The production of
The VVV catalogue of
(periodic) variable stars
Niall Miller
Prof. Phil Lucas, Dr. Yi Sun
University Of Hertfordshire
VVV(X)
red -------------------------mid IR
- Started in 2010
- 4-meter VISTA telescope in the Z, Y, J, H, & Ks filters.
- ~50 - 80 epochs (~200 measurements*)
- ~700 million stars
- ~10 million variable stars
*this is highly variable
VVV(X)
- Started in 2010
- 4-meter VISTA telescope in the Z, Y, J, H, & Ks filters.
- Survey plan affords multiple data points per epoch
Minniti D., et al., 2010, New Astronomy, 15, 433
Red = Original VVV
Blue = Extension to overlap with VHS
VVV(X)
- Started in 2010
- 4-meter VISTA telescope in the Z, Y, J, H, & Ks filters.
- Survey plan affords multiple data points per epoch
Minniti D., et al., 2010, New Astronomy, 15, 433
vvvsurvey.org
Dark Green = 1
Light Green = 2
Magenta = 3
Red = 4
Yellow = 5
Red = Original VVV
Blue = Extension to overlap with VHS
How to reliably extract info
What we don't know:
- Light Curve shape
- Light Curve photometric accuracy
- Light Curve contamination
- Other sources of perturbation
What we know:
- Hopefully a star?
- Probably variable
How to reliably extract info
What we don't know:
- Light Curve shape
- Light Curve photometric accuracy
- Light Curve contamination
- Other sources of perturbation
What we know:
- Hopefully a star?
- Probably variable
How to reliably extract info
What we don't know:
- Light Curve shape
- Light Curve photometric accuracy
- Light Curve contamination
- Other sources of perturbation
What we know:
- Hopefully a star?
- Probably variable
Pre Processing
- Remove linear trend
- Running sigma clip
- Other cuts, VVV has lots of contamination
- Reduce temporal resolution
- Reduce photometric uncertainty
Remove linear trend
Contamination?
- Phase folding techniques
-Finds period which produces ‘cleanest’ phase fold
- Nothing assumed about the structure of the data*
- Computationally expensive
- Not practical to perfectly tune
Lomb Scargle
- The most common
- Fourier based
- Is a fitting technique appropriate?
Phase Dispersion Minimisation & Conditional Entropy
But how do we actually know?
How do we know if a star is periodic?
How do we know if we have extracted the right period?
Manual inspection can't be the only way?!?
Checking 200 took me ~ 1hr, ∴all 10 million will take ~ 7 years if only I was funded for that long…
- Compare against other things?
- Analyse the light curve?
- Analyse the periodogram?
Analysis of the periodogram?
There are many methods which try to verify periodicity by analysis of the light
curve.
- A significance test
- A comparison of values to known periodic/aperiodic sources
- More sophisticated statistical approach (e.g Baluev)
All assume noise is correctly modelled/accounted for/ignored
Recurrent Neural Network (RNN) - likely your second NN
For each example of X and Y I have…
Given X, what does the RNN think Y is?
loss = how wrong it was
We can use loss to calculate the direction to take for the next guess (Ã la gradient descent )
Inform the network on how wrong it was
Machine learning is good at lying so we need to check
Same star with 4 different periods identified with different
methods
Each method has produced a visually different phase
folded light curve
Can it definitely identify periodic variables?
Multiple different simulated and real
datasets are tested for this NN
method and the previous Baluev
method for specificity and
sensitivity.
NN method performed better than
Baluev method without the
requirement for a Lomb-Scargle
periodogram
ROC curve
What do we get from all of this?
We have 100,000* confident periodic variables
Of which 35,000 in Gaia, 9,000 in WISE, 40,000 in TESS, 25,000 in 2MASS –i.e likely new things here!
Period range: 0.001 - ~1500 days
Multiple time-series statistics separate to periodicity *likely to increase
Example to find more YSOs with SPICY
Can we use the carefully selected YSOs from the SPICY catalogue to
parameterise VVV YSOs?
Example to find more YSOs with SPICY
Possibly Potentially Candidate YSOs
553 potentially new objects
