Comments (4)
stevengiacalone
commented on June 16, 2024
It's hard to say exactly what's happening without seeing your code, but I recommend increasing the number of systems simulated per scenario by an order of magnitude or two. You can do this by entering "N=int(1e7)" or "N=int(1e8)" as an argument when running .calc_probs(). This usually helps lower the scatter in FPP. Let me know if this doesn't help and I can look into it more closely.
On a side note, I would discourage using TRICERATOPS on this TOI. TRICERATOPS results are only reliable for planet candidates smaller than ~8 R_Earth. This TOI has a transit depth of >0.5% and the host star is most likely a sub-giant (given its logg, Teff, and plx) meaning the host star is probably larger than 1 R_Sun. This implies that the transiting object is above the radius limit.
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priyashmistry
commented on June 16, 2024
Thank you for the reply, I tried the solution you provided but it gave me a memory error.
Here I'm attaching a link to the .ipynb file of my Jupyter notebook and .csv files for transit data and contrast curve data. Let me know anything else is needed.
https://drive.google.com/drive/folders/1pxxL7j6hUh8aCaXC2WVsK4QCUYljXqF0?usp=sharing
---------------------------------------------------------------------------
MemoryError Traceback (most recent call last)
File :9, in
File C:\Personal\Internships\ExoplanetStatisticalValidation\envs\triceraenv\lib\site-packages\triceratops\triceratops.py:718, in target.calc_probs(self, time, flux_0, flux_err_0, P_orb, contrast_curve_file, filt, N, parallel, drop_scenario, verbose, flatpriors, exptime, nsamples, molusc_file)
712 if verbose == 1:
713 print(
714 "Calculating TP scenario "
715 + "probabilitiey for " + str(ID) + "."
716 )
--> 718 res = lnZ_TTP(
719 time, flux, flux_err, P_orb,
720 M_s, R_s, Teff, Z,
721 N, parallel, self.mission,
722 flatpriors,
723 exptime, nsamples
724 )
725 # self.res_TTP = res
726 j = 0
File C:\Personal\Internships\ExoplanetStatisticalValidation\envs\triceraenv\lib\site-packages\triceratops\marginal_likelihoods.py:127, in lnZ_TTP(time, flux, sigma, P_orb, M_s, R_s, Teff, Z, N, parallel, mission, flatpriors, exptime, nsamples)
125 u2_arr = np.full(N, u2)
126 companion_fluxratio = np.zeros(N)
--> 127 lnL[mask] = -0.5*ln2pi - lnsigma - lnL_TP_p(
128 time, flux, sigma, rps[mask],
129 P_orb, incs[mask], a_arr[mask], R_s_arr[mask],
130 u1_arr[mask], u2_arr[mask],
131 eccs[mask], argps[mask],
132 companion_fluxratio=companion_fluxratio[mask],
133 exptime=exptime, nsamples=nsamples
134 )
135 else:
136 for i in range(N):
File C:\Personal\Internships\ExoplanetStatisticalValidation\envs\triceraenv\lib\site-packages\triceratops\likelihoods.py:468, in lnL_TP_p(time, flux, sigma, R_p, P_orb, inc, a, R_s, u1, u2, ecc, argp, companion_fluxratio, companion_is_host, exptime, nsamples)
431 def lnL_TP_p(time: np.ndarray, flux: np.ndarray, sigma: float,
432 R_p: np.ndarray, P_orb: float, inc: np.ndarray,
433 a: np.ndarray, R_s: np.ndarray,
(...)
438 exptime: float = 0.00139,
439 nsamples: int = 20):
440 """
441 Calculates the log likelihood of a transiting planet scenario by
442 comparing a simulated light curve and the TESS light curve.
(...)
466 lnL (numpy array): Log likelihood.
467 """
--> 468 model = simulate_TP_transit_p(
469 time, R_p, P_orb, inc, a, R_s, u1, u2,
470 ecc, argp,
471 companion_fluxratio, companion_is_host,
472 exptime, nsamples
473 )
474 lnL = 0.5*(np.sum((flux-model)**2 / sigma**2, axis=1))
475 return lnL
File C:\Personal\Internships\ExoplanetStatisticalValidation\envs\triceraenv\lib\site-packages\triceratops\likelihoods.py:345, in simulate_TP_transit_p(time, R_p, P_orb, inc, a, R_s, u1, u2, ecc, argp, companion_fluxratio, companion_is_host, exptime, nsamples)
343 else:
344 F_dilute = F_comp / F_target
--> 345 flux = (flux + F_dilute)/(1 + F_dilute)
346 return flux
MemoryError: Unable to allocate 35.7 GiB for an array with shape (1781578, 2693) and data type float64```
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stevengiacalone
commented on June 16, 2024
It looks like the issue was that the transit wasn't quite centered at zero. I've uploaded a new notebook to the drive folder that shows more consistent results. I've also binned the data down to a few hundred points, which speeds up the code and helps prevent memory errors (this is okay to do as long as the shape of the transit is not being affected).
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priyashmistry
commented on June 16, 2024
Got it, thank you for the help!!
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Related Issues (20)
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