# Publications

• A study of extragalactic planetary nebulae populations based on spectroscopy. I. Data compilation and first findings

We compile published spectroscopic data and [O III] magnitudes of almost 500 extragalactic planetary nebulae (PNe) in 13 galaxies of various masses and morphological types. This is the first paper of a series that aims to analyse the PN populations and their progenitors in these galaxies. Although the samples are not complete or homogeneous, we

9
2020
• MASTER Transient Discovery Report for 2020-09-26
Pogrosheva, T. et al.

9
2020
• μ<SUB>⋆</SUB> masses: weak-lensing calibration of the Dark Energy Survey Year 1 redMaPPer clusters using stellar masses

We present the weak-lensing mass calibration of the stellar-mass-based μ⋆ mass proxy for redMaPPer galaxy clusters in the Dark Energy Survey Year 1. For the first time, we are able to perform a calibration of μ⋆ at high redshifts, z > 0.33. In a blinded analysis, we use ∼6000 clusters split into 12 subsets spanning the ranges 0.1 ≤ z < 0.65 and μ⋆

Pereira, M. E. S. et al.

9
2020
• A precise architecture characterization of the π Mensae planetary system★

Context. The bright star π Men was chosen as the first target for a radial velocity follow-up to test the performance of ESPRESSO, the new high-resolution spectrograph at the European Southern Observatory's Very Large Telescope. The star hosts a multi-planet system (a transiting 4 M⊕ planet at ~0.07 au and a sub-stellar companion on a ~2100-day

Damasso, M. et al.

10
2020
• Exploring the Stellar Age Distribution of the Milky Way Bulge Using APOGEE

We present stellar age distributions of the Milky Way bulge region using ages for ∼6000 high-luminosity ( $\mathrm{log}(g)\lt 2.0$ ), metal-rich ([Fe/H] ≥ -0.5) bulge stars observed by the Apache Point Observatory Galactic Evolution Experiment. Ages are derived using The Cannon label-transfer method, trained on a sample of nearby luminous giants

Hasselquist, Sten et al.

10
2020
• The CARMENES search for exoplanets around M dwarfs. A deep learning approach to determine fundamental parameters of target stars

Existing and upcoming instrumentation is collecting large amounts of astrophysical data, which require efficient and fast analysis techniques. We present a deep neural network architecture to analyze high-resolution stellar spectra and predict stellar parameters such as effective temperature, surface gravity, metallicity, and rotational velocity

Passegger, V. M. et al.