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All posts for the month May, 2015

Disintegrating planets

Posted by admin on May 28, 2015
Posted in: BSU Journal Club.
Simulated dust tails around the disintegrating planet EPIC 201637175 b. From Sanchis-Ojeda+ (2015).

Simulated dust tails around the disintegrating planet EPIC 201637175 b. From Sanchis-Ojeda+ (2015).

Fun research group meeting today. We discussed research papers on a new class of extrasolar planet, ultra-short period planets. Most of these planets are small and rocky, and some of them are so small, rocky, and hot that they are actively disintegrating.

We first discussed Spitzer Space Telescope observations by Demory and colleagues of 55 Cnc e, a rocky planet with a mass and radius eight and two times the Earth’s, respectively, but almost 100 times closer to its star than the Earth is to the Sun. Its surface temperature is about 2000 K, hot enough to form a molten rock lake on the planet’s dayside.

Demory and colleagues looked at 55 Cnc e’s transits and eclipses from 2011 and 2013 and found that they changed quite a lot. Why they’ve changed isn’t clear. Demory et al. speculated that perhaps the planet exhibits extreme volcanic activity, similar to Jupiter’s moon Io, and the erupted material has gone into orbit around the star, causing variable transits and eclipses.

We next discussed the discovery of a new disintegrating rocky planet using data from the K2 mission by Sanchis-Ojeda and colleagues.

This planet, EPIC 201637175 b, zips around its star every 9 hours, and because it’s so hot (1500 K), its rocky surface is evaporating, leaving behind a dust tail, like a comet. Subtle indications of the dust tail appear in the K2 measurements as tell-tale bumps in the light curve, suggesting the dust is scattering light in complicated and surprising ways.

More follow-up work will help us understand this new extreme class of planet, perhaps even allow us to figure out what they’re made of and where they came from.

Attendees at today’s group meeting include Jennifer Briggs, Emily Jensen, Liz Kandziolka, and Tyler Wade.

Edison and The Electric Chair

Posted by admin on May 16, 2015
Posted in: Books. Tagged: , .
From http://amzn.to/1B4bBhV.

From http://amzn.to/1B4bBhV.

Just finished Mark Essig‘s Edison and The Electric Chair. I received the book as a surprise gift, not having heard of it before. Very glad I did.

The book covers a period in American history that I knew next to nothing about — the Gilded Age and the War of the Currents. It traces Thomas Edison‘s work developing and marketing many of the key elements of the US’s electrical landscape, from the light bulb to the distribution system. Along the way, Essig weaves in elements and anecdotes from Edison’s life to paint a fascinating and nuanced picture.

Essig also describes the political landscape of Edison’s time and how social movements to reduce human and animal suffering at the turn of the last century drove the search for more humane execution methods — the most common method at the time was hanging.

Although I couldn’t put it down, I was a little disappointed by how light the book was on the scientific and technical aspects of the story. For instance, Essig spends many chapters on the public debate that raged during the War of the Currents over whether direct or alternating current was more dangerous (seems it’s AC). But he never really resolves the debate or explains our current understanding.

So it’s really more a history of science and technology than a popular science book but still a very engaging read.

 

 

The Challenge of Motion Capturing a Toddler

Posted by admin on May 11, 2015
Posted in: Data Science. Tagged: , .

As part of a project, I’m trying to learn how to do motion capture on videos. Fortunately, there’s Python support for the OpenCV computer vision library.

I adapted some motion capture code I found online that uses the Shi-Tomasi Corner Detector scheme to find good features to track — regions in a grayscale video frame that have large derivatives in two orthogonal directions.

Then the code estimates the optical flow using the Lucas-Kanade method, which applies a least-squares fit to solve for the two-dimensional velocity vector of the corner features.

As a test case, I used a video of Alice singing the “Ito Maki Maki” song.

The shiny tracks in the video show the best-fit model. Interestingly, the corner detection scheme chooses to follow the glints in her eyes and on her lip. The motion tracker does a good job following the glints until she blinks and swings her arm across her face.

The code I used is posted below.

import numpy as np
import cv2
cap = cv2.VideoCapture('IMG_0986.mov')
size = (int(cap.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH)),
int(cap.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT)))

# params for ShiTomasi corner detection
feature_params = dict( maxCorners = 100,
qualityLevel = 0.3,
minDistance = 7,
blockSize = 7 )

# Parameters for lucas kanade optical flow
lk_params = dict( winSize = (15,15),
maxLevel = 2,
criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))

# Create some random colors
color = np.random.randint(0,255,(100,3))

# Take first frame and find corners in it
ret, frame = cap.read()
old_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
p0 = cv2.goodFeaturesToTrack(old_gray, mask = None, **feature_params)

# Create a mask image for drawing purposes
mask = np.zeros_like(frame)

images = list()

height , width , layers = frame.shape

fourcc = cv2.cv.CV_FOURCC('m', 'p', '4', 'v')
video = cv2.VideoWriter()
success = video.open('Alice_singing.mp4v', fourcc, 15.0, size, True)

ret = True
while(ret):
  print(ret)

  frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

# calculate optical flow
  p1, st, err = cv2.calcOpticalFlowPyrLK(old_gray, frame_gray, p0, None, **lk_params)

# Select good points
  good_new = p1[st==1]
  good_old = p0[st==1]

# draw the tracks
  for i,(new,old) in enumerate(zip(good_new,good_old)):
    a,b = new.ravel()
    c,d = old.ravel()
    cv2.line(mask, (a,b),(c,d), color[i].tolist(), 2)
    cv2.circle(frame,(a,b),5,color[i].tolist(),-1)

  img = cv2.add(frame,mask)

  video.write(img)
  ret,frame = cap.read()

# Now update the previous frame and previous points
  old_gray = frame_gray.copy()
  p0 = good_new.reshape(-1,1,2)

cap.release()
video.release()
cv2.destroyAllWindows()

Direct Detection of Reflected Light from 51 Pegasi b

Posted by admin on May 3, 2015
Posted in: BSU Journal Club. Tagged: , .
Artistic rendering of 51 Peg b, from http://en.wikipedia.org/wiki/51_Pegasi_b.

Artistic rendering of 51 Peg b, from http://en.wikipedia.org/wiki/51_Pegasi_b.

For the majority of exoplanets, astronomers study the planets via indirect means, by looking for their gravitational tugs on their host stars or the shadows they cast when occult their stars. Consequently, the things astronomers learn about exoplanets often involve systematic uncertainties, usually related to uncertainties about our knowledge of the stellar properties.

For example, by measuring a planet’s gravitational tugs on its star, astronomers can estimate the planet’s mass but only if they also know the star’s mass. It’s a little like watching two dancers spinning hand-in-hand, with one in black and the other in white,  and then trying to estimate the weight of the dancer in black based on how the dancer in white spins.

But in last week’s journal club, we discussed a recent study from Martins and colleagues that may have thrown white clothes on one of the most famous exoplanets, 51 Pegasi b, and revealed its dance moves.

51 Peg was the first exoplanet discovered around a Sun-like star. It’s a gas giant, like Jupiter, but unlike Jupiter, it orbits its host star every four days and is almost 100 times closer to its host star than Jupiter is to our Sun.

Martins and colleagues conducted ground-based spectroscopic observations of the 51 Peg system as the planet revolved about its host star. In principle, this orbital motion causes the spectral features imprinted on light reflected from the planet’s atmosphere to be Doppler-shifted.

Detecting the light reflected from a planet and resolving it spectrally is a bit like trying to discern the color of a football fan’s t-shirt against the glare of stadium lights, only much harder.

However, Martins and colleagues found tentative indications of light reflected from 51 Peg b’s atmosphere. By modeling the Doppler-shifting of the subtle spectral signals, they were able to estimate the planet’s mass (0.46 times Jupiter’s) and its radius (almost twice Jupiter’s, if it’s about twice as reflective as Jupiter).

Journal club attendees included Jennifer Briggs, Nathan Grigsby, Emily Jensen, and Liz Kandziolka.