in range (0, len(cat A)):
    for m in range (0, len(cat B)):
        calculate offset = angulardistance (A, B)
        if offset < radius
            if offset = smallest value so far
            return best_match = (A, B, offset)

Where A, B are RA, Dec values for a catalog.

These have a connection to big data, and to the whole Billions and Billions thing in the Universe. I will talk about in following entries.

Matlab FFT on Sinusoidal Code

amp = 3;
freq = 5;
phi = pi/4;
fs = 50;
Ts = 1/fs;
l = 1; %second samples

sineWaveFunc(amp,freq,phi,Ts,l)

plot(t, s)
xlabel('time')
ylabel('Amplitude')
title('sineWave')

myPow = calcPower(fs, freq, s);

myFFT = findFFT(s);
%calculate power

N = length(s) + 1;  % to be even :)
S = fft(s,N);
SdB = 20*log10(S);  % or SdB = mag2db(S);
freqs = (0:N/2-1)*fs/N;

figure(2)
plot(freqs, SdB(1:N/2))
xlabel('frequency')
ylabel('amplitude')
title('spectrum')

function sinFFT = findFFT(s)
N = length(s) + 1;
sinFFT = fft(s, N);
%plot(sinFFT, N)
end

function pow = calcPower(fs, freq,s)
Nsamp = round(fs/freq);

pow = (1/Nsamp)*sum(abs(s(1:Nsamp)).^2);
end

function [s , t] = sineWaveFunc(amp,freq,phi,Ts,l)
t = 0:Ts:l;
s = amp*sin(2*pi*freq*t + phi);
end

DSP and Astronomy

10/3/2019 Astronomy = (DSP + Statistics + Data Science)

Look at this slide about windowing from DSP Guru Mitra

Sequence/Pulse Windowing is the act of turning a infinite length sequence into a finite length sequence by applying a window sequence

So that is what this massive phallic construct does:

Which can be broken down into geek speak like this:

Radio Telescopes are windows on the Universe

So if you have the infinities of a pulsar signal (That's artist embellishment right there) and apply all those DeDispersions, FFTs, phase binning. Then we can get a limited sequence called a power spectrum in terms of frequency and time.

Its just a windowed sequence of values

Or we can use it to blow each other up and all that other planetary chauvinism stuff.

Matlab Discrete Sinusoid

2/4/2019 Creating Discrete Sinusoidal

I'm going to go learn Cyclic Spectroscopy for Radio Astronomy Applications. Got distracted, plotted Sinusoids in Matlab instead.

I think it looks pretty

a = -80;
b = 80;
n = a:b; %sample length
fs = 0.0125;
phi = 0;
Amp = 6;

x = Amp*sin(2*pi*fs*n - phi);
x2 = (-Amp)*x

clf
stem(n, x, '-o')
axis([0 b -2 2]);
grid;
title('sinusoid')
xlabel('Time index n')
ylabel('Amplitude')
axis

%plotting an evil twin
hold on

stem(n, x2, '-*')

Knowledge Discovery with WEKA

30/3/2019 Introduction to WEKA. The Waikato Environment for Knowledge Analysis

The hype for all this AI stuff is getting severely overblown. But that being said predictive algorithms will be an important tool for scientists in any field in the years to come.

Astronomy is no different.

Neural Networks have been used to classify pulsar candidates in this paper:

• V. Morello, E. D. Barr, M. Bailes, C. M. Flynn, E. F. Keane: “SPINN: a straightforward machine learning solution to the pulsar candidate selection problem”, 2014; arXiv:1406.3627. DOI: 10.1093/mnras/stu1188.

Astronomy datasets are notorious for putting the BIG in BIG Data. Not only are the objects being studied are separated from Earth by large distances but also, the true nature of the signals that they produce are magnificently intense.

The paper states that it as the data mounts up in the future, it will no longer be feasible to process it using a typical 'human cogitator' i.e. Grad Students approach to vet the data. That's where the Data Mining and Machine Learning will comes in.

Twinkle Twinkle little Pulsar, But is that what you really are?

So i guess it is important for yours truly to get to grips with it. Thats where WEKA comes in.

WEKA is an an environment that can be used to analyze datasets using prebuilt data mining algorithms.

We can test the same data set with different data mining algorithms to take a measure of their performance. This is done by finding their percentage of correctly classified and their time taken to build model.

The following is a description of the Classifier/ Data Mining Algorithm/ Learner that we use to analyze the attributes/feature and put the data into class.

Data mining’s eclectic nature fostered this inconsistency in naming—the field encompasses contributions from statistics, artificial intelligence (Machine Learning),and database management;each field has chosen different names for the same concept. 

One-R (One Rule

Naive Bayes (probabilistic Classifier with strong independence assumption)

IBK (K Nearest Neighbour)

J48 (unpruned C4.5 decision tree)

Random Forest (Random Decision Forests)

MLP (Multi Layer Perceptron)

SMO (Support Vector Machines)

Data-set	Classifier	Percent correct (%)	Time to Construct (s)
Weather-nominal	One-R	42.8571	0.00
	Naïve Bayes	57.1429	0.00
	IBK	57.1429	0.00
	J48	50.000	0.00
	Random Forest	71.4286	0.02
	MLP	71.4386	0.03
	SMO	64.2857	0.01
Iris	One-R	92.0000	0.01
	Naïve Bayes	96.0000	0.01
	IBK	95.3333	0.00
	J48	96.0000	00.0
	Random Forest	95.3333	0.06
	MLP	97.3333	0.14
	SMO	96.0000	0.04
Pima Diabetes	One-R	65.1042	0.00
	Naïve Bayes	76.3021	0.005
	IBK	70.1823	0.00
	J48 (with missing values)	73.8281	0.02
	J48 (Remove Corrupt Instances) J48 (Padded Corrupt Instances)	74.6228 74.0885	0.01 0.01
	Random Forest	75.7813	0.18
	MLP	75.3906	0.40
	SMO	77.3438	0.03
Soybean	One-R	39.9707	0.01
	Naïve Bayes	92.9722	0.01
	IBK	91.215	0.00
	J48	91.5081	0.04
	Random Forest	92.9722	0.19
	MLP	93.4114	20.00
	SMO	93.8507	0.49

Repeating the model building resulted in faster build time but no changes in accuracy.

Support Vector Machine can be not bad