Search

We present deep g- and r-band Magellan/Megacam photometry of two dwarf galaxy candidates discovered in the Dark Energy Survey (DES), Grus i and Indus ii (DES J2038–4609). For the case of Grus i , we resolved the main sequence turn-off (MSTO) and ∼2 mags below it. The MSTO can be seen at g0 ∼ 24 with a photometric uncertainty of 0.03 mag. We show Grus i to be consistent with an old, metal-poor (∼13.3 Gyr, [Fe/H] ∼ −1.9) dwarf galaxy. We derive updated distance and structural parameters for Grus i using this deep, uniform, wide-field data set. We find an azimuthally-averaged halflight radius more than two times larger (∼151+21−31 pc; ∼) and an absolute V-band magnitude ∼−4.1 that is ∼1 magnitude brighter than previous studies. We obtain updated distance, ellipticity, and centroid parameters that are in agreement with other studies within uncertainties. Although our photometry of Indus ii is ∼2–3 magnitudes deeper than the DES Y1 public release, we find no coherent stellar population at its reported location. The original detection was located in an incomplete region of sky in the DES Y2Q1 data set and was flagged due to potential blue horizontal branch member stars. The best-fit isochrone parameters are physically inconsistent with both dwarf galaxies and globular clusters. We conclude that Indus ii is likely a false positive, flagged due to a chance alignment of stars along the line of sight. [ABSTRACT FROM AUTHOR]

In this paper we investigate how implementing machine learning could improve the efficiency of the search for Trans-Neptunian Objects (TNOs) within Dark Energy Survey (DES) data when used alongside orbit fitting. The discovery of multiple TNOs that appear to show a similarity in their orbital parameters has led to the suggestion that one or more undetected planets, an as yet undiscovered "Planet 9", may be present in the outer solar system. DES is well placed to detect such a planet and has already been used to discover many other TNOs. Here, we perform tests on eight different supervised machine learning algorithms, using a data set consisting of simulated TNOs buried within real DES noise data. We found that the best performing classifier was the Random Forest which, when optimized, performed well at detecting the rare objects. We achieve an area under the receiver operating characteristic (ROC) curve, (AUC) = 0.996 ± 0.001. After optimizing the decision threshold of the Random Forest, we achieve a recall of 0.96 while maintaining a precision of 0.80. Finally, by using the optimized classifier to pre-select objects, we are able to run the orbit-fitting stage of our detection pipeline five times faster. [ABSTRACT FROM AUTHOR]