:

# DrugChat: Towards Enabling ChatGPT-Like Capabilities on Drug Molecule Graphs

Youwei Liang∗ Ruiyi Zhang∗ Li Zhang  Pengtao Xie  University of California San
Diego  *Equal contribution

###### Abstract

A ChatGPT-like system for drug compound analysis could be a game-changer in
pharmaceutical research, accelerating drug discovery, enhancing our
understanding of structure-activity relationships, guiding lead optimization,
aiding drug repurposing, reducing the failure rate, and streamlining clinical
trials. In this work, we make an initial attempt towards enabling ChatGPT-like
capabilities on drug molecule graphs, by developing a prototype system
DrugChat. DrugChat works in a similar way as ChatGPT. Users upload a compound
molecule graph and ask various questions about this compound. DrugChat will
answer these questions in a multi-turn, interactive manner. The DrugChat
system consists of a graph neural network (GNN), a large language model (LLM),
and an adaptor. The GNN takes a compound molecule graph as input and learns a
representation for this graph. The adaptor transforms the graph representation
produced by the GNN into another representation that is acceptable to the LLM.
The LLM takes the compound representation transformed by the adaptor and
users’ questions about this compound as inputs and generates answers. All
these components are trained end-to-end. To train DrugChat, we collected
instruction tuning datasets which contain 10,834 drug compounds and 143,517
question-answer pairs. The code and data is available at
https://github.com/UCSD-AI4H/drugchat

##  1 Introduction

The process of drug discovery and development is a time-intensive and costly
endeavor, often taking years and billions of dollars to bring a single drug to
market (Avorn, 2015). This process involves the exploration and understanding
of vast chemical spaces and the intricate relationships between molecular
structures and their biological activities, commonly known as structure-
activity relationships (SAR) (Idakwo et al., 2020). Traditional methods
(Rycker et al., 2018) often involve laborious iterative testing, with a high
rate of late-stage failures. Recent advancements in computational chemistry
and chemoinformatics (Zeng et al., 2022) have offered some respite, but there
is still a pressing need for tools that can intuitively understand and
generate meaningful insights from the complex data inherent in molecular
graphs of drug compounds.

This technical report introduces the concept of applying ChatGPT-like
capabilities to drug molecule graphs, aiming to revolutionize the way we
interact with and understand these complex entities. By transforming these
molecular graphs into a form amenable to AI analysis, we can enable dynamic
exploration of chemical spaces, efficient prediction of compound properties,
and intelligent suggestions for drug design and optimization. A ChatGPT-like
AI system capable of understanding drug compound molecule graphs and answering
various questions about these drugs could revolutionize pharmaceutical
research in several ways:

  * •

Speeding up Drug Discovery. A ChatGPT-like AI system could drastically cut
down the time required for initial stages of drug discovery by providing
immediate insights into a compound’s potential therapeutic uses, side effects,
and contraindications based on its structure.

  * •

Predicting Drug Interactions. A ChatGPT-like AI system could predict potential
interactions between new drug candidates and existing drugs. By comparing the
molecular structures of thousands of known substances, the system could
identify possible conflicts or synergies, helping researchers to better
anticipate how a new drug might behave in the real world.

  * •

Understanding Structure-Activity Relationships (SAR). SAR (Idakwo et al.,
2020) is a crucial aspect of drug design. A ChatGPT-like AI system could help
researchers understand the relationship between a drug’s chemical structure
and its biological activity. It could also help predict what modifications to
the chemical structure might enhance its effectiveness or reduce unwanted side
effects.

  * •

Guiding Lead Optimization. During the drug discovery process, ‘lead’ compounds
(those that show promise in initial screenings) (Hughes et al., 2011) are
typically optimized for better efficacy, reduced toxicity, and improved
pharmacokinetics. A ChatGPT-like AI system could offer suggestions for
structural modifications to enhance these parameters, guiding researchers in
the right direction and saving valuable time.

  * •

Supporting Drug Repurposing. A ChatGPT-like AI system could also aid in drug
repurposing (Pushpakom et al., 2019) efforts. By understanding the structural
properties of existing drugs, it could identify candidates that may be
effective against diseases they were not initially developed to treat. This
could help breathe new life into existing drugs and offer more rapid routes to
treatment for challenging diseases.

  * •

Reducing the Failure Rate. The failure rate in drug discovery is high (Hughes
et al., 2011), often due to unforeseen toxicity or efficacy issues that emerge
late in development. By providing more accurate predictions about a drug’s
properties and effects at the outset, a ChatGPT-like AI system could help
reduce these costly late-stage failures.

  * •

Streamlining Clinical Trials. A ChatGPT-like AI could help design more
effective clinical trials by predicting how a drug will interact with other
medications or conditions, enabling researchers to target their trials more
effectively and recruit suitable patient cohorts.

Developing a ChatGPT-like system for drug molecule graphs is highly
challenging. First, one of the primary challenges lies in the representation
of molecular graphs. Unlike text data, which is sequential and has a well-
defined structure, molecular graphs are inherently non-sequential and highly
complex, with no clear start or end point. Developing a methodology to
translate these graphs into a format that a GPT model can process is crucial.
Second, another significant challenge is capturing and understanding the
intricate structure-activity relationships (SAR) in drug compounds. These
relationships are often not straightforward and can involve subtle
interactions between different parts of a molecule. Third, to train such a
system, large datasets of molecular structures, along with their associated
biological activities, side effects, and other properties, would be required.
The generation or compilation of such datasets is a non-trivial task, given
the proprietary nature of much of this information and the sheer diversity of
the chemical space.

In this technical report, we make an initial attempt towards enabling ChatGPT-
like capabilities on drug molecule graphs, by developing a prototype system
DrugChat. DrugChat works in a similar way as ChatGPT. Users upload a compound
molecule graph and ask various questions about this compound. DrugChat will
answer these questions in a multi-turn, interactive manner.

The DrugChat system consists of a graph neural network (GNN) (Hu et al.,
2020), a large language model (LLM) (Chiang et al., 2023), and an adaptor. The
GNN takes a compound molecule graph as input and learns a representation for
this graph. The adaptor transforms the graph representation produced by the
GNN into another representation that is acceptable to the LLM. The LLM takes
the compound representation transformed by the adaptor and users’ questions
about this compound as inputs and generates answers. All these components are
trained end-to-end. To train DrugChat, we collected instruction tuning
datasets which contain 10,834 drug compounds and 143,517 question-answer
pairs.

The major contributions of this work are as follows:

  * •

We develop DrugChat, a prototype system aiming to achieve ChatGPT-like
capabilities on drug molecule graphs. DrugChat allows users to interactively
ask open-ended questions about drug compounds and provides informative
answers.

  * •

We collect instruction tuning datasets which contain 10,834 drug compounds and
143,517 question-answer pairs. These datasets enable the training of ChatGPT-
like models for drug compounds and are publicly available.

  * •

To our best knowledge, DrugChat represents the first system that bridges graph
data with large language models (LLMs), which enables interactive
conversations on graphs. Our system seamlessly integrates graph neural
networks with LLMs and can be easily extended to analyze other graph data
beyond compound molecule graphs.

##  2 Related Works

AI-based drug properties analysis (Paul et al., 2021) is a promising approach
to significantly reduce costs and time associated with the traditional drug
discovery and development pipeline. For example, ImageMol (Zeng et al., 2022)
is an unsupervised pretraining method that processes images of chemical
structures and outputs molecular properties, such as drug metabolism, brain
penetration, toxicity, and molecular target profiles like beta-secretase
enzyme and kinases. ImageMol was pretrained on 10 million unlabeled drug-like,
bioactive molecules through predicting molecular targets of candidate
compounds. It was assessed on 51 benchmark datasets and demonstrated high
accuracy in identifying anti-SARS-CoV-2 molecules across 13 high-throughput
experimental datasets.

Large language models (LLMs) (Brown et al., 2020) have demonstrated
outstanding capabilities in generating inventive text, responding to reading
comprehension queries, mathematical reasoning, etc. Nevertheless, the weight
parameters of some of the most powerful LLMs are not publicly available, which
considerably hinders academic research. Moreover, early LLMs were limited to
processing text information as input, restricting their ability to understand
information in other modalities. Consequently, researchers have developed an
array of publicly accessible LLMs, including multi-modal variants, to address
these challenges.

LLaMA (Touvron et al., 2023), an LLM with 65 billion parameters, was developed
by Meta AI 111https://ai.facebook.com/blog/large-language-model-llama-meta-
ai/. This model is trained on publicly accessible datasets including English
CommonCrawl, C4, Github, Wikipedia, Gutenberg Project, ArXiv, and Stack
Exchange. This training dataset encompasses 20 languages with the most
speakers. Although it is smaller than GPT-3, LLaMA demonstrates superior
performance on many benchmarks, including commonsense reasoning, question
answering, reading comprehension, code generation, etc. Vicuna (Chiang et al.,
2023) is an open-source chatbot trained by fine-tuning LLaMA on around 70,000
user-shared conversations with ChatGPT
222https://lmsys.org/blog/2023-03-30-vicuna/. Vicuna was evaluated using
GPT-4. MiniGPT-4 (Zhu et al., 2023) is a vision-language model capable of
processing images and generating textual answers
333https://minigpt-4.github.io/. For instance, it can create stories and poems
from input images and offer solutions to problems depicted in images.
MiniGPT-4 incorporates a linear projection layer to align the visual
information from a pretrained vision encoder with a large language model.
LLaVA (Liu et al., 2023) is another multi-modal model that integrates a vision
encoder with an LLM to facilitate general-purpose visual and language
understanding 444https://llava-vl.github.io/. Different from MiniGPT-4, LLaVA
is trained on multi-turn conversation data. The projection layer and the LLM
are both updated in the training stage while only the visual encoder
parameters are frozen. The training data was created by converting raw image-
text pairs into a suitable instruction-following format using ChatGPT/GPT-4.

##  3 Drug Instruction Tuning Data

To train DrugChat, we curate instruction tuning data for drug compounds, from
two sources - ChEMBL and PubChem. Table 1 summarizes the statistics of the
curated two datasets.

Table 1: Dataset statistics. Dataset | Number of Drugs | Number of Question-Answer Pairs
---|---|---
ChEMBL | 3,892 | 129,699
PubChem | 6,942 | 13,818

###  3.1 ChEMBL Drug Instruction Tuning Dataset

In this section, we outline the process of constructing the ChEMBL Drug
Instruction Tuning dataset. We gathered data from the ChEMBL
website555https://www.ebi.ac.uk/chembl/, which houses information for a total
of 2,354,965 chemical compounds. We downloaded the sqlite version of the data
dump, which was last updated on February 28,
2023666https://ftp.ebi.ac.uk/pub/databases/chembl/ChEMBLdb/latest/. Out of the
entire dataset, we identified 14,816 compounds that contain drug information.
After applying further filtering to exclude drugs with insufficient
descriptive information, we ended up with a dataset containing 3,892 drugs.

For each drug, we initially gathered its SMILES string, representing the
molecular structure. Subsequently, we obtained various molecular features,
including the complete molecular formula and its classification as an acid,
base, or neutral compound. Additionally, we collected drug-specific
properties, such as the mechanism of action and therapeutic applications.
Based on these attributes, we manually crafted 129,699 question-answer pairs.
Question-answer (QA) pairs for an exemplar drug in the ChEMBL Drug Instruction
Tuning Dataset is shown in Table 2.

Table 2. Question-answer (QA) pairs for an exemplar drug in the ChEMBL Drug
Instruction Tuning Dataset.

SMILES of the drug | CC(=O)/N=c1/sc(S(N)(=O)=O)nn1C
---|---
QA Pair 1 |  Q: Please provide a description of this drug’s mechanism of action. A: Carbonic anhydrase II inhibitor.
QA Pair 2 |  Q: Is this compound an acid, a base, or neutral? A: Neutral.
QA Pair 3 |  Q: How many rotatable bonds does this compound have? A: 1.
QA Pair 4 |  Q: Determine if this drug is administered as a racemic mixture, a single stereoisomer, an achiral molecule, or has an unknown chirality. A: An achiral molecule.
QA Pair 5 |  Q: Does this compound satisfy the rule-of-three criteria? A: No.
QA Pair 6 |  Q: How many violations of Lipinski’s Rule of Five are there for this compound, using the HBA_LIPINSKI and HBD_LIPINSKI counts? A: 0.
QA Pair 7 |  Q: Is it known whether this drug is administered parenterally? A: No.
QA Pair 8 |  Q: Is this compound a small molecule polymer, such as polystyrene sulfonate? A: No.
QA Pair 9 |  Q: What is the calculated ALogP value for this compound? A: -1.42.
QA Pair 10 |  Q: Is this molecule characterized by a small molecular structure or a protein sequence? A: It has a small molecule structure.
QA Pair 11 |  Q: Determine if this molecule is inorganic, meaning it contains only metal atoms and fewer than two carbon atoms. A: No.
QA Pair 12 |  Q: What is the highest development stage achieved for this compound across all indications? Please respond with Approved, Phase 3 Clinical Trials, Phase 2 Clinical Trials, Phase 1 Clinical Trials, Early Phase 1 Clinical Trials, or Clinical Phase Unknown. A: Approved.
QA Pair 13 |  Q: What is the first recorded year of approval for this drug? A: 1959.
QA Pair 14 |  Q: Is there a black box warning associated with this drug? A: No.
QA Pair 15 |  Q: Is it known if this drug is the first approved in its class, regardless of the indication or route of administration, acting on a specific target? A: No.
QA Pair 16 |  Q: How many hydrogen bond acceptors does this compound have? A: 6.
QA Pair 17 |  Q: Is this drug used for therapeutic purposes, rather than for imaging, additives, or other non-therapeutic applications? A: Yes.
QA Pair 18 |  Q: What is the polar surface area (PSA) value of this compound? A: 107.41.
QA Pair 19 |  Q: Has this approved drug been withdrawn due to toxicity reasons for all indications, populations, and doses in at least one country (not necessarily the US)? A: No.
QA Pair 20 |  Q: How many violations of Lipinski’s Rule of Five (using HBA and HBD definitions) are there for this compound? A: 0.
QA Pair 21 |  Q: How many heavy (non-hydrogen) atoms does this compound have? A: 14.
QA Pair 22 |  Q: How many aromatic rings does this compound have? A: 1.
QA Pair 23 |  Q: Determine the type of availability for this drug. A: Prescription only.
QA Pair 24 |  Q: Is it known whether this drug is taken orally? A: Yes.
QA Pair 25 |  Q: How many hydrogen bond acceptors are there in this compound, calculated according to Lipinski’s original rules (i.e., counting N and O atoms)? A: 7.
QA Pair 26 |  Q: Is the drug administered in this specific form, such as a particular salt? A: Yes.
QA Pair 27 |  Q: Determine if this compound is a prodrug. A: No.
QA Pair 28 |  Q: What is the molecular weight of this compound’s parent molecule? A: 236.28.
QA Pair 29 |  Q: How many hydrogen bond donors are there in this compound, calculated according to Lipinski’s original rules (i.e., counting NH and OH groups)? A: 2.
QA Pair 30 |  Q: Which USAN substem can this drug or clinical candidate name be matched with? A: Zolamide.
QA Pair 31 |  Q: What is the definition of this compound’s USAN stem? A: Carbonic anhydrase inhibitors.
QA Pair 32 |  Q: What is the molecular formula of this compound, including any salt that it may have? A: C5H8N4O3S2.
QA Pair 33 |  Q: How many hydrogen bond donors does this compound have? A: 1.
QA Pair 34 |  Q: What is the classification of this molecule? Please respond with Small Molecule, Protein, Antibody, Oligosaccharide, Oligonucleotide, Cell, Enzyme, Gene, or Unknown. A: Small molecule.

###  3.2 PubChem Drug Instruction Tuning Dataset

In this section, we outline the process of constructing the PubChem Drug
Instruction Tuning Dataset. The data was collected from the PubChem
website777https://pubchem.ncbi.nlm.nih.gov/, which houses information of
66,469,244 chemical compounds. We utilized the data version last updated on
May 9, 2023888https://pubchem.ncbi.nlm.nih.gov/classification/. Among these
compounds, 19,319 possess drug information. After filtering out drugs lacking
detailed textual descriptions, we retained 6,942 drugs.

For each drug, we gathered its SMILES string and descriptions from databases
including ChEBI, LOTUS, and YMDB. In total, we acquired 13,818 compound
description summaries and curated 13,818 question-answer (QA) pairs. QA pairs
for an exemplar drug in PubChem are provided in Table 3.

Table 3: Question-answer (QA) pairs for an exemplar drug in the PubChem Drug Instruction Tuning Dataset. Compound Name | Acetylcarnitine
---|---
Canonical SMILES | CC(=O)OC(CC(=O)[O-])C[N+](C)(C)C
ChEBI Database |  Q: Please describe this drug. A: O-acetylcarnitine is an O-acylcarnitine having acetyl as the acyl substituent. It has a role as a human metabolite. It is functionally related to an acetic acid. It is a conjugate base of an O-acetylcarnitinium.
LOTUS Database |  Q: Please describe this drug. A: Acetylcarnitine is a natural product found in Pseudo-nitzschia multistriata, Euglena gracilis, and other organisms with data available.
YMDB Database |  Q: Please describe this drug. A: L-Acetylcarnitine is a metabolite found in or produced by Saccharomyces cerevisiae.

##  4 Method

An overview of DrugChat is provided in Figure 1. It takes a compound molecule
graph as input and allows users to ask multi-turn questions about this
compound. For each question, DrugChat generates an answer. DrugChat consists
of a graph neural network (GNN), a large language model (LLM), and an adaptor
between GNN and LLM. The GNN learns a representation for the compound molecule
graph. The adaptor (which is a linear transformation matrix) transforms the
graph representation into an LLM-compatible soft prompt vector. The LLM takes
a user-question and the graph prompt vector as inputs and generates an answer.
We employ a pretrained GNN from (Hu et al., 2020) and a pretrained LLM -
Vicuna13b (Chiang et al., 2023). When training DrugChat, we fix the weight
parameters of the GNN and LLM, and only update the adaptor’s weights. Given a
drug in the instruction tuning data and a question about this drug, the drug’s
graph is first fed into the GNN to produce a representation vector which is
then fed into the adaptor to produce a prompt vector. The prompt vector and
the question are fed into the LLM to generate an answer. A negative log
likelihood loss between the generated answer and groundtruth answer is
calculated. The adaptor is trained by minimizing this loss. Next, we introduce
each component in DrugChat.

Figure 1: An overview of the DrugChat framework. Figure 2: A demonstration of
the DrugChat framework. Figure 3: A demonstration of the DrugChat framework.

###  4.1 Graph Neural Networks (GNNs)

A GNN (Hu et al., 2020) utilizes the graph structure and node features to
learn multiple layers of representation vectors for each node $v$ and a
representation vector $h_{G}$ for the entire graph $G$. The representation of
each node is updated by aggregating features from its neighboring nodes. At
layer $k$, the representation $h_{v}^{k}$ of node $v$ encompasses information
from nodes within the $k$-hop network neighborhood of $v$. Formally,
$h_{v}^{k}$ is calculated as follows:

$h_{v}^{k}=\sigma(h_{v}^{k-1},AGG(\\{h_{u}^{k-1},u\in\mathcal{N}(v)\\})),$ (1)

where $AGG$ represents a function that aggregates information from neighboring
nodes. $\sigma$ is a function that combines information from the node feature
of the previous layer and neighboring nodes. $\mathcal{N}(v)$ indicates all
neighboring nodes for node $v$. To acquire the representation vector for the
entire graph $G$, a pooling function $f$ (which is permutation-invariant, such
as averaging) extracts information from all node features at the final layer
$K$:

$h_{G}=f(\\{h_{v}^{K},v\in G\\}).$ (2)

###  4.2 Large Language Models (LLMs)

LLMs utilize the Transformer (Vaswani et al., 2017) decoder to model the
conditional probability $p_{\theta}(n_{i}|n_{<i})$ for token $n_{i}$ in a
language model. The Transformer decoder applies a multi-head self-attention
block (Vaswani et al., 2017) to the input context tokens and uses a position-
wise feedforward network to calculate the probabilities over output tokens.
Given the context vector of tokens, the token generation probability is
computed as follows:

$\begin{split}&h_{0}=N_{i-1}W_{e}+W_{p}\\\
&h_{l}=transformer\\_block(h_{l-1})(1\leq l\leq m)\\\
&p(n_{i}|N_{i-1})=Softmax(h_{m}W_{e}^{T})\end{split}$ (3)

where $N_{i-1}=(n_{1},n_{2},...,n_{i-1})$ denotes the context vector of
tokens, $m$ refers to the number of layers, $W_{e}$ denotes the token
embedding matrix, and $W_{p}$ represents the positional embedding matrix.

###  4.3 Aligned Graph-Text Generation

At this stage, we create a prompt for every training graph-text pair, which
allows the LLM to generate descriptions from drug compound graphs. We utilize
a prompt template that adheres to the conversational format of Vicuna-13b:

$\begin{split}&\textbf{Q:}<Graph><GraphFeature></Graph><Instruction>\\\
&\textbf{A:}<Desc>\\\ \end{split}$ (4)

In this prompt, $<GraphFeature>$ is a soft prompt that symbolizes the graph
structure feature encoded by the linear projection layer. $<Instruction>$
serves as a directive sentence, prompting the LLM to generate descriptions for
the drug, such as “Describe the mechanism of this drug”. During the training
stage, $<Desc>$ is populated with descriptive text from human experts to train
the linear projection layer. In the testing stage, $<Desc>$ remains empty, and
the model is expected to generate descriptive text for the provided drug
structure.

##  5 Results

We tested DrugChat on compound graphs that are not contained in the training
data. Figure 2 and 3 show two examples. DrugChat can answer diverse multi-turn
questions about compounds, such as “what makes this compound unique?”, “what
diseases can this compound potentially treat?”. Note that these questions are
not contained in the training data. We will perform a systematic quantitative
evaluation by collaborating with pharmaceutical scientists.

##  6 Conclusions, Limitations and Future Work

In this report, we present the DrugChat framework, designed to answer
questions and generate text descriptions for drugs from their molecular
graphs. We develop the ChEMBL Drug Dataset and the PubChem Drug Dataset to
train the DrugChat model. With further development and evaluation, DrugChat
has the potential to enable conversational analysis of drug compounds.

A potential limitation of DrugChat is language hallucination. Since DrugChat
incorporates an LLM module, it may occasionally produce untrustworthy answers
and descriptions for drugs, which hampers its practical application in real
drug discovery pipelines. If DrugChat generates seemingly convincing yet
incorrect text descriptions, it could mislead human decision-makers and
potentially result in adverse consequences.

In the future, this issue could be mitigated by utilizing higher-quality
training data and implementing effective filtering strategies. Additionally,
more advanced GNN encoders and LLMs will play a crucial role in addressing
this challenge. As the number of users increases, human feedback can also be
utilized to fine-tune the DrugChat model through reinforcement learning
techniques (Ouyang et al., 2022).

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# X-RAY BURST AND PERSISTENT EMISSION PROPERTIES OF THE MAGNETAR
SGR 1830$-$0645 IN OUTBURST

George Younes Department of Physics, The George Washington University,
Washington, DC 20052, USA<EMAIL_ADDRESS>Astronomy, Physics and Statistics
Institute of Sciences (APSIS), The George Washington University, Washington,
DC 20052, USA  Chin-Ping Hu Department of Physics, National Changhua
University of Education, Changhua 50007, Taiwan  Karishma Bansal Jet
Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109,
USA  Paul S. Ray Space Science Division, U.S. Naval Research Laboratory,
Washington, DC 20375, USA  Aaron B. Pearlman McGill Space Institute (MSI)
Fellow.  Department of Physics, McGill University, 3600 rue University,
Montréal, QC H3A 2T8, Canada  FRQNT Postdoctoral Fellow.  McGill Space
Institute, McGill University, 3550 rue University, Montréal, QC H3A 2A7,
Canada  Division of Physics, Mathematics, and Astronomy, California Institute
of Technology, Pasadena, CA 91125, USA  Franz Kirsten Department of Space,
Earth and Environment, Chalmers University of Technology, Onsala Space
Observatory, 439 92, Onsala, Sweden  Zorawar Wadiasingh Department of
Astronomy, University of Maryland, College Park, Maryland 20742, USA
Astrophysics Science Division, NASA Goddard Space Flight Center, Greenbelt, MD
20771, USA.  Center for Research and Exploration in Space Science and
Technology, NASA/GSFC, Greenbelt, Maryland 20771, USA  Ersin Göğüş Sabancı
University, Faculty of Engineering and Natural Sciences, İstanbul 34956 Turkey
Matthew G. Baring Department of Physics and Astronomy - MS 108, Rice
University, 6100 Main Street, Houston, Texas 77251-1892, USA  Teruaki Enoto
Extreme Natural Phenomena RIKEN Hakubi Research Team, Cluster for Pioneering
Research, RIKEN, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan  Zaven
Arzoumanian  Astrophysics Science Division, NASA Goddard Space Flight Center,
Greenbelt, MD 20771  Keith C. Gendreau  Astrophysics Science Division, NASA
Goddard Space Flight Center, Greenbelt, MD 20771  Chryssa Kouveliotou
Department of Physics, The George Washington University, Washington, DC 20052,
USA<EMAIL_ADDRESS>Astronomy, Physics and Statistics Institute of Sciences
(APSIS), The George Washington University, Washington, DC 20052, USA  Tolga
Güver Istanbul University, Science Faculty, Department of Astronomy and Space
Sciences, Beyazıt, 34119, Istanbul, Turkey  Istanbul University Observatory
Research and Application Center, Istanbul University 34119, Istanbul Turkey
Alice K. Harding  Theoretical Division, Los Alamos National Laboratory, Los
Alamos, NM 87545, USA  Walid A. Majid Jet Propulsion Laboratory, California
Institute of Technology, Pasadena, CA 91109, USA  Division of Physics,
Mathematics, and Astronomy, California Institute of Technology, Pasadena, CA
91125, USA  Harsha Blumer  Department of Physics and Astronomy, West Virginia
University, Morgantown, WV 26506, USA  Center for Gravitational Waves and
Cosmology, West Virginia University, Chestnut Ridge Research Building,
Morgantown, WV 26505, USA  Jason W. T. Hessels  Anton Pannekoek Institute for
Astronomy, University of Amsterdam, Science Park 904, 1098 XH, Amsterdam, The
Netherlands, ASTRON, Netherlands Institute for Radio Astronomy, Oude
Hoogeveensedijk 4, 7991 PD Dwingeloo, The Netherlands  Marcin P. Gawroński
Institute of Astronomy, Faculty of Physics, Astronomy and Informatics,
Nicolaus Copernicus University, Grudziadzka 5, 87-100 Toruń, Poland
Vladislavs Bezrukovs Engineering Research Institute Ventspils International
Radio Astronomy Centre, Ventspils University of Applied Sciences, Inzenieru
street 101, Ventspils, LV-3601, Latvia  Arturs Orbidans  Engineering Research
Institute Ventspils International Radio Astronomy Centre, Ventspils University
of Applied Sciences, Inzenieru street 101, Ventspils, LV-3601, Latvia

###### Abstract

We report on NICER X-ray monitoring of the magnetar SGR 1830$-$0645 covering
223 days following its October 2020 outburst, as well as Chandra and radio
observations. We present the most accurate spin ephemerides of the source so
far: $\nu=0.096008680(2)$ Hz, $\dot{\nu}=-6.2(1)\times 10^{-14}$ Hz s-1, and a
significant second and third frequency derivative terms indicative of non-
negligible timing noise. The phase-averaged 0.8–7 keV spectrum is well fit
with a double-blackbody (BB) model throughout the campaign. The BB
temperatures remain constant at 0.46 and 1.2 keV. The areas and flux of each
component decreased by a factor of 6, initially through a steep decay trend
lasting about 46 days followed by a shallow long-term one. The pulse shape in
the same energy range is initially complex, exhibiting three distinct peaks,
yet with clear continuous evolution throughout the outburst towards a simpler,
single-pulse shape. The rms pulsed fraction is high and increases from about
$40\%$ to $50\%$. We find no dependence of pulse shape or fraction on energy.
These results suggest that multiple hotspots, possibly possessing temperature
gradients, emerged at outburst-onset, and shrank as the outburst decayed. We
detect 84 faint bursts with NICER, having a strong preference for occurring
close to the surface emission pulse maximum; the first time this phenomenon is
detected in such a large burst sample. This likely implies a very low altitude
for the burst emission region, and a triggering mechanism connected to the
surface active zone. Finally, our radio observations at several epochs and
multiple frequencies reveal no evidence of pulsed or burst-like radio
emission.

.

##  1 Introduction

The variable emission from magnetars, spanning timescales from milliseconds to
years, constitutes distinctive character that separates them from the rest of
the isolated neutron star family. Magnetars emit bright, hard X-ray short
bursts with luminosities ranging from $10^{37}$ to $10^{41}$ erg s-1 (e.g.,
Collazzi et al. 2015) and sub-second duration. Often accompanying these
bursts, magnetars enter a period of elevated persistent flux level which could
exceed their quiescent emission by as many as three orders of magnitude, i.e.,
outbursts (e.g., Coti Zelati et al. 2018). Historically, these properties were
observed in magnetars with a narrow range of spin periods around the $2-12$
seconds and large spin-down rates $\dot{P}\sim 10^{-11}$–$10^{-13}$ s s-1.
Assuming magnetic-dipole braking, these timing properties imply large dipolar
field strengths, typically in the range of $B\sim 10^{13}-10^{15}$ G at the
equator, and young spin-down ages of few thousand years (e.g., Kouveliotou et
al. 1998, see also Mereghetti 2008; Kaspi & Beloborodov 2017 for reviews). The
large available magnetic energy is putatively the main source of power in
magnetars since, for most, the rotational energy loss is incapable of powering
their X-ray output in quiescence and/or during outbursts (Thompson & Duncan
1996; Thompson et al. 2002; Turolla et al. 2015).

While the aforementioned temporal characteristics are certainly representative
of the bulk of the magnetar population, magnetar-like activity has now been
observed from several other classes of isolated neutron stars: low B-field
magnetars, e.g., SGR 0418+5729 ($B\sim 6\times 10^{12}$ G, Rea et al. 2013),
high B-field rotationally-powered pulsars (PSR J1846$-$0258 and PSR
J1119$-$6127 Gavriil et al. 2008; Archibald et al. 2016; Göğüş et al. 2016),
and a central compact object with a very long spin-period of about 6.67 hrs
(Rea et al. 2016). Moreover, a wind nebula, an otherwise common
characteristics of young rotation-powered pulsars, has been detected around a
typical magnetar, Swift J1834.9$-$0846 (Younes et al. 2016). These results
strengthen the case for a connection, perhaps evolutionary, among all isolated
neutron stars (Viganò et al. 2013).

On 2020 October 10, the BAT instrument onboard the Neil Gehrels Swift
Observatory (Swift) triggered on a short, soft gamma-ray burst from the
direction of the Galactic plane, and prompt XRT observations revealed a
bright, previously unknown X-ray source, now dubbed SGR 1830$-$0645 (Page et
al. 2020). Using the same data set, Göğüş et al. (2020) reported the detection
of coherent pulsations with a spin-period of 10.4 s, confirmed with NICER few
hours later (Younes et al. 2020b). NICER performed intense monitoring of the
source in the ensuing days, and subsequently (11 days later) revealed the
detection of a spin-down rate, $\dot{\nu}\approx 9.0\times 10^{-14}$ Hz s-1,
flux decay, and the detection of numerous short X-ray bursts (Ray et al.
2020). All of these characteristics fall within the realm of typical magnetar
properties, and implies that SGR 1830$-$0645 is a new addition to the magnetar
family (see also Coti Zelati et al. 2021). So far, the source has not been
detected in radio at several frequencies (Surnis et al. 2020; Maan et al.
2020; Coti Zelati et al. 2021).

In this paper, we present the detailed soft X-ray and radio properties of SGR
1830$-$0645 following its first-detected outburst utilizing the NICER X-ray
telescope, Chandra telescope, and several radio facilities. We introduce our
observations and data reductions in Section 2. Our results are presented in
Section 3. We discuss the implications of our findings in Section 4, including
our assumed fiducial distance of 4 kpc to the source.

##  2 Observations and data reduction

###  2.1 X-rays

We observed SGR 1830$-$0645 with NICER starting approximately 4 hours after
its discovery with BAT on 2020 October 10 at 14:29 UTC (Page et al. 2020).
Prior to the sun-constrained period which started on 2020 November 17, thirty-
seven observations were carried out (obs IDs 3201810101 to 3201810137). We
restarted the NICER campaign on 2021 February 10. Our current analysis
includes data extending up to 2021 May 21 (up to obs. ID 4201810118). NICER
consists of 56 co-aligned X-ray concentrating optics covering a
30$\hbox{${}^{\prime}$}^{2}$ field of view and providing a collecting area of
1900 cm2 at 1.5 keV. It offers high precision timing and spectral
capabilities, while affording a low background (Gendreau et al. 2016). We
processed NICER data using NICERDAS version v007a, as part of HEASOFT version
6.27.2. Good time intervals were created from raw level 1 event files using
the nicer tool nicerl2. We use the default filtering criteria as described in
NICER Data Analysis Guide111https://heasarc.gsfc.nasa.gov/docs/nicer/data_a
alysis/nicer_analysis_guide.html, except the underonly_range has been relaxed
to $0-300$. This background is mainly due to optical loading and only affects
the low energy part of the spectrum, below the 0.8–7 keV range we consider for
all of our analyses. This choice for the energy range is driven by the
hydrogen column density in the direction of the source and its soft spectrum.
We extracted background spectra utilizing the nibackgen3C50 method (Remillard
et al. 2021) and added a conservative 20% systematic uncertainty to the
estimated background number counts per NICER-energy channel.

We observed the field of SGR 1830$-$0645 with the Chandra X-Ray Observatory
Advanced CCD Imaging Spectrometer (ACIS, Garmire et al. 2003) for 4.6 ks on
2020 October 13 (Observation ID: 24841). The target was positioned at the
nominal aimpoint of the S3 chip of the ACIS-S detectors. The observation was
operated in 1/8th subarray mode, which provided a temporal resolution of 0.4
s. We reduced the data using the Chandra Interactive Analysis of Observations
(CIAO222https://cxc.cfa.harvard.edu/ciao/ version 4.12) and the calibration
database of Chandra CALDB version 4.9.2.1.

We utilize Xspec v12.11.0 (Arnaud 1996) for the NICER X-ray spectral analysis.
We do not attempt any spectral analysis of the Chandra data due to heavy pile-
up. We use the latest NICER response and ancillary files in the CALDB release
xti20200722. We group the data to have 50 counts per energy bin and utilize
the $\chi^{2}$ statistics for model parameter estimation and error
calculation. We quote all errors at the $1\sigma$ ($68\%$ uncertainty level)
unless otherwise noted.

###  2.2 Radio Campaigns

European stations. Between 2020 Oct 11 and 2021 Feb 04, we observed SGR
1830$-$0645 for a total of 68.2 hrs (non-overlapping) with several European
stations that are also part of the European VLBI333Very Long Baseline
Interferometry Network (EVN). The stations involved and the frequencies
covered varied between observations as summarized in Table 1. Several stations
observed simultaneously at complementary frequencies in an effort to cover as
large a bandwidth as possible. At each participating station we recorded the
raw voltage data in VLBI Data Interchange Format (VDIF, Whitney et al. 2010)
with the local VLBI backends (DBBC2). Each station recorded dual-polarisation
(circular) 2-bit data which were subsequently transferred to Onsala Space
Observatory for processing. We used the same pipeline as outlined in Kirsten
et al. (2020) which creates total intensity filterbanks. Depending on
observing frequency, the time and frequency resolution of the filterbanks are
between $64-1024\,\mu$s and $7.812-2000\,$kHz, respectively.

Table 1: Summary of the radio observations of SGR 1830$-$0645 with EVN dishes Stationa | Bandb | Bandwidth [MHz]c | SEFD [Jy]d | Fluence limits [Jy ms]e | Time observed [hrs]
---|---|---|---|---|---
Wb | P | 60 | 2100 | 65 | 34.0
O8 | L | 100 | 350 | 8 | 46.9
Tr | C | 250 | 260 | 4 | 3.4
Nt | L | 230 | 740 | 12 | 2.0
Mc | C | 240 | 170 | 3 | 2.0
Ir | L | 100 | 700 | 17 | 18.0
Ib | C, X | 250, 250 | 620, 650 | 10, 10 | 11.75, 8.60
Total telescope time/total time on source [hrs]f | 126.6, 68.2
a Wb: Westerbork RT1, O8: Onsala 25m, Tr: Toruń, Nt: Noto 32m, Mc: Medicina
32m, Ir: Irbene 32m, Ib: Irbene 16m
b P: 300–364 MHz; L: 1336–1720 MHz; C(Mc,Ib): 4798–5310 MHz; C(Tr): 6500–6756
MHz; X: 8287–8543 MHz
c Effective bandwidth accounting for RFI and band edges.
d From the EVN status page. SEFD refers to System Equivalent Flux Density.
e Assuming a $7\sigma$ detection threshold
f Total time on source accounts for overlap between the participating
stations.

Deep Space Network. The Deep Space Network (DSN; Pearlman et al. 2019) is a
world-wide array of radio telescopes that are primarily used for spacecraft
communication. The DSN radio telescopes are located at three main sites
(Canberra, Australia; Goldstone, California; and Madrid, Spain), which each
host a steerable 70 m radio antenna and several smaller 34 m radio dishes.
When these radio telescopes are not being utilized to communicate with
spacecraft, they can be used to perform radio observations of magnetars, high
magnetic field pulsars, and fast radio burst (FRB) sources (e.g., see Majid et
al. 2017; Pearlman et al. 2018a; Majid et al. 2020; Pearlman et al. 2020b, c).

We performed a continuous radio observation of SGR 1830-0645 using the Deep
Space Network (DSN) 34-m diameter radio telescope (DSS-34), located at the
Canberra Deep Space Communications Complex (CDSCC) in Tidbinbilla, Australia,
for 5.15 hr starting at 2020 October 12 07:00:01 UTC. The pulsar backend was
used to simultaneously record data at $S$-band (center frequency: 2.2 GHz,
bandwidth: 118 MHz) and $X$-band (center frequency: 8.3 GHz, bandwidth: 441
MHz). Power spectral density measurements at both frequency bands were
channelized and saved in digital polyphase filterbanks with a frequency and
time resolution of 1 MHz and 512 us, respectively. Initial results from this
radio observation were presented in Pearlman et al. (2020a).

Green Bank Telescope. The 100-m Robert C. Byrd Green Bank Telescope (GBT)
observed SGR 1830$-$0645 for 92 minutes at S-band (center frequency: 2.0 GHz,
bandwidth: 800 MHz) and for 90 minutes at C-band (center frequency: 6.0 GHz,
bandwidth: 1500 MHz) on October 13, 2020, for a total exposure of 182 minutes
using the Versatile GBT Astronomical Spectrometer (VEGAS) pulsar backend in
incoherent dedispersion and total intensity mode. The S and C bands data were
recorded using 4096 and 3072 frequency channels with a sampling time of 81.92
$\mu$s and 43.69 $\mu$s, respectively. The data were analyzed using the
PRESTO444http://www.cv.nrao.edu/~sransom/presto/ software package.

##  3 Results

###  3.1 Localization

We used our Chandra observation to derive the SGR 1830$-$0645 localization. We
selected all events in the 0.5 - 8 keV band detected with the S3 detector of
ACIS-S, and used the wavdetect tool of CIAO to search in the entire field for
point sources whose detection significance is in excess of 5$\sigma$. The
search resulted in only one bright source with a rate of $1.37\pm 0.02$ counts
s-1 at RA, Dec: 277.673520, -6.754696 degrees, respecitvely, which corresponds
to RA: 18h30m41.s64, Dec: -06∘45′ 16.′′9 (J2000). We estimated the positional
uncertainty by the 90% photon containment region around the source position as
0.73′′ (statistical only, with another $0.8\hbox{${}^{\prime\prime}$}$
systematic uncertainty). No obvious extended emission is observed in our short
Chandra exposure at the arcsecond or arcminute scales.

###  3.2 Timing

To develop a phase-connected timing model we computed a set of pulse times of
arrival (TOAs) from all NICER observations and fit them to a model. We compute
TOAs using an unbinned maximum likelihood technique described in Ray et al.
(2011). This technique uses an analytic pulse profile template in the
likelihood calculation. Because the main pulse changes from double peaked
early in the outburst (around MJD 59154) to a single peak later, we used two
templates. We tried several different template alignments, i.e., a single wide
Gaussian, a multi-gaussian model, and aligning the fundamental of a Fourier
series fit to the pulse profile. We chose the ones where the timing residuals
showed the smallest discontinuity at the switchover date. This turned out to
be a single wide Gaussian, with slightly different widths for the early and
late data (FWHM = 0.39 and 0.32 respectively). Using these templates, we
computed TOAs for each segment of data of typical duration 400–1200 s. Using
PINT (Luo et al. 2021), we fit the TOAs to a timing model of the form
$\phi(t)=\phi_{0}+\nu(t-t_{0})+1/2\dot{\nu}(t-t_{0})^{2}+1/6\ddot{\nu}(t-t_{0})^{3}+\ldots$,
truncated at the highest significant (at the $5\sigma$ level) term, which in
this case was $\dddot{\nu}$. Given the high NICER cadence, we were able to
maintain phase coherence throughout our monitoring campaign. The parameters of
the best-fit timing model are presented in Table 2 and the residuals are
displayed in Figure 1. We find that the SGR 1830$-$0645 spin frequency is
$\nu=0.096008680(2)$ Hz and spin-down rate is $\dot{\nu}=-6.2(1)\times
10^{-14}$ Hz s-1, which imply a dipole field strength at the equator $B\approx
2.7\times 10^{14}$ G, a spin-down age $\tau\approx 24.4$ kyr, and a spin-down
luminosity $|\dot{E}|\approx 2.4\times 10^{32}$ erg $s^{-1}$.

Figure 1: Residuals, in seconds, of a timing model fit to the SGR 1830$-$0645 TOAs as derived from 400 to 1200 seconds of NICER data. The gap constitutes the sun-constrained period. The model includes up to the fourth term in the Taylor expansion of the phase evolution, and it is summarized in Table 2. Table 2: SGR 1830$-$0645 Timing Parameters  Parameter | Value
---|---
R.A. (J2000) | 18:30:41.64
Decl. (J2000) |  $-6$:45:16.9
Time Scale | TDB
Ephemeris | DE405
Epoch (MJD) | 59132.0
$\nu$ (Hz) | 0.096008680(2)
$\dot{\nu}$ (Hz s-1) | $-6.2(1)\times 10^{-14}$
$\ddot{\nu}$ (Hz s-2) | $4.7(3)\times 10^{-21}$
$\dddot{\nu}$ (Hz s-3) | $-2.7(4)\times 10^{-28}$
Valid Range (MJD)  | 59132.7–59355.7
$\chi^{2}$/dof | 177/175
RMS residual (ms) | 95
Inferred Parameters
$B$ (G) | $2.7\times 10^{14}$
$\tau$ (kyr) | 24.4
$|\dot{E}|$ (erg s-1) | $2.4\times 10^{32}$

Figure 2: SGR 1830$-$0645 persistent emission pulse profile evolution with
time in the 0.8–2 keV (left-panel), 2–7 keV (middle-panel), and 0.8–7 keV
range (right-panel). All profiles are corrected for the background. Colors
represent the days during which the data is accumulated post 2020 October 10.
Two rotational cycles are shown. The y-axis is shifted arbitrarily for
clarity. See text for more details.

The time-integrated soft X-ray pulse profile of SGR 1830$-$0645 initially
shows a complex shape, with 3 prominent peaks, each separated by $\approx 0.2$
cycles. We searched for any temporal evolution by building pulse profiles
using observations spanning one to several days, accumulating for each profile
an exposure ranging from 4 ks to 40 ks. The right panel of Figure 2 displays
the 0.8–7 keV pulse profiles with 70 phase bins color-coded by time interval
in days from MJD 59132.0. The prominent peaks are clearly visible up to day 21
post outburst, after which the profile simplifies to a prominent single peak.
However, the fainter peak around rotational phase of 0.6 remained visible up
until the last observation prior to the sun-constraint period, 37 days post-
outburst. We note a clear decrease in the separation between the peaks as the
outburst evolves. A more detailed analysis of this behavior will be presented
elsewhere. The pulse profiles following the sun-constrained period clearly
exhibit a simple broad single-peak pulse.

We also constructed pulse profiles in two energy bands, namely 0.8–2 keV and
2–7 keV (Figure 2, left and middle panels, respectively). These energy ranges
reflect the contribution from the two thermal models necessary to describe the
0.8–7 keV spectrum (see Section 3.3). In both energy ranges, the three peaks
are present, and at similar phases. The only subtle distinction in the
profiles is the dip between the peaks, where it is more prominent at hard
X-rays.

We fit the background-corrected pulse profiles to a Fourier series to measure
the root-mean-square pulsed fraction (${\rm PF_{rms}}$, Woods et al. 2004). We
consider the smallest number of harmonics that provide the best fit to each
profile according to an F-test. We find that the first seven harmonics are
required to provide an adequate fit to the early pulse shapes. Yet, three
harmonics are adequate to result in satisfactory fit to the data post sun-
constrained period. The source exhibits a highly pulsed signal, with ${\rm
PF_{rms}}$ narrowly varying between $36\%$ and $42\%$ during the first month
of the outburst (bottom panel of Figure 3). Subsequent to the sun-constrained
period, the source pulse fraction increased to $\approx 50$%. We find no
significant variation in the rms PF with energy.

Figure 3: Panel (a). Number of SGR 1830$-$0645 X-ray bursts per day as
observed by NICER. Panel (b). Decay of the hot (red squares) and warm (black
squares) BB emitting areas throughout the source outburst. Panel (c). Decay of
the absorption-corrected flux of the hot (red squares) and warm (black
squares) BB components in the 0.5-10 keV range. In panels (b) and (c) the
dotted lines are the best fit broken-linear decay function. Panel d. RMS
pulsed fraction evolution with time. See text for more details. Figure 4:
Panel (a). Example NICER spectra from the first observation post outburst (4
hours after the source discovery, black dots) and 138 days later (blue dots).
The dashed and dotted lines are the best fit absorbed warm and hot BB models,
respectively, while the solid line is their sum. Panels (b) and (c).
Residuals, in units of standard deviation $\sigma$, of each spectrum from the
best fit absorbed 2BB model.

###  3.3 Persistent Emission Spectroscopy

We fit the 0.8–7 keV X-ray spectra extracted from all 37 observations taken
prior to the sun-constrained period simultaneously, with an absorbed two
blackbody (BB) model. We link the hydrogen column density among all the
spectra since we do not expect any large and variable absorption intrinsic to
the magnetar. Initially, we let all other parameters free to vary, i.e., the
temperatures and size of the apparent emitting area. We find a reduced
$\chi^{2}$ between 0.9 and 1.1 for all spectra, which have degrees of freedom
(dof) ranging from 40 to 440. We observe strong variations in the size of the
apparent emitting areas for the warm and hot BB components. The temperatures
on the other hand remain essentially constant through all observations,
varying only at $<2\sigma$ level. Hence, we linked the temperatures of both
components between all the spectra and refit the data. This resulted in an
equally good fit with for all spectra. We find a hydrogen column density in
the direction of the source $N_{\rm H}=(1.17\pm 0.01)\times 10^{22}$ cm-2,
while the temperatures of the warm and the hot BB are $0.47\pm 0.01$ keV and
$1.22\pm 0.01$ keV, respectively. We also fit these spectra with the sum of a
BB and a power-law, affected by interstellar absorption. This model
consistently provide a worse fit compared to the 2BB with $\chi^{2}$ larger
than that of the latter for the same number of dof. This is consistent with
the 0.5-30 keV broad-band spectral result presented in Coti Zelati et al.
(2021) which found that a purely thermal model is the best fit to the soft
X-ray part of the spectrum. Hence, for the remainder of the spectral analysis,
we adopt the 2BB model.

We then fit the post sun-constrained period spectra simultaneously with an
absorbed thermal model. We fixed the hydrogen column density to the value as
derived above. We find that a single thermal component cannot adequately
describe the spectral shape of these spectra, and a 2BB model is still
required. We started by allowing the areas and temperatures to vary freely.
Once again, the temperatures of all spectra were consistent within
uncertainties, hence, were linked. This final fit resulted in a reduced
$\chi^{2}$ of about 1 for all spectra. The temperature of the warm and hot BB
components are $0.48\pm 0.02$ keV and $1.22\pm 0.05$ keV, respectively. Figure
4 shows the $EF_{\rm E}$ spectra extracted from a pre- and post-sun-
constrained observation in black and blue, respectively, demonstrating that
the flux decay lacks significant spectral shape evolution.

The temporal evolution of the emitting area, $R^{2}$ and the absorption-
corrected 0.5-10 keV flux of each BB component is shown in Figure 3. Each of
these parameters for both BB components follow a similar trend, and are well
fit with a broken first-order polynomial function (dashed-lines). The fits
result in $\chi^{2}$ of 89, 96, 77, 105, for 59 dof, for the warm and hot
areas and corresponding fluxes, respectively. The break occurs shortly after
the start of the sun-constrained period at $t_{\rm break}=46\pm 2$ days or
around 2020 November 27. We find a flux decay rate of $-5.7(2)\times 10^{-13}$
erg s-1 cm-2 day-1 and $-3.0(5)\times 10^{-14}$ erg s-1 cm-2 day-1, before and
after $t_{\rm break}$, respectively. The ratio of the BB fluxes shows no
apparent variation with an average $<F_{\rm hot}/F_{\rm warm}>\approx 1.9\pm
0.2$. The areas of the hot and warm components shrank from 0.28 km2 to 0.04
km2 and 6.0 km2 to 1.0 km2, respectively.

###  3.4 X-ray Bursts

Figure 5: Fluence distribution of bursts in SGR 1830$-$0645 detected with
NICER. For fluences $\gtrsim 6\times 10^{-11}$ erg cm-2, the distribution
approximately follows $F^{-0.5}$, as shown with a red, dashed line.

We searched for bursts in the X-ray band with the NICER data sets in the
energy range of $0.7$-$8$ keV. We use the Bayesian block technique to search
for significant X-ray flux variability in individual event files (Scargle et
al. 2013). The blocks with duration shorter than $\sim$1 s are further
examined to exclude those with high backgrounds and multiple blocks in one
burst. Moreover, blocks close to the boundaries of a GTI have also been
excluded. The detection significance of a burst is defined through the Poisson
probability of detecting a number of photons in a block, given an estimated
non-burst count rate calculated from nearby 1 s intervals. We have identified
84 short bursts with Poisson probabilities lower than $2.6\times 10^{-7}$.
This corresponds to a $5.2~{}\sigma$ detection significance, and implies a
false alarm rate of $\leq 1$ throughout our campaign.

Among the 84 bursts, the averaged Bayesian block duration is about 0.029 s
with a standard deviation of 0.023 s. The average photon count rate for the
burst ensemble is $470\pm 14$ counts s-1, corresponding to an unabsorbed flux
of $(4.5\pm 0.2)\times 10^{-9}$ erg cm-2 s-1in the energy range of 0.7–8 keV
(assuming a blackbody with a temperature of 1.5 keV; as derived through our
burst spectral analysis below). The burst with the highest fluence occurred at
MJD (TDB) 59139.864807 with a duration of 0.014 s and an averaged flux of
$3.3\pm 0.5\times 10^{-8}$ erg cm-2 s-1. The fluence distribution of the
bursts is shown in Figure 5. The high-fluence tail, which covers the range
6–30 $\times 10^{-11}$ erg cm-2, can be described by a power-law with an index
of $\approx 1.5$. To estimate the uncertainty of this index, we performed
$10^{5}$ times of Monte Carlo simulations. In each simulation, we generated
Poisson distributed photons in each burst with a mean value equal to the
observed one, and calculated the corresponding fluence. The result suggests an
uncertainty on the PL index of 0.3.

Figure 6: Average pulse profile of SGR 1830$-$0645 (gray) in the energy range
0.8–7 keV, using all X-ray observations reported in this paper. The black
profile represents the histogram of the bursts phase folded using the spin
parameters as reported in Table 2.

We investigate the spin phase distribution of these short bursts using the
timing model presented in Table 2. Figure 6 shows the burst phase distribution
along with the pulse profile of SGR 1830$-$0645 as derived from all
observations. It is clear that the burst phase distribution is not uniform,
with its peak occurrence coinciding with the peak of the persistent emission
X-ray pulse profile. We apply the Anderson Darling test to assess the burst
phase against a uniform distribution which yielded an AD statistic of 4.5 with
a corresponding p-value of 0.005. Moreover, we performed simulation to test
the significance of this apparent non-uniformity in phase. In each simulation,
we generate 83 mock bursts located at randomly distributed spin phases. For
$10^{5}$ trials, only $\approx 450$ of them result in AD statistics higher
than the observed value. This implies that the null hypothesis of the observed
burst phase distribution being drawn from a uniform distribution can be
rejected at the $3\sigma$ level. Finally, we searched for any phase dependence
of the mean photon energy and the fluence of bursts, but found no clear trend.

We accumulated a total of 1111 bursts photons in the 0.7–8 keV range. We
merged photon events with nimpumerge and generated an averaged burst spectrum
in the burst time intervals. We fit the spectrum with the Cash statistics
(Cash 1979), assuming that the background constitutes the non-burst epochs. We
excluded the data-sets taken in 2021 due to the dramatic decrease in the rate
of bursting activity. The burst spectrum can be described equally well with an
absorbed PL or BB model. We fix the hydrogen column density $N_{\rm H}$ to
$1.17\times 10^{22}$ cm-2 as derived through the persistent emission spectral
analysis. We find a photon index of $0.4_{-0.3}^{+0.5}$ (C${}_{\rm
stat}=408.5$ with 448 degrees of freedom) for the PL component, or $kT=1.5\pm
0.1$ keV and a radius of $4.1\pm 0.3$ km ($C^{2}=362.6$ with 448 degrees of
freedom) when considering the BB model. We further divided bursts into two
groups: bursts associated with the X-ray pulse (on-pulse, phase 0.8–1.3) and
the X-ray valley (off-pulse, phase 0.3–0.8). No significant difference is
obtained between the spectra of these two groups. The on-pulse and off-pulse
spectra are shown in Figure 7.

Figure 7: (a) The NICER spectra of the accumulated on-pulse bursts (black
circles) and off-pulse bursts (blue squares). The black and blue lines
represent the best-fit power-law model. (b) and (c): Residuals of each
spectrum from the best-fit power-law model in terms of $\sigma$.

###  3.5 Radio upper-limits

Deep Space Network. We dedispersed the $S$-band and $X$-band data from DSS-34
with trial DMs between 10 and 2000 pc cm${}^{\text{--3}}$ and independently
searched the data in each frequency band for periodic radio emission. We used
a GPU-accelerated Fast Folding Algorithm (FFA) to search for pulsed radio
emission with periods between 9 and 12 s. We also used a GPU-accelerated
Fourier Domain Acceleration Search (FDAS) pipeline, which employs a matched
filtering algorithm to correct for Doppler smearing, to search for
periodicity. No statistically significant periods, with a signal-to-noise
(S/N) ratio above 7.0, were found after folding the data modulo each of the
period candidates identified by the two algorithms. We also folded the data
utilizing the NICER ephemerides at the DSN epoch, as well as using trial
periods around the predicted 10.42 s rotational period of the source. We do
not find any pulsed emission at or near the expected signal. For an assumed
duty cycle of 10%, we place the following (7-sigma) upper limits on the SGR
1830-0645 radio flux density during our radio observation: $<$ 0.14 mJy at
$S$-band and $<$ 0.08 mJy at $X$-band.

We also searched the de-dispersed $S$-band and $X$-band data for radio bursts
using a Fourier domain matched filtering algorithm, where each dedispersed
time-series was convolved with boxcar functions with logarithmically spaced
widths between 512 us and 153.6 ms. Candidates with detection signal-to-noise
ratios above 7.0 were saved and classified using a GPU-accelerated machine
learning pipeline based on the FETCH (Fast Extragalactic Transient Candidate
Hunter) software package (Agarwal et al. 2020). The dynamic spectra of the
candidates were also visually inspected for verification. No radio bursts were
detected above a 7-sigma fluence detection threshold of 1.8 Jy ms for a 1-ms
burst at $S$-band and 1.0 Jy ms for a 1-ms burst at $X$-band.

European stations. We searched the filterbanks from each station for single
pulses using Heimdall555https://sourceforge.net/projects/heimdall-astro/ in
the DM-range $\mathrm{10-2000\,pc\,cm^{-3}}$. Classification of candidates was
performed via FETCH Agarwal et al. (2020) and manual inspection of the most
promising candidates. No attempt of folding the data around the rotational
period was done. We found no bursts above a S/N threshold of 7 in any of the
observations, constraining the fluence of any potential bursts as summarized
in Table 1.

Green Bank Telescope. The data were searched for periodic emission, both
completely blind and by folding the data around the known period, and for
bright single pulses in the DM range of 10–1500 pc cm-3. We do not detect any
significant periodic or single-pulse emission. For periodic emission, we place
a 7-sigma upper limit on the average flux density of 7.8 $\mu$Jy and 6.2
$\mu$Jy at S and C band respectively, assuming a 10% duty cycle. The
dedispersed data were also searched for fast radio bursts using Heimdall and
candidates were run through the GPU accelerated convolutional neural network
FETCH (Agarwal et al. 2020) to distinguish between radio frequency
interference (RFI) and astrophysical signals. We did not detect any radio
bursts in our data and the 7-sigma fluence upper limits are 0.07 Jy ms and
0.04 Jy ms at 2.0 GHz and 6.0 GHz, respectively, assuming a pulse width of 1
ms.

##  4 Summary and discussion

In this paper, we present the results of a NICER heavy-cadence monitoring
campaign of the recently discovered magnetar SGR 1830$-$0645, covering the
time interval from 4 hours to 223 days post outburst-onset. This dense
observing campaign allowed us to track the X-ray spectral and temporal
evolution of the source in exquisite detail, while affording an analysis of
low-level bursting activity that occurred throughout the outburst. We
complement NICER with a Chandra observation that provided the precise sky
localization of the source. Finally, we present the results of a suite of SGR
1830$-$0645 radio observations taken at several frequencies from multiple
epochs during the source outburst.

###  4.1 General properties

The NICER observations of SGR 1830$-$0645 allowed us to build an accurate
timing solution to the rotational properties of the source over several
months. The temporal characteristics we inferred, $\nu=0.096008680(2)$ Hz and
$\dot{\nu}=-6.2(1)\times 10^{-14}$ Hz s-1, are quite typical of the magnetar
family, as well as the derived intrinsic properties: $B=2.7\times 10^{14}$ G,
$\tau=24.4$ kyr, and $|\dot{E}|=2.4\times 10^{32}$ erg $s^{-1}$ (see also Coti
Zelati et al. 2021). The long baseline of the observations also revealed the
presence of higher order frequency derivatives, likely indicating a
significant level of timing noise (typical of magnetars during outburst
epochs, e.g., Younes et al. 2015; Archibald et al. 2020; Hu et al. 2020). All
the above properties, along with the strong bursting activity and the
persistent flux decay, cement the origin of SGR 1830$-$0645 as the latest
addition to the magnetar family.

The Galactic coordinates of SGR 1830$-$0645 (section 3.1) locate this magnetar
close to the positions of known magnetars SGR 1833$-$0832, Swift
J1834.9$-$0846, and 1E 1841$-$045\. These three sources are thought to be on
the Galactic Scutum–Centaurus Arm (Table 7 and Figure 17 of Enoto et al.
2017). The distances of the latter two sources, Swift J1834.9$-$0846, and 1E
1841$-$045, are measured at 4.2 kpc and 8.5 kpc from the associated supernovae
remnants (SNRs) W41 (Leahy & Tian 2008) and Kes 73 (Tian & Leahy 2008),
respectively. Due to the lack of any SNR around SGR 1830$-$0645, we assume a
fiducial distance of SGR 1830$-$0645 at 4 kpc as the close part of this spiral
arm. The Chandra observation reveals no significant extended emission around
the source that may be interpreted as an X-ray wind nebula, in-line with the
majority of the magnetars (with the exception of Swift J1834.9$-$0846, Younes
et al. 2016).

###  4.2 X-ray evolution during outburst

SGR 1830$-$0645 pulse profile at the onset of the outburst shows a complex
morphology, exhibiting three clearly distinct peaks. The initial separation of
the peaks is about 0.2 rotational phases, which seem to decrease with time. By
day 21 post-outburst, the profile simplified to a broad single peak, though
the weakest peak around phase 0.65 remained visible until the last observation
prior to the sun-constraint period. Complex profile shapes are quite common
for magnetars during their outburst epochs. For instance, 4U 0142+61 double-
peaked profile revealed extra peaks following the source 2006 and 2015
outbursts (Archibald et al. 2017, see also, e.g., Gavriil et al. 2011; Rea et
al. 2013). These changes commonly revert back to a simpler shape as the
outburst wanes. This is also the case for SGR 1830$-$0645, where the pulse
profiles following the sun-constrained period, i.e., four months post-
outburst, evince a single broad pulse.

We find no strong energy dependence on pulse shape, except for a deeper, more
pronounced trough between the two main peaks in the energy range 2–7 keV
compared to the lower 0.8–2 keV band. Yet, these two energy ranges are
dominated by each of the two BB components used to fit the 0.8–7 keV phase-
averaged spectrum (Figure 4). These two BB components evolve similarly
throughout the outburst, i.e, their temperatures remain constant while their
areas decrease, driving a very similar flux decay trend (Figure 3). Hence, it
is evident that these two spectral components are not distinct. It is
plausible that the X-ray emitting area is not isothermal, but rather possesses
a temperature gradient, for example a hot core surrounded by a cooler ring,
which are evolving concurrently. The fact that the hotter component is of
slightly higher flux than the cooler one (see Figure 4) provides clues to the
concentration of energy deposition in the persistent emission activation zone.
The increase of the RMS pulse fraction after the sun-constrained down-time is
possibly caused by the lower effective emitting area of both components (e.g.,
Hu et al. 2020).

The three distinct peaks in the profile at the onset of the outburst clearly
indicate asymmetric, non-uniform heating pattern on the surface, yet with
clear evolution towards a simpler, more localized region. In the magnetar
model, it is natural to attribute the soft X-ray active regions to being
proximate to magnetic field line footpoints on the surface. These footpoints
may or may not be associated with dipolar field morphology. Perturbations to
this baseline field configuration have been developed in the twisted field
scenario (e.g., Thompson et al. 2002; Beloborodov 2009), via analogy to solar
corona contexts. These introduce toroidal field components driven by inner
magnetospheric currents, and it is possible that such magnetospheric field
morphology modifications could permeate magnetar atmospheres. Moreover the
currents will naturally bombard the stellar surface (Beloborodov & Li 2016),
heating it at the footpoints of twisted field lines. Thus, the hot emission
zones may be physically connected to and/or co-located with bombardment
footpoints. The pulse phase migration and merging of the peaks seen in Figure
2 as the outburst evolves would suggest hot spot coalescence and field line
footpoint mobility. The rate of pulse profile evolution can potentially
constrain twisted field models of magnetar activation, suggesting insightful
paths for future studies on the theory/observation interface for magnetars.

Processes that originate in the crust may form an alternate scenario to
bombardment by twisted fields for the hot spot evolution. The rate and
character of evolution would be governed by bulk material properties such as
viscosity, heat conductivity, and the depth of subsurface energy injection
(e.g., Beloborodov & Levin 2014; Lander 2016; Lander & Gourgouliatos 2019;
Kwang-Hua 2018). These scenarios will be investigated in a separate study.

###  4.3 Phase Distribution of Short Bursts

Since our initial observation, NICER frequently detected typical short
magnetar bursts from SGR 1830$-$0645, revealing a total of 84 with an average
duration of about 30 ms. Much of the burst activity transpires during the
early stages of the outburst evolution, i.e., during the steep flux decline
and the strong pulse shape variation (Figures 2 and 3. The fluence ranged from
3 to 30 $\times 10^{-11}$ erg cm-2. Brighter bursts have also been detected
throughout the outburst by large field of view instruments such as Fermi-GBM
and Swift-BAT (Fletcher & Fermi GBM Team 2021; Klingler et al. 2021), though
at a much lower rate. This is qualitatively consistent with the steep $\log
N$-$\log S$ distribution of the NICER events, $N(>F)\propto F^{-0.5}$. This PL
fluence distribution seems to be universal amongst magnetars (e.g., Cheng et
al. 1996; Göğüş et al. 1999; Scholz & Kaspi 2011; Collazzi et al. 2015; Younes
et al. 2020a), and may be consistent with either the crust quake or magnetic
reconnection scenarios which are usually invoked as their triggering mechanism
(e.g., Thompson & Duncan 1995, 2001; Lyutikov 2003, 2015; Elenbaas et al.
2016).

The apparent phase correlation of the bursts with that of the persistent
thermal surface hot-spot emission (Figure 6) is the strongest evidence yet of
such phase-selection in observability of short bursts in magnetars.
Previously, a similar preference for burst occurrence close to the persistent
emission pulse maximum has been reported in the radio-emitting magnetar XTE
J1810$-$197, however, in a much smaller sample of four bursts (Woods et al.
2005). Observe that this phase variation of burst arrival times contrasts the
approximate phase-uniformity apparent in the recent burst storm of SGR
1935+2154 (Younes et al. 2020a).

This strong correlation suggests that approximate spatial colocation of bursts
and soft X-ray activity is highly likely, with the burst plasma emitting at
relatively low altitudes. Given the $R^{2}\sim 16$ km2 apparent area of the
bursts, one expects that occultation of the bursts by the star at some pulse
phases is readily achievable. We envisage that the bursts could originate in
twisted field line zones near quasi-polar footpoints of closed field lines
(Chen & Beloborodov 2017). Polarization-dependent radiative transport in
optically-thick $e^{+}\,e^{-}$ plasmas in the presence of magnetar-strength
B-fields could impart strong beaming of radiation (e.g., Barchas et al. 2021),
which could bias observability of bursts at particular phases where the
observer samples magnetic field directions roughly parallel to the observer’s
line of sight. This beaming is strong if the typical NICER-measured burst
photon energy of $3kT\sim 3-5$ keV is well below the local cyclotron energy
(roughly 2 MeV at the surface): such a domain arises if the altitude of the
bursts is less than about $5-7$ stellar radii assuming the local magnetic
field is not disrupted. These constraints pose a challenge for reconnection
models of magnetar bursts (e.g., those invoking a relativistic tearing
instability in an equatorial current sheet, Lyutikov 2003; Komissarov et al.
2007; Elenbaas et al. 2016) and may point to a polar crustal origin (e.g.,
Lander et al. 2015; Thompson et al. 2017) for the mechanism and locale of
these magnetar short bursts. Future polarization studies of magnetar bursts,
for instance with LEAP (McConnell et al. 2017) or AMEGO (McEnery et al. 2019),
could elucidate the source geometry and hone in on the altitude and magnetic
colatitudes for burst emission.

Finally, since magnetar short bursts could underpin some FRBs (Wadiasingh &
Timokhin 2019; Suvorov & Kokkotas 2019; Bochenek et al. 2020; CHIME/FRB
Collaboration et al. 2020; Mereghetti et al. 2020a), our result of burst-phase
dependence has interesting implications for mechanisms, observability, and
periodic windowing of extragalactic FRBs, beyond the scope of this paper.

###  4.4 Radio behavior

The non-detection of SGR 1830$-$0645 at MHz and GHz frequencies is
commensurate with the majority of magnetar sources. So far, only six magnetars
have been detected in the radio band. With the exception of SGR 1935+2154, the
properties of the radio emission from magnetars follow a general trend; it is
transient, usually appearing during outburst epochs, and the radio spectrum is
flat or inverted as opposed to the steep spectrum detected in canonical radio
pulsars (e.g., Camilo et al. 2006; Torne et al. 2015; Pearlman et al. 2018b;
Camilo et al. 2016; Esposito et al. 2020). Yet, the radio emission from SGR
1935+2154 is unique to date. Hours after its 2020 April burst storm (Younes et
al. 2020a), the source emitted the brightest radio flash ever detected from
the Milky Way, with properties reminiscent of extragalactic fast radio bursts
(Bochenek et al. 2020; CHIME/FRB Collaboration et al. 2020), simultaneous to a
bright, spectrally-unique short X-ray burst (Mereghetti et al. 2020b; Li et
al. 2021; Younes et al. 2021; Ridnaia et al. 2021; Tavani et al. 2021). Many
large radio campaigns ensued (weeks of on-source observations), however, only
three other radio bursts were detected with two having fluences between 10 and
100 Jy ms (Kirsten et al. 2020). Our radio observations of SGR 1830$-$0645
were sensitive to such brightness levels (Table 1). Furthermore, months after
SGR 1935+2154 had returned to quiescence, the FAST radio telescope detected
very weak pulsed emission from the source (Zhu et al. 2020). Such intermittent
radio emission raises the possibility that magnetars may be more prone to low-
frequency radiation than is currently thought. Hence, future regular radio
monitoring programs of magnetars both in outburst and during quiescent periods
may be key to unravelling the nature of the low-frequency emission of these
sources and their connection to extragalactic FRBs.

## Acknowledgments

G.Y. is partly funded through the NASA NICER GI program grant 80NSSC21K0233.
M.G.B. acknowledges the generous support of the National Science Foundation
through grant AST-1813649. Z.W. acknowledges support from the NASA
postdoctoral program. ZW acknowledges partial support by NASA under award
number 80GSFC21M0002. F.K. acknowledges support from the Swedish Research
Council. A.B.P is a McGill Space Institute (MSI) Fellow and a Fonds de
Recherche du Quebec – Nature et Technologies (FRQNT) postdoctoral fellow.
C.-P.H. acknowledges support from the the Ministry of Science and Technology
in Taiwan through grant MOST 109-2112-M-018-009-MY3. This work is based in
part on observations carried out using the 32-m radio telescope operated by
the Institute of Astronomy of the Nicolaus Copernicus University in Toruń
(Poland) and supported by a Polish Ministry of Science and Higher Education
SpUB grant. A portion of this research was performed at the Jet Propulsion
Laboratory, California Institute of Technology and the Caltech campus, under a
Research and Technology Development Grant through a contract with the National
Aeronautics and Space Administration. U.S. government sponsorship is
acknowledged. W.A.M. thanks the CDSCC staff and the DSN scheduling team for
their rapid response in scheduling and carrying out the radio observations
with the DSN.

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