New catalogue of Pre-Main Sequence objects using Gaia

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Presentation transcript:

New catalogue of Pre-Main Sequence objects using Gaia Miguel Vioque University of Leeds R. D. Oudmaijer (University of Leeds, UK), M. Schreiner (Desupervised, Denmark), D. Baines (ESAC, Spain), and R. Pérez-Martínez (Isdefe,Spain) Gaia’s view of Pre-Main Sequence Evolution, Leeds, 20th of June 2019

Looking for new Pre-Main Sequence (PMS) objects in Gaia! Main characteristics of PMS objects: Infrared excesses H𝛼 emission Photometric variability

Looking for new Pre-Main Sequence (PMS) objects in Gaia! Main characteristics of PMS objects: Infrared excesses H𝛼 emission Photometric variability High mass PMS objects (Herbig Be stars) are very similar to Classical Be stars

Perform an homogeneous selection, distance and position independent! Looking for new Pre-Main Sequence (PMS) objects in Gaia! Perform an homogeneous selection, distance and position independent! Main characteristics of PMS objects: Infrared excesses H𝛼 emission Photometric variability High mass PMS objects (Herbig Be stars) are very similar to Classical Be stars

Distance and position independent! We used a Neural Network for this: Selection of the characteristics: From Gaia: 𝑩 𝒑 , 𝑮, 𝑹 𝒑 and 2 variability indicators From AllWISE: 𝑱, 𝑯, 𝑲 𝒔 , 𝑾𝟏,𝑾𝟐,𝑾𝟑,𝑾𝟒 From IPHAS & VPHAS+: 𝒓− 𝑯 𝜶 Distance and position independent! Create all possible colours Remove all linear dependency (PCA)

Cross-match Gaia DR2 x AllWISE x IPHAS and VPHAS+ Input Sample = 4,151,538 sources Construction of the Training Set: 848 Pre-Main Sequence objects 163 are Herbig Ae/Be stars (high mass end, all available) 775 Classical Be stars (all available). 471,111 random sources with all the characteristics.

Cross-match Gaia DR2 x AllWISE x IPHAS and VPHAS+ Input Sample = 4,151,538 sources Construction of the Training Set: 848 Pre-Main Sequence objects 163 are Herbig Ae/Be stars (high mass end, all available) 775 Classical Be stars (all available). 471,111 random sources with all the characteristics.

Training the Neural Network

Trained Neural Network Input Sample = 4,151,538 sources

Probability Map

693 Classical Be candidates Probability Map 693 Classical Be candidates 1309 either 8470 PMS candidates 4,140,511 other

693 Classical Be candidates Classical Be Completeness 𝟖𝟐.𝟑±𝟏.𝟏% Probability Map 693 Classical Be candidates Evaluation on Test Set PMS Completeness 𝟕𝟖.𝟖±𝟏.𝟒% Classical Be Completeness 𝟖𝟐.𝟑±𝟏.𝟏% 1291 either 8470 PMS candidates 4,140,511 other

There is a large contamination between categories! Cross-match Gaia DR2 x AllWISE x IPHAS and VPHAS+ Input Sample = 4,151,538 sources Construction of the Training Set: There is a large contamination between categories! 848 Pre-Main Sequence objects 163 are Herbig Ae/Be stars (high mass end, all available) 775 Classical Be stars (all available). 471,111 random sources with all the characteristics.

There is a large contamination between categories! Cross-match Gaia DR2 x AllWISE x IPHAS and VPHAS+ This algorithm cannot assess itself, we need a totally independent analysis Input Sample = 4,151,538 sources Construction of the Training Set: There is a large contamination between categories! 848 Pre-Main Sequence objects 163 are Herbig Ae/Be stars (high mass end, all available) 775 Classical Be stars (all available). 471,111 random sources with all the characteristics.

HR diagram Training Set PMS candidates

HR diagram Training Set Classical Be candidates

3131 potential Herbig Ae/Be (682 with good Gaia solution)

Coordinates

Coordinates

Coordinates

Coordinates

Spatial distribution Within 1.5 Kpc All (up to 5 Kpc)

Spatial distribution Within 1.5 Kpc All (up to 5 Kpc)

Mid-IR excess vs. Mass (lower limits) Candidates Training Set

Variability vs. Mass (lower limits) Candidates Training Set

Variability vs. Mass (lower limits) Candidates Training Set Vioque et al. 2018

Candidates Cody & Hillenbrand, 2018

Caveats Candidates H𝛼 emission Main Sequence Reddening

Caveats Candidates H𝛼 emission Main Sequence Reddening

Caveats Planetary Nebula! H𝛼 emission Main Sequence Reddening Candidates H𝛼 emission Main Sequence Reddening

Results: We retrieve 8470 new PMS candidates. 3131 (682) potential Herbig Ae/Be stars. We retrieve 693 new Classical Be stars candidates. We retrieve 1309 candidates of belonging to either one of the two categories. Completeness 𝟕𝟖.𝟖±𝟏.𝟒% Completeness 𝟖𝟐.𝟑±𝟏.𝟏% Near- and mid- infrared excesses are the most important characteristics followed by H𝛼 and variability which are equaly important