Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Removing systematic errors for exoplanet search via latent causes
Authors: Bernhard Schölkopf, David Hogg, Dun Wang, Dan Foreman-Mackey, Dominik Janzing, Carl-Johann Simon-Gabriel, Jonas Peters
ICML 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We analyze two simulated data sets that illustrate the identifiability statements from Sections 2.1 and 2.2. ... In Fig. 6, we present corrected light curves for three typical stars of different magnitudes, using both CPM and PDC. ... Fig. 7 presents our CDPP comparison of CPM and PDC, showing that our method outperforms PDC. |
| Researcher Affiliation | Academia | Bernhard Sch olkopf EMAIL Max Planck Institute for Intelligent Systems, 72076 T ubingen, GERMANY David W. Hogg EMAIL Dun Wang EMAIL Daniel Foreman-Mackey EMAIL Center for Cosmology and Particle Physics, New York University, New York, NY 10003, USA Dominik Janzing EMAIL Carl-Johann Simon-Gabriel EMAIL Jonas Peters EMAIL Max Planck Institute for Intelligent Systems, 72076 T ubingen, GERMANY |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/jvc2688/Kepler Pixel Model. |
| Open Datasets | Yes | We obtained the data from the Mikulski Archive for Space Telescopes (MAST) (see http://archive.stsci. edu/index.html). |
| Dataset Splits | Yes | We train the model separately for each month, which contains about 1300 data points. Standard L2 regularization is employed to avoid overfitting, and parameters (regularization strength and number of input pixels) were optimized using cross-validation. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'the function gam (penalized regression splines) from the R-package mgcv' but does not provide specific version numbers for R or the mgcv package. |
| Experiment Setup | Yes | Specifically, we use 4000 predictor pixels from about 150 stars... Standard L2 regularization is employed to avoid overfitting, and parameters (regularization strength and number of input pixels) were optimized using cross-validation. We train the model separately for each month, which contains about 1300 data points... we report results where the AR component uses as inputs the three closest future and the three closest past time points, subject to the constraint that a window of 9 hours around the considered time point is excluded. |