# ABYSS II: Identification of young stars in optical SDSS spectra and their
properties

Serat Saad Department of Physics and Astronomy, Vanderbilt University, VU
Station 1807, Nashville, TN 37235, USA  Kaitlyn Lane  Department of Physics
and Astronomy, Vanderbilt University, VU Station 1807, Nashville, TN 37235,
USA  Marina Kounkel Department of Physics and Astronomy, University of North
Florida, 1 UNF Dr., Jacksonville, FL 32224, USA  Department of Physics and
Astronomy, Vanderbilt University, VU Station 1807, Nashville, TN 37235, USA
<EMAIL_ADDRESS>Keivan G. Stassun Department of Physics and Astronomy,
Vanderbilt University, VU Station 1807, Nashville, TN 37235, USA  Ricardo
López-Valdivia Universidad Nacional Autónoma de México, Instituto de
Astronomía, AP 106, Ensenada 22800, BC, México  Jinyoung Serena Kim Steward
Observatory, Department of Astronomy, University of Arizona, 933 North Cherry
Avenue, Tucson, AZ 85721, USA  Karla Peña Ramírez Centro de Astronomía
(CITEVA), Universidad de Antofagasta, Av. Angamos 601, Antofagasta, Chile  Guy
S. Stringfellow Center for Astrophysics and Space Astronomy, Department of
Astrophysical and Planetary Sciences, University of Colorado 389-UCB, Boulder,
CO 80309, USA  Carlos G. Román-Zúñiga Universidad Nacional Autónoma de México,
Instituto de Astronomía, AP 106, Ensenada 22800, BC, México  Jesús Hernández
Universidad Nacional Autónoma de México, Instituto de Astronomía, AP 106,
Ensenada 22800, BC, México  Scott J. Wolk Center for Astrophysics, Harvard &
Smithsonian, 60 Garden St., Cambridge MA. 02138, USA  Lynne A. Hillenbrand
Department of Astronomy, California Institute of Technology, Pasadena, CA
91125, USA

###### Abstract

We developed a tool that measures equivalent widths of various lines in low
resolution optical spectra, and it was applied to stellar spectra obtained as
part of SDSS-V and LAMOST programs. These lines, such as Li I which directly
indicates stellar youth, or optical H I and Ca II which in emission indicate
activity associated with stellar youth, are commonly seen in YSOs. We observe
several notable differences in the properties of these lines between YSOs and
the field stars. Using these data, we devise a set of criteria through which
it is possible to confirm the youth of stars that have been observed by the
ABYSS program, as well as to identify likely young stars that have
serendipitously been observed by other programs. We examine the decrement of H
lines seen in emission in CTTSs, and estimate the properties of the accretion
stream that is responsible for the production of these lines. Finally, we
examine the evolution of Li I as a function of age, and characterize the
scatter in its abundance that appears to be intrinsic in young M dwarfs.

††software: TOPCAT (Taylor, 2005), BOSS Net (Sizemore, 2024), LineForest
(Kounkel et al., 2023)

##  1 Introduction

In its observing strategy, Sloan Digital Sky Survey in its fifth iteration
(SDSS-V) is set to obtain optical and near IR spectra of several million stars
through the Milky Way Mapper (MWM) program (Almeida et al., 2023). This will
be achieved with both the BOSS (Baryonic Oscillation Spectroscopic Survey) and
APOGEE (APO Galactic Evolution Experiment) spectrographs, using a state of the
art fiber robotic positioner (Pogge et al., 2020) that allows the simultaneous
observation of up to 500 targets. As part of the SDSS-V MWM program the APOGEE
& BOSS Young Star Survey (ABYSS) is expected to produce multi-epoch spectra of
$>$100,000 photometrically identified young star candidates with ages $<$30
Myr (Kounkel et al., 2023b, hereafter Paper I).

To-date, single epoch spectra of several tens of thousands of such candidates
have been observed, allowing us to perform an initial characterization of
their properties. Since the initial selection of the ABYSS targets was not
free of contamination, it is necessary to use spectroscopic criteria to
separate out older field stars from the bona fide young stellar objects (YSOs)
to enable any follow up work. We take advantage of the fact that optical
spectra of young stars typically exhibit a number of unique features (e.g., Li
I absorption, H$\alpha$ emission, inflated radii) that are not present in the
field stars, allowing us to perform their identification.

The Li I 6708 Å absorption line has particular importance. Li I is present in
the ISM, and as such, it is included in the chemical composition of YSOs as
they form. However Li is easily destroyed: as soon as the internal temperature
of a star reaches $3\times 10^{6}$ K, it is rapidly processed in nuclear
reactions (Clayton, 1983). In higher mass stars with radiative envelopes, a
trace fraction of Li I will persist near the photosphere regardless of the
internal temperature. However, when fully or mostly convective stars reach the
age where they have sufficiently contracted to reach that temperature in the
core/convective boundary layer, Li I becomes depleted everywhere within its
envelope. Thus, Li I will not be detected in low mass field stars, and its
detection can act as an unambiguous confirmation of stellar youth (e.g.,
Briceno et al., 1997; Jeffries et al., 2014; Gutiérrez Albarrán et al., 2020)
although some care needs to be applied with regards to the solar-type stars
(Žerjal et al., 2019). Additionally, at its strongest, Li I absorption
typically has equivalent widths of only 0.5 Å, and this feature is found near
several Fe I lines. Thus, care needs to be taken in order to minimize the
confusion and contamination.

In the case of YSOs with dusty disks, they often exhibit strong and wide
emission lines from the accretion stream shock and outflows. The strongest of
those lines in the optical spectra is H$\alpha$, often presenting equivalent
widths (EqW) of tens to hundreds of Å (White & Basri, 2003). Particularly
energetic shocks are capable of exciting other transitions of H (Kwan &
Fischer, 2011; Wilson et al., 2022; Campbell et al., 2023), as well as a
number of other elements, including various O, Fe, He, S, N, and others
(Hamann & Persson, 1992a, b, c; Hamann, 1994; Baldovin-Saavedra et al., 2012;
Ballabio et al., 2022). Young stars exhibiting accretion are known as
Classical T Tauri Stars (CTTSs). Since disks tend to be short-lived, CTTSs are
not ubiquitous among all young stars. Broad emission lines can help to cleanly
separate out YSOs from the field stars, although a few rare types of more
evolved stars (e.g, cataclysmic variables) could also exhibit them
(Echevarria, 1988). Additionally, in some rare cases H$\alpha$ in CTTSs can be
so strong, it may confuse the data processing pipelines in large surveys such
as SDSS, which could erroneously result in masking such a feature as a cosmic
ray.

Young stars also tend to be $10^{2}-10^{4}$ times more magnetically active
than the field stars (e.g., Skumanich, 1972; Feigelson et al., 2007; Kounkel
et al., 2022). This activity produces a number of emission lines, most notably
in H$\alpha$ and other H lines, as well as Ca (Vaughan et al., 1978; White &
Basri, 2003; Briceño et al., 2019), although they are significantly weaker and
narrower than the lines dominated by accretion. Young stars with emission
lines driven primarily from activity are known as Weak Lined T Tauri Stars
(WTTSs). Activity-driven emission is significantly longer lived however, it is
able to persist for several Gyr in late M dwarfs, and and for several 100s of
Myr in early M dwarfs (West et al., 2008; Newton et al., 2017). However, any
emission in G & K dwarfs is a clearer indication of stellar youth.

In addition to the above features that are primarily observed in the optical
regime, with sufficiently high signal to noise, $\log g$ can also be used as a
reliable tracer in many young stars. Low mass pre-main sequence (PMS) stars
are still inflated over their main sequence counterparts, as such they have a
systematically lower $\log g$ in comparison to the field stars. For high mass
stars, it is the inverse: because OB (and to a lesser extent A) stars evolve
so rapidly, catching them on the main sequence is more likely than not to
signify their youth in comparison to those stars that have already evolved
into giants. Although there has been difficulty in accurately calibrating
$\log g$ measurements of young stars in the past, recent efforts to process
SDSS spectra (both APOGEE & BOSS) now allow $\log g$ of low mass YSOs to be an
independent estimate of age (Olney et al., 2020; Sprague et al., 2022).

Each individual tracer may provide a strong indication of stellar youth, and,
for some sources, particularly if the scope of the study is limited to e.g.,
an individual star forming region, simple criteria are capable of producing
clean sample. However, any individual criteria are significantly easier to
apply to select low mass KM dwarfs, in comparison to OBAFG stars, and stars
with the age of a few Myr are easier to identify than those with an age of
20-30 Myr.

Dealing with the data from a large all-sky spectroscopic survey results in its
own challenges. Given that YSOs tend to be somewhat rare, any selection
criteria have to be carefully adjusted, as even a small fraction of the field
stars in the the parameter space similar to that of the young stars can
overwhelm the sample, while any cuts that are too strict may exclude a
significant fraction of bona fide YSOs that could have been easily
identifiable in a smaller study. As such, using a combination of different
criteria is advantageous for developing a cleaner but still comprehensive
sample. At the same time, however, it is necessary to efficiently and
carefully extract relevant spectral features in a manner that could be applied
to all of the stars regardless of their mass, evolutionary status, or the
unique properties than a star may exhibit.

In this paper we aim to develop a pipeline for measuring equivalent widths of
various youth-sensitive lines in low resolution optical spectra, such as those
produced with BOSS and LAMOST. Using these data, we develop a classifier for
identification of young stars, and we study the evolution of the properties of
these lines as a function of age.

##  2 Data

BOSS is a low resolution optical spectrograph with $R\sim$1800 with the
typical pixel scale of $\sim$1 Å, covering a wavelength range of 3600–10400
Å(Smee et al., 2013). Twin instruments are mounted at two observatories, at
Apache Point Observatory (APO; Gunn et al., 2006; Blanton et al., 2017) and
Las Campanas Observatory (LCO; Bowen & Vaughan, 1973), to ensure a complete
coverage of the entire sky. Each spectrograph is capable of observing up to
500 spectra within 3∘ and 2∘ field of view respectively. In prior iterations
of SDSS, BOSS was primarily to observe extragalactic targets, obtaining
spectra of only a few thousand of stars in total (Abdurro’uf et al., 2022),
although it did include a couple of fields rich in PMS stars (Suárez et al.,
2017). Since the transition to SDSS-V in 2021, it has observed well over
400,000 stars, including more than 17,000 stars targeted by ABYSS.

LAMOST (Large Sky Area Multi-Object Fiber Spectroscopic Telescope) is a
spectrograph very similar to BOSS in terms of its resolution, although it does
have a slightly narrower wavelength range of 3700–9000 Å, and it can observe
up to 4000 stars simultaneously (Yan et al., 2022). Beginning its operations
in 2011, it has obtained spectra of more than 10 million stars. So far there
have been only a few studies utilizing LAMOST specta of young stars (Liu et
al., 2021; Wang et al., 2022; Hernández et al., 2023; Lin et al., 2023),
however, in LAMOST DR8 there have been more than 13,000 serendipitously
observed stars that are included in ABYSS SDSS targeting (most of which to-
date are yet to be observed with BOSS). Given the similarity between BOSS &
LAMOST, it is beneficial include these data in the analysis.

Fundamental stellar parameters for the data from both of these spectrographs
have been extracted using BOSS Net (Sizemore L. et al. submitted), a neural
net that is capable of predicting self-consistent $T_{\rm eff}$ and $\log g$
(with respective precision of 0.008 dex and 0.1 dex at SNR$\sim$15) values
that are calibrated to theoretical models for stars of all types, including
PMS stars, main sequence sources, brown dwarfs, red giants, subdwarfs, white
dwarfs, as well as OB stars. Additionally, it provides an independent estimate
of stellar radial velocities (RV). This is important, since RVs that are
included in LAMOST data releases have a number of artefacts, systematic
offsets on order of up to 10 km s-1 that vary depending on sky position and
spectral type. Accurate RVs are crucial for accurate centroiding of spectral
lines, especially those that are weak and narrow.

##  3 Methods

###  3.1 Measuring line widths

Figure 1: An example of a spectral lines (H$\alpha$ and Li I) of a PMS star in
BOSS spectra. The red line is centered on the feature, the blue line is the
manually defined continuum over which the line is integrated to measure EqW.

Here we describe the development of the LineForest pipeline which extracts a
number of youth sensitive features from the optical spectra.

####  3.1.1 Initial measurements

Different lines present different challenges in the ability of measuring
accurate EqW. Some lines are quite strong, such as H$\alpha$, with absolute
widths (AbW) that can vary with spectral type, and depending on the range over
which the line is integrated, the EqW would either be underestimated, or it
could be contaminated by the flux well outside of the line. On the other hand,
Li I is a relatively weak absorption line; typically not exceeding EqW of 0.5
Å(Briceno et al., 1997). A number of lines such as Fe I and CN can often blend
in together with Li I when its strength decreases to $<$0.2 Å. One of the
methods through which Li I EqW has been measured in a large census in the past
was through identifying the closest matching spectrum of an evolved star and
subtracting it out such that only Li I remains (Žerjal et al., 2019), however
while it is possible to do this in high resolution and high SNR spectra, such
an approach creates a challenge for BOSS, particuarly because $>70$% of visits
have SNR$<$30\. We attempted a number of other approaches - Gaussian profile
fitting, automated continuum determination, automated width estimation – all
of which had some degree of success in a subset of stars, but none could be
generalized across all sources even within a single class.

We found that the most reliable approach to measuring EqW was through manually
defining each line in each spectrum, which is an adaptive approach that allows
to measure the width of the entire line, and enables skipping sources that do
not present a feature in either emission or absorption. This task is not
feasible to do for all stars observed in a large survey due to a significant
person-power requirement. However, doing it on a subset of stars can create a
vetted set of training labels for data driven machine learning applications
which can then be packaged into an automated pipeline.

For this purpose, we selected a random set of $\sim$3500 spectra, among which
about 1000 sources targeted by ABYSS, and the remaining were chosen to create
a representative subset of targets from other programs. Those other programs
include OB stars, brown dwarfs, white dwarfs, cataclysmic variables, and red
giants, featuring $\sim$250 stars in each class. For all those sources we have
measured line properties of the 23 most prominent transitions, while another
29 lines were measured in a subset of 350 spectra to enable the model to learn
the shape of the continuum at these transitions. This represents a total of 52
lines (out of which a half are Balmer or Paschen H series lines that fall in
the BOSS wavelength range). The features are listed in Table 1.

Using a custom-built interactive code previously used in Campbell et al.
(2023), we recorded, for each line in each source, the wavelength and flux
($\lambda_{1}$, $F_{1}$) near the start of the line as well as those near the
end of the line ($\lambda_{2}$, $F_{2}$). These set of points define the
continuum under which the spectrum was integrated to estimate EqW (Figure 1).
By convention, absorption lines are defined with positive EqW, and emission
lines are defined with negative EqW. The difference $\lambda_{2}-\lambda_{1}$
corresponds to AbW. In cases where no apparent line was observed, both EqW and
AbW were recorded as 0. In cases where significant blending was suspected,
depending on the exact profile the line was either recorded as 0, or the
continuum was drawn over the portion of the line most likely to be associated
with the given element. While such approach is imperfect, the neural net is
capable of interpolating through these measurements to achieve a more general
solution.

We do note that the measured line properties do not take into account various
physical effects that may alter them, for example veiling due to accretion,
and model fitting is required in order to characterize it (e.g., Fang et al.,
2020, 2021). We defer full analysis of the effects of veiling in young stars
to future works in the series.

Low resolution spectra such as BOSS may present a challenge in identifying
some weaker lines if the radial velocity is very uncertain, particularly for
sources with very low SNR. As a consequence of this, if during manual
measurements a confident identification was not possible, nothing would be
recorded for it, thus attempting to condition the neural net to disregard it.

However, it should be noted that in some cases, some lines may be
systematically misattributed. For example, He transitions are most commonly
observed in OB-type stars, although they can also be seen in emission in low
mass PMS stars as a result of certain processes related to accretion. For
instance, in a number of low mass main sequence stars, the Fe I 6678 Å
absorption feature has almost identical wavelength to He I 6678 line. It is
unlikely that the lines are blended in single stars as they are prominent at
completely different $T_{\rm eff}$ regimes, however, for the sake of self-
consistency, all of the likely misattributed measurements were preserved in
the sample.

In some cases there is considerable blending, for example, Pa 13, Pa 15, and
Pa 16 are located extremely close to the lines of Ca II triplet. Significant
confusion is also presetnt between Ca H and H$\epsilon$ lines. While some
effort was made to define the continuum in such a way as to minimize the
contribution from the neighboring strong features. Particularly, in young low
mass stars, Calcium lines are in emission, with only weak Pa lines. It was not
always possible to separate the lines cleanly, and thus in some of the
approaches described below, there are cases with partial confusion among those
lines. Reasonable judgement should be exercised in using the catalog for the
appropriate $T_{\rm eff}$ ranges for each line, and referring back to the
spectra of the sources is suggested for the unexpected features.

Table 1: Set of lines used in this study

Line |  $\lambda$ $a$$a$footnotemark:  | $\Delta\lambda$ | Precision | Recall | EqW $\sigma$ | AbW $\sigma$ | Notes $c$$c$footnotemark:
---|---|---|---|---|---|---|---
| (Å) | (Å) |  |  | (dex)${}^{b}$${}^{b}$footnotemark:  | (dex)${}^{b}$${}^{b}$footnotemark:  |
H$\alpha$ | 6562.8 | 200 | 0.98 | 0.96 | 0.08 | 0.09 |
H$\beta$ | 4861.3 | 200 | 0.98 | 0.94 | 0.09 | 0.1 |
H$\gamma$ | 4340.5 | 200 | 0.95 | 0.93 | 0.1 | 0.12 |
H$\delta$ | 4101.7 | 200 | 0.94 | 0.94 | 0.1 | 0.11 |
H$\epsilon$ | 3970.1 | 200 | 0.95 | 0.94 | 0.08 | 0.1 | [E]; Ca II H blend
H8 | 3889.064 | 200 | 0.91 | 0.66 | 0.08 | 0.06 |
H9 | 3835.391 | 200 | 0.91 | 0.71 | 0.11 | 0.1 |
H10 | 3797.904 | 200 | 0.82 | 0.77 | 0.12 | 0.08 |
H11 | 3770.637 | 200 | 0.9 | 0.72 | 0.1 | 0.08 |
H12 | 3750.158 | 50 | 0.85 | 0.79 | 0.14 | 0.09 |
H13 | 3734.369 | 50 | 0.77 | 0.65 | 0.13 | 0.09 |
H14 | 3721.945 | 50 | 0.87 | 0.68 | 0.09 | 0.07 |
H15 | 3711.977 | 50 | 0.87 | 0.81 | 0.12 | 0.15 |
H16 | 3703.859 | 50 | 0.88 | 0.7 |  |  |
H17 | 3697.157 | 50 | 0.56 | 0.38 | 0.11 | 0.2 |
Pa7 | 10049.4889 | 200 | 0.9 | 0.84 | 0.13 | 0.14 |
Pa8 | 9546.0808 | 200 | 0.93 | 0.9 | 0.12 | 0.11 |
Pa9 | 9229.1200 | 200 | 0.92 | 0.85 | 0.11 | 0.12 |
Pa10 | 9014.909 | 200 | 0.83 | 0.72 | 0.16 | 0.17 |
Pa11 | 8862.782 | 200 | 0.86 | 0.78 | 0.08 | 0.12 |
Pa12 | 8750.472 | 200 | 0.86 | 0.92 | 0.11 | 0.11 |
Pa13 | 8665.019 | 200 | 0.73 | 1.0 | 0.07 | 0.08 | [E]; Ca II 8662.140 Å blend
Pa14 | 8598.392 | 200 | 0.75 | 0.9 | 0.11 | 0.08 |
Pa15 | 8545.383 | 200 | 0.83 | 0.83 |  |  | [E]; Ca II 8542.089 Å blend
Pa16 | 8502.483 | 200 | 0.62 | 1.0 |  |  | [E]; Ca II 8498.018 Å blend
Pa17 | 8467.254 | 200 | 0.73 | 0.8 |  |  |
Ca II | 8662.140 | 50 | 0.99 | 0.96 | 0.06 | 0.07 | [L]; Pa 13 blend
Ca II | 8542.089 | 50 | 0.98 | 0.98 | 0.06 | 0.07 | [L]; Pa 15 blend
Ca II | 8498.018 | 50 | 0.97 | 0.94 | 0.07 | 0.07 | [L]; Pa 16 blend
Ca II K | 3933.6614 | 200 | 0.94 | 0.92 | 0.08 | 0.08 |
Ca II H | 3968.4673 | 200 | 0.96 | 0.92 | 0.07 | 0.08 | [L]; H$\epsilon$ blend
He I | 6678.151 | 50 | 0.92 | 0.83 | 0.13 | 0.08 | [E]; Fe I 6678 Å blend
He I | 5875.621 | 50 | 0.71 | 0.55 | 0.13 | 0.1 | [E]
He I | 5015.678 | 50 | 0.43 | 0.51 | 0.15 | 0.17 | [E]; Fe II 5018 Å blend
He I | 4471.479 | 50 | 0.81 | 0.62 | 0.14 | 0.11 | [E]
He II | 4685.7 | 50 | 0.08 | 0.38 |  |  | [E]
N II | 6583.450 | 50 | 0.74 | 0.66 | 0.19 | 0.11 |
N II | 6548.050 | 50 | 0.65 | 0.53 | 0.21 | 0.12 |
S II | 6716.440 | 50 | 0.81 | 0.74 | 0.12 | 0.08 |
S II | 6730.816 | 50 | 0.69 | 0.59 | 0.16 | 0.1 |
Fe II | 5018.434 | 50 | 0.76 | 0.85 | 0.15 | 0.16 |
Fe II | 5169.030 | 50 | 0.81 | 0.81 | 0.09 | 0.08 |
Fe II | 5197.577 | 50 | 0.57 | 1.0 |  |  |
Fe II | 6432.680 | 50 | 0.71 | 0.5 |  |  |
O I | 5577.339 | 50 | 0.8 | 0.53 | 0.12 | 0.06 |
O I | 6300.304 | 50 | 0.47 | 0.56 | 0.19 | 0.11 |
O I | 6363.777 | 50 | 0.2 | 0.25 |  |  |
O II | 3727.42 | 50 | 0.82 | 0.53 | 0.23 | 0.17 | [E]; Fe I 3727 Å blend
O III | 4958.911 | 50 | 0.5 | 0.33 |  |  | [E]
O III | 5006.843 | 50 | 0.78 | 0.78 | 0.16 | 0.06 | [E]
O III | 4363.85 | 50 | 1.0 | 0.33 |  |  | [E]
Li I | 6707.760 | 50 | 0.88 | 0.64 | 0.12 | 0.09 | [L]; Fe I 6707 Å blend

aafootnotetext: All wavelengths are given in air, typically taken from Kramida
et al. (2023).

bbfootnotetext: Scatter between the manual measurements and the predictions;
reported only if the test set has $>$10 spectra with EqW$>$0.2 Å

ccfootnotetext: E: Line is most prominent in early type stars, L: Line is most
prominent in late type stars

####  3.1.2 Model architecture & Training

We categorize the studied lines into two groups: one contains lines that are
typically broad, and the other groups those lines that are typically narrow.
Broad lines (which include most of H lines, as well as Ca H & K) are evaluated
over a $\pm$200 Å window centered on the doppler-corrected line. Narrow lines
(all of the remaining elements, as well as high order lines in the Balmer
series, particularly those found close to the edge of the wavelength range of
BOSS) are evaluated over a $\pm$50 Å window. We note that the width of the
“broad” lines is driven by the maximum width seen in these lines across the
entire sample, such as e.g., white dwarfs. And, although many lines that are
typically narrow can be significantly broadened to widths of 100s of km s-1
,thorough accretion or winds (e.g., Banzatti et al., 2019; Sicilia-Aguilar et
al., 2020), even in their most extreme cases they can easily fit within the 50
Å window.

The flux in these windows is linearly reinterpolated onto uniformly-spaced,
128 element array with a pixel scale of $\sim$3.15 and $\sim$0.79 Å
respectively. Also, in order to normalize the flux in these windows, we
transform it onto the log space.

The lines can significantly vary in strength. As such, to normalize their
properties, we also take the log of the absolute value of EqW and AbW.
However, while the AbW are always positive, EqW can be either positive or
negative. In order to preserve this information, we add the sign of EqW to the
output: the sign is set to +1 if EqW$>$0, -1 if EqW$<$0, and it is left at 0
if the line is undetected.

The model was constructed in TensorFlow, using a convolutional neural network
(CNN) architecture. The data are passed through through 6 convolutional
layers, with a convolutional kernel of 8 pixels; each layer breaks the data
into an increasingly larger number of filters, from 8 to 32. Each
convolutional layer is followed by a maxpooling layer to reduce the
dimensionality of the data, and a $\tanh$ activation function. The outputs of
the first six layers are flattened, and then are passed through 3 fully
connected layers with 128, 256, and 128 neurons with ReLU activation function,
after which they predict EqW, AbW, and EqW sign (which is also used as a
classifier for the detection). In training, we used mean square error loss,
masking it for EqW and AbW in sources where the line is undetected.

Two models are trained, one for all of the broad lines, and one for all of the
narrow lines (which refers solely to the adopted window size, and not the
properties of the lines themselves). Doing so allows the CNN to better develop
the ability to recognize the center of the window and to extract its
properties. These models, however, can’t be used as a general tool for
extracting any lines not listed in Table 1, as the unfamiliar continuum can
skew the weights. For example, the model trained on broad lines is generally
able to reproduce EqW measurements of the narrow lines and vice versa (with
the outputs scaled accordingly to their input pixel scales), but there are
some systematic offsets, a large number of false positives and false
negatives, as well as a significantly larger scatter than when the dedicated
model is used.

####  3.1.3 Testing

The labelled data were split 80:20 into the train and test sets. We evaluate
the performance of the models on the test set, and consider the line as
“detected” in a given spectrum if the prediction for the EqW sign is $>$0.5 or
$<-0.5$. Overall, across the sample the typical root mean squared in both EqW
and AbW measurement is 0.09 dex For each transition, we examine the number of
true positives (tp), false positives (fp), false negatives (fn), and evaluate
both the precision (tp/(tp+fp)), and recall (tp/(tp+fn)). They are recorded in
Table 1.

The model is performing extremely well with strong lines (e.g., H$\alpha$,
with precision and recall of 98% and 96% respectively). In Li I, precision is
relatively high, at 88%, but its recall is somewhat lower, at 64% - in these
cases, the fp and fn sources are typically those that are borderline
detections, with EqW$<$0.05 Å. Some transitions do have quite low precision
and recall. For instance, the He II 4685.7 Å line has 8% precision and 38%
recall, however, this transition is extremely rare, recorded for only 29 out
of $\sim$3500 sources in the train set, of which only 8 are in the test
sample. Thus, the ratios for this line are heavily affected by the small
number statistics. When we evaluate the line on the full set of stars observed
by BOSS (Section 3.1.4), when it is seen in absorption in cool stars, it is
likely to be confused with other nearby features, but in stars with $T_{\rm
eff}$$>$30,000 K, the absorption line measurements seem to be more robust, as
do the rare cases when it is seen in emission, particularly for the ABYSS
targets.

####  3.1.4 Evaluation

The resulting model is made available in Kounkel et al. (2023). We apply
LineForest to all of the stellar sources observed by BOSS to-date, including
all of the legacy data observed prior to SDSS-V. However, we note that the
legacy SDSS data archive does not provide robust RV measurements as SDSS-V.
This is mainly due to poor wavelength calibration, which was only identified
at the beginning of the current iteration of the survey. As such, measurements
of weak and narrow lines from legacy data spectra may be of lesser quality
than in other datasets.

All of the lines were chosen to ensure they are detectable within the
wavelength range of BOSS. LAMOST, however, has a narrower coverage, thus we
only record the properties of the lines in the range of Pa11 to H$\epsilon$
inclusively.

We estimate the uncertainties in all of the measurements using a technique
established in (Olney et al., 2020), through computing several different
realizations of a line by scattering the flux randomly by the reported
uncertainties, passing each realization through the model, and averaging the
predictions. In total, 100 iterations were made, and 1$\sigma$ errors were
computed from 16, 50, and 84th percentiles.

The line measurements are reported only for the sources in which the line is
detected confidently in at least 30 of these iterations, this suppresses the
model from very noisy features. Sources with significant noise are also likely
to have very uncertain RV measurements, thus making it difficult to identify
weak lines in the first place. For example, 65% of YSOs have $\sigma_{\rm
RV}<$5 km s-1. On the other hand, for YSOs with confident Li I detection, 75%
of them have $\sigma_{\rm RV}<$5 km s-1, thus Li I measurements do indeed
appear to be suppressed in noisy data.

###  3.2 YSO Classifier & Sample Definition

The lines that have been measured with LineForest can be used to improve the
identification of PMS stars. In order to utilize all of the features, we built
a fully connected neural net using TensorFlow. We include $T_{\rm eff}$, $\log
g$, all of the EqW and AbW, as well as six fluxes, from Gaia ($G$, $G_{BP}$,
$G_{RP}$) and from 2MASS ($J$, $H$, $K$), for a total of 164 inputs.

The network consists of three layers with 256, 512, and 1024 neurons connected
using ReLU, a dropout layer with a rate of 0.5, distilling everything to a
layer with a single output with a sigmoid activation function that returns the
probability of a source being pre-main sequence.

The initial training set consisted of all of the ABYSS sources that have been
observed to-date, both with BOSS and with LAMOST, all of the SDSS-V BOSS
sources, as well as a random subset of $\sim$1.4 million LAMOST field stars.
80% of the sample was used in training, and the remaining 20% was used for
testing. Since YSOs are relatively rare in the total training set, accuracy is
an imperfect metric. Rather, we used a F1 score (the harmonic mean of the
precision and recall) to evaluate the performance of the model.

While the targeting from ABYSS is rather comprehensive, it does include a
significant fraction of more evolved field stars. Additionally, ABYSS may have
missed some YSOs that have nonetheless have been serendipitously targeted by
other programs. This mislabeling affects the quality of the model. To
compensate for this, we reevaluate some of the labels.

First, we excluded ABYSS targets that are found at high galactic latitudes,
such as $|b|>30^{\circ}$. We similarly excluded sources with RVs larger than
what is typically found for the clustered sources at a given distance, sources
that have $\log g$$<$3.4 or $\log g$$>$5.1, cool magnetically inactive stars
with very low H$\alpha$ emission, all of the sources without Li I measurement
and, finally, K dwarfs with insufficiently high Li I absorption. Also, we
included all of the sources with H$\alpha$ emission that is consistent with
originating from a protoplanetary disk, as well as K dwarfs with Li I
absorption. By training a model using this sample, then we were able to
examine the outputs, particularly for sources that the model identified as
high confidence YSOs but were originally labelled as field stars. In that
sample we interactively identified the sources that are consistent with being
associated with young clusters and star forming regions that appeared to stand
out as the overdensities in the plane of the sky, verifying that they are also
clustered in proper motion and parallax phase space. These sources were then
re-labelled as YSOs, and the model was retrained. This procedure was repeated
a number of times until the number of “discovered” YSOs was negligibly small.

The final model was trained from a sample of 34,407 YSOs (down from 57,036
ABYSS targets). It achieves F1 score of 0.633 on the test sample, with
precision of 0.752 and recall of 0.543 at a probability cut-off of 50%. A
higher probability cut-off improves precision, although we note that in that
case, many of the “false positives” may actually be bona fide YSOs found in
areas that are not strongly clustered in our sample. The bulk of the sources
that are missing are found at higher $T_{\rm eff}$ that are difficult to
recover using traditional techniques as well, nonetheless we do note that,
somewhat surprisingly, we are able to classify YSOs across the whole $T_{\rm
eff}$ range, and we are able to autonomously recover many of more distant
populations that lack low mass stars in our sample (e.g., Cygnus X). The bulk
of the information necessary for the classification is carried by just $T_{\rm
eff}$, $\log g$, and EqW from H$\alpha$ and Li I, nonetheless other features
improve the recovery of sources at these higher $T_{\rm eff}$.

In total, we are able to identify, to-date, 17.9 K stars observed with SDSS-V
BOSS as YSOs with probability $>$0.5, from which 11.1 K have a probability
$>$0.8. Similarly, there are, 9371 stars in LAMOST DR8 with probability
$>$0.5, of which 6105 stars have probability $>$0.8. Finally, there are 285 /
225 stars in the legacy SDSS data. Unsurprisingly, most of the sources are
concentrated along the galactic plane and the Gould’s belt, with only a few
sources found at higher galactic latitudes (Figure 2)

Figure 2: On-sky distribution of identified YSO candidates with probability
$>$0.5

###  3.3 Ages

To examine the evolution of the sample (Section 5), it is necessary first to
determine the ages of the stars. For this purpose we use Sagitta (McBride et
al., 2021), a neural net that has two components. It first classifies sources
into those that can be identified as pre-main sequence based on their
photometry (G, BP, RP, J, H, K), parallax, and average extinction towards a
given line of sight. Then, using the same input features, it estimates
photometric ages for the individual PMS stars. It has been trained on the
average ages within a subcluster in which a given star is found, and at the
moment this pipeline offers the most stable performance with respect to
$T_{\rm eff}$ (i.e., producing self-consistent ages for G, K, and M-type stars
in a given population), in comparison to the more traditional techniques that
rely on the theoretical isochrones (Kounkel et al., 2023a).

Nonetheless, Sagitta is only reliable for the stars that are classified as
PMS. Higher mass stars quickly reach the main sequence, and as such, their
photometry stops being a reliable indicator of their ages in comparison to the
lower mass stars of the same age. For this reason, in the total sample of
stars with spectroscopic YSO probability $>$0.5, there are a number of sources
ages of which has to be derived through other means to avoid biases in the
overall distribution (Figure 3).

To fill in this gap we only retain ages for those stars with Sagitta-derived
PMS probability of $>$0.5. We then calculate the 3-d distance from a lower
probability to a closest higher probability source. We then assign the age of
that closest high probability source if the separation is $<$30 pc. We have
experimented with different cut-offs to observe the effects on the analysis
presented later in the paper, to achieve an appropriate trade-off in terms of
completeness at higher $T_{\rm eff}$ while maintaining robustness of the
sample. Although this separation is considerable, it does incorporate within
it a typical uncertainty in the parallax, and it is also permissive for the
inclusion of the more diffuse populations. On the other hand, in denser
regions, the closest neighbor is usually well under that limit.

Such substitution has been done for 26% of the full sample, with the bulk of
it dominated by hotter stars. Among sources with $T_{\rm eff}$$>$5000 K, 74%
of sources have their ages substituted, on the other hand, this is the case
for only 12% of cooler stars, most of which have age $>$10 Myr.

Figure 3: Top: Kiel diagram of the sample, color coded by the photometrically
derived probability of a star being PMS using Sagitta (McBride et al., 2021).
Bottom: Adopted ages for the stars in the sample; for the sources with PMS
prob.$<$0.5, the assumed age is that of a closest high probability neighbor.
Figure 4: Distribution of H$\alpha$ as a function of temperature for the YSOs
in our sample. The red line shows the adopted separation between CTTSs and
WTTSs. The yellow lines are the ranges for the selection from White & Basri
(2003).

Figure 5: EqW measurements in BOSS spectra for some of the lines used in this
study. The colored background (color coded by the density of points) represent
field stars of different evolutionary stages, black contours are the sources
identified as YSOs.

###  3.4 Identification of CTTSs

White & Basri (2003) have developed a traditional method to select CTTSs based
on H$\alpha$ emission strength as a function of spectral type, employing 4
flat cuts. The simplicity of its implementation becomes a downside when
dealing with a very large sample, as the transition between these cuts become
very pronounced. Briceño et al. (2019) have improved on this approach by
developing a much more continuous selection, but their approach relies
strongly on spectral types of the stars. Commonly used transformations between
spectral type and $T_{\rm eff}$ exist (e.g., Pecaut & Mamajek, 2013), but
since we do have measurements of $T_{\rm eff}$ available directly, it is much
more optimal to use a selection criterion that does not rely on such a
conversion.

After examining the distribution of H$\alpha$ EqW as a function of $T_{\rm
eff}$, we notice a clear concentration of sources with weak emission lines
(i.e, WTTSs). On the other hand, among those sources with stronger emission
lines (i.e, CTTSs), there is a much more significant scatter (Figure 4). Using
this overdensity of WTTSs, we define a cut

$\begin{split}\log T_{\rm eff}<3.7:H\alpha<-436.95\log T_{\rm eff}^{2}+\\\
3227.7\log T_{\rm eff}-5963.2\\\ \log T_{\rm
eff}>3.7:H\alpha<-2.55\end{split}$ (1)

to select out CTTSs, which we then compare against White & Basri (2003) with
the transformation from Pecaut & Mamajek (2013) as a guide.

##  4 Results

We present LineForest measurements for 23,903 stars identified as YSO
candidates in LAMOST spectra with probability $>$0.1 in Table 2. Outputs for
all of the BOSS spectra (including non-YSOs), will be incorporated in the
subsequent SDSS data releases.

Table 2: LAMOST PMS candidates  Column | Unit | Description
---|---|---
obsid |  | LAMOST unique identifier
RA | deg | Right ascention in J2000
Dec | deg | Declination in J2000
log $T_{\rm eff}$ | [K] | Effective temperature from Sizemore et al.
$\sigma$ log $T_{\rm eff}$ | [K] | uncertainty in log $T_{\rm eff}$
log g |  | Surface gravity from Sizemore et al.
$\sigma$ log g |  | Uncertainty in log g
RV | km s-1 | Radial velocity from Sizemore et al.
$\sigma$ RV | km s-1 | Uncertainty in RV
PMS |  | Probability from the spectroscopic classifier of the
|  | object being a PMS candidate, reported for PMS$>$0.1
_eqw | Å | Equivalent width, all the lines in the sample
$\sigma$ _eqw | Å | Uncertainty in _eqw
_abw | Å | Absolute width, all the lines in the sample
$\sigma$ _abw | Å | Uncertainty in _abw

###  4.1 General line properties

We examine the overall differences in the measured properties of the lines
between stars identified as young and the significantly more evolved field
stars (Figure 5).

As expected, Li I shows an obvious signature of evolution. In our sample, we
observe how the YSO candidate distribution clearly peaks near 0.45 Å at
$T_{\rm eff}$$\sim$4000 K, while only a trace amount of Li I is observed at
this $T_{\rm eff}$ in the field stars, largely within the errors. While there
is some variance in its distribution with $T_{\rm eff}$, it is not
significant. On the other hand, in YSOs Li I abundance significantly decreases
towards the cooler end, since these stars become fully convective, resulting
in a higher internal $T_{\rm eff}$ that is available for Li I depletion.
Similarly, towards the hotter end, the internal temperature of the star itself
increases significantly, even though the convective envelope shrinks. The
overall shape of Li I distribution in young stars is consistent to what has
been observed in the past (e.g., Jeffries et al., 2014, 2023). We discuss
evolution of Li I in greater detail in Section 5.

H$\alpha$ is another notable line. It is commonly seen in emission in cool
stars, and in absorption at stars with $T_{\rm eff}$$>$6500 K, reaching a
maximum EqW at $\sim$10,000 K. There is a difference in the EqW in early-type
stars, with younger stars typically having stronger absorption lines; this
difference is most likely driven by different $\log g$ distribution (Sizemore
L. et al. submitted). Young high mass stars are expected to be on the main
sequence, on the other hand, since high mass stars have short lifetimes, older
stars rapidly evolve away from it.Among late type stars the difference is much
more significant. This is apparent not just in CTTSs (which show H$\alpha$
emission lines far in excess of what is observed in the field), but in WTTSs
as well. Evolved M dwarfs in the field can be divided into inactive (with
H$\alpha$ EqW$\sim$0 Å), and those that are magnetically active, where
H$\alpha$ has stronger emission (Newton et al., 2017). Both are present in
SDSS-V sample. Active M dwarfs are usually considered to be relatively young,
with ages younger than a few Gyr (West et al., 2008). Here we are able to
observe a significant evolution between these active M dwarfs and WTTSs, with
H$\alpha$ emission lines being 1.5-2 times stronger in WTTSs than in somewhat
older field stars. This is an indication of the magnetic activity decreasing
in strength as stars age.

A line behavior similar to that of H$\alpha$ is observed in other H lines as
well, both in the Balmer and in the Paschen series, though, higher energy
lines are less likely to be seen in cooler stars. Similar trends can also be
observed in Ca lines as well – they also show evolution of $\log g$ among
early type stars (although whether YSOs have stronger or weaker lines usually
depends on the precise type of transition), and they also separate late type
stars based on their age. The Ca triplet usually consists of narrow lines that
are seen in emission only in some CTTSs. And although they are primarily seen
in absorption, low mass YSOs tend to have weaker Ca triplet lines. This effect
is likely to be also primarily driven by the magnetic activity filling the
lines (but not to the degree of creating emission lines), as these lines are
magnetically sensitive, and the excess flux in them typically correlates with
emission in Ca H & K (Martin et al., 2017). However, veiling could also have
some influence on these lines.

Some differences are observed between the Ca triplet, and the Ca H & K lines.
The latter two tend to have very broad absorption lines in which a narrow
emission component may emerge. This results in a discontinuity at EqW=0 Å as
the pipeline transitions from measuring the full absorption line to only
measuring its emission portion. Low mass YSOs are commonly seen with Ca H & K
in emission, this is only rarely the case in the field stars (in which case
they are most likely to be younger than a few 100 Myr, Cunningham et al.,
2020).

CTTSs produce emission lines from many different transitions. Often (e.g., in
He I or Fe II lines), this occurs in cool stars, with $T_{\rm eff}$$<$4500 K
presenting very strong H$\alpha$ EqW$<$-60 Å. Some transitions (e.g., O II or
N II) can produce emission lines across all $T_{\rm eff}$. In early type stars
this may be a more reliable indicator of accretion than H$\alpha$.

###  4.2 Hydrogen decrements in CTTSs

Figure 6: Left: An example of the decrement of EqW of H lines in the Balmer
series, normalized by EqW of H$\gamma$ ($n_{u}=5$). The black line shows the
best fitted model from Kwan & Fischer (2011). H$\epsilon$ is likely
contaminated from Ca H. Right: Distribution of the derived Temp and log n
models for the sources with cleanly defined decrements in Balmer lines. Points
are color coded by the uncertainty in log n, note that most confident
measurements tend to have log n$>$11\.

While CTTSs are defined using the strength of H$\alpha$, it is not uncommon to
see emission lines in other H lines as well. Of 6444 CTTSs, 3085 show emission
in H$\beta$, 1966 in H$\gamma$, 1215 in H$\delta$, 1021 in H$\epsilon$, etc.

When multiple lines are available, since each of them has different excitation
temperature, it is possible to probe the physical conditions such as
temperature (Temp) and density (log n) of the emitting gas (i.e, the accretion
stream). This was previously done in Campbell et al. (2023) using Brackett
lines observed with APOGEE, through comparing the ratios of equivalent widths
of these lines to the models from Kwan & Fischer (2011).

We attempt to replicate this experiment using the Balmer lines (Figure 6). In
CTTSs we isolate the H lines in all of the sources that appear to be in
emission. We then compared them to the models from Kwan & Fischer (2011).
Since some of the H lines are likely to be optically thick (and thus their EqW
would not scale accurately with the abundances), we examined different subsets
of these lines, normalizing all EqWs by the strongest line in the subset.

Because of this we discarded H$\alpha$ and H$\beta$ from the analysis; the
higher order lines on the other hand produced a more self-consistent fit.
Thus, we limited the sample only to the sources that have H$\gamma$ and at
least two higher energy lines to construct a decrement. Similarly, we
attempted to examine the decrements in the Paschen series, but while results
were broadly consistent to the Balmer series fit, there was significant
uncertainty in the fit, thus they were excluded from the analysis.

Through the model comparison, we identified the Temp and log n of the gas. We
note that we have not corrected EqWs for veiling or extinction, thus the
presented results should be considered preliminary, and done only for the
sources with a clearly defined decrement. An estimation of these parameters
will be done in the subsequent papers in the series.

We find that Temp of the accretion shock is typically $\sim$6000–10,000 K, and
that density is $\sim 10^{11-11.5}$ cm-3 for the sources with the most
confident measurements. There is no strong correlation with the properties of
the star itself, such as $T_{\rm eff}$, $\log g$, age, or any other.

For comparison, Campbell et al. (2023) find a much larger range in Temp, from
4000K to 16,000 K (with the sources with hotter Temp preferentially being Be
stars) and density ranging from $\sim 10^{12}$ cm-3 at 5000 K, to $\sim
10^{11}$ cm-3 at 12,500 K. Over the range of the overlapping Temp, there is a
relative agreement in the derived log n.

##  5 Discussion: Li I evolution with age

Figure 7: Comparison of Li I measurements reported here to those from Gaia-ESO
spectra from Binks et al. (2022), color-coded by $T_{\rm eff}$ of the star.
The black line shows the best fit between the two.  Figure 8: Li I
distribution as a function of $T_{\rm eff}$ across different age bins. Yellow
dots show the raw data, black squares show the median Li I EqW in 0.1 dex
$T_{\rm eff}$ bins, and the black error bars show the 16-84 percentile
scatter. The grey errorbar shows the typical uncertainty in the individual
measurements. The red dashed line shows the relationship of Li I as a function
of $T_{\rm eff}$ and age from Jeffries et al. (2023), the blue solid line
shows the polynomial fit presented in Equation 2

To assess the reliability of the measurements, in Figure 7 we compare our
measurements of Li I with the measurements reported by Binks et al. (2022)
from Gaia-ESO spectra. Gaia-ESO spectra have significantly higher resolution
($R\sim$15,000) than BOSS or LAMOST spectra, making these measurements
significantly more robust. Indeed, the comparison does show significant
scatter, but this scatter is mostly reproduced by our reported uncertainties.
Furthermore, there is an excellent correlation between the two sets of
measurements, particularly at $T_{\rm eff}$$\leq$4500 K, although Gaia-ESO
measurements appear to be systematically larger by $\sim$0.05 Å likely due to
the differences in the continuum definition.

In young stars Li I is known to precipitously deplete as they get older.
Recently, Jeffries et al. (2023) have developed an empirical model of Li I
depletion using Gaia-ESO measurements of 52 open clusters with age ranges from
2 Myr to 6 Gyr. While it serves as a good picture of the overall global
evolution of Li I, the sampling at the younger age ranges is somewhat poor,
and no attempt was made to distinguish between clusters younger than 10 Myr.
Since our sample has more than an order of magnitude more star younger than 25
Myr, we reexamine the trend at these age ranges.

To do this, we separate the sources 0.1 dex bins both in age and in $T_{\rm
eff}$, and we find the median, as well as 16th and 84th percentile of the
distribution to check the scatter (Figure 8). We also find the typical
uncertainty in Li I measurement of the sources in the bin (to evaluate the
significance of the scatter), as well as the weighted average error for the
median itself. Since the external comparison does show our uncertainties as
robust, even with considerable individual errors, a sufficiently large
statistical sample allows improving on finding the mean of the distribution.

The relation from Jeffries et al. (2023) appears to be somewhat inconsistent
in describing our Li I measurements of these young stars. In part it is due to
the systematics in the assumptions of age and $T_{\rm eff}$, as well as the
aforementioned systematic offsets in our determination of Li I, but also it
can be attributed to the sparse young sample they used. Instead, we fit a
polynomial to the data

$\begin{split}{\rm Li\ I\
EqW}=a_{0}+a_{1}t+a_{2}t^{2}+a_{3}t^{3}+a_{4}t^{4}+\\\
b_{1}T+b_{2}Tt+b_{3}Tt^{2}+b_{4}Tt^{3}+\\\
c_{2}T^{2}+c_{3}T^{2}t+c_{4}T^{2}t^{2}+\\\ d_{3}T^{3}+d_{4}T^{3}t+\\\
e_{4}T^{4}\end{split}$ (2)

where $t$ is $\log_{10}$ age in years, and $T$ is $\log_{10}$$T_{\rm eff}$ in
K, and the coefficients are listed in Table 3. This relationship is valid for
3200$<$$T_{\rm eff}$$<$6000 K, and for ages $<$30 Myr.

Table 3: Fitted coefficients for Li I estimation  Coefficient | Value | Coefficient | Value
---|---|---|---
$a_{0}$ | -5148.728 | $b_{4}$ | 0.4496906
$a_{1}$ | -523.1078 | $c_{2}$ | -2422.647
$a_{2}$ | 106.6105 | $c_{3}$ | 1.814952
$a_{3}$ | -9.375462 | $c_{4}$ | -0.4258554
$a_{4}$ | 0.2786480 | $d_{3}$ | 413.0113
$b_{1}$ | 6216.321 | $d_{4}$ | 0.3173983
$b_{2}$ | 31.22195 | $e_{4}$ | -26.54814
$b_{3}$ | -5.656809 |  |

We find that, at all age ranges, at $T_{\rm eff}$$>$4500 K, the scatter is
statistically reproducible by the measurement uncertainties, but at cooler
$T_{\rm eff}$, the scatter becomes almost 2 times larger than the
uncertainties (Figure 9). This supports the conclusions of Binks et al. (2022)
that at low $T_{\rm eff}$ Li I abundance in the chromosphere is influenced by
the variability in the spot coverage of stars (as opposed to erroneous age
measurements that would lead to unrealistic age spread in a given cluster).

Figure 9: Ratio between the typical scatter in Li I in each age and $T_{\rm
eff}$ bin, and the typical uncertainty in Li I measurements in that bin. The
black line shows the fitted average across all age ranges.  Figure 10:
Evolution of Li I as a function of age, as characterized by Equation 2.

Comparing Li I at the neighboring age bins, we find that there is a
significant depletion from $\sim$1.5 to 3 Myr, after which it stalls until 10
Myr, and beyond that point the abundances once again continue to decline
(Figure 10. This is apparent both in the fitted expression for estimating
average Li I EqW, as well as in comparing the medians computed from the data
directly. It is not entirely clear what causes this stalling.

##  6 Conclusions

We present a set of measurements of equivalent widths youth-sensitive lines in
optical BOSS & LAMOST spectra, including lines such as H$\alpha$ and Li I, as
well as a number of others. These measurements were taken using a newly
developed data-driven pipeline LineForest that was trained on manual
measurements of EqWs of $\sim$3500 stars. Although there is a significant
emphasis in using these data for characterizing young stars, LineForest is
capable of measuring EqWs of these lines in all stellar spectra.

Combining EqWs, AbWs, as well as $T_{\rm eff}$, $\log g$, and colors, we
developed a classifier that identifies stars younger than a few 10s of Myr.
This classifier is most effective in identifying late type YSOs, however, it
is capable of confirming youth in the bulk of early-type stars, as well as
some of the solar-type stars.

We observe a number of differences in the line properties of YSOs and the more
evolved field stars. In particular, activity-sensitive lines (such as the
lines of Ca, and the lines of H) tend to show stronger emission (or weaker
absorption) in WTTSs than in magnetically active field dwarfs. This is
consistent with the decrease in magnetic activity strength as stars become
older. We also observe emission lines that are attributable to accretion
across a number of elements, some of which are only seen in late-type stars,
while others are prominent in stars of all masses.

We examine the decrement of the H lines in the Balmer series for CTTSs, and
use these data to estimate the properties of the accretion stream. While the
results are preliminary due to the further need to correct the lines for
processes such as veiling or extinction, we do find Temp. and log n of the
streams to be somewhat consistent with previous studies.

Finally, we examine the evolution of Li I as a function of age. We find that
Li I abundance decreases strongly in the first couple of Myr, but then it
stalls until the age of 10 Myr, after which point it continues to deplete. We
also find that late-type stars appear to exhibit significant scatter in Li I
EqWs regardless of the age of the star, and that Li I abundances may be
strongly influenced by the variability in the magnetic activity.

CGRZ acknowledges support from projects CONACYT CB2018 A1-S-9754 AND UNAM-
PAPIIT AG101723. Funding for the Sloan Digital Sky Survey V has been provided
by the Alfred P. Sloan Foundation, the Heising-Simons Foundation, the National
Science Foundation, and the Participating Institutions. SDSS acknowledges
support and resources from the Center for High-Performance Computing at the
University of Utah. The SDSS web site is www.sdss.org. SDSS is managed by the
Astrophysical Research Consortium for the Participating Institutions of the
SDSS Collaboration, including the Carnegie Institution for Science, Chilean
National Time Allocation Committee (CNTAC) ratified researchers, the Gotham
Participation Group, Harvard University, Heidelberg University, The Johns
Hopkins University, L’Ecole polytechnique fédérale de Lausanne (EPFL),
Leibniz-Institut für Astrophysik Potsdam (AIP), Max-Planck-Institut für
Astronomie (MPIA Heidelberg), Max-Planck-Institut für Extraterrestrische
Physik (MPE), Nanjing University, National Astronomical Observatories of China
(NAOC), New Mexico State University, The Ohio State University, Pennsylvania
State University, Smithsonian Astrophysical Observatory, Space Telescope
Science Institute (STScI), the Stellar Astrophysics Participation Group,
Universidad Nacional Autónoma de México, University of Arizona, University of
Colorado Boulder, University of Illinois at Urbana-Champaign, University of
Toronto, University of Utah, University of Virginia, Yale University, and
Yunnan University. This work has made use of data from the European Space
Agency (ESA) mission Gaia (https://www.cosmos.esa.int/gaia), processed by the
Gaia Data Processing and Analysis Consortium (DPAC,
https://www.cosmos.esa.int/web/gaia/dpac/consortium). Funding for the DPAC has
been provided by national institutions, in particular the institutions
participating in the Gaia Multilateral Agreement.

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