A Gaussian Process Model of Quasar Spectral Energy Distributions
Authors: Andrew Miller, Albert Wu, Jeff Regier, Jon McAuliffe, Dustin Lang, Mr. Prabhat, David Schlegel, Ryan P. Adams
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct three experiments to test our model, where each experiment measures redshift predictive accuracy for a different train/test split of spectroscopically measured quasars from the DR10QSO dataset [13] with confirmed redshifts in the range z (.01, 5.85). Our experiments show that accurate redshift measurements are attainable even when the distribution of training set is different from test set by directly modeling the SED itself. Our method dramatically outperforms [2] and [3] in split (iii), particularly for very high redshift fluxes. |
| Researcher Affiliation | Academia | Andrew Miller , Albert Wu School of Engineering and Applied Sciences Harvard University acm@seas.harvard.edu, awu@college.harvard.edu Jeffrey Regier, Jon Mc Auliffe Department of Statistics University of California, Berkeley {jeff, jon}@stat.berkeley.edu Dustin Lang Mc Williams Center for Cosmology Carnegie Mellon University dstn@cmu.edu Prabhat, David Schlegel Lawrence Berkeley National Laboratory {prabhat, djschlegel}@lbl.gov Ryan Adams School of Engineering and Applied Sciences Harvard University rpa@seas.harvard.edu |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks that are clearly labeled as such. |
| Open Source Code | No | The paper does not include an unambiguous statement that the authors are releasing the code for the work described in this paper, nor does it provide a direct link to a source-code repository. |
| Open Datasets | Yes | We conduct three experiments to test our model, where each experiment measures redshift predictive accuracy for a different train/test split of spectroscopically measured quasars from the DR10QSO dataset [13] with confirmed redshifts in the range z (.01, 5.85). |
| Dataset Splits | Yes | We conduct three experiments to test our model, where each experiment measures redshift predictive accuracy for a different train/test split of spectroscopically measured quasars from the DR10QSO dataset [13] with confirmed redshifts in the range z (.01, 5.85). Our experiments split train/test in the following ways: (i) randomly, (ii) by r-band fluxes, (iii) by redshift values. ... For computational purposes, we limit our training sample to a random subsample of 2,000 quasars. ... Basis validation We examined multiple choices of K using out of sample likelihood on a validation set. |
| Hardware Specification | No | The paper states 'This work used resources of the National Energy Research Scientific Computing Center (NERSC)', but it does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper mentions software tools like 'autograd [9]' and 'LBFGS [12]' and 'native python', but it does not specify version numbers for any of these or other software components, which is required for reproducibility. |
| Experiment Setup | Yes | In the following experiments we set K = 4, which balances generalizability and computational tradeoffs. ... For each test quasar, we construct an 8-chain parallel tempering sampler and run for 8,000 iterations, and discard the first 4,000 samples as burn-in. |