Copyright by Benjamin Thomas Kidder 2020

Copyright by Benjamin Thomas Kidder 2020 The Dissertation Committee for Benjamin Thomas Kidder certiﬁes that this is the approved version of the following dissertation:

Advances in Manufacturing and Metrology of Silicon Immersion Gratings and Spectroscopy of Young, Low-mass Stars with IGRINS

Committee:

Daniel Jaﬀe, Supervisor

Adam Kraus

Gary Hill

Chris Sneden

Andrea Isella Advances in Manufacturing and Metrology of Silicon Immersion Gratings and Spectroscopy of Young, Low-mass Stars with IGRINS

Benjamin Thomas Kidder

DISSERTATION Presented to the Faculty of the Graduate School of The University of Texas at Austin in Partial Fulﬁllment of the Requirements for the Degree of

DOCTOR OF PHILOSOPHY

THE UNIVERSITY OF TEXAS AT AUSTIN August 2020 Dedicated to my wife Naomi Acknowledgments

I am eternally grateful to my many mentors and collaborators who made this work possible. Engineer Cindy Brooks guided me in my work with silicon diﬀractive optics and encouraged me to experiment and explore my own avenues of research. Michelle Grigas provided me with a huge amount of knowledge and training in silicon processing, which was the basis for any success I have had in making silicon gratings. My graduate student predecessor, Michael Gully-Santiago, laid a strong foundation and helped develop many of the techniques we still employ today in our work with silicon gratings. I am grateful for the support and camaraderie of my fellow graduate students Bri- ana Indahl, Raquel Martinez and Rebecca Tippens and Emily Lubar among others. I am thankful to my committee members Chris Sneden, Adam Kraus, Gary Hill and Andrea Isella for always being willing to provide guidance when and for contributing many suggestions and ideas that have immensely improved the quality of my research. I also acknowledge the mentorship and contributions from my senior colleagues Ricardo L´opez-Valdivia, Kim Sokal, Hwiyun Kim, and Kyle Kaplan without whom my work with IGRINS would not have been possible.

I am thrilled to acknowledge support from NASA APRA grant NNX15AD97G and the Giant Magellan Telescope Organization grant GMT-INST-CON-00993.

v I am also very grateful for support from the Department of Astronomy in the form of the David Benfield Memorial Fellowship in Astronomy. This work used the Immersion Grating Infrared Spectrometer (IGRINS) that was developed under a collaboration between the University of Texas at Austin and the Ko- rea Astronomy and Space Science Institute (KASI) with the financial support of the US National Science Foundation under grants AST-1229522 and AST- 1702267, of the University of Texas at Austin, and of the Korean GMT Project of KASI. Based on observations obtained at the Gemini Observatory, which is operated by the Association of Universities for Research in Astronomy, Inc., under a cooperative agreement with the NSF on behalf of the Gemini partnership: the National Science Foundation (United States), the National Research Council (Canada), CONICYT (Chile), Ministerio de Ciencia, Tecnolog´ıa e InnovaciónProductiva (Argentina), and Ministérioda Ciência, Tecnologia e Inova¸cão(Brazil). 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.

I wish to extend a special thank you to Greg Mace and my advisor Dan Jaﬀe. Greg Mace has provided me with an unprecedented level of support and kindness over the course of my graduate career and has contributed in some way to nearly every piece of this document. My advisor, Dan Jaﬀe, has

vi contributed immensely to professional and personal growth, has taught me to think, question and problem solve in ways I never imagined possible for myself and has always made space and time to help and mentor me even when he had none to spare.

vii Advances in Manufacturing and Metrology of Silicon Immersion Gratings and Spectroscopy of Young, Low-mass Stars with IGRINS

Publication No.

Benjamin Thomas Kidder, Ph.D. The University of Texas at Austin, 2020

Supervisor: Daniel Jaﬀe

Silicon immersion gratings will allow the Giant Magellan Telescope Near-IR Spectrograph (GMTNIRS) to achieve continuous coverage over the entire J, H, K, L and M photometric bands with resolution R∼65,000 at J, H and K and R∼80,000 at L and M. I describe the manufacturing process and metrology techniques for silicon immersion gratings. I overview updates to the production process and new grating metrology techniques that I have developed in order to successfully manufacture silicon immersion gratings for GMTNIRS. Most of the changes to our manufacturing process that I have contributed to, were required because of our need to produce gratings on silicon substrates larger than those we have used in the past. Gratings for J, H and K will be blazed at R3, while the L and M gratings will be blazed at R4 to

viii achieve the desired resolution. The higher blaze angle of the L and M gratings requires that we use 150mm diameter substrates rather than the 100mm substrates that our standard process was built for. In order to accommodate the larger substrates my colleagues and I constructed a custom UV exposure system for contact printing of grating lines, and constructed ﬁxtures for coating and etching of the larger substrates. Additionally, we implemented new process checks and metrology techniques to improve our overall grating yield for 100mm and 150mm gratings. These updates to our process have resulted in the successful production of a complete set of gratings for GMTNIRS.

In addition to my work in the area of manufacturing silicon immersion gratings, I have completed two observation based projects using our exist- ing instrument, the Immersion Grating Near-Infrared Spectrometer (IGRINS), which employs a silicon immersion grating as its primary dispersing element. The first of these projects uses IGRINS to provide updated membership information for young low-mass stars in relation to the TW Hya young moving group. Nearby young moving groups provide unique samples of similar age stars for testing the evolution of physical properties. Incomplete and/or incorrect group membership classifications reduce the usefulness of the group, which we assume to be coeval. With near-infrared spectra of two candidate members of the TW Hya Association, 2MASS J12354615−4115531 (TWA 46) and 2MASS J12371238−4021480 (TWA 47), we test their membership by adding radial velocity measurements to the literature. We find that 2MASS J12354615−4115531 is a close spectroscopic binary system with

ix a center-of-mass radial velocity of -6.5±3.9 km s−1. This radial velocity and a Gaia parallax produces a TWA membership probability of 41.9% using the Banyan Σ tool for 2MASS J12354615−4115531. The spectrum of 2MASS J12371238−4021480 shows that it appears to be a single star with a radial velocity consistent with the TW Hya Association and a membership probability of 99.5%. The reduced probability of TWA 46 as a true member of TWA highlights the importance of high-resolution, near-infrared spectra in validating low-mass moving group members.

The second astronomical observation based project in this document concerns veiling in spectra of young stars in the Taurus-Auriga star forming region. I present measurements of the H and K band veiling for 142 young stellar objects in the Taurus-Auriga star forming region using high-resolution spectra from IGRINS. In addition to providing measurements for rH and rK we produce low-resolution spectra of the excess emission across the H and K bands. We fit single-temperature blackbodies to the excess spectra of 42 members of our sample and measure near-IR excess temperatures ranging from 1200-2400K. We compare the luminosity of the excess continuum emission in Class II and Class III young stellar objects and find that a large number of Class III sources display a significant amount of excess flux in the near- infrared. We conclude that the mid-infrared SED slope and therefore young stellar object classification is a poor predictor of the amount of remaining inner-disk (<1AU) material. I find that 6 members of our sample contain a prominent feature in their H-band excess spectra of unknown origin that is

x deﬁnitively inconsistent with a single or multi-temperature blackbody.

xi Table of Contents

Acknowledgments v

Abstract viii

List of Tables xiv

List of Figures xv

Chapter 1. Introduction 1 1.1 Advances in the Manufacturing and Metrology of Silicon Im- mersion Gratings ...... 1 1.2 Near-Infrared Spectroscopy of Young Stars with IGRINS ...... 4

Chapter 2. Approaching Perfection in the Manufacture of Sili- con Immersion Gratings 8 8 2.2 Wavefront Error from Interferometry ...... 11 2.3 Wavefront PSF and Periodic Errors ...... 13 2.4 Comparison to Previous Products ...... 18 2.5 Conclusions and Future Work ...... 19

Chapter 3. Silicon Immersion Gratings on 150mm Material 21 21 3.2 Process Updates ...... 23 3.3 Updated Process Results ...... 26 3.4 Comparison to Previous Products ...... 33 3.5 Conclusion and Future Work ...... 34

xii Chapter 4. GMTNIRS/MagNIFIES Grating Overview 36 4.1 Introduction ...... 36 4.2 Updates to Spin-coating and Patterning ...... 38 4.3 Updates to Grating Predictive Metrology ...... 41 4.4 Updates to PSF Metrology ...... 44 4.5 GMTNIRS/MagNIFIES Grating Summary ...... 52

Chapter 5. Radial Velocities of Low-mass Candidate TWA Mem- bers 54

Chapter 6. The IGRINS YSO Survey: Veiling Spectra of Pre- Main-Sequence Stars in Taurus-Auriga 55 6.1 Introduction ...... 55 6.2 Observations ...... 58 6.3 Analysis ...... 60 6.4 Discussion ...... 73 6.4.1 Excess Temperatures ...... 76 6.4.2 Evidence for Features in Excess spectra ...... 78 6.4.3 Veiling and Accretion ...... 85 6.5 Summary and Conclusions ...... 87

Bibliography 89

Vita 109

xiii List of Tables

2.1 Comparison of K1 to previous gratings ...... 19

3.1 Comparison of LM1 to previous gratings ...... 34

1 Summary of complete set of GMTNIRS/MagNIFIES primary and backup silicon immersion gratings...... 53

1 Measurments of rH and rK for members of YSO sample. . . . 66

1 Measurments of rH and rK for members of YSO sample. . . . 67

1 Measurments of rH and rK for members of YSO sample. . . . 68

1 Measurments of rH and rK for members of YSO sample. . . . 69 2 Results of blackbody fits to the near-IR excess spectra of 47 members of our YSO sample. For two-temperature fits, only the temperature for the cool component is displayed. *Denotes single temperature fits...... 72 2 Results of blackbody fits to the near-IR excess spectra of 47 members of our YSO sample. For two-temperature fits, only the temperature for the cool component is displayed. *Denotes single temperature fits...... 73

xiv List of Figures

2.1 K1 silicon immersion grating after patterning and etching . . . 11 2.2 K1 wavefront from interferometry ...... 14 2.3 K1 direct PSF measurement ...... 15 2.4 K1 grating point spread function dispersion ...... 16 2.5 K1 grating point spread function cross-dispersion ...... 16

3.1 Thinned photoresist results ...... 24 3.2 Updated UV exposure tool ...... 26 3.3 150mm and 100mm gratings side-by-side ...... 28 3.4 LM1 wavefront from interferometry ...... 29 3.5 LM1 1-D photoresist linewidth measurements vs. interferogram 32

4.1 Curved contact fringes ...... 41 4.2 Line width mapping example ...... 43 4.3 Line width mapping vs. interferometry ...... 44 4.4 Beam placement window for PSFmodeler.py ...... 46 4.5 PSF window for PSFmodeler.py ...... 48 4.6 L-Backup PSF crosscut ...... 49 4.7 PSF window for PSFmodeler.py following slit convolution . . . 50 4.8 Dispersion axis crosscut of PSF following slit convolution . . . 51 4.9 Final interferometer measurements of GMTNIRS/MagNIFIES silicon immersion gratings (25mm beam) ...... 53

6.1 Veiling ﬁt example ...... 63 6.2 Veiling spectrum example ...... 65 6.3 Excess two-temperature blackbody ﬁt ...... 71 6.4 H vs. K veiling and excess luminosities ...... 75

6.5 Distribution of LH and LK for Class II and Class III YSOs . . 76 6.6 Excess temperature distribution for Taurus members . . . . . 78

xv 6.7 Excess spectra for objects with H-band feature ...... 79 6.8 Veiling ﬁts for H-band feature objects ...... 80 6.9 Residual cross-correlation for H-band feature object FR Tau . 84 6.10 Brackettγ and near-IR excess ...... 86

xvi Chapter 1

Introduction

1.1 Advances in the Manufacturing and Metrology of Silicon Immersion Gratings

Silicon immersion gratings are an important emerging technology in near-infrared (near-IR) astronomy that allow spectrographs to be compact while achieving continuous coverage at high-resolution. Our research group at UT Austin, led by Dr. Dan Jaﬀe, has been developing processes and producing silicon gratings for more than two decades. The Immersion Grating Near-Infrared Spectrometer (IGRINS) uses a silicon immersion grating, manufactured by our research group, as its primary disperser to achieve continuous

λ coverage over the H and K photometric bands at a resolution of R= ∆λ =40,000. IGRINS has now been in operation for 6 years, travelling between the McDon- ald, Lowell and Gemini South Observatories. IGRINS is an incredible instrument in its own right, but was also created as a prototype for the planned Giant Magellan Telescope Near-Infrared Spectrograph (GMTNIRS). GMTNIRS will acheive continuous coverage over the J,H, and K bands at R∼65,000 and over the L and M bands at R∼80,000. While IGRINS uses a single silicon immersion grating, GMTNIRS will require 5 gratings for the instrument (one for each photometric band) as well as 5 additional backup gratings. The optical design

1 for GMTNIRS requires that the L and M band gratings be signiﬁcantly longer than any gratings our group had previously produced. In order to produce the larger gratings for the L and M band channels of GMTNIRS we had to update our entire production process to accommodate larger silicon substrates. The large number of gratings needed, changes to our production process necessi- tated by the larger substrates, and the fact that the yield from our process has historically been somewhat unpredictable, made the task of manufacturing gratings for GMTNIRS an unprecedented challenge. During my time with the silicon grating group in the UT Austin Astronomy department I was able to make several important contributions that led to the successful production of all silicon immersion gratings for GMTNIRS.

I began manufacturing silicon immersion gratings for GMTNIRS im- mediately after beginning my graduate studies in the UT Austin Astronomy Department. In order to evaluate the quality of a grating once it has be successfully etched into a silicon surface the ﬁrst thing we evaluate is the peak- to-valley (PV) error in the wavefront which we measure using a ZYGO optical interferometer. One of the ﬁrst silicon immersion gratings I completed was one of the best in terms of PV wavefront error ever completed by our group. I presented results of metrology for this grating (designated K1) and an overview of the process that produced it at the 2016 SPIE Astronomical Instruments + Telescopes conference and include the associated conference paper[55] in this document as Chapter 2. The successful production and impressive PV measurements of the GMTNIRS K-band channel silicon immersion grating, K1,

2 were a testament to the strong foundation laid by my colleagues and prede- cessors for producing silicon immersion gratings. I continued to manufacture silicon immersion gratings for GMTNIRS following the success of K1, but was unable to achieve the same level of success in terms of the PV measurements of the final gratings. I was able to produce a passable set of gratings for the H and K band channels of GMTNIRS. However, the difficulty I found in attempting and failing to reproduce gratings matching the quality of K1 led me to focus my research efforts on improving the consistency of our manufacturing techniques. I was able help implement some of these improvements through the development of an updated production process for producing silicon immersion gratings on the 150mm silicon substrates needed to create the L and M band gratings for GMTNIRS. I contributed to the design of our new UV exposure system used for contact printing immersion gratings, built and calibrated the new UV system, and tested new techniques for improving the contact printing process and overall grating yield in terms of substrates used per successful grating. I presented on the updates to our production process and the successful production of our group’s first grating on 150mm material at the 2018 SPIE meeting and include the conference paper[57] here as 3.

Following the successful completion of our ﬁrst M-band grating for GMTNIRS, documented in Chapter 3, we completed the remainder of the L and M band primary and backup gratings on 150mm substrates. We then chose to revisit the H and K band gratings, because the ﬁrst set contained spectral ghosts (discussed in 2) at the 0.1% level. We completed a new set

3 of H and K-band gratings for GMTNIRS that are free of spectral ghosts. We then completed a set of J-band gratings which have our smallest and therefore most challenging requirement for PV wavefront error. The rapid completion of an entirely new set of GMTNIRS gratings at relatively high yield following the production of our ﬁrst M-band grating on 150mm material was possible with several further updates to our production process including a method I introduced to predict grating quality at a point in the process where the substrate can still be recycled. I describe these most recent updates to our process and to our metrology techniques in Chapter 4. I want to note that a decision was made following the publication of Chapter 3 to initially com- mission GMTNIRS on the Magellan telescopes under the name MagNIFIES before eventually moving it to the Giant Magellan Telescope as GMTNIRS. However this decision did not signiﬁcantly impact grating development and to avoid confusion I will continue to refer to the instrument as GMTNIRS throughout this document.

1.2 Near-Infrared Spectroscopy of Young Stars with IGRINS

Over the course of the last decade, the ability of astronomers to directly observe protoplanetary disks through interferometry has led to incredible discoveries about their structure and evolution (e.g. HL Tau[2]). However, the inner regions (<1AU) of the closest disk-bearing young stellar objects (YSOs) still cannot be spatially resolved with current technology as this would require

4 spatial resolution more than ten times that of ALMA’s maximum resolution. However, it is important that we continue to develop methods to study this inner disk region because many important star-disk interactions occur here such as infall, accretion, stellar winds and dust sublimation at the inner dust rim. Measuring absorption line veiling in the near-IR is an important tool for studying inner disk regions in YSOs. Veiling describes the effect that non-photospheric continuum has on photospheric absorption lines. The measured equivalent width (EW) of an absorption lines from the photosphere of young star is reduced when non-photospheric continuum (e.g. disk emission) is present. By comparing high-resolution spectra of disk-bearing YSOs with spectra of disk-less YSOs at a similar effective temperature (Teff ) we can measure the veiling and therefore the amount of emission from non-stellar sources in the inner disk at different wavelengths. IGRINS is an ideal instrument for studying the inner disk through line veiling because it achieves continuous coverage over the entire H and K-bands at high-resolution. The H and K-bands are of particular interest because they probe the peak emission of the inner sublimation wall of YSO disks[18, 90, 93] and allows us to measure veiling for hundreds of absorption lines in order to create a low-resolution spectrum of the excess continuum.

Over the course of my graduate career I experimented with several different methods for measuring veiling in YSOs using IGRINS spectra. When I started my graduate studies, our research group had just commissioned IGRINS on the Harlan J. Smith Telescope at McDonald observatory, and had

5 begun a comprehensive survey of YSOs in the Taurus-Auriga star-forming region. I initially attempted to measure veiling in disk-bearing (Class I and II) members of Taurus by using a simple reduced χ2 fitting routine to artificially veil spectra of presumably disk-less (Class III) members of Taurus. Upon evaluating several of my Class III templates I found evidence that many of the template spectra were not entirely free of veiling. This realization that many Taurus Class III members may not be free of veiling eventually led me to observe members of the TW Hya (TWA) young moving group (YMG) to use as templates using IGRINS on the Gemini South Telescope. TWA is slightly older (∼8Myr[106]) than Taurus and contains almost entirely Class III YSOs. While evaluating our TWA templates, my collaborators and I found a previously undiscovered spectroscopic binary. This object, 2MASS J12354615−4115531, had previously been identified as a member of TWA, but upon measuring the radial velocity (RV) of the system we found that it is unlikely to be a member. We published our results on 2MASS J12354615−4115531 in the Astrophysical Journal[56] and I have included the paper here as Chapter 5.

I used the remaining TWA template spectra to develop a series of software tools in Python for measuring veiling in Taurus YSOs. By the time I had completed my observations of TWA members to use as templates, our group had observed a total of ∼160 members of Taurus as part of the IGRINS YSO Survey. My goal was to measure veiling for as many of these objects as possible, so I developed a method for automating the entire process of measuring veiling including template selection, radial velocity correction, vsini matching,

6 Brackett emission line removal, ﬁtting of the veiling, and generation of low- resolution excess spectra. Through this process I was able to produce H and

K-band veiling measurements (rH and rK ) for 142 members of Taurus, measure temperatures of the excess continuum for 46 members, and discovered an emission-like feature in the low resolution H-band excess spectra for 6 Taurus members. My collaborators and I also found that there is signiﬁcant overlap in terms of H and K-band veiling between Class II and Class III YSOs, leading us to conclude that the mid-infrared slope which determines YSO class is a poor predictor of the amount of remaining inner disk material. I describe our approach to measuring veiling for members of Taurus and the results of this project in my paper, currently in preparation, which I include here as Chapter 6.

7 Chapter 2

Approaching Perfection in the Manufacture of Silicon Immersion Gratings

2.1 Introduction 1

Silicon immersion gratings allow for the construction of compact near- IR spectrographs that achieve continuous wavelength coverage over a large bandwidth at high resolution. An immersion grating disperses incident light in an optical material with index of refraction n>1. Light enters the immersion medium through a polished entrance face of the material, is dispersed by the grating, and leaves the material with an angular dispersion that is ∼n-times larger than would result from an otherwise identical non-immersed grating. The increase in phase difference over the diffracted wavefront between the immersion medium and air means that errors in groove placement that cause phase differences across the wavefront must be n-times smaller than would be acceptable for a front-surface grating. An immersion grating operating at the diffraction limit must have peak-to-valley surface error in the direction of

1This chapter was published as a conference paper [55] for 2016 SPIE Astronomical Instruments + Telescopes conference. My personal contribution to this work included performing the majority of grating manufacturing procedures for gratings referenced in the text, performing post-production analysis, writing the majority of the text and creating all ﬁgures.

8 propegation (PV error) of less than λ/4[6] where λ is the operating wavelength in immersion. Silicon is opaque to wavelengths shorter than ∼1.15 µm[107] and has an index of refraction of 3.44, so a grating that exceeds the λ/4 quality requirement at this cutoﬀ wavelength will have a PV error of less than 85 nm. We have produced a silicon immersion grating blazed at R3 (hereafter K1) that meets this requirement for diﬀraction limited performance and has a PV error of 79 nm over a 25 mm beam.

We produce silicon immersion gratings using a contact printing method in which we coat the polished surface of a puck of silicon with a passivation layer (silicon nitride) and a UV-sensitive photoresist (PR) layer. We then pattern lines onto the surface using contact photolithography, etch the passivation layer in a reactive ion etch process and then etch grooves into the silicon substrate in a potassium hydroxide solution. Achieving PV wavefront error . λ/4 is difficult given that a number of the silicon processing steps can introduce large-scale errors. Brooks et al. (2014)[10] linked the introduction of large-scale errors to non-uniformity in the illumination of the substrate by the UV exposure system during the patterning step. Large-scale errors with PV greater than λ/4 will degrade the point spread function (PSF) and limit the resolving power and efficiency of instruments operating near the diffraction limit. However, when the slit is significantly wider than the diffraction limit, large-scale surface errors will affect neither resolving power nor efficiency.

Overall PV error is perhaps the best stand-alone measurement of a grating’s overall quality, but periodic errors in the groove placement across a

9 grating’s surface that are much smaller than the λ/4 criterion for the overall PV, can introduce spectral ghosts. Ghosts of suﬃcient strength can negatively impact science results of instruments employing these gratings. Marsh et al. (2007)[81] provides an extensive discussion of the eﬀects of repetitive and large- scale error on the PSF.

In order to limit the level of periodic errors and attempt to produce gratings that consistently achieve PV <λ/4 at the silicon cutoﬀ in immersion, we have honed our contact printing method by optimizing our UV exposure system, introducing additional process checks and inspections, and carefully evaluating large-scale and periodic errors. These improvements to our standard process have allowed us to produce several instrument quality gratings including K1. We fabricated K1 using our standard process techniques described in detail by Marsh et al. (2007)[81] and Brooks et al. (2014)[10].

K1 will be used for the K-band channel of the Giant Magellan Telescope Near Infrared Spectrograph (GMTNIRS)[67]. GMTNIRS is a planned near- IR spectrograph that will achieve continuous wavelength coverage over the J,H,K,L and M photometric bands at R∼65,000 at J,H, and K and R∼80,000 at L and M. This instrument will use a total of ﬁve immersion gratings (one for each channel)[50]. K1 surpasses the requirement for diﬀraction limited performance at it’s intended operating wavelength range in the K-band. In the center of the K-band at 2.2 µm K1 has a PV error of ∼ λ/8. The PV error for K1 also surpasses the λ/4 criterion in the H-band with a PV wavefront error of ∼ λ/6 at 1.6 µm. However, the J-band extends down to the 1.15µm

10 silicon cutoﬀ wavelength where the PV error is λ/4.

Figure 2.1 K1 after patterning and etching of grooves. In this image the grooves run top to bottom along the large patterned and etched area on the face of the silicon puck. This puck is 100 mm in diameter and 30 mm thick. Before this grating can be used in immersion the puck shown in this image will be cut into a prism, an entrance face will be polished and coated with an anti-reﬂective material, and a reﬂective coating will be deposited on the grating surface.

2.2 Wavefront Error from Interferometry

K1 has the smallest PV error of any silicon grating we have produced using contact printing photolithography. The PV error is 79 nm over a 25 mm

11 beam, which is the beam-size of the spectrograph that will eventually house this grating. We measure the surface error of our gratings in the direction of propegation using a commerical ZYGO interferometer operating at 633 nm to illuminate the front-surface of the grating in littrow. Marsh et al. (2007)[81] demonstrated that front-surface measurements of silicon gratings using optical interferometry accurately predict their performance in immersion.

We can account for most of the surface errors that have a measurable effect on the spectral PSF as a combination of low-order aberrations and high- spatial frequency periodic errors[10, 42]. Both of these components of the overall surface error contribute to the PV error of K1. We have determined that of the process steps involved in producing a silicon grating, the uniformity of UV dose across the surface during the patterning step has the largest effect on groove displacement in the final product[10]. Figure 2.2 shows the full interferogram over a 25 mm beam as well as the model containing the first 9 Zernike terms in the Noll sequence[94], and the residual from subtracting the first 36 Zernike terms. The Zernike model composed of the first 9 Zernike terms reflects large-scale surface errors. The residual created by subtracting the first 36 Zernike terms shows the high-spatial frequency periodic error in the surface.

The large-scale distortion shown in the center panel of Figure 2.2 is roughly elliptical over the full surface, leading us to believe that the cause may be non-uniformity of the UV beam incident on the PR layer during patterning. If this distortion is not introduced in the patterning step, then it may be a

12 product of non-uniform thickness of the PR layer that arises during the spin- coating process step. The origin of the high-spatial frequency error visible in the right panel of ﬁgure 2.2 is also not well understood. In this grating and in past products these distortions appear running parallel to the grating lines[42]. This makes it highly improbable that they are a product of the high-amplitude Fizeau contact fringes that are readily visible during patterning and can change in spatial-frequency and orientation with each contacting. We also detect ghosts in the spatial direction which are caused by shift in groove placement that is periodic along the length of the grooves. These ghosts are discussed further in Section 2.3. This periodic error has a spatial period of ∼325 µm so they are not readily visible in the right panel of Figure 2.2. While these distortions of the line edge positions over the grating surface pose an obstacle to further improving the quality of our gratings, we welcome the opportunity to evaluate and correct these problems. It is only because we have succeeded in reducing all other sources of surface error that we are able to detect these defects.

2.3 Wavefront PSF and Periodic Errors

Periodic errors in the line edge position of a grating can result in spectral ghosts surrounding the diﬀractive order peak. In a contact printed grating, periodic errors in the line edge positions can result from contact fringes during patterning, or from position errors already present in the photomask. We have worked with the company that produces our photomasks to reduce the

13 Figure 2.2 Image of the surface of K1 obtained by measuring the reflection of a 25 mm beam in littrow at R3. Left: The full interferogram with a PV surface error of 79 nm. Center: A fit of the first 9 Zernike terms which shows the large-scale surface error. The PV error of this model is 51 nm. Right: Residual of a 36 term Zernike model subtracted from the full interferogram in order to show the high-spatial frequency component of the wavefront distortion. The PV error of the residual is 61 nm. incidence of spectral ghosts in the masks themselves. We measured the PSF of K1 by illuminating the grating in littrow at R3 using a 25 mm laser beam operating at 633 nm and then focusing the reflected beam onto a detector using a lens with a focal length of 800 mm. This measurement is not diffraction limited due to the fact that the 25 mm beam is imperfectly collimated. Figure 2.3 shows the 2-dimensional PSF. Figure 2.4 shows crosscuts of the PSF in the dispersion and spatial directions. K1 does not exhibit any spectral defects above our detection limit at a fraction of ∼4×10−5 the intensity of the main peak, but does contain ghosts in the spatial direction (perpendicular to dispersion).

14 Figure 2.3 PSF of K1 which was obtained using 633 nm HeNe collimated laser with 25 mm beam-size. Ghosts in the spatial direction (perpendicular to the dispersion direction) are visible above and below the main peak.

15 Figure 2.4 Crosscut of the PSF of K1 in the dispersion directions. This panel illustrates that no ghosts are present surrounding the main peak like those that appear in the spatial direction. The main peak of the PSF in the dispersion direction is not in precise agreement with the measurement of the mirror PSF; however, this can be attributed to imperfections in the optical setup used to acquire this measurement.

Figure 2.5 Crosscut of the PSF of K1 in the spatial direction. Peaks at 0.21◦ and 0.42◦ are ghosts that result from periodic changes in groove placement along the length of the grooves.

16 While K1 is free of spectral ghosts along the dispersion axis, we have detected sets of symmetric ghosts in the direction perpendicular to dispersion at 0.21◦ and 0.42◦ from the main peak respectively. The higher intensity set of ghosts at 0.21◦ are visible in Figure 2.3 and both sets are shown in detail by the vertical crosscut of the PSF in Figure 2.4. These ghosts appear at the same position and intensity in all gratings that have been patterned with the photomask that was used to produce K1. A Fourier transform of the interferogram shown by Figure 2.2 in the spatial direction reveals that the defect has a spatial period of ∼325µm. We have received confirmation from our photomask supplier that errors with this spatial period can be explained by the mask-writing process. Having identified the source of these periodic errors we will be able to resolve this issue in future products. As for K1, the higher intensity ghosts (10−3 of the main peak), are unlikely to significantly hinder science results from GMTNIRS. We will also be able to use differencing techniques to suppress resulting unwanted artifacts in the spectra by a factor of ∼10. Nevertheless, the potential consequences of these defects will need to be carefully evaluated and included in the instrument documentation since the potential effects of ghosts in the spatial direction in a cross-dispersed spectrograph with many spectral orders are much more difficult to predict than if the same ghosts appeared in the dispersion direction.

17 2.4 Comparison to Previous Products

We have previously reported on a silicon immersion grating, CA1[42], which has a PV surface error of 108 nm over a 25 mm beam, slightly above that of K1. The intensities of the spectral ghosts in K1 are also signiﬁcantly less than CA1; however the spatial ghosts present in the spectrum of K1 are a new problem that are not present in CA1 and their potential impact on science results is not yet well understood. Most recently we reported on another silicon immersion grating ,G05[10], which we produced after several changes and new metrology techniques were introduced into our production process. G05 was measured to have PV error of 185 nm. K1 surpasses this grating in terms of overall PV surface error by a factor of 2. However, K1 is not the ﬂattest grating in terms of PV error that our group has produced. Gully-Santiago et al. (2014)[41] reported on a silicon immersion grating, G07, patterned using electron beam (e-beam) lithography that has a PV error of 58 nm. Unfortu- nately, the intensity of spectral ghosts in G07 is much higher than those that typically appear in contact printed gratings. However, a more recent grating patterned using e-beam lithography, K04, has a PV wavefront error of 36 nm and spectral ghosts at 10−4 of the main peak. This suggests that e-beam lithography may prove to be a viable alternative to contact photolithography. Table 2.1 shows a comparison between the performance of K1 and other silicon gratings from recent reports. The measurements given in this table only scratch the surface of the metrology that must be performed on each of these gratings in order to properly evaluate their unique defects. It should be noted

18 that the periodic errors present in CA1 and K1, in the dispersion and spatial directions respectively, would not be detectable if the large-scale groove displacement error was signiﬁcantly larger. It is only because the gratings we have begun to produce for GMTNIRS, including K1, have such a low level of large-scale surface error that we are able to evaluate and improve periodic errors with very small amplitudes. The periodic errors in the spatial direction present in K1 were found to have an amplitude of ∼12 nm.

Table 2.1 Comparison of K1 to previous gratings Spectral Ghost Process Width (µm) Pitch (µm) Blaze 25mm PV (nm) Amplitude CA1 Contact 17.4 27.4 R3 107.6 9×10−4 G07 E-beam 50 80 R3 57 .5 7×10−3 G05 Contact 50 80 R3 183.6 <10−3 K04 E-beam 0.62 2.35 18◦ 36.0 1×10−4 K1 Contact 29.5 33.7 R3 79.1 <4×10−5

2.5 Conclusions and Future Work

The ultimate achievement in the production of silicon immersion gratings is to consistently produce gratings that meet the requirement for diffraction limited performance at the shortest wavelength to which silicon is trans- parent. K1 meets this requirement with a PV error of 79 nm over a 25 mm beam. However, our work to reduce surface errors in our gratings will not be complete until each grating we produce exceeds this requirement. We have honed our contacting process to minimize the effects of large-amplitude contact fringes. Our ability to consistently eliminate the effects of these fringes

19 will be essential as we evaluate possible sources for the elliptical large-scale and the high spatial frequency surface error components discussed in Section 3.4. Determining the sources of these errors will require a careful comparison of similar defects observed in all recent gratings produced using our standard process.

20 Chapter 3

Silicon Immersion Gratings on 150mm Material

3.1 Introduction 1

Silicon immersion gratings will allow the Giant Magellan Telescope Near-IR Spectrograph (GMTNIRS)[50] to achieve continuous coverage over the J, H, and K photoemtric bands at R∼65,000 and the L and M photometric bands at R∼80,000 in a single exposure, and will also allow the instrument to be compact. Silicon immersion gratings disperse incident light while it is immersed in optical grade silicon. Silicon’s high refractive index (n∼ 3.44) results in a factor of n increase in the angular dispersion of the diﬀracted light exiting the material. GMTNIRS will use separate silicon immersion gratings for each of the 5 photometric bands that it covers. We have previously manufactured silicon immersion gratings blazed at R3 on 100mm substrates with maximum thickness 30mm. The J,H and K-band gratings for GMTNIRS will be manufactured on these 100mm substrates that our standard production

1This chapter was published as a conference paper [57] for 2018 SPIE Astronomical Instruments + Telescopes conference. My personal contribution to this work included performing the majority of grating manufacturing procedures for gratings referenced in the text, performing post-production analysis, writing the majority of the text and creating all ﬁgures.

21 process was developed for. However, the manufacturing of the GMTNIRS L and M-band gratings has posed a signiﬁcant challenge, because these gratings must be blazed at R4 in order to achieve the desired resolution. This increase in the incident angle necessitates that the gratings be manufactured on 150mm diameter, 35mm thick substrates. This substantial increase in substrate size has required us to alter every step of our manufacturing process in some way to accommodate the larger substrates. The implementation of this updated process is now complete and we have used it to produce a silicon grating for the GMTNIRS M-band spectrograph that has peak-to-valley surface error (PV)

. λ/8 in immersion at the intended operating wavelength.

We produce silicon immersion gratings using a contact printing process in which silicon boules are ﬁrst x-ray aligned and cut to acheive the desired blaze angle. The cut silicon substrates are polished on a single face and coated with a passivation layer of silicon nitride. The substrates are coated with photoresist and patterned with grating lines through contact printing. The passivation layer is subjected to reactive-ion etching (RIE) to create a hard silicon nitride mask on the surface. Grooves are etched into the silicon in a potassium hydroxide solution. This process is discussed in further detail by Brooks et al. (2014)[10]. Aside from the cutting, polishing and passivation coating, all of these steps are performed in-house by members of our research group. The photoresist coating and RIE steps of our process utilize semiconductor industry tools that are used to pattern and etch silicon wafers. In developing our standard process for 100mm substrates we created custom ﬁxtures for

22 these tools to accommodate our substrates. For the photoresist coating and RIE steps we were able to create updated ﬁxtures to accommodate 150mm substrates. However, commercially available UV exposure systems are not as easily altered to accommodate thick silicon substrates. We previously built a custom UV exposure system for contact printing on 100mm substrates, but because of the need for a larger UV beam diameter and mask contacting stage to pattern on 150mm material, we opted to design and construct an entirely new UV exposure tool.

3.2 Process Updates

The first step in our production process upon receiving the polished and silicon-nitride-coated substrates is spin-coating them with photoresist. Pho- toresist spin-coating is typically performed on wafers that are held in place by a vacuum chuck as they are rotated at high speeds. We previously constructed a modified vacuum chuck for the spin-coating of 100mm silicon substrates. We produced a scaled up version of our custom vacuum chuck in order to accommodate 150mm substrates. The moment of inertia of the 150mm substrates is a factor of ∼2.5 larger than that of the 100mm substrates. The large moment of inertia for the 150mm substrates led us to reduce the rotational velocity for our spin coating to avoid damage to our spin tool and substrates. How- ever, this resulted in significant non-uniformity in the photoresist layer. We were able to improve the uniformity by coating photoresist at the reduced spin speed with a less viscous photoresist (Figure 3.1).

23 Figure 3.1 Images of resist coatings on two 150mm diameter substrates. Left: Our standard photoresist coated at a slower rotational velocity than we use for 100mm diameter substrates. Right: Reduced viscosity photoresist coated at the same reduced rotational velocity.

The UV exposure step in our manufacturing process proved to be the most signiﬁcant hurdle in scaling up our process for 150mm substrates. We previously used a custom built UV exposure system for patterning on 100mm R3 silicon substrates. This older UV exposure system produced a collimated beam with ∼88% UV uniformity over 100mm. In order to pattern on 150mm substrates, we designed and built an updated UV exposure system that in- cludes a larger mask aligner and collimated UV beam (Figure 3.2). In design- ing our new UV system for 150mm substrates we updated the optical design in an attempt to improve the UV beam uniformity. We were able to achieve ∼93% UV uniformity over the 150mm beam. We also made changes to the

24 mask contacting system in order to reduce the eﬀects of contact fringes, which produce periodic variations in the grating linewidths. Grating linewidth variation translates into wavefront distortion of incident light on the ﬁnal etched grating.

The use of 150mm silicon substrates also required us to update both our RIE nitride etching and potassium hydroxide wet silicon etching processes. For the RIE process step we created an updated process holder for the same RIE tool that we use for silicon nitride etching of 100mm substrates. The updates to our wet etching equipment however, required us to employ a larger ultrasonic bath and an updated bath cooling system. We also opted to increase the temperature of the potassium hydroxide bath used for etching of the grooves in order to increase the overall silicon etch rate. Increasing the silicon etch rate was desirable to us because the large groove depths for the GMTNIRS M and L band gratings (up to 50µm) would have required etches as long as 10 hours using the bath temperatures we had previously used for etching smaller pitched grooves.

25 Figure 3.2 Newly constructed UV exposure tool for 150mm diameter silicon substrates. Left: UV exposure tool after assembly was completed. Right Top: UV tool mask alignment stage with 150mm substrate loaded. Right Bottom: UV tool mask alignment stage with 150mm substrate and mask loaded and visible contact fringes.

3.3 Updated Process Results

Using our updated silicon immersion grating production process we have successfully manufactured a GMTNIRS M-band grating (hereafter LM1) that exceeds our requirements for peak-to-valley surface error (PV) by a factor of >2. For instruments operating near the diﬀraction limit, large-scale grating surface errors with PV greater than λ/4 will degrade the point-spread-function

26 (PSF)[6]. In order to achieve this requirement at the blue end of the M-band in immersion, a grating must have PV surface error .325nm. LM1 greatly surpasses this requirement with PV surface error of ∼160nm over the full grating surface, and ∼83nm over a 25mm beam (the beamsize used in the preliminary design for GMTNIRS). We measure the PV surface error of the front surface of our gratings at 633 nm using a ZYGO interferometer. Marsh et al. 2007[81] showed that an interferometric measurement of the front surface of a silicon immersion grating provides an accurate estimate of the grating surface error in immersion. The grating surface is shown in Figure 3.3 alongside the GMTNIRS K-band grating K1[55] which was made using a 100mm diameter R3 silicon substrate.

The full surface interferogram of LM1 can be broken down into large- scale aberrations and high-frequency errors through Zernike analysis (Figure 3.4). In 100mm diameter gratings manufactured using our previous UV exposure tool we frequently found that the large-scale aberrations revealed by the lowest-order Zernike terms appeared to be roughly circular[55]. The low-order Zernike terms from the Zernike ﬁt to the LM1 interferogram reveal large-scale aberrations that are not circular and are dissimilar in appearance and have a lower amplitude than the large-scale aberrations measured in many of our previous products. We assert that this change in the shape and amplitude of aberrations is most likely due to the introduction of a less-viscous photoresist in the manufacturing of LM1. We concluded that the improved photoresist uniformity is more likely responsible for the change in the large-scale aberra-

27 tions than a change in beam uniformity from the previous UV exposure tool because we have observed the aforementioned circular pattern in the large- scale aberrations in gratings made with both UV tools.

Figure 3.3 LM1 grating after silicon etching (right) manufactured using a 150mm diameter substrate next to K1[55] which was manufactured on a 100mm diameter substrate.

28 Figure 3.4 Interferogram of the surface of LM1 measured from the grating front surface at 633nm (not in immersion) in Littrow. Left: Full surface interferogram of LM1. Center: LM1 interferogram with first 9 Zernike terms subtracted to reveal high spatial frequency surface errors. Right: Zernike fit using the first 36 Zernike terms to reveal low spatial frequency abberrations.

29 In addition to introducing new equipment and methods to our silicon immersion grating manufacturing process to accommodate 150mm substrates, we introduced a new metrology technique to our process in order to increase our overall yield of gratings that meet our PV surface-error requirements. Af- ter we coat a silicon substrate with photoresist and pattern the resist using our UV tool, we have the option of removing the photoresist layer, cleaning the substrate and reapplying the photoresist layer through a simple and inex- pensive process. This option has been useful in the past when we have found visible defects in the photoresist layer, or had reason to believe that there may have been a problem during the patterning step. However, until now we have not been able to predict the PV of a grating before grooves are etched into the silicon to produce the ﬁnal grating. We have recently discovered that by measuring photoresist line widths using a high-power microscope and custom edge detection software we are able to produce a rough estimation of the PV surface error that will be present in the ﬁnal grating. During the production of LM1, our PV prediction technique allowed us to reject several photoresist patterns that were predicted to have PV surface errors near or above our λ/4 requirement. Had these patterns been etched, we would have needed to go through the expensive and time consuming process of having the resulting gratings polished away and the substrates recoated with silicon nitride, but instead we were able to simply dissolve the photoresist layer and recoat them.

We have invested a signiﬁcant amount of time and eﬀort into research and development of our PV surface error prediction technique, but LM1 is

30 one of the first gratings we have processed to completion since its introduction into our process. A comparison between the photoresist line widths measured before RIE, and the surface error in the final grating measured through interferometry (Figure 3.5) support the accuracy of our prediction technique and its ability to estimate whether or not a given photoresist pattern will produce a grating that meets our λ/4 requirement. Figure 3.5 also indicates that there is some disagreement between the linewidth variation measured after patterning and the interferogram of the final grating. However, this technique is only meant to produce a rough estimate of the PV so that we can make an informed decision as to whether or not it should go through nitride and silicon etching, and so far it has performed extremely well in that regard.

31 Figure 3.5 A comparison of the variation of LM1 photoresist line widths measured across the center of the pattern using a microscope mounted CCD, and a crosscut of the interferogram of the ﬁnal grating measured in Littrow after silicon etching.

32 3.4 Comparison to Previous Products

In comparing the surface error from interferometry of LM1 to gratings made with our previous UV tool and production process LM1 fares remarkably well (Table 3.1). LM1 has PV surface error over a 25mm beam of ∼83nm which is only slightly greater than that of K1[55], the grating with the lowest PV we have ever produced through contact printing. This is an important achievement, because in addition to using our new UV tool to manufacture 2 M-band and 2 L-band gratings, we intend to use it to manufacture 2 J-band gratings, for which the PV surface error requirement is much tighter. Silicon becomes opaque at ∼1150nm[107], so this means that the λ/4 requirement at the blue end of the J-band in immersion is ∼85nm. The fact that LM1 meets the λ/4 requirement for a J-band grating gives us conﬁdence that we will be able to manufacture J-band gratings for GMTNIRS using our new UV tool and elements of the process that were used to manufacture LM1. We also note that LM1 has PV surface error that is 78% of the surface error measured for CA1, which is a grating that is currently being used in the Immersion Grating Infrared Spectrometer (IGRINS)[74].

LM1 is comparable in PV surface error to the best gratings our group has ever produced using contact printing. However, gratings produced with electron beam (e-beam) lithography still tend to have a lower PV surface error than gratings made with contact lithography. Our group has produced several gratings in collaboration with NASA’s Jet Propulsion Laboratory, written using e-beam lithography that surpass any contact grating we have produced[41].

33 However, e-beam gratings typically contain spectral ghosts. Ghosts are spuri- ous peaks in the spectrum produced by a grating, and are caused by periodic errors in the grating groove positions. This means that an e-beam grating with exceptionally low PV surface error may not meet an instrument’s spec- iﬁcations because of ghosts. Both of the e-beam gratings referenced in Table 3.1 contain ghosts that are readily detectable in a monochromatic spectrum produced by the front surface of the grating. LM1 does not appear to contain any signiﬁcant periodic errors that would produce ghosts and meets the PV surface requirement for a J-band grating.

Table 3.1 Comparison of LM1 to previous gratings Spectral Ghost Process Groove (µm) Pitch (µm) Blaze 25mm PV (nm) Amplitude CA1 Contact 17.4 27.4 R3 107.6 9×10−4 G07 E-beam 50 80 R3 57 .5 7×10−3 G05 Contact 50 80 R3 183.6 <10−3 K04[11] E-beam 0.62 2.35 18◦ 36.0 1×10−4 K1[55] Contact 29.5 33.7 R3 79.1 <4×10−5 LM1 Contact 64.9 109.9 R4 83.0 <4×10−5

3.5 Conclusion and Future Work

Updating our production process to successfully manufacture silicon immersion gratings on 150mm material is a crucial achievement for our research group. We have successfully produced a grating that not only meets the λ/4 requirement in the M-band, but that exceeds the ideal limit[55] for silicon immersion grating PV surface error over a 25mm beam. However, one

34 of the most signiﬁcant problems with our contact printing process in the past has been inconsistency and an inability to predict the outcome of our process until silicon etching is complete. We will continue to produce L and M-band gratings for GMTNIRS and, in the process, characterize the consistency with which we can produce gratings that meet or exceed our requirements as LM1 does. We will also continue to improve our technique for predicting the PV surface error from the resist pattern. Our current objective is to hone our updated process through the production of more gratings for GMTNIRS and to improve our PV prediction technique so that we are able to consistently produce R3 and R4 silicon immersion gratings with PV surface error<85nm.

35 Chapter 4

GMTNIRS/MagNIFIES Grating Overview

4.1 Introduction

At the SPIE Astronomical Telescopes + Instrumentation, 2016 conference[55], I presented an overview of a grating manufactured for the K-band channel of the Giant Magellan Telescope Near Infrared Spectrograph (GMT- NIRS)[50] (included in this document as Chapter 2). Since the publication of the 2016 SPIE paper we have implemented a number of important updates to our production process and post-production metrology for silicon immersion gratings that have helped improve the consistency and overall grating yield as well as our ability to characterize gratings upon completion. Our research group implemented some of these changes as part of scaling up our process to accommodate 150mm substrates used in making the L and M band gratings for GMTNIRS which are described in my second ﬁrst-author SPIE paper[57] and is included as Chapter 3.

Following the production of our ﬁrst M-band grating (described in Chapter 3) we were able to successfully produce the remaining L and M-band gratings on 150mm substrates. Upon completion of the L and M primary and backup gratings we chose to revisit the H and K band gratings. Our original

36 set of H and K band gratings, one of which is the focus of Chapter 2 all met the λ/4 requirement in immersion, but contained spectral ghosts in the cross- dispersion direction. At the time of writing Chapter 2, we concluded that the spectral ghosts would most likely not signiﬁcantly hinder science with GMT- NIRS, but following completion of the L and M band gratings we decided that it would be a good use of our remaining resources to revisit the H and K band gratings. We were able to produce 2 H-band and 2 K-band gratings as well as 2 J-band gratings (primary and backup for each band) that meet or surpass the λ/4 requirement and contain no detectable ghosts.

In this chapter I overview our production process as of the writing of this document including all updates to our process that were used to produce the grating LM1 (aka M-primary) described in Chapter 3 that we then ap- plied to our production of 100mm gratings. I also discuss two pieces of custom software that I have written and incorporated into our manufacturing process: one used for photoresist pattern quality evaluation and another for analysis of the finished gratings point spread function (PSF) using data from interferometry. Also in this chapter I summarize metrology of all 10 of the completed GMTNIRS gratings. It should be noted that the grating referred to as K1 in Chapter 2 will no longer be used in GMTNIRS as we have since produced a qualified grating that does not contain spectral ghosts. To avoid confusion we adopt a new naming convention in this chapter for the finished gratings and will refer to gratings as ”primary” or ”backup” for a given band rather than assigning them alpha-numeric references as in previous chapters.

37 4.2 Updates to Spin-coating and Patterning

In Chapter 2 I outlined changes to our production process implemented to allow production of silicon immersion gratings on 150mm substrates. We have now used the process outlined in Chapter 2 to successfully produce all 4 (1 primary and 1 backup per band) L and M band gratings for GMTNIRS. Following the successful production of the L and M-band gratings on 150mm substrates, we chose to use several of our updated process techniques in an attempt to improve consistency and yield for gratings produced on the smaller 100mm substrates. Previous to the L and M band production we had successfully produced gratings for the H and K bands that contained spectral ghosts in the cross-dispersion direction (see Chapter 1) that may have had an impact on science performed with the planned spectrograph. We had also produced J-band gratings that did not meet the λ/4 requirement, but may have still qualified for use in the spectrograph after a more detailed performance analysis. We chose to attempt to remake gratings for the J, H and K bands because the L and M band production time was shorter-than-expected and we had a number unused 100mm substrates. We had moderate expectations for producing new gratings for the J,H and K-band channels, but we were ultimately able to successfully produce new primary and backup gratings for all 3 of these channels. The H-band primary and backup, while meeting the λ/4 requirement both have a surface defect when inspected by eye that resembles a thin film coating the grooves. Based on interferometry this defect does not significantly impact the surface error of the front-surface grating in Littrow,

38 but we still may revisit the H-band gratings if given the opportunity.

In order to successfully produce the gratings for the J,H and K-band channels of GMTNIRS we incorporated several elements from our updated process for production of gratings on 150mm substrates. The ﬁrst step in the production process that we altered based on our 150mm process was the photoresist spin-coating step. We used a solvent thinned photoresist to achieve a more uniform coating on 150mm substrates spun at a slower rotational velocity than we had previously used to spin-coat 100mm substrates. For spin-coating the substrates used in our second attempt at producing the J,H and K-band gratings, we used the solvent thinned resist, but at the faster spin speed that we had previously used with unaltered photoresist on 100mm substrates. Us- ing the thinned photoresist with the faster spin speed allowed us to acheive an exceptionally uniform photoresist coating, which by my account seemed to make the process of contacting the photomask to the substrate during the patterning step easier and more reproduceable.

In addition to updating our spin-coating process we chose to use our new UV exposure system for 150mm substrates to perform the patterning step on the 100mm substrates for the new set of J,H and K-band. We made this choice based on the the improved uniformity of UV light across the beam over our older UV system, and the success of the new system in producing the L and M-band gratings. After attempting to contact print on several 100mm substrates we attached the mask aligner from our previous UV system onto the new one in an attempt to improve the quality of mask-substrate contact

39 and reduce the effect of contact fringes. The mask aligner is a detachable part of the UV system that holds the photomask on a tip-tilt stage so that the can be precisely aligned to be in parallel with the photoresist coated surface of the silicon substrate. The mask aligner for our new UV system supports the photomask only at its corners while the mask aligner for our previous UV system supported the photmask with a lip around the entire edge of the mask. The fact that the newer mask-aligner held the mask only at the corners had no noticeable effect on the quality of the L and M band gratings, but when used with the smaller 100mm substrates we found that it was nearly impossible to reduce the frequency of the contact fringes. We also found that the contact fringes took on circular or bowed shape (Figure 4.1). If the mask and substrate surface are assumed to be perfectly flat, when there is a wedge-shaped gap between mask and substrate the fringe pattern will consist of straight, parallel fringes. Warped fringes when contacting almost certainly indicate that the photomask is being deformed during the contact printing step. I hypothesized that the reason we never witnessed this effect in our previous UV system was because the mask-aligner supported the photomask at all of its edges rather than just its corner, preventing the mask from deforming enough to see an effect in the fringe pattern. I also assumed that the reason we did not see the impact of this effect in the patterning of 150mm substrates is because the larger area of the silicon surface made it easier for the user to adjust the contact in such a way that the deformation of the mask is counteracted by the silicon pressing up on the mask. We found that by replacing the new mask-

40 aligner with the old one, the warping of the contact fringes during patterning was greatly reduced.

Figure 4.1 Image of curved fringes during mask alignment and contacting step.

4.3 Updates to Grating Predictive Metrology

For the new J,H and K band gratings we implemented a more standard- ized version of the metrology technique describe in Chapter 3 for predicting the quality of a grating after the photoresist pattern is developed. After we expose the photoresist layer with UV light and develop the pattern, we are left with a pattern of straight parallel lines which must be uniform in terms of line-center placement and half-width to

λ sin θ i 4

41 where θi is the incident angle of light on the grating surface and λ is the minimum operating wavelength for a given grating in immersion. We refer to the half-width rather than full-width of the lines because the placement of the far edge of a grating groove (relative to the light source), rather than the full groove width, is what aﬀects the wavefront.

The photomasks we use surpass our line-center placement and half- width requirements for our smallest operating wavelength at the short end of the J-band (1.15µm or 0.33µm in immersion) by a factor of ∼10, so we can assume that large-scale surface errors are a result of non-uniformity in the exposure of the pattern and not the pattern on the mask itself. Changes in the level of exposure across the photoresist-coated substrate surface affect the half-width of the patterned lines in the photoresist, but not the placement of the line centers. This means that by measuring the line half-widths in the developed photoresist pattern we can predict the wavefront uniformity for the resulting grating. This technique gives us the advantage of being able to evaluate the potential quality of a grating mid-process at a point where we can, if necesssary, rework the substrate by cleaning off the photoresist pattern, and avoid wasting expensive silicon substrates. In Chapter 3 we showed promising results of this method by comparing line half-widths measured after patterning with a crosscut of the wavefront from the final grating measured by front-surface interferometry. We have since improved this technique by measuring linewidths in a 2-dimensional grid across the grating surface. We then interpolate the resulting low-resolution image and scale the x-axis so that the

42 final image is analogous to an image of the wavefront from the finished (front- surface) grating measured in Littrow using a ZYGO interferometer. Figure 4.2 shows the map of the line widths after photoresist pattern develop and Figure 4.3 shows the interpolated and scaled image in comparison to the interferometric measurement of the completed front surface grating in Littrow. The agreement in terms of morphology between the line-width map and the wavefront from interferometry is very good, and illustrates that we can now successfully predict large-scale grating errors while a given substrate can still be reworked with minimal effort and cost.

Figure 4.2 Map of line widths over a 90x30mm central region of the photoresist pattern for what became our H-backup grating for GMTNIRS.

43 Figure 4.3 Comparison of the interpolated and scaled linewidth map to the wavefront from front surface interferometry of our H-backup grating.

4.4 Updates to PSF Metrology

In order to qualify silicon immersion gratings in the past we have used three key specifications to decide whether or not a grating is suitable to be used in an instrument: peak-to-valley (PV) wavefront error < λ/4, spectral ghosts amplitude < 10−3 relative to the main peak of the Littrow order, and minimal surface defects such as scratches and roughness that affect grating efficiency (evaluated on a case-by-case basis). Measuring the PV wavefront error using a ZYGO interferometer is typically the first measurement we perform to learn whether or not a grating is suitable for use. However, if a grating does not meet the λ/4 requirement it does not necessarily mean that the grating should not be used. Optical aberrations affect the PSF in different ways and for a slit spectrograph like GMTNIRS the affects of surface errors in the grating on the PSF are mitigated if the slit is wider than the diffraction limit of the tele-

44 scope. This means that when a grating does not meet the λ/4 requirement we need to understand how the grating surface errors will affect the instrument PSF. We perform a direct measurement of the PSF when performing our ghost measurements, but the optical system we use is not finely calibrated or stable enough to reliably measure the affects of large-scale surface errors on the PSF. However, by taking the Fourier transform of the wavefront from a grating using interferometry we can generate a PSF. We can then convolve the grating PSF with an image of a slit to model the PSF for an instrument employing a given grating. I developed a software package written in Python that generates grating PSFs from ZYGO interferometer data and convolves them with an artificial slit image. This software allows us to more confidently and consistently qualify or disqualify gratings that do not meet the λ/4 requirement.

Here I provide a brief overview of the software and how it allows us to generate a simple model of the instrument PSF based on the interferogram of a given grating surface. Upon initializing the software the user can load in data from the ZYGO interferometer, and place a beam corresponding to the instrument beam size anywhere on the wavefront image (Figure 4.4).

45 Figure 4.4 Load window of PSFmodeler.py that allows the user to load in data from ZYGO interferometer, adjust pixel scale for diﬀerent levels of zoom on the interferometer, adjust beam size, and overlay the beam on the image of the wavefront.

46 Following placement of the beam the user may proceed to take the Fourier transform of the selected area. The upon selecting a beam size and placement the software interpolates the wavefront onto a 2 times finer grid and applies a Gaussian taper the edges of the beam rather than a sharp cutoff. Us- ing a Gaussian taper to define the edges of the beam reduces the amplitude of the airy rings in the PSF, making it easier to detect deviation from an ideal PSF. The software then takes the 2D Fourier transform of the tapered wavefront. The PSF is initially generated by the software using default set- tings, but once the default PSF is generated by the software, the user can adjust the edge taper of the beam, interpolation, and most importantly the operating wavelength for the grating. The PSF window displays a 2D image of the PSF as well a horizontal (dispersion axis) and vertical (cross-dispersion axis) cross-cuts of the 2D PSF (Figure 4.7). The cross-cuts are plotted against cross-cuts of an ideal PSF that is generated by taking the Fourier transform of a completely flat wavefront that has undergone the same beam tapering operation as the grating wavefront from interferometry.

47 Figure 4.5 PSF display window of PSFmodeler.py that allows the user to load in data from that allows the user to view the raw PSF and make adjustments to how the PSF is generated.

PSFmodeler.py allows for closer inspection of the crosscuts of the PSF. Figure 4.6 shows a crosscut along the dispersion axis of the PSF for our L- backup grating. L-backup has a PV wavefront error of 203nm, which is less than λ/4 at the operating wavelength in immersion, but still has an eﬀect on the PSF. Figure 4.6 shows that this 203nm non-uniformity in the wavefront places added power in the ﬁrst Airy ring compared to the ideal PSF.

48 Figure 4.6 Crosscut inspection window of PSFmodeler.py that allows the user to examine eﬀects of grating surface errors on the PSF. This crosscut is from our L-backup grating which has PV wavefront error of 203nm.

Once the PSF is calculated PSFmodeler.py can convolve a slit image with the PSF image generated using ZYGO interferometer data. Convolving the image of the slit with the PSF generates a simple model of the instrument PSF. This allows us to predict how surface errors in a grating will affect line profiles from astronomical sources observed by the instrument (in this case GMTNIRS). Figure 4.7 shows the PSF display window for PSFmodeler.py after the grating PSF has been convolved with the slit image. Figure 4.8 shows a dispersion axis crosscut of the slit-convolved PSF and a more detailed view of the affect of the grating wavefront errors in our L-backup grating on the final PSF.

49 Figure 4.7 PSF of our L-backup grating after it has been convolved with the slit image.

50 Figure 4.8 Dispersion axis crosscut of the PSF of our L-backup grating after it has been convolved with the slit image.

51 4.5 GMTNIRS/MagNIFIES Grating Summary

We have successfully completed a set of 10 gratings (5 primary and 5 backup) for GMTNIRS/MagNIFIES. All 5 of the primary gratings in this set meet the λ/4 requirement for the operating wavelength in immersion. All 5 of the backup gratings either meet the λ/4 requirement or come close and have been qualiﬁed using PSFmodeler.py as described in Section 4.4. The optical design for GMTNIRS/MagNIFIES required us to produce 4 gratings for the L and M channels on 150mm substrates as well 6 gratings for the J,H and K band channels on the 100mm substrates that our process was initially developed for. However, after updating our process to accommodate 150mm substrates, the improved consistency and yield of our process allowed us to manufacture new gratings for the H and K band channels, to replace earlier sets that contained spectral ghosts that arose from a defect in the photomask (Chapter 2). We were also able to produce a set of J-band gratings, one of which is the best silicon immersion grating in terms of PV wavefront error that we have ever produced with contact printing. The J-Primary grating has a full-surface PV wavefront error of 75nm and a PV of 55nm over a 25mm beam. We provide a summary of the PV measurements and grating parameters for the set of 10 silicon immersion gratings for GMTNIRS/MagNIFIES in Table 1 and images of the wavefronts from interferometery in Figure 4.9.

52 Table 1. Summary of complete set of GMTNIRS/MagNIFIES primary and backup silicon immersion gratings.

Grating Groove Width Pitch PV λ/4 Blaze Substrate Diameter (µm) (µm) (nm) (nm) (mm)

J-Primary 11.43 18.18 55 85 71.6◦ 100 J-Backup 11.43 18.18 95 85 71.6◦ 100 H-Primary 18.03 27.03 101 108 71.6◦ 100 H-Backup 18.03 27.03 129 108 71.6◦ 100 K-Primary 21.83 33.33 105 147 71.6◦ 100 K-Backup 21.83 33.33 108 147 71.6◦ 100 L-Primary 42.43 71.43 132 236 76◦ 150 L-Backup 42.43 71.43 202 236 76◦ 150 M-Primary 64.9 109.9 83 332 76◦ 150 M-Backup 64.9 109.9 101 332 76◦ 150

Figure 4.9 Set of wavefront measurements from ZYGO interferometer for 10 gratings currently designated as primary or backup gratings for GMT- NIRS/MagNIFIES.

53 Chapter 5

Radial Velocities of Low-mass Candidate TWA Members

54 Chapter 6

The IGRINS YSO Survey: Veiling Spectra of Pre-Main-Sequence Stars in Taurus-Auriga

6.1 Introduction

The Taurus-Auriga star forming region (also referred to as simply Tau- rus) is an important population for studying young stellar object (YSO) disks because it is young (1-5Myr [35, 62]), nearby (140pc, [34, 53]) and contains YSOs at a range of key evolutionary stages. Lada and Wilking [63] devised a classification scheme, that is still in use today, to separate YSOs into categories (Class I, II and III) based on the mid-infrared (mid-IR) slope of their spectral energy distributions (SEDs). Class I objects are dominated by infrared excess and have accretion disks and residual circumstellar envelopes. Class II objects have dissipated their envelopes, but still have disks and therefore a significant amount of flux in the mid-IR. Class III sources are defined by a lack of mid-IR flux, having dissipated the bulk of their disks. Taurus is made up primarily of Class II and III YSOs, making it a useful sample for studying the disk dissi- pation process. For the outer disk (>10AU), advances in radio interferometric instruments and techniques in recent years have given us the ability to study disk structure in Taurus Class I and II YSOs in unprecedented detail (e.g. [2]) with with maximum angular resolutions of 0.”012 (∼12AU for Taurus).

55 However, classiﬁcations based on the mid-IR SED slope and interferometric studies that reveal structure in the outer disk tell us nothing about the evolution and physical processes in the inner disk at <1AU from the forming star. However, it is important for us to study these regions by any other means available, because a number of key star-disk interactions occur here, including accretion, winds, and dust sublimation at the inner rim of the disk. High- resolution near-infrared (near-IR) spectroscopy shortward of 5 µm is one such means of studying the inner disk because it allows us to constrain the excess (non-photospheric) continuum emission in this region by measuring veiling of photospheric absorption lines.

The bulk of the excess in the near-IR has been attributed to an inner sublimation wall at a single temperature that agrees with typical dust sublimation (1400K-2000K), inwards of which the intensity of radiation from the star does not allow dust to survive [18, 90, 93]. Since the identification of the inner dust rim as the primary source of near-IR excess, more information about the properties of the dust rim have been uncovered. Isella and Natta [49] ar- gued that the inner dust rim is rounded off rather than a sharp cutoff at all disk scale heights because of the density gradient in protoplanetary disks and the dependence of the dust sublimation temperature on density, and showed through modelling of the dust rim that the resulting near-IR excess from a curved inner dust rim is minimally dependent on disk inclination. Muzerolle et al. [90] found that the near-IR excess in lower mass YSOs took the shape of a ∼1400K blackbody which is taken to be the dust sublimation temperature

56 ([21] and references therein). However, McClure et al. [84] found evidence that the inner dust rims around low-mass YSOs in the Taurus star forming region are best ﬁt by higher temperature models (1600-2000K). The presence of hotter-than-expected dust at the sublimation radius illustrates the need for measurements of the excess for a larger sample of Taurus YSOs as well as convincing explanations for the higher dust temperatures.

The second contributor to the near-IR excess is accretion shock from gas within the dust rim falling onto the young star. Gullbring et al. [40] found that the excess in the U-band is closely related to accretion rates for T Tauri stars. Folha and Emerson[27] proposed that the excess contribution from accretion shock in T Tauri stars, while peaking in the UV, could at least partially account for the high excess ﬂux in the near-IR. Even though it is now understood that the accretion shock only directly accounts for a fraction of the near-IR excess, the level and shape of the excess ﬂux in the near-IR can inform us about the contributions of the dust sublimation wall and the accretion shock, as well as properties of the emitting dust and the physical shape of the wall itself.

We can determine spectral energy distribution (SED) for the excess ﬂux by comparing line equivalent widths (EW) in excess-free template or model spectrum to those in a YSO spectrum. This method of determining the excess has the advantage of being entirely insensitive to reddening. By performing this comparison for many absorption lines we can produce a veiling spectrum, from which we can derive a low-resolution spectrum of the excess ﬂux by

57 correcting for the SED shape of the YSO photosphere [26, 84].

Previous studies of veiling and near-IR excess in Taurus have focused on subsets of <20 Taurus members [4, 26, 52, 84]. Here, we automated a process for measuring near-IR excess for a large sample of Taurus members. We use high-resolution near-IR spectra from the Immersion Grating Near Infrared spectrometer (IGRINS) to measure veiling and produce excess spectra for 142 members of the Taurus-Auriga star forming region. IGRINS is an optimal instrument for this project because it provides continuous coverage across the entire H and K photometric bands at high resolution. The broad coverage at high-resolution allowed us to measure the veiling in our sample of Taurus YSOs using a high number of photospheric absorption lines for a large sample of YSOs with maximal sensitivity to absorption line EW which is necessary to accurately measure veiling.

6.2 Observations

We constructed our sample of Taurus YSOs from the IGRINS reduced science spectra archive by identifying targets classified as Taurus members by Luhman et al.[69] with a signal to noise ratio (SNR) >30 in both the H and K bands. We found that our software for fitting the veiling produced poor fits for veiling values of rλ>6, so we excluded objects with veiling approximated to be above this value which included all Class I sources. Our final sample of 145 YSO members of Taurus consists of 91 Class II and 54 Class III YSOs based on classifications from [24, 54, 69, 102], and [61].

58 IGRINS covers the entirety of the H and K photometric bands at a resolution of ∼45,000 [95, 114]. IGRINS uses a silicon immersion grating [42] as its primary disperser in order to achieve such broad wavelength coverage at high-resolution. Our IGRINS Taurus YSO sample was obtained from 2014 to 2017 following the instrument’s commissioning in 2014 on the 2.7m Harlan J. Smith Telescope (HJST) at McDonald Observatory. A portion of the sample was also obtained during IGRINS’ time as a visiting instrument at Lowell Observatory on the 4.3m Discovery Channel Telescope (DCT). Our Taurus YSO sample is composed of ∼610 individual observations. For this work we chose not to focus on changes in veiling over time for individual objects, so we combined multi-epoch observations of the same object into a single spectrum in order to increase signal-to-noise and, where possible, average over variations in the veiling.

We developed a method for measuring line veiling in Class II sources using Class III members of the same Taurus sample as templates, because ideally we want our veiling templates to be coeval with our targets. However, with a younger sample of templates such as Class III members of Taurus we could never be sure whether or not our templates contained small amounts of veiling so we ultimately decided to use members of a slightly older association in order to increase the likelihood that our templates have negligible veiling. To this end we observed all Class III members of the TW Hydra Association (TWA), an ∼8 Myr young moving group (YMG) [106], to use as templates for measuring veiling in our Taurus sample. We observed a total of 27 members of

59 TWA with IGRINS on the Gemini South telescope in 2018. We selected TWA targets to observe based on membership information from Gagne et al.[30] and selected 21 of the observed members to use as templates. We excluded several members that had low-SNR spectra, were spectroscopic binaries, and objects with very high vsini (>40km/s).

We reduced all observations using the IGRINS pipeline package version 2.2 [66]. The wavelength solution was determined using telluric absorption and OH emission lines. OH emission lines were removed prior to spectral extraction through A-B pair subtraction, and the remaining telluric absorption features were removed by dividing the spectrum by that of an A0V telluric standard. All A0V spectra used for telluric correction in this work were observed within ∼1 hour of the target spectra, with the same telescope and instrument conﬁgu- ration, and at a similar airmass. The ﬁnal product of the IGRINS pipeline is a one-dimensional, telluric-corrected spectrum. All spectra used in our analysis were barycenter velocity corrected prior to any analysis using ZBARYCORR [113].

6.3 Analysis

We measured veiling values as a function of wavelength for members of the IGRINS Taurus YSO sample by comparing line equivalent widths of Taurus member spectra with those of TWA member template stars. We created an automated pipeline to measure veiling for all members of our Taurus sample. For each individual Taurus YSO spectrum we began by selecting up to 10

60 templates from our TWA sample that were closest to the target in based on measurements from López-Valdivia et al. (in prep). We then carried out the full veiling fitting routine using each selected TWA template. We selected the best template at the end of the fitting process by calculating χ2 for each veiled template and adopting the template fit with the smallest χ2 value.

In order to measure veiling using each selected TWA template for a given Taurus member we first performed a cross-correlation between the template and target spectrum to measure and then correct any RV offset between the two objects. We then isolated a portion of the target and template spectra at ∼1.56µm to use for fitting rotational broadening. We selected this portion of the spectrum for fitting the broadening, because it contains absorption lines that are present in the entire range of spectral types in our Taurus sample (K0- M7.5). We then identified which spectrum, template or target, contained the broader absorption lines. We fit for the offset in vsini between the two objects by broadening the slower rotator using the technique described by Gray[38]. Using the measured vsini offset we broaden the entire H and K band spectra of the slower rotator to match the broadening of the faster one. We then exclude regions of the spectrum near wavlengths of the Brackett series of hydrogen lines because many members of our sample are actively accreting and their spectra contain the Brackett series in emission. We separate the spectra into 180 sections between the Brackett regions and fit the veiling for each individual section with the Markov chain Monte Carlo (MCMC) ensemble sampler emcee [28] by adding artificial veiling to the TWA template spectrum with the

61 formula

F + ri Fv = ri + 1

where Fv, F and ri are the veiled flux, the template flux and the veiling for a given section respectively [8, 45]. When fitting each section we normalize the template and target continuum level by their respective median values, and include a continuum offset free parameter when fitting the veiling so that the choice of continuum normalization does not significantly impact the veiling result. Results of the fitting routine for adjacent portions of the spectrum of FS Tau A are shown in Figure 6.1. We chose to use FS Tau A as a fitting routine example because it has a significant amount of veiling (rH =0.76), its spectrum is relatively free of telluric contamination and its excess spectrum contains a visible slope that agrees well with a two-temperature blackbody.

62 Figure 6.1 Adjacent sections of the spectrum of FS Tau A in H (top) and K(bottom) fit with a template spectrum of TWA 13B. The dashed vertical lines represent boundaries of each section. We show the rotationally broadened template with and without the best-fit veiling. Best-fit veiling values for each section are displayed above each section of spectrum. In both the top and bottom panels the left sections contains fewer and/or weaker lines than the four other panels. The top and bottom left sections are examples of veiling fits that are down-weighted when we take the weighted average across 5 sections to generate our veiling spectrum.

63 We then bin the veiling values by 5 and perform a weighted average of the veiling for each set of 5 sections. We determine weights using an algorithm that estimates the number and average depth of absorption lines in each section. Sections of spectra that contain fewer and/or weaker absorption lines, and therefore less relevant information for determining veiling, are down-weighted when binning the measurements. We take the weighted standard deviation for each set of 5 sections as the error. The result from this process is a low resolution spectrum of the veiling across the H and K bands. At this point the low-resolution veiling spectrum is in units of photospheric flux at a given wavelength. I.e. the veiling spectrum contains the shape of the YSO photosphere SED when we want to study the shape of the excess alone. To correct for this we use flux-calibrated spectra from the IRTF spectral library [101] with the same spectral types as our targets to calculate the average flux for each corresponding section of spectrum for which we measure a veiling value in our Taurus objects to produce a flux-calibrated stellar spectrum with identical resolution to our low-resolution veiling spectrum. We then multiply the low-resolution stellar spectrum and our veiling spectrum to produce a low- resolution spectrum of the excess flux. An example of the veiling spectrum before and after the photosphere correction is shown in Figure 6.2.

For our sample of 142 Taurus objects we provide measurements of the H and K band veiling in Table 1. We determined H and K band veiling and uncertainties by taking the mean and standard error in the mean respectively for the veiling spectrum in each band.

64 Figure 6.2 Top: Veiling spectrum for FS Tau A. Measurements of the veiling for sections of the H and K bands are binned by 5 and the weighted mean and standard deviation are taken to be the veiling value for that bin and the uncertainty respectively. The result is a low-resolution veiling spectrum. Bottom: We correct the shape of the excess for the SED shape of the stellar continuum using a ﬂux-calibrated template from the IRTF library [101].

65 Table 1. Measurments of rH and rK for members of YSO sample.

a b Object Alt. Name SpT YSO Class rH σH rK σK Ref. Class

J04292373+2433002 GV Tau A+B 0.01 0.01 0.09 0.02 K5 I 1,2,3,4 J04410424+2557561 Haro 6-32 A 0.01 0.01 0.24 0.02 M5 III 2,3,4,5 J04335252+2256269 XEST 17-059 A 0.01 0.01 0.25 0.02 M5.75 III 2,3,4,5 J04394488+2601527 0.01 0.01 0.08 0.02 M5 II 2,3,4 J04144739+2803055 XEST 20-066 0.02 0.01 0.12 0.01 M5.25 III 2,4,5 J04190110+2819420 V410-Xray6 A 0.02 0.01 0.13 0.01 M5.5 II 2,3,4 J05071206+2437163 0.02 0.01 0.10 0.02 K6 III 2,4,5 J04331003+2433433 V830 Tau 0.03 0.01 0.06 0.02 K7.5 III 1,2,3,4,5 J04352450+1751429 HBC 412 A+B 0.03 0.01 0.07 0.02 M2.6 III 1,2,4,5 J04131414+2819108 LkCa 1 0.03 0.01 0.07 0.02 M3.6 III 1,2,3,4,5 J04311444+2710179 JH 56 0.03 0.01 0.06 0.02 K8 III 1,3,4,5 J04321885+2422271 V928 Tau A+B 0.03 0.01 0.18 0.02 M0.8 III 1,2,3,4,5 J04192625+2826142 V819 Tau 0.03 0.01 0.04 0.01 K8 III 1,3,4,5 J04220313+2825389 LkCa 21 0.03 0.01 0.05 0.01 M2.5 III 1,2,3,4,5 J04410470+2451062 IW Tau A 0.03 0.01 0.15 0.01 M0.9 III 1,2,3,4,5 J04270280+2542223 DF Tau A 0.04 0.01 0.21 0.02 M2 II 1,2,3,4 J04321456+1820147 V827 Tau A 0.04 0.01 0.04 0.01 M2 III 1,2,4,5 J04355892+2238353 XEST-09-042 0.04 0.01 0.05 0.02 K8 III 2,3,4,5 J04162810+2807358 LkCa4 A 0.04 0.01 0.03 0.01 M2 III 1,2,3,4,5 J04432023+2940060 CIDA 14 A 0.04 0.01 0.09 0.02 M5.5 II 2,4 J04190197+2822332 V410-Xray 5a 0.04 0.01 0.18 0.02 M5.5 III 2,3,4,5 J04321606+1812464 MHO-5 0.04 0.01 0.08 0.02 M6 II 2,4 J04141458+2827580 FN Tau 0.05 0.01 0.17 0.02 M3.5 II 1,2,3,4 J04330197+2421000 MHO-8 A 0.05 0.01 0.01 0.01 M6 III 2,3,4,5 J04411681+2840000 CoKu Tau4 A 0.05 0.01 0.20 0.02 M1.1 II 1,2,4 J04183158+2816585 CZ Tau A 0.05 0.01 0.14 0.01 M4.2 II 1,2,3,4 J04184703+2820073 Hubble 4 A 0.05 0.01 0.03 0.01 K8 III 1,2,3,4,5 J04173893+2833005 LkCa 5 0.05 0.01 0.05 0.02 M2.2 III 1,2,3,4,5 J04294155+2632582 DH Tau A 0.05 0.01 0.10 0.02 M2.3 II 1,2,3,4 J04323034+1731406 GG Tau Aa+Ab 0.05 0.01 0.19 0.02 K7.5 II 1,2,4 J04334871+1810099 DM Tau 0.05 0.01 0.10 0.01 M3 II 1,2,4 J04315844+2543299 J1-665 0.05 0.01 0.21 0.02 M4.9 III 1,2,3,4,5 J04320926+1757227 L1551-51 0.06 0.01 0.19 0.02 K6 III 1,2,4,5 J04442713+2512164 0.06 0.01 0.06 0.02 M7.25 II 2,3,4 J04325323+1735337 0.06 0.01 0.15 0.01 M2 III 4,5 J04300357+1813494 UX Tau B 0.06 0.01 0.16 0.02 M2 III 1,4,2 J04391741+2247533 VY Tau A 0.07 0.01 0.16 0.02 M2 II 2,4

66 Table 1 (cont’d)

a b Object Alt. Name SpT YSO Class rH σH rK σK Ref. Class

J04322627+1827521 MHO-7 0.07 0.01 0.23 0.02 M5.25 III 2,4,5 J04233919+2456141 FT Tau 0.07 0.01 0.15 0.01 M3 II 1,2,3,4 J04355349+2254089 HP Tau G3 0.07 0.01 0.11 0.01 M0.6 III 1,2,3,4,5 J04265629+2443353 0.07 0.01 0.19 0.02 K6-M3.5 I 1,2,3,4 J04053087+2151106 HBC 362 0.07 0.01 0.25 0.02 M2.7 III 1,2,4,5 J04324373+1802563 L1551-55 0.07 0.01 0.45 0.04 K6 III 1,2,4,5 J04202606+2804089 0.08 0.01 0.25 0.02 M3.5 II 2,3,4 J04352089+2254242 FF Tau A+B 0.08 0.01 0.20 0.02 K8 III 1,2,3,4,5 J04154278+2909597 0.08 0.01 0.23 0.02 M1 II 2,3,4 J04341099+2251445 JH108 0.09 0.01 0.11 0.01 M1.5 III 1,2,3,4,5 J04144928+2812305 FOTau A 0.09 0.01 0.32 0.03 M3.5 II 1,2,3,4 J04311578+1820072 MHO-9 0.09 0.01 0.12 0.01 M4.25 III 2,4,5 J04324303+2552311 UZTau Aa 0.09 0.01 0.22 0.02 M1.9 II 2,3,4 J04390163+2336029 0.09 0.01 0.25 0.02 M4.9 II 2,3,4 J04420548+2522562 0.09 0.01 0.13 0.01 M0.5 III 1,2,3,4,5 J04194127+2749484 LkCa 7 A+B 0.09 0.01 0.13 0.01 M0 III 1,2,3,4,5 J04554535+3019389 0.09 0.01 0.30 0.03 M4.75 II 2,4 J04361909+2542589 LkCa 14 0.10 0.01 0.34 0.03 K5 III 1,2,3,4,5 J04420732+2523032 LkHa 332 A+B 0.10 0.01 0.14 0.01 M2.5 III 2,4,3 J04174965+2829362 V410-Xray1 0.10 0.01 0.09 0.02 M3.7 II 2,3,4 J04191281+2829330 FQ Tau A 0.11 0.01 0.34 0.03 M3.5 II 1,2,3,4 J04221675+2654570 CFHT-Tau-21 0.11 0.01 0.06 0.02 M1.5 II 2,3,4 J04305137+2442222 ZZ Tau A 0.11 0.01 0.15 0.01 M4.3 II 2,3,4 J04352737+2414589 DN Tau 0.12 0.01 0.29 0.03 M0.3 II 1,2,3,4 J04560201+3021037 HBC 427 0.12 0.02 0.40 0.04 K6 III 1,2,4,5 J04181710+2828419 V410-Anon13 0.13 0.02 0.10 0.02 M5.75 II 2,3,4 J04330622+2409339 GHTau A 0.15 0.02 0.45 0.04 M2 II 1,2,3,4 J04181078+2519574 V409 Tau 0.15 0.02 0.43 0.04 M1 II 2,3,4 J04214323+1934133 0.15 0.02 0.67 0.06 M2.4 II 1,2,4 J04132722+2816247 Anon1 A 0.15 0.02 0.21 0.02 M0.5 III 1,2,3,4,5 J04294247+2632493 DSTau 0.16 0.02 0.40 0.04 M0.7 III 2,3,4,5 J04551098+3021595 GMAur 0.16 0.02 0.35 0.03 K6 II 1,2,4 J04413882+2556267 0.16 0.02 0.61 0.06 M0 II 1,2,4 J04043984+2158215 HBC 361 0.17 0.02 0.28 0.03 M3.2 III 1,2,4,5 J05030659+2523197 V836Tau 0.18 0.02 0.38 0.04 M0.8 II 2,4 J04321786+2422149 CFHT-Tau-7 0.20 0.02 0.11 0.01 M6 III 2,3,4,5 J04394748+2601407 0.20 0.03 0.32 0.03 M7 II 2,3,4

67 Table 1 (cont’d)

a b Object Alt. Name SpT YSO Class rH σH rK σK Ref. Class

J04193545+2827218 FR Tau 0.20 0.03 0.26 0.02 M5.25 II 2,3,4 J04180796+2826036 V410-Xray3 A 0.21 0.03 0.19 0.02 M6.25 III 2,3,4,5 J04335470+2613275 IT Tau A 0.21 0.03 0.47 0.04 K5 II 1,2,3,4 J04465305+1700001 DQ Tau 0.21 0.03 0.46 0.04 M0.6 II 1,2,4 J05075496+2500156 CIDA 12 0.22 0.03 0.43 0.04 M4 II 2,4 J04294568+2630468 KPNO-Tau-5 0.24 0.03 0.07 0.02 M7.5 III 2,3,4,5 J04324911+2253027 JH112 B 0.24 0.03 0.60 0.06 K5.5 II 1,2,3,4 J04330781+2616066 KPNO-Tau-14 0.25 0.03 0.46 0.04 M6 III 2,3,5 J04144786+2648110 CX Tau 0.25 0.03 0.64 0.06 M2.5 II 1,2,3,4 J04333678+2609492 IS Tau A 0.26 0.03 0.52 0.05 M0 II 1,2,3,4 J04302961+2426450 FX Tau A 0.27 0.03 0.69 0.07 M2.2 II 1,2,3,4 J04320329+2528078 0.29 0.04 0.13 0.01 M6.25 III 2,3,4,5 J04312405+1800215 MHO-4 0.29 0.04 0.16 0.02 M7 III 2,4,5 J04173372+2820468 CY Tau 0.29 0.04 0.93 0.09 M2.3 II 1,2,3,4 J04354093+2411087 CoKu Tau 3 A 0.30 0.04 0.63 0.06 M0.5 II 1,2,3,4 J04183112+2816290 DD Tau A 0.30 0.04 1.06 0.10 M3.5 II 1,2,3,4 J04383528+2610386 HV Tau 0.31 0.04 0.39 0.04 M4.1 III 1,4,2 J04430309+2520187 GOTau 0.33 0.04 0.79 0.07 M2.3 II 1,2,3,4 J04244457+2610141 I04216+2603 0.33 0.04 0.83 0.08 M2.8 II 1,2,3,4 J04323058+2419572 FYTau 0.34 0.04 0.86 0.08 M0.1 II 1,2,3,4 J04265352+2606543 FVTau A 0.34 0.04 0.25 0.02 M0 II 1,2,3,4 J04215563+2755060 DETau 0.36 0.04 1.67 0.16 M2.3 II 1,2,3,4 J04315056+2424180 HKTau A 0.36 0.05 0.75 0.07 M1 II 2,3,4 J04155799+2746175 0.38 0.05 0.44 0.04 M5.5 II 2,3,4 J04354203+2252226 XEST 08-033 0.39 0.05 0.75 0.07 M4.75 III 2,3,4,5 J04323034+1731406 GG Tau Aa+Ab 0.39 0.05 1.02 0.10 K7.5 II 1,2,4 J04333405+2421170 GI Tau 0.40 0.05 1.04 0.10 M0.4 II 1,2,3,4 J04391779+2221034 LkCa 15 0.41 0.05 1.16 0.11 K5.5 II 1,2,3,4 J04355684+2254360 Haro 6-28 A 0.41 0.05 1.20 0.11 M2 II 2,3,4 J04144730+2646264 FP Tau 0.41 0.05 1.79 0.18 M2.6 II 2,3,4 J04183110+2827162 V410 Tau A+B+C 0.42 0.05 0.54 0.05 K5 III 1,2,3,4,5 J04141760+2806096 CIDA-1 0.42 0.05 1.02 0.10 M5 II 1,2,3,4 J04352020+2232146 HO Tau 0.42 0.05 1.26 0.12 M3.2 II 1,2,3,4 J04141291+2812124 V773 Tau 0.43 0.05 0.89 0.09 K4 II 1,2,3,4 J04553695+3017553 LkCa 19 0.43 0.05 2.22 0.23 K2 III 1,2,4,5 J04295156+2606448 IQ Tau 0.45 0.06 1.13 0.11 M1.1 II 1,2,3,4 J04333456+2421058 GK Tau 0.45 0.06 1.16 0.11 K6.5 II 1,2,3,4

68 Table 1 (cont’d)

a b Object Alt. Name SpT YSO Class rH σH rK σK Ref. Class

J04354733+2250216 HQ Tau A 0.45 0.06 1.32 0.13 K2 II 2,3,4 J04420777+2523118 V955 Tau A 0.45 0.06 0.93 0.09 K7 II 1,2,3,4 J05074953+3024050 RW Aur A 0.46 0.06 1.94 0.18 K2 II 1,2,4 J04251550+2829275 0.47 0.06 1.18 0.11 M6.5 III 3,4,5 J04185170+1723165 HBC 376 0.47 0.06 1.05 0.10 K4 III 2,4,5 J04345542+2428531 AA Tau 0.48 0.06 1.08 0.10 M0.6 II 1,2,3,4 J04191583+2906269 BP Tau 0.49 0.06 0.92 0.09 M0.5 II 1,2,3,4 J04294247+2632493 DI Tau A+B 0.51 0.06 1.06 0.10 M0.7 III 2,3,4,5 J04270469+2606163 DG Tau 0.51 0.06 1.38 0.13 K7 II 1,2,3,4 J04324911+2253027 JH 112 A 0.52 0.06 1.52 0.15 K5.5 II 1,2,3,4 J04312382+2410529 V927 Tau A+B 0.52 0.06 0.55 0.05 M4.5 III 1,2,3,4,5 J04423769+2515374 DP Tau A 0.61 0.08 1.35 0.13 M0.8 II 1,2,3,4 J04382134+2609137 GM Tau 0.64 0.08 1.20 0.11 M5 II 2,3,4 J04392090+2545021 GN Tau 0.67 0.08 1.61 0.15 M2.5 II 1,2,3,4 J04330664+2409549 V807 Tau A 0.68 0.08 1.32 0.13 K7 II 1,2,3,4 J04355277+2254231 HP Tau A 0.70 0.09 2.13 0.20 K4 II 1,2,3,4 J04141358+2812492 FM Tau 0.70 0.09 1.37 0.13 M4.5 II 1,2,3,4 J04514737+3047134 UY Aur A 0.72 0.09 2.56 0.25 K7 II 1,2,4 J04220217+2657304 FS Tau A 0.72 0.09 1.35 0.13 M0 II 1,2,4 J04474859+2925112 DS Tau 0.73 0.09 1.48 0.14 M0.4 II 1,2,4 J04335200+2250301 CI Tau 0.73 0.09 2.02 0.19 K5.5 II 1,2,3,4 J04465897+1702381 Haro 6-37 Aa 0.76 0.09 1.75 0.17 K8 II 1,2,4 J04390637+2334179 0.78 0.10 0.38 0.04 M7.5 III 2,3,4,5 J04314007+1813571 XZ Tau 0.82 0.10 2.44 0.24 M2 II 1,2,4 J04300399+1813493 UX Tau A 1.03 0.13 3.67 0.42 K0 II 1,2,4 J04333906+2520382 DL Tau 1.05 0.13 2.46 0.23 K5.5 II 1,2,3,4 J04304425+2601244 DK Tau A 1.11 0.14 2.53 0.24 K8.5 II 1,2,3,4 J04215943+1932063 T Tau Aa 1.31 0.16 3.65 0.35 K0 II 1,2,4 J04382858+2610494 DO Tau 1.38 0.17 3.46 0.34 M0.3 II 1,2,3,4 J04333935+1751523 HN Tau A 1.57 0.19 3.27 0.33 K5 II 1,2,4 J04324303+2552311 UZ Tau E 1.78 0.22 4.89 0.56 M1.9 II 2,3,4 J04245708+2711565 IP Tau 2.00 0.25 3.92 0.51 M0.6 II 1,2,3,4 J04321583+1801387 V826 Tau 2.29 0.28 3.20 0.32 K7 III 1,2,4,5 J04141700+2810578 CW Tau 2.62 0.32 6.30 0.89 K3 II 1,2,3,4 J04470620+1658428 DR Tau 2.97 0.37 6.49 0.77 K6 II 1,2,4

aSpectral types for all members of our sample were adopted from [70] with the exception of the spectral type for CIDA 12 which was adopted from [69]. b(1)Kenyon & Hartmann 1995[54]; (2)Luhman et al. 2010[69]; (3)Rebull et al. 2010[102]; (4)Esplin et al. 2014[24]; (5)Kraus et al. 2017[61]

69 Using the excess spectra we measured the temperature of the near-IR excess for 47 members of our sample. The purpose of this measurement is to test a scenario in which the inner dust rim at a single temperature is the dominant source of excess in the near-IR, but the measured temperature is inaccurate when the excess emission arises from more than one temperature component. Therefore, we used emcee to fit either a single or two-temperature blackbody curve to the excess spectra based on whether or not each YSO was found to be actively accreting. For each YSO we determined whether to fit a single or two-temperature blackbody based if the YSOs spectrum contained the Brackett γ (Brγ) line of atomic hydrogen in emission (see section 6.4.3 for details). We assume that YSOs that display Brγ in emission are actively accreting and that the flux from the accretion shock will contribute to the excess flux in the near-IR. For YSOs that we determined to be active accretors, we include a hot (>5000K) and cool (<2500K) temperature component in our temperature fit. The excess flux from accretion peaks in the U-band, and our excess spectra only cover only the H and K bands, so it is not possible to measure the temperature of the accretion flux with any accuracy, but including the hot temperature component allows us to improve the accuracy of our temperature fit to the near-IR excess. An example of one such fit is show in Figure 6.3. We excluded objects with H-band veiling <0.1 from our temperature fits in order to avoid fitting objects with veiling less than our typical uncertainties. After performing the temperature fitting routine we also excluded objects with temperature uncertainties >300K concluding that these

70 measurements were too unreliable to contribute to any conclusions concerning the temperature of the inner dust rim. The resulting near-IR excess temperature measurements are provided in Table 2. For YSO excess spectra ﬁt with two temperature components, we display only the cool component because uncertainties in the temperature of the hot component are generally in excess of 1000K.

Figure 6.3 Example two-temperature blackbody ﬁt to the H and K band excess spectrum of Taurus sample member FS Tau A.

71 Table 2. Results of blackbody fits to the near-IR excess spectra of 47 members of our YSO sample. For two-temperature fits, only the temperature for the cool component is displayed. *Denotes single temperature fits.

Object Alt. Name Excess Temperature σ− σ+ Fcool/Fhot (K) (K) (K) at 2µm

J04553695+3017553 LkCa 19 610* 60 60 J04183112+2816290 DD Tau A 1090 80 70 10.8 J05074953+3024050 RW Aur A 1180 70 60 12.5 J04144928+2812305 FO Tau A 1180* 50 40 J04514737+3047134 UY Aur A 1300 80 50 8.5 J04413882+2556267 I04385+2550 1310 120 120 17.8 J04330622+2409339 GH Tau A 1320 80 50 26.4 J04214323+1934133 I04187+1927 1320 110 100 13.8 J04215563+2755060 DE Tau 1350 90 90 11.1 J04173372+2820468 CY Tau 1350 80 80 20.3 J04314007+1813571 XZ Tau 1360 100 100 9.4 J04352020+2232146 HO Tau 1390 100 100 11.8 J04391779+2221034 LkCa 15 1440 80 90 19.5 J04382858+2610494 DO Tau 1450 130 100 5.4 J04354733+2250216 HQ Tau A 1450* 110 100 J04355277+2254231 HP Tau A 1460 90 80 19.5 J04215943+1932063 T Tau Aa 1470 110 80 6.3 J04361909+2542589 LkCa 14 1470 160 100 81.8 J04295156+2606448 IQ Tau 1470 100 70 7.7 J04141760+2806096 CIDA-1 1480 70 90 16.5 J04333456+2421058 GK Tau 1530 80 80 11.6 J04355684+2254360 Haro 6-28 A 1540 120 120 11.0 J04333405+2421170 GI Tau 1540 110 80 8.0 J04304425+2601244 DK Tau A 1560 100 60 5.3 J04270280+2542223 DF Tau A 1560 100 100 10.2 J04382134+2609137 GM Tau 1580 50 60 32.6 J04191281+2829330 FQ Tau A 1580 100 90 25.5 J04324911+2253027 JH 112 A 1590 100 100 20.5 J04335200+2250301 CI Tau 1610* 40 40 J04333906+2520382 DL Tau 1620 120 90 6.8 J04333935+1751523 HN Tau A 1630 140 140 11.4 J04423769+2515374 DP Tau A 1640 110 120 8.8 J04392090+2545021 GN Tau 1660 90 110 13.2 J04141358+2812492 FM Tau 1700 120 110 6.8 J04465897+1702381 Haro 6-37 Aa 1720 110 110 10.3 J04302961+2426450 FX Tau A 1720 100 120 10.6

72 Table 2 (cont’d)

Object Alt. Name Excess Temperature σ− σ+ Fcool/Fhot (K) (K) (K) at 2µm

J04245708+2711565 IP Tau 1750 120 110 9.0 J04335470+2613275 IT Tau A 1760* 160 130 J04323034+1731406 GG Tau Aa+Ab 1790 130 130 10.2 J04420777+2523118 V955 Tau A 1810 150 140 7.2 J04315056+2424180 HK Tau A 1830 130 130 11.7 J04345542+2428531 AA Tau 1870 130 130 8.3 J04141291+2812124 V773 Tau 1880* 160 140 J04330664+2409549 V807 Tau A 1910 150 130 6.7 J04474859+2925112 DS Tau 1950* 50 50 J04220217+2657304 FS Tau A 1960 150 140 9.7 J04294155+2632582 DH Tau A 2040 130 140 12.4 J04191583+2906269 BP Tau 2180 160 130 6.5

6.4 Discussion

Veiling (rλ) is a measure of the excess continuum in units of the photospheric flux for a given wavelength. The photospheric flux varies from source to source so it is useful to examine the luminosity (density) of the excess in the H and K bands in addition to the veiling because it is independent of both the luminosity of the star and distance from the observer. We use H and Ks magnitudes from 2MASS [105] and parallaxes from Gaia [12] to convert our measured veiling values to excess luminosity densities LH and LK. We find a linear relationship between rH or rK as well as between LH and LK within our uncertainties for both the Class II and Class III populations within our Taurus sample (Figure 6.4). By performing a linear fit to the luminosity densities and the H and K bands We find that for the Class II population LK = 1.14LH

73 24 −1 and for the Class III population LK = 0.59LH − 1 × 10 W µm . The difference in the slopes of these fits reveals that the near-IR excesses of the Class III population in our Taurus sample are significantly hotter than those of the

Class II population. The best-ﬁt lines for LK vs. LH for the Class II and Class III populations correspond to excess temperatures of 1400K and 2500K respectively. The relationship between rH and rK for our sample of Taurus members in Class II and Class III YSOs remain consistent for low veiling values (rH <0.1). The large The fact that we see linear relationships for very small veiling values indicates that we are most likely detecting veiling in this regime even when veiling values of r=0 fall within our uncertainties. The detection of small amounts of veiling is an important result, especially for Class III YSOs, because it shows that some inner disk material may remain well into the Class III phase after the bulk of the disk has dissipated.

74 Figure 6.4 Left: Relationship between veiling values in the H and K photometric bands (rH and rK ) for our sample of 139 Taurus members. Right: Relationship between H and K-band excess luminosity derived from the veiling measurements using 2MASS magnitudes and Gaia parallaxes

25 −1 The region where LH<1×10 Wµm in Figure 6.4 is populated primarily by Class III sources. However, there are a signiﬁcant number of Class

II sources here as well. Figure 6.5 shows the distribution of LH and LK for the Class II and Class III members of our YSO sample. 30% of our measured excess luminosities for Class II sources fall below the mean value for the Class III sources. YSO classiﬁcations are determined using the SED slope in the near to mid-IR and indicate how much material remains in the disk over a large range of circumstellar radii. The fact that we see a signiﬁcant overlap of

25 −1 Class II and Class III sources for values of LH <1×10 Wµ is evidence that YSO class is a poor indicator of how much material remains at small (<1AU) radii.

75 Figure 6.5 Distribution of LH and LK for Class II and Class III YSOs

6.4.1 Excess Temperatures

The distribution of excess temperatures for the 47 sample members for which we could determine a reliable temperature is shown in Figure 6.6. The maximum dust sublimation temperature in the ISM is ∼1500K [7]. How- ever, we find that only 40% of our sample fall at or below this temperature. Our finding that the continuum excess temperatures for our Taurus sample are concentrated above the ∼1500K sublimation temperature is not surpris- ing given that it mirrors results of McClure et al.[84] for T Tauri stars and Monnier and Millan-Gabet[89] for Herbig Ae/Be stars. The 1500K value for the sublimation temperature is derived from properties of graphite dust grains found in the interstellar medium [20], however both grain size and composition are likely to differ significantly in a protoplanetary disk environment. Mon-

76 nier Millan-Gabet [89] suggest that grains with a>1µm that may form in a denser environment like a protoplanetary disk. The presence of highly refrac- tory dust species may also be responsible for increasing the dust sublimation temperature [84, 85, 100].

Another possible explanation for our measured excess temperatures approaching 2000K is a contribution from dust-free gas within the inner sublimation radius. Fischer et al. [26] found that in Taurus members with high accretion rates, the emitting regions were, in many cases, larger than the surface areas of the star. They concluded that at least some of the excess ﬂux in the I,Y and J bands was from dust-free gas. A contribution from gas at temperatures exceeding 2000K within the sublimation radius may explain some of the higher excess temperatures in our sample and may be responsible for skewing many of our single-temperature ﬁts towards higher temperatures.

77 Figure 6.6 Distribution of excess continuum temperatures for 47 members of our member sample.

6.4.2 Evidence for Features in Excess spectra

For six members of of our sample (V927 Tau A+B, GM Tau, FR Tau, 2MASS J0425155 0+2829275, 2MASS J04390637+2334179 and 2MASS J04155799+2746175) we find evidence for a broad spectral feature in the H- band that is inconsistent with single or multiple temperature blackbody emission. We show the excess spectra for these six objects in Figure 6.7 and the veiling fits near the peak of the feature in Figure 6.8. For all six of these objects, we find that the amplitude of the H-band feature is larger than the individual uncertainties in the excess flux measurements, and the feature spans at least 10 data points. We explored several possible explanations for the presence

78 of this feature in the H-band excess spectrum including template miss-match, large star spot coverage and disk atmosphere. We hypothesize that the H-band feature could arise if the chosen Class III TWA template used to ﬁt the veiling is signiﬁcantly hotter than the Taurus YSO. The wavelength and broad nature of the feature is strikingly similar to the broad spectral feature produced by M and brown dwarf atmospheres. The similar feature in M and brown dwarfs is the result of H2O absorption bands at either end of the H-band. In M-dwarf atmospheres this feature increases in relative strength in later spectral types.

Figure 6.7 Excess spectra for six YSOs containing a spectral feature in the H- band. We display the excess spectra in units of normalized ﬂux, but provide the veiling at the peak of the feature for each YSO.

79 Figure 6.8 Veiling fits for the 6 YSOs containing a spectral feature in the H- band. We show fits in the region at 1674nm because it is close to the peak of the spectral feature and contains relatively deep absorption lines to illustrate the quality of the fits.

The six objects with excess spectra containing this feature have spectral types ranging from M4.5 to M7.5 and effective temperatures of ∼3000K (López-Valdivia et al. in prep). The coolest template in our sample of TWA objects has a spectral type of M5.5. It is possible that if the H-band feature in our coolest template source is significantly weaker than in each of these 6 Taurus YSOs the veiling fit would erroneously produce this feature in the excess spectrum. However we find that 13 other members of our Taurus YSO sample with spectral types M4.5-M7 are measured to have little to no veiling in the H-band (rH <0.1), making the template miss-match explanation less

80 likely. We attempted to reproduce the H-band feature by performing a ﬁt veiling to a TWA template M5.5 YSO using another M2 template source. We were able to reproduce a feature in the H-band, but the amplitude of the feature was smaller than the individual uncertainties in the excess spectrum, and the individual veiling ﬁts were very poor compared to those for the 6 Taurus YSOs displaying the H-band excess feature.

Another possible origin of the H-band excess feature in these 6 YSOs is the presence of star spots covering a large portion of the stellar surface. Gully-Santiago et al. [43] found that IGRINS spectrum Taurus member LkCa 4 was well fit by a two-temperature stellar atmosphere model that included a cooler star spot component covering 80% of the stellar surface. [43] also found that in some spectral regions, the cooler component of the stellar surface presents as veiling due to heavy line-blanketing and rotational broadening. In all our Taurus YSO sample objects containing this feature, the K-band excess spectrum is relatively flat within uncertainties. We expect that we would detect similar feature in the K-band at spectral types later than simM9, so in the case that the H-band feature results from star spots we expect the spotted region to have an effective temperature >2400K based on the temperature scale from Luhman et al. [72]. If we assume that these six objects contain spotted regions at a temperature of 2400K and hotter surface regions at ∼3000K (based on temperatures from López-Valdivia et al. in prep) we find that these 6 YSOs would need to have spot coverage of 30-60% to produce the H-band feature in their excess spectra.

81 While a scenario in which the H-band excess feature arises from the surface of the star itself is a simpler explanation, we also explore the possibility that this feature may arise in the disk. In the case of star spots, we suggest that the feature may be the result of a cooler component of the stellar photosphere, but it is not beyond reason that the feature is the result of the same H2O absorption bands, but from an atmosphere of the inner disk itself. A broad spectral feature arising from H2O absorption bands in an accretion disk has been documented in FU Orionis type stars starting with FU Ori itself [15]. However, if the H-band feature is the result of an accretion disk we would expect to see evidence of accretion for each of these 6 YSOs. Instead, we only detect the Brγ line in emission, a reliable txracer of accretion [14], in 2 of the 6 YSOs (FR Tau and GM Tau).

In the cases of both star spots and an accretion disk atmosphere, we expect there to be evidence in the high-resolution IGRINS spectra for the presence of absorbing material that is significantly cooler than the assumed temperature of the YSO photosphere. In order to look for evidence of cooler material, we cross-correlated sections of the residuals from the veiling fits with the IGRINS spectrum of a late-type (M8.5, [115]) Taurus YSO 2MASS J04221332+1934392. The purpose of this test is to look for agreement between the veiling fit residuals, and a cool stellar spectrum. In the case that the cool component causing the H-band excess feature does arise in the disk, we would expect the cross-correlation function to return a broader ”boxy” profile rather than a roughly Gaussian peak, as the lines would be subject to rotational

82 broadening resulting from a Keplerian disk rather than a rotating star. The strongest agreement we found between the veiling fit residuals and M8.5 stellar spectrum are in several regions of the spectrum of FR Tau. These sections show a roughly Gaussian peak in the cross-correlation function, which is more consistent with a stellar line profile than one resulting from a a stellar spectrum being cross-correlated with the spectrum of a corotating disk. Figure 6.9 analysis shows the result of one such cross-correlation for FR Tau. However, In the vast majority of veiling fit sections we find no evidence of agreement between the residuals and the M8.5 stellar spectrum. This test adds a small amount of merit to the explanation that the H-band excess arises from cool spots on the star itself, but it is also possible that absorption lines arising in an accretion disk would be too heavily broadened for us to detect in the residuals. Using Baraffe et al. [5] pre-main-sequence evolutionary models to estimate the luminosity and mass of a 3000K star we find that in order to maintain a temperature >2400K the orbiting material would have to be .0.01 AU from its host star. Assuming that the inner disk is Keplerian, the orbital velocity would exceed 100 km/s. If the feature we find in the excess spectrum is the result of optically thick material very close to the stars, extreme line broadening due to orbital velocities in excess of 100 km/s could explain the fact that we do not see clear evidence of absorption lines in the residuals from the veiling fits of the high-resolution IGRINS spectra.

83 Figure 6.9 Top: Veiling ﬁt for a K-band section of FR Tau. Middle: Cross correlation function for veiling ﬁt residuals and IGRINS spectrum Taurus YSO 2MASS J04221332+1934392 (M8.5). Bottom: Residuals and IGRINS spectrum of 2MASS J04221332+1934392 aligned in velocity space based on cross- correlation.

84 6.4.3 Veiling and Accretion

Emission lines of atomic hydrogen arise in T Tauri stars from magnetospheric accretion of disk material onto the star [14]. Muzerolle et al. [91] showed that the luminosity of the Brackett γ line in emission correlates well with disk accretion rate. In order to evaluate the contribution of accretion to the near-IR excess we compare our measurements of the excess in the near-IR to Brγ luminosity (LBrγ) (Figure 6.10). We measure the properties of strong Br-γ emission lines, defined here as those with emission in at least 4 continuous resolution elements (∼30 km s1) above 3 times the adjacent continuum noise. We define the endpoints of each detected emission line as the point where line reaches 0.5% of the peak flux relative to the continuum. We com- pute equivalent widths (EWs) using a trapezoidal Riemann sum between these endpoints. To determine errors on the line parameters, we run a 104 iteration Monte Carlo bootstrap simulation adding Gaussian noise at the level of the adjacent continuum, taking the larger of the 68% confidence intervals as our uncertainty. We converted our measured Brγ EWs to line luminosities by using our veiling measurements to deveil K-band luminosities calculated using

2MASS magnitudes and distances from Gaia. To deredden LBrγ we calculated the deveiled H-K colors for our sample using 2MASS magnitudes and our veiling measurements. We used intrinsic H-K colors for pre-main sequence dwarfs from Cox [17] using temperatures provided by L´opez-Valdivia et al. (in prep) to calculate E(H-K). We then use the CCM[16] extinction law to calculate and correct for the reddening of LBrγ.

85 Figure 6.10 Left: Comparison of Brackett γ EW and H-band veiling. This plot shows our raw measurements before dependence on the individual stellar luminosities and distances have been taken out. Right: Comparison LBrγ calculated from Brγ EWs and H-band excess luminosity density calculated from the veiling measurments.

In comparing our measurements of the excess to LBrγ as a tracer of accretion, we ﬁnd that for a majority of our sample there is little to no correlation between excess and LBrγ. However, the average veiling value does increase for

−5 sources with LBrγ >3×10 L . It appears that there is a stronger correlation between for larger values of LBrγ based on two strongly accreting members of our sample: DR Tau and T Tau. The result that near-IR excess is propor- tional to the accretion luminosity in the near-IR for the strongest accretors is consistent with previous assertions (e.g. Johns-Krull et al. [52]) that accretion luminosities that are a large fraction of the photospheric luminosity will significantly enhance disk heating and therefore increase excess ﬂux in the near-IR.

86 The right-panel of of Figure 6.10 illustrates that the eﬀect of accretion shock signiﬁcantly enhancing disk heating and near-IR excess in turn only occurs in

−4 our sample for values of LBrγ & 1 × 10 L .

6.5 Summary and Conclusions

We present measurements of rH and rK for 142 YSO members of the Taurus star forming region. We calculate these measurements using high- resolution H and K band IGRINS spectra of these 142 YSOs as well template spectra of members of the TWA young moving group. We generate low-resolution H and K band spectra of the excess for all members of the YSO sample and measure excess temperatures for 42 members. We ﬁnd that our measurements of the excess temperatures in Taurus YSOs are consistent with or slightly higher than previous studies of YSOs with strong near-IR excess.

We ﬁnd that on average the near-IR excess is stronger in Class II YSOs, but that a signiﬁcant fraction of Class III sources have a non-zero excess in the H and K bands, with the excesses of several Class III sources exceeding the average excess value for Class II sources. This indicates that the mid-IR SED slope α which traces the amount of remaining outer disk material is a poor predictor of how much material remains in the inner disk at <1AU.

We identify six objects that display a prominent spectral feature in their the H-band excess spectra. We do not come to any concrete conclusions surrounding the origin of the feature, but we speculate that this type of feature could arise from a gaseous disk photosphere within the inner dust rim.

87 Now that we have automated a process for measuring veiling in IGRINS spectra we intend to use our software tools to measure veiling in the Rho- Ophiuchus star forming region which has a higher spatial density of YSOs than Taurus and is also presumed to be slightly younger. In this work we present analysis of many multi-epoch observations that have been combined into a single spectrum in order to increase the signal-to-noise ratio. We now intend to measure the veiling for individual epochs for YSOs with many observations in order to search for changes in veiling over time. We also plan to perform follow- up observations on the six objects whose excess spectra contain a prominent feature in the H-band.

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108 Vita

Benjamin Thomas Kidder was born in Seattle, Washington in 1991, the son of Drs. Paul and Paulette Kidder. He received the Bachelor of Science degree in Physics from the University of Redlands in Spring of 2014. He enrolled in the University of Texas at Austin astronomy program in Fall of 2014.

Permanent address: [email protected]

This dissertation was typeset with LATEX† by the author.

†LATEX is a document preparation system developed by Leslie Lamport as a special version of Donald Knuth’s TEX Program.

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